EFFECTUAL ENTREPRENEURIAL EXPERTISE EXISTENCE AND B

EFFECTUAL ENTREPRENEURIAL EXPERTISE: EXISTENCE AND BOUNDS

Saras D. Sarasvathy

University of Virginia

The Darden Graduate School of Business Administration

100 Darden Boulevard

Charlottesville, VA 22903

Tel: (434) 982-2079

Fax: (434) 243-5020

e-mail: SarasvathyS@

Nicholas Dew

Naval Postgraduate School

1 University Circle

Monterey, CA 93943

Tel: (831) 656-3622

Fax: (831) 656-3407

e-mail: NDew@

Stuart Read

IMD

Chemin de Bellerive 23, P.O. Box 915

CH-1001 Lausanne, Switzerland

Tel: + 41 (0)21 618 01 11

Fax: + 41 (0)21 618 07 07

e-mail: @

Robert Wiltbank

Willamette University

Atkinson Graduate School of Management

Salem, OR 97301

Tel: (503) 370-6955

Fax: (503) 370-3011

e-mail: Wiltbank@

We would like to thank the Ewing Marion Kauffman Foundation for funding portions of

the empirical work undertaken for this paper. We thank Herb Simon and Lester Lave for

their invaluable conversation and Anil Menon for his help in honing the ideas in this paper

and for suggesting the Borda count. We also thank the four external coders for their

participation. Finally, we are grateful to numerous colleagues for helping us improve the

paper by providing comments on earlier drafts.

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EFFECTUAL ENTREPRENEURIAL EXPERTISE: EXISTENCE AND BOUNDS

SUMMARY

Recent theorizing in entrepreneurship has proposed effectuation as a baseline model of

entrepreneurial expertise that goes beyond the “toolbox” of basic business skills such as

market research and business planning. This study seeks to empirically delineate key

elements of entrepreneurial expertise as compared with basic business knowledge. Think

aloud protocols were extracted from 27 expert entrepreneurs and 37 MBA students who

were asked to identify the market for a single new product. Analyses revealed that 89% of

experts used effectuation more frequently than causation, while MBA students

demonstrated a noticeably opposing preference, with 81% using causation more than

effectuation.

KEYWORDS

Expertise, Effectuation, Uncertainty, Decision-Making, Bounded Rationality, Protocol

Analysis

RUNNING HEAD

Effectual Entrepreneurial Expertise

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INTRODUCTION

That entrepreneurs create firms is a simple fact. But that entrepreneurs often create

firms in the absence of markets is an idea that has been gaining ground with researchers.

For example, Venkataraman (1997: 126) identified this as a central phenomenon to be

explained by entrepreneurship research. In a seminal article that brought this phenomenon

to the attention of management scholars, Shane and Venkataraman (2000) pointed out the

further complication due to the fact that much of the information required to bring new

markets into existence itself does not come into existence until those markets are created

(Arrow 1974). Thereafter, during an in-depth discussion of research perspectives in

entrepreneurship, six eminent researchers -- Arrow, Kamien, Olson, Sexton, Simon &

Venkataraman (1999) highlighted several intellectual issues of interest in entrepreneurship

including the importance of decision-making in the absence of markets (Sarasvathy, 2000).

In an attempt to tackle this central research question in entrepreneurship,

Sarasvathy (2001) proposed effectuation as the dominant model for entrepreneurial

decision-making, particularly in the absence of pre-existent markets. Further theoretical

development of effectuation theory has positioned it in the landscape of strategy making as

useful in situations where predictability is low, but controllability of the situation is high

(Wiltbank et al 2006). This paper seeks to empirically establish the existence of effectual

reasoning in entrepreneurial decision making and to suggest bounds on its use as compared

with business knowledge that embodies causal reasoning.

The next section briefly describes effectuation and outlines its theoretical

precedents. Thereafter, the central hypothesis is developed and quantitatively tested

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through protocol analyses of the cognitive processes of 27 expert entrepreneurs and 37

MBA students who were asked to identify the market for a given new product. Finally, a

more detailed qualitative content analysis of the protocols is used to inductively develop a

process model of effectual reasoning and contrast it with causal models that form part of

basic MBA education similar topics.

Results indicate that over 63% of the experts used effectuation more than 75% of

the time. 78% of the MBA students did not use effectuation at all. Content analysis of

expert protocols, in stark contrast to the novices, reveals an almost exact reversal of the

commonly accepted marketing decision models based on causal reasoning. Causal

reasoning prescribes that decision makers proceed by first identifying a potential market

for a product and then devise marketing strategies to capture a sizable share of it through

the segmentation-targeting-positioning process (Kotler, 1991). As expected, this process

was followed by the MBA students two thirds of the time. The experts in this study,

however, used effectual reasoning to: (i) start with a single customer; (ii) generalize the

profile of that customer into a larger segment; (iii) add segments in an iterative fashion;

and, (iv) end up literally creating a market for their product. In fact, starting with the exact

same product, the 27 expert entrepreneurs ended up building 18 different companies many

of them in completely disparate industries, where the 37 MBA students created fewer than

half as many different companies.

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CENTRAL HYPOTHESIS: EFFECTUATION AND ENTREPRENEURIAL

EXPERTISE

Effectuation is the inverse of causation. Effectual reasoning is not merely a

deviation from causal reasoning. It is a distinct mode of reasoning based on an entirely

separate logic than the logic behind causal reasoning. Causation models are based on a

logic of prediction, i.e., to the extent that you can predict the future, you can control it.

Effectuation, instead, is based on a logic of control, i.e., to the extent that you can control

the future, you do not need to predict it.

Causal reasoning is useful in domains where the future is predictable, goals are

known, and an exogenous environment serves as the ultimate selection mechanism. But it

does not provide useful criteria for action in domains where these three characteristics are

absent – namely, in problem spaces characterized by Knightian uncertainty (Knight, 1921),

Marchian goal ambiguity (March, 1982) and Weickian enactment (Weick, 1979). The

absence of markets problem in entrepreneurship, alluded to in the beginning of this paper,

can be more generally modeled as the problem of making decisions in the absence of a

predictable future, clearly known goals and an independent environment. It is in precisely

such problem spaces that effectuation provides principles, techniques and criteria for

action. Effectual reasoning, therefore, integrates the Knightian, Marchian, and Weickian

problem spaces and develops techniques that provide a new, but theoretically fully funded

alternative to causal reasoning. For a detailed description of the two modes of reasoning

and their differential application to problems in entrepreneurship see Sarasvathy (2001), as

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well as later sections in the current paper, where we induce the detailed process model

from qualitative analysis of the expert protocols.

A simple example should begin to clarify and distinguish between the two types of

processes. Imagine a chef assigned the task of cooking dinner. There are two ways the

task could be organized. The first case would be where the host or client has picked out a

menu in advance. All the chef needs to do is to list the ingredients needed, shop for them

and then actually cook the meal. This is a process of causation. It starts with a given menu

and focuses on selecting between effective ways to prepare the meal.

The second case would be when the host asks the chef to look through the

cupboards in the kitchen for possible ingredients and utensils, and cook a meal. Here, the

chef has to imagine possible menus based on the given ingredients and utensils, select one,

and then prepare the meal. This is a process of effectuation. It starts with given

ingredients and utensils, and focuses on preparing one of many possible desirable meals

with them.

To extend this overly simple example to business, imagine the manufacture of a

product. In the case of causal reasoning, the blueprints of the product are provided in

advance, together with its costs, and estimates of market demand; the manufacturer needs

simply to procure the raw materials and process and assemble them according to the

predetermined plan. In the case of effectuation, the manufacturer has a general idea that

might lead to a product that could be marketed profitably. Gillette was looking for

something customers would have to purchase repeatedly (McKibben, 1998). While he was

shaving one morning, it occurred to him that a non-permanent razor might fit his

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specification. He then had to develop a cheap, effective removable-blade razor, generate an

adequate initial market, and so on, always modifying his plans as he gained new

knowledge and new stakeholders from his initial efforts.

Studies of Expertise – and Their Usefulness for Understanding Effectual Reasoning

Expertise in any area entails certain common cognitive processes among the

experts who solve problems within the given area (Chi, Glaser et al. 1982). The extraction

of these processes has been the central goal of hundreds of protocol analysis studies in the

past 30 to 40 years. Some examples from business include: Decision making

(Montgomery & Svenson, 1989); Accounting (Belkaoui, 1989); Argumentation in

management consulting (Young 1989); and Software cost estimation (Mukhopadhyay,

Vicinanza, & Prietula, 1992). Protocol analysis has also been used extensively in studying

decision making processes of experts in areas other than business, such as chess (Charness

1989), medical diagnosis (Johnson et. al., 1981), mathematics (Webb, 1975), and scientific

discovery (Qin, & Simon, 1990).

In a detailed investigation into conceptual and methodological issues involving

verbal protocols, Ericsson and Simon (1993) provide examples from over two hundred

empirical studies that use protocol analysis. They emphasize the advantages of using think

aloud protocols over other methods, particularly methods calling for retrospective recall

such as interviews or pure stimulus-response methods such as questionnaires. Think-aloud

protocols call for concurrent verbalization – i.e., subjects are required to think aloud

continuously as they solve the problems. The transcriptions of their taped verbalization

form the basic data to be analyzed. The essential logic behind the use of protocol analysis,

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widely accepted by the researchers who use it, can be summarized as follows: While

retrospective recall allows the subject to make up good stories about how they believe they

solve problems, and stimulus-response methods force us to deduce subjects’ decision

processes after the fact, concurrent verbalization allows the researcher to look directly

inside the black box of cognitive processing, because of the structure of the short term

memory system of the human brain (Ericsson & Simon, 1980).

Protocol analysis has been used successfully in management to separate expert

managers from novices in their problem solving (Isenberg, 1986) and in entrepreneurship

to differentiate entrepreneurs from bankers in how they manage risks (Sarasvathy, Simon,

& Lave, 1998). In the current study, entrepreneurial expertise is embodied in the

entrepreneurial process. Prior researchers have categorized several tasks associated with

this process such as: locating a business opportunity; accumulating resources; marketing

new products; producing the product; building an organization; and responding to

government and society (Gartner, 1985). Each task involves uncertainties including those

due to non-existent markets. Making decisions in the face of such uncertainties, therefore,

constitutes the main content of entrepreneurial expertise.

Entrepreneurial expertise and decision-making under uncertainty

Historically, the research on decision-making under uncertainty can be divided into

(a) the normative development of rational decision models (MacCrimmon, Wehrung, &

Stanbury, 1986) and (b) empirical investigations into bounds on that rationality in actual

decision makers (Kahneman & Tversky, 1990).

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The normative development is rooted in the conceptual distinction between “risk”

and “uncertainty” (Knight, 1921). The commonly used statistical metaphor of the urn

containing different colored balls serves to illustrate the difference between the two

(Kamien, 1994). Problems involving risk are akin to a speculative game with an urn

containing 5 green balls and 5 red balls. The drawer of a red ball is awarded a prize of

$50. For any given draw, we can precisely calculate the probability of getting a red ball,

because we know the underlying distribution of balls inside the urn from which we are

making the draw. Problems involving uncertainty involve the same award of $50 for the

draw of a red ball -- except this time we do not know how many balls are in the urn, of

which colors, or even if there are any red balls at all in the distribution. In statistical

terminology, decisions involving the first type of urn with the known distribution call for

classical analytical techniques; and the decisions involving the second type of urn with the

unknown distribution call for estimation techniques. Once the underlying distribution is

discovered through estimation procedures, the urn with the unknown distribution is

transformed, as it were, into the urn with the known distribution and becomes susceptible

to analytical techniques.

Real life examples of risk include all types of insurance, some areas of the stock

markets, and gaming of various types. Forecasting demand for very well established

products such as Coca Cola or personal computers nowadays also fall within this category.

Some real life examples of uncertainty include dealing with environmental pollution,

global warming, genetic cloning, and commercialization of innovations, particularly

radical innovations.

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Experiments by researchers developing normative models have demonstrated that

human beings in general prefer the “risky or known distribution” urn over the “uncertain or

unknown distribution” urn (Ellsberg, 1961). But entrepreneurship researchers have

speculated that since entrepreneurs have been shown to have a high tolerance for

ambiguity, they would have a preference for the urn with the unknown distribution

(Kamien, 1994).

Both normative approaches have been qualified by researchers who have shown

that human beings in general are not strictly rational (Simon, 1959). Instead, their

rationality is bounded by cognitive limitations such as physiological constraints on

computational capacity (Payne, Bettman, & Johnson, 1993); and psychological limitations

such as biases and fallacies (Bar-Hillel, 1980; Tversky & Kahneman, 1982). Yet this does

not imply that decision makers are irrational. Rather, the evidence suggests that within

certain bounds, decision makers use heuristics that often lead to very effective decisions

(Gigerenzer, Hell, & Blank, 1988).

The arguments from both perspectives – rationality and bounded rationality – can

be summarized as follows. If the decision makers believe they are dealing with a

measurable or relatively predictable future, they will tend to systematically gather

information and invest some effort on a reasonable analysis of that information, within

certain bounds. Similarly, if they believe they are dealing with relatively unpredictable

phenomena, they will try to gather information through experimental and iterative learning

techniques aimed at first discovering the underlying distribution of the future.

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The concept of effectuation suggests a rather different logic for the choice process:

"Whatever the initial distribution of balls in the urn, I will continue to acquire red balls and

put them in the urn. I will look for other people who own red balls and induce them to

become partners and put their balls in the urn. As time goes by, there will be so many red

balls in the urn that almost every draw will obtain one. On the other hand, if I and my

acquaintances have only green balls, we will put them in the urn, and when there are

enough, will create a new game where green balls win." Of course, such a view may

express hopes rather than realities, and many entrepreneurs in the real world do fail. This

fact does not negate the hypothesis that they are often more concerned with molding, or

even creating, the part of the world with which they are concerned than with predicting it

and reacting to the prediction.

For the purposes of this study, the first approach– i.e., decision models dealing with

the “known distribution” -- is called ANL (for analytical approaches). ANL includes

traditional market research techniques such as focus groups and questionnaires and/or

hiring professionals to study the market and come up with decision alternatives. The

second – i.e., decision models dealing with the “unknown distribution” – is deemed BAN

(for Bayesian approaches – “Bayesian” stands for all types of statistical estimation using

iterative discovery procedures). BAN primarily includes test marketing, trial ballooning

and other systematic techniques of experimentation and iterative learning aimed at

discovering the structure and shape of the potential market.

At their logical core, both ANL and BAN are causation models. They require the

decision maker to start with a pre-defined potential market and seek information about it

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using two different categories of approaches – analytical (ANL) and estimation (BAN). In

contrast, effectuation, referred to as EFF for hypothesis testing, incorporates the logic of

control, and involves attempts to shape and create the potential market rather than divine it

through analytical or estimation techniques. EFF includes statements not only attesting to

subjects’ ability and desire to shape the environment through commitments from key

stakeholders, but also codifies explicit comments rejecting techniques of ANL and BAN.

The central hypothesis for the first stage of data analysis can now be stated as:

Hypothesis: When faced with creating a firm that markets a new product, expert

entrepreneurs, as opposed to novice MBA students, prefer effectuation (EFF) to the

two types of traditional market research techniques involving analysis (ANL) and

estimation (BAN).

METHOD

First, entrepreneurial expertise was operationalized as a set of criteria for sample

selection. Second, a research instrument was developed to capture the information seeking

tasks involved in discovering and/or creating the market for a new product. Third, a

comparison sample of novices was selected. Fourth, subjects completed the think aloud

task and their concurrent verbal protocols were collected. Coding and analysis of the

protocols proceeded in two stages. In the first stage, the hypothesis was tested and the

existence of effectual reasoning in expert protocols and relative non-existence among

MBA students established. In the second, a process model of effectuation contained in the

expert protocols was inductively extracted and contrasted with causal models widely used

in the MBA curriculum.

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Subjects

Current literature on expertise distinguishes experts using a construct called

“Deliberate practice” (Ericsson and Lehmann 1996). Exceptionally high task performance

is consistently associated with experts as they solve complex problems in their domain

more quickly, more easily, and more accurately than novices (Simon and Simon 1978;

Larkin, McDermott et al. 1980; Charness, Reingold et al. 2001). But experience alone is

not sufficient for expertise. The systematic differences between experts and novice

individuals within a domain nearly always reflect attributes acquired by experts during

their lengthy period of deliberate practice (Ericsson and Lehmann 1996). This literature

has established that deliberate practice takes time. Over three decades of research in the

area has converged on the original “10-year rule” that Simon and Chase (1973) posited.

While not hard and fast, the rule suggests that it takes a minimum of 10 years of deliberate

practice for a novice to ascend to the rank of expert.

For the purposes of this study, an expert entrepreneur is defined as a person who,

either as an individual or as part of a team, has founded one or more companies, remained

with at least one company they founded for more than ten years, and had taken it public.

The public company requirement not only satisfies a very stringent definition of

entrepreneurial expertise, but also provides additional data about the actual experience of

the subjects in the form of annual reports, press kits etc.

Two sources were utilized to identify possible expert entrepreneurs for the study:

(1) A list of the one hundred most successful entrepreneurs from 1960 to 1985, compiled

by the venture capitalist, David Silver (Silver, 1985); and, (2) The list of national winners

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of the Entrepreneurs of the Year awards, compiled by Ernst & Young. Together, the two

sources drew their members from a pool that included virtually every enduring company

created by an entrepreneur in the US from 1960 until 1996. As clearly outlined in their

publications, both sources used several evaluation procedures and qualification criteria to

select their lists from the complete populations of entrepreneurial companies in their

respective times. Thus the sample for this study was drawn indirectly from the complete

population of entrepreneurs at large, and directly from a complete population of expert

entrepreneurs.

The characteristics of the final pool of expert entrepreneurs suggest the sample is

fairly representative of the population of expert entrepreneurs. Subjects from 17 states

across the U.S. were all male, 90% American, aged between 40 and 82, with two thirds

having graduate degrees. While all subjects were male, there is no reason to believe that it

would make the sample less representative since the percentage of female entrepreneurs

who fulfilled the necessary criteria in the original population was less than one half of 1%

to begin with. On average subjects had founded seven new ventures, with the minimum

number being three. Besides founding a company, actively running it, and taking it public,

the subjects have a variety of entrepreneurial experiences including multiple ventures,

failures both before and after their successes, mergers and acquisitions, major PR coups

and disasters, taking a public company private, etc. The companies they built range in

annual sales from $200 Million to $6.5 Billion. The companies also span a very wide

range of industry groups, including retail goods and services, household products such as

teddy bears, ice cream and razors, security services, contract programming, computers,

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software, telecommunications, media, biotechnology, environmental technologies, steel,

railroads, power plants, and more.

Protocols were collected from 27 experts and 37 novices. Based on precedents in

the “deliberate practice” literature on expertise, we sought a control group of novices who

would have enough experience in basic business knowledge so as to understand and tackle

the problems in the research instrument, yet could be starkly contrasted with the experts in

terms of their entrepreneurial experience. We chose 37 graduate students in business

administration. The subjects in the novice group were 97% American, aged between 26

and 46, with primary experience in managerial roles in large and complex organizations.

Their backgrounds spanned a wide range of occupations, including pilots, acquisitions,

supply and logistics, manpower recruitment and management, operations and maintenance,

and medical services. Comparing this novice group with the expert entrepreneurs on key

indicators of entrepreneurial expertise showed that the groups were dichotomous. 87% of

the MBA students had never founded a firm and, of those that had, only one had started

multiple ventures (in that case, two).

Using a sample of MBA students as the novice (control) group in this study raises

two issues. First, what makes these subjects novice entrepreneurs and not novice

managers, novice doctors or novice chess players? And, second, might a better sample of

novice entrepreneurs exist? To the first question, while there is no doubt that MBA

students are also novice managers and perhaps even novice doctors or chess players, that

does not preclude them from being considered novice entrepreneurs. In fact, MBA

students are as likely as any other group of individuals, if not more likely, to pursue an

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entrepreneurial career. Furthermore, there is an established tradition of using students in

expertise experiments in spite of the fact that their “novice-ness” may extend to

dimensions not of interest to the study (Lehmann and Norman 2005, Andersson 2004).

For example, Isenberg (1986) used 12 general managers and 3 college undergraduates in a

think-aloud protocol study to develop and test a model of managerial decision making.

This view is generalized by researchers in the discipline of psychology where novices are

defined as people who have not experienced the 10-year process of deliberate practice

involved in becoming expert (Simon and Chase 1973).

Addressing the second question, using MBA students as the novice sample in this

study provides the advantage that it allows us to ensure a common baseline of knowledge

in business fundamentals. Without a comparable knowledge across both our novice and

expert samples, the findings from the business specific task used in our protocol might be

confounded simply by lack of familiarity with business in general. For example, a sample

of people with a stated desire to become an entrepreneur, or even with a small amount of

entrepreneurial experience, might also be an effective novice sample. However, a random

sample of these would likely provide two sources of variation – one from the lack of basic

business knowledge and another from the lack of entrepreneurial experience. In selecting

the novice sample we weighed the costs and benefits of the two sources of variation and

found that the benefits of comparability across basic business knowledge outweighed any

costs due to the stark contrasts in entrepreneurial experience – in fact, we found the latter

to be of added value than a cost to the experimental choice. This is especially important

because our findings in this particular study do not hinge on interest and/or genuine

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intention to start the business in the protocol, but around the knowledge base that drives

the steps and processes of starting and operating that business.

Both during the experiment and afterwards, several subjects – both expert

entrepreneurs and MBA students - mentioned they found the problems interesting, realistic

and absorbing. Several experts commented that the problems reminded them of actual

decisions they had to make in their real life entrepreneurial experience.

The Research Instrument

Subjects were given a detailed description of an imaginary product called

Venturing. Since the expert sample varied (intentionally) in all aspects except

entrepreneurial expertise, the decision problem used in the study had to be chosen so as not

to technologically or otherwise bias some subjects against others. Therefore

entrepreneurship itself was made the product for which the subjects had to identify/create a

market. Venturing, as described fully in Appendix 1, is an imaginary game of

entrepreneurship. Based on the description, subjects were asked to answer the following

five questions:

1. Who could be your potential customers for this product?

2. Who could be your potential competitors for this product?

3. What information would you seek about potential customers and competitors -

list questions you would want answered.

4. How will you find out this information - what kind of market research would

you do?

5. What do you think are the growth possibilities for this company?

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Protocols from Question 4, hereafter called the MR (Market Research) question,

were used to test the central hypothesis. Thereafter, protocols from all the questions were

analyzed to develop the grounded process of effectuation that the subjects used.

DATA ANALYSIS 1: QUANTITIATIVE TESTS OF THE HYPOTHESIS

Coding

First, the protocols from the MR question were read several times to identify

relevant semantic chunks for coding. The semantic chunk is the primary unit of analysis

for hypothesis testing. A semantic chunk can range from a single phrase or sentence to a

string of sentences that hang together to make a single meaningful point about the decision

at hand. Second, the chunks were coded by the authors and external coders. The external

coders did not participate in the study in any way except for this particular task, and were

unaware of the hypothesis. The semantic chunks were coded based on a coding scheme

segregating them into three main categories ANL, BAN and EFF -- developed especially

to test the hypothesis in the study. Coded chunks are quoted throughout the ensuing text.

Experts are numbered E1 to E30 and novices are numbered N1 to N37. These numbers are

parenthesized at the end of each quoted chunk for the reader’s convenience.

ANL included statements suggesting the use of traditional market research methods

such as focus groups and surveys and/or hiring professionals to conduct such market

research. Examples of semantic chunks coded ANL include: “I would also go to the

market-- like 4 or 5 market research companies to find out whether they had done any

research in this area.” [E8]; “It’s easy to hire a research company, I think. Outsource that.

Yeah, because that will probably take a lot of time. I mean, to find kids, you’d have to

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walk the malls and walk the schools, or whatever. To find home-based people, you’d have

to probably knock on doors. Not very exciting. To find professionals, just getting time

from them would probably be very challenging. I would think outsourcing would probably

be a good idea” [N27]; “I would want to do some market research relative to who I think

my core customer groups are and what percentage are more likely to buy.” [E28]

BAN included statements suggesting the use of systematic iterative learning

methods such as test marketing and trial ballooning. Examples of semantic chunks coded

BAN include: “As far as the school boards, that would be a much more involved and

difficult research project and almost seat of the pants.” [E23]; “I’d probably do some test

marketing.” [E7]; “Or you try it on a kind of trial and error basis. If you see it works, it

comes back to you and you change it before you officially launch it.” [N24]. Examples of

semantic chunks coded as EFF are used throughout the section titled “Data Analysis 2”.

Tests of the Hypothesis

Of the total of 306 semantic chunks used for hypothesis testing, 71 (23%) were

coded ANL, 48 (16%) were coded BAN and 187 (61%) were EFF. The external coders

found 13 of the 306 chunks not applicable to the question under consideration, i.e., the way

the subjects would do market research. Four of these pertained to one subject, E10. A total

of 40 mismatches were found between coders, included the 13 disputed chunks mentioned

above. Inter-coder reliability was calculated at 94% (James et al, 1993).

------------------------------

Insert Table 1 about here

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Comparing the expert entrepreneurs and MBA students, experts used EFF in their

cognitive processes 75% of the time, whereas the students used EFF 16% of the time.

Almost two-thirds (66%) of semantic chunks coded for the novices were ANL, compared

to 10% ANL for experts. There was no significant difference between experts (15%) and

novices (16%) in their use of BAN reasoning.

We used ANOVA to compare the differences between the expert and student

groups. For our analyses, we calculated three percentages for each subject; causal chunks

as a percent of total, Bayesian chunks as a percentage of total, and effectual chunks as a

percentage of total. We then compared experts and novices in their use of the three types

of thoughts. Novices demonstrated an overwhelming use of analytical thoughts, (F =

49.10, p < .001), experts demonstrated an overwhelming use of effectual thoughts (F =

55.45, p < .001), and there was no significant difference between the groups in their use of

Bayesian thoughts (F = .02, p = .89).

Because we had 27 experts and 37 novices in our data set, and one of the

assumptions inherent to ANOVA is that of balanced group sizes, we conducted three

additional analyses, each using a different strategy for eliminating approximately 10 novice

subjects, to test the robustness of our results. In the first, we eliminated the 11 subjects

from the novice group who had experience in a small firm, had new venture experience, or

had started a new venture. Our results were unchanged. In the second, we eliminated the

10 oldest subjects in the novice group from the complete data set, and again our results

were unchanged. For completeness, we eliminated, the 10 youngest subjects in the novice

group from the complete data set, and our results were still unchanged.

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For additional rigor, a second test of the hypothesis was conducted. Given the fact

that different subjects had a different total number of semantic chunks, this test examined

whether the more loquacious subjects were skewing the numbers in favor of the

hypothesis. We employed a method called the Borda count from the social choice literature

(Saari, 1994). For each subject, the absolute numbers of chunks in the three categories

were converted into relative magnitudes of 0s, 1s and 2s. This is a very conservative

measure, for if a subject such as E21 made no statement pertaining to ANL, 1 statement

pertaining to BAN and 9 statements of EFF, his Borda count would be 0 for ANL, 1 for

BAN and only 2 (instead of 9) for EFF.

The total Borda count for the experts came to 21 ANL; 23 BAN; and 49 EFF. This

means that even after removing the slightest possibility of relative loquacity there is an

overwhelming preponderance of EFF statements – more than twice over ANL and BAN.

Among novices there is an overwhelming preponderance of ANL statements: 40 ANL, 12

BAN and 10 EFF. This means novices made four times more ANL statements than EFF

and three times more ANL statements than BNL. These results establish provisional

acceptance of the hypothesis that in creating the market for a new product expert

entrepreneurs show a clear preference for effectuation, whereas MBA students, as

expected, prefer traditional market research methods. In fact, more than 63% of all the

statements made by 74% of the expert entrepreneurs (20 out of 27) were statements of

effectuation; 7 of the 27 experts did not make any statements other than EFF.

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DATA ANALYSIS 2: DEVELOPING A PROCESS MODEL OF EFFECTUATION

Having discovered that all but 4 of the 27 expert entrepreneurs did not use causal

reasoning to any meaningful extent, the focus of the analysis shifted to the task of

identifying the specific process model of effectuation the 23 expert entrepreneurs used.

The contents of the protocols were analyzed using simple process tracing methods, such as

those developed and employed by researchers in cognitive science as a preliminary step to

writing expert systems (Haines, 1974). This qualitative content analysis involved two

stages. In Stage 1 of the qualitative analysis, the coded chunks from the MR question were

revisited, listing specific suggestions made by the subjects in lieu of using causal

reasoning, grouping similar suggestions together into common categories and identifying

and counting repeating patterns in the suggestions. A similar approach was used in Stage 2

of the qualitative analysis, except transcriptions from all five questions were analyzed, with

the added initial step of first identifying all relevant semantic chunks pertaining to

identifying the market for Venturing.

Two independent researchers, not including the external coder for the Market

Research question, participated in developing the frequency counts used in this analysis.

In the ensuing sections describing the results of the analyses, a large number of original

quotes from the experts’ protocols are presented in support of each frequency count used.

These quotes not only support the emerging decision model discovered in the data, but also

reinforce the high reliability (over 90%) between the researchers.

Exactly as suggested by the model of effectual reasoning, the experts started their

decision-making process with a given set of means, rather than a pre-determined goal

22

(Stage i). Three categories of “means” emerged from the data. Experts selected their first

“customer” based on any one or a combination of the three categories: (1) Who they (the

subjects) were; (2) What they knew; and (3) Whom they knew. Initial customer selection

based on who the subjects were included statements such as: “I’d rather sell to corporate

America because I don’t like schools” [E22]; “I’d rather be in the education business than

in the game business” [E2]; and “I am intrigued by games, I really am, I think it is an

exciting area” [E3]. The second category of “what they knew” had two sources for initial

customer selection. Subjects in this category either used their previous work experience or

an analogy of something they had experienced in one way or another. Five experts used

other games (such as Monopoly, Mousetrap, Sim City, Civilization, etc.) as analogies and

talked about either themselves or their kids enjoying computer games, and so making kids

or well-to-do adults the first customers. Three had direct experience selling other types of

toys and games or had been involved in educational software startups as potential

investors. Under the third category of “whom they know”, experts often selected a

strategic partner as their first customer. Six experts selected a business school professor

they knew as their first customer. Even some of the subjects who selected their first

customer from the earlier two categories suggested making strategic partners of the first

few. For example, E26, quoted earlier said, “Traditional market research says, you do very

broad based information gathering, possibly using mailings. I wouldn’t do that. I would

literally, target, as I said initially, key companies who I would call flagship, do a frontal

lobotomy on them…. The challenge then is really to pick your partners, and package

yourself early on before you have to put a lot of capital out.”

23

Converting initial customers into strategic partners was the most popular method of

developing a customer/segment definition (Stage ii). Another strongly preferred method

was to talk directly and sell to customers/strategic partners at a very early stage. Seven

experts suggested selling even before the product was developed or produced. For

example, E1 said, “Somebody once told me the only thing you need is a customer and I

think I’d start instead of asking all the questions I’d go and say.. try and

make some sale. I’d make some… just judgments about where I was going -- get me and

my buddies -- or I would go out and start selling. I’d learn a lot you know.. which people..

what were the obstacles.. what were the questions.. which prices work better and just DO

it. Just try to take it out and sell it. Even before I have the machine. I’d just go try to sell

it. Even before I started production. So my market research would actually be hands on

actual selling. Hard work, but I think much better than trying to do market research”. E4

said, “Every product that potential customers are using, when critically examined, might

give you insight on one aspect of your particular product. So you don’t have to yourself go

and do massive experiments. You can actually, by looking at half a dozen different

products, you might actually learn about customer behavior, their need and their aspiration

and.. dynamics. So, without even going and building a product, you might want to get

some understanding of the dynamics of that particular market that you’re.. since it doesn’t

exist, that’s the best you can do”.

The process for moving from a single customer or strategic partner to a market

consisted of two additional stages: (iii) Adding segments either through the development

of additional products for the initial segment or through strategic partnerships; and, (iv)

24

Defining a market through a strategic vision for the company. One particular protocol

(that of E5 from question 5) is presented in Appendix 2 in its entirety since it illustrates

these last two stages spectacularly. Table 2 contains the process tracing based on this

protocol. The process tracing is used along with data from question 1 and 4 to synthesize

the complete process of effectuation used by E5.

------------------------------

Insert Table 2 about here

------------------------------

E5 selected his initial customer through his previous experience. He had recently

published a book and selected potential entrepreneurs or students of entrepreneurship, the

audience for his book, as his first customers. Then, in question 4, he suggested

understanding his first customer by going to his publisher -- “So the places I would go

would be the first rocks I would turn over and I would undoubtedly find leads within leads

in the places that I talked about.” If he were not in such a position, he would, he said,

“…try to find a mentor who had written a successful business book, convince the mentor

that this is a great product and get in that way.”

Having thus identified a customer and defined a target customer segment in terms

of current readers of books on entrepreneurship, he began question 5 by stating he did not

believe this product had great market potential. But he proceeded to consider abstract

aspects of both the product and the initial customer segment, defining both tentatively in

terms of a theoretical single market as any learning in an interactive situation where

simulation is a benefit. Gradually thereafter, adding new segments in an iterative process

of refining and re-formulating his original definitions, he ended up creating a market (or

25

more precisely, in this experiment, the vision of a market) defined as any organization in a

learning situation with technical requirements from which, in his own words “…you could

see a several hundred million dollar company coming from it.” Other experts used other

words but the pattern was repeated. For example, E8 said, “It’s sort of like driving a car

down the street. You never point the car and just hang on to the wheel without moving it.

As the car moves along you have to adjust the steering wheel to keep it going even in the

straight direction.”

Comparing the Process Followed by MBA students

After finding that novices had a strong preference for a causal mode of reasoning

(78% of novices made no EFF statements at all) we also conducted a qualitative data

analysis of the causal reasoning process followed by novices. The first striking pattern in

novices’ decision-making processes was the considerable faith they placed in the efficacy

of market research. Novices made statements such as: “If your market research and your

survey shows that there is a benefit, then I think there is no reason why schools wouldn’t

use this as a teaching tool for something as part of their curriculum” (N35); “[Y]ou would

want to do some kind of market research. You don’t want to go out there unknowing. You

need to have something to work with” (N10) and; “I think once you get the market

research down and you find out what’s actually out there between your competitors and

what the customers want, I think there is some possibility.” (N18). The unquestioned

assumption that market research was appropriate to the scenario stood in sharp contrast

with expert entrepreneurs who largely rejected traditional market research in their decision

process, making comments such as: “I mean… I don’t believe in market research actually”

26

(E1) and; “You can’t go out and survey customers and say, o.k. what kinda car do you

really want? and so forth” (E7).

Novices then proceeded to enumerate several alternative market research methods

they would use. There were three frequently mentioned methods, each of which is

commonly found in marketing textbooks (Kotler 1991). The first was Internet and other

public sources of information. The MBA students made comments such as: “I’d get on the

Internet and Google about everything” (N15); “I would try to find out as much as I could

on my own through the Internet or research materials, publications, current publications,

periodicals” (N23) and; “What kind of market research would you do? Google is a good

one” (N11). The second research method was surveys. N36 said that, “I would probably

want to develop a type of survey that I could have people actually write down their

answers, so that I can evaluate it”. N11 said, “Questionnaire. Maybe online survey… [I]f

it is an online survey, I know that the people who answer that will already be computer

savvy, Internet aware.” A third frequently favored market research method was focus

groups, drawing comments such as, “I guess just focus groups. Show people the game if

it’s available. If it’s not available, tell them about it and see what they think of the idea”

(N17) and; “You could… focus groups for different… you could bring in different kinds of

customers… just bring them in and ask them what they would be looking for if they had a

program like this” (N34).

The process followed by the MBA students was vividly illustrated by N19, whose

protocol for question 4 of the problem set is presented in Table 3. N19 starts by saying he

would want to know about existing markets (“the markets”) and competitors in those

27

markets, without hesitating for a moment to consider whether a market for venturing pre-exists. Then he addresses the universe of all theoretically possible customers, referring to

“potential customers who’d be interested in the product anywhere?” and saying that he

would “Think of all the potential markets out there”, which is the common starting point

recommended in marketing textbooks (Kotler, 1991). He mentions a rudimentary

segmentation of the assumed market and turns to the Internet as a means of gathering data

on these segments. The process trace finishes with the admonition that for the Venturing

product “[Y]ou could just market the hell out of it and it doesn’t really have to be all that

good.” At no point does N19 actually define initial target customers, or talk about how he

will reach them and get them to buy. This pattern is repeated in other novice protocols. The

result was typically a free-floating analysis of a theoretical market, which stood in sharp

contrast with the concrete way the expert entrepreneurs said they would build a wide

variety of specific markets for Venturing.

------------------------------

Insert Table 3 about here

------------------------------

Putting It All Together: Causation and Effectuation

When put together as a process model, it became increasingly clear that the process

emerging out of the expert data was an inversion of the causal reasoning we teach students

in entrepreneurship classes. Figure 1 graphically contrasts the effectuation process with

the Segmentation-Targeting-Positioning process, clearly illustrating the reversal of the

causal direction.

------------------------------

Insert Figure 1 about here

28

------------------------------

In this model, the decision maker does not start with a pre-determined effect or a

pre-defined market to be created. Instead the process begins with identifying a set of

possible causes as given (who the decision maker is, what he/she knows and whom he/she

knows), and proceeds to choose between several possible effects in a contingent manner,

taking advantage of new opportunities as they arise. The evidence shows effectuation is

intrinsically path-dependent and contingent rather than goal-driven and planned.

A comparison of the different stages of effectuation identified in the data with the

stages involved in the textbook marketing model of Segmentation-Targeting-Positioning

illustrates the causal inversion at the core of effectuation. In the textbook version, the

process starts with a pre-defined market consisting of all possible customers for the

product (Kotler, 1991). Information is gathered about this pre-defined market using

techniques such as focus groups, surveys, etc. The market is then divided up into segments

using relevant segmentation variables. Thereafter, based on an evaluation of their potential

for achievable market shares, one or a few particular segments are selected and targeted.

Finally, the product is positioned within the target segment/s in an optimal manner subject

to resource and technological constraints.

A decision maker who uses the textbook model to create a market for Venturing

would begin with a universe of all possible customers (just as N19 did). This market

would therefore be defined as all people who are computer literate and interested in either

computer games, learning about entrepreneurship or both. This pre-defined market would

then be segmented based on variables such as age, spending power, previous purchases of

29

computer games and/or entrepreneurship education, geographical location etc. Information

would be gathered about each of these segments and some evaluation criteria would be

developed based on size, growth potential, risk-return profile etc. One or more segments

would then be selected as target segments with a view to maximizing potential return.

Marketing strategies including distribution, pricing and promotion would be crafted and

Venturing would be carefully positioned to capture the hearts and pockets of the individual

customers in the target segment. None of the expert entrepreneurs in this study, including

the four who suggested using traditional market research techniques, actually used this

top-down causal model for creating the market for Venturing.

DISCUSSION

This paper set out to empirically establish the existence of effectual reasoning in

entrepreneurial expertise as compared with students taught on current MBA curricula, as

well as to suggest bounds between causation and effectuation in how expert entrepreneurs

bring into existence future products and firms in the absence of current markets for them.

The evidence gathered here provides strong support for the hypothesis that expert

entrepreneurs have a preference for using effectual reasoning in creating markets for new

products. In fact, over 63% of experts used effectuation more than 75% of the time. By

comparison, 78% of the MBA students did not use effectuation at all. Furthermore, starting

with the exact same imaginary product Venturing, the effectual reasoning used by experts

led them to build 18 different companies, several of which involved completely disparate

industries. Independent evidence suggests this phenomenon is not peculiar to this study,

and that it occurs in the real world: one study of a single MIT invention showed how eight

30

entrepreneurs developed eight different companies based on their prior knowledge and

experience, rather than competing within the same pre-determined market, as causal logic

would predict (Shane, 2000).

It is important to note expert entrepreneurs did not use effectuation processes

across the board, and some MBA students did use effectuation to a limited extent. In

addition, both experts and novices suggested using test-marketing approaches to some

degree, a decision model we term Bayesian (BAN). Furthermore, independent evidence

shows expert entrepreneurs recognize that once the market has been created, a more

traditional and causal decision model is both useful and necessary (Sarasvathy et. al.,

1998). In fact, experts expressed lucid notions on the differences between an

“entrepreneurial” decision context and a “managerial” one. There is also evidence that a

large proportion of successful founders of new ventures do not survive through the full

growth cycle leading to an enduring firm with sustainable market leadership. For example,

approximately 50% of founding entrepreneurs in venture capital backed firms are fired

before the firm reaches a successful exit. Discovering and investigating the details of this

transition point from entrepreneurship-driven organization to the birth of a strategic

management-driven organization suggests important areas for future research.

A second potentially fruitful area for further research might address to what extent

effectuation is a trait and to what extent it is a direct result of entrepreneurial experience

and expertise development. We use the term “trait” here to mean a medium-term constant

attribute of a person – some aspect of ability or behavior that remains stable over time. As

a result of conversations with psychologists, we have come to acknowledge that there may

31

be a traits aspect to effectuation. In other words, there may be some human beings who are

naturally better at or prefer the use of this logic. But the data also suggest that as a group,

expert entrepreneurs have learned to prefer an effectual logic for actions in the early stages

of firm formation. This is based on the observation that experts overwhelmingly preferred

effectuation to causal approaches in the Venturing problem, though they were equally

capable of using a causal logic.

A third and final potentially fruitful area of further research would be to develop

and test the implications of effectuation for new venture success and failure. Explaining

new venture success/failure has been one of the holy grails of entrepreneurship research. It

is necessary, therefore, to consider what implications this study has for that question. We

attempt below a brief outline of possible theorization in this regard.

The essence of effectuation is the use of non-predictive strategies including the

affordable loss principle. In contrast, a causal approach involves calculating the levels of

investment required to achieve certain levels of expected return and predicating actual

plans and implementation on those calculations. Because entrepreneurs that use

effectuation invest only what they can afford to lose, their pattern of investments mirrors

the growth (shrinkage) of the firm. This means at any given point in time, should failure

occur, the effectuator is likely to lose less in terms of investment than the entrepreneur who

invests using a causal logic.

The corollary to this, of course, is that the effectuator may not make adequate

investments in time to exploit a really large or extremely fast-growing opportunity, and

therefore may lose out on the upside, either to other stakeholders or to competitors. In

32

either case, we could speculate that effectuation may or may not reduce a firm’s

probability of failure but it does reduce the costs of failure. Furthermore, the fact that each

failure occurs earlier and at a lower level of investment might have some positive

implications for the effectual entrepreneur. For any given level of initial investment in a

new venture, the effectual entrepreneur survives longer. Lower costs of failure mean more

experiments, so the effectuating entrepreneur reaps more benefits from cumulative

learning. Lastly, even if we assume that only small successes are dependent on expertise

whereas homeruns are drawn from a random distribution, it is plausible to suggest that the

effectual entrepreneur gets to explore more opportunities than does the causal entrepreneur.

In other words, effectuation gives the entrepreneur more shots at the jackpot.

In sum, this paper has established the existence and delineated the bounds of

effectual reasoning in expert entrepreneurial decision making in the absence of markets for

new products. While further research is required to carve out its logical structure in more

detail and identify its theoretical and empirical links with performance, early speculation

suggests provocative possibilities in this regard.

33

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37

TABLE 1

Descriptives of Expert and Novice Samples

Expert Subjects (N = 27)

Variable Mean s.d. Minimum Maximum

Year of birth

Ventures started

Years worked for those

Count of Analytical thoughts

Count of Baysian thoughts

Count of Effectual thoughts

Year of birth

Ventures started

Years worked for those

Count of Analytical thoughts

Count of Baysian thoughts

Count of Effectual thoughts

1943

7.3

21.6

0.9

1.3

6.5

1970

0.2

0.46

1.3

0.4

0.3

8.8

7.4

9.3

1.1

1.7

4.7

4.9

0.4

1.3

1

0.7

0.7

1918

3

12

0

0

0

1959

0

0

0

0

0

1953

40

43

4

6

20

1979

2

5

4

3

3

Novice Subjects (N = 37)

38

TABLE 2

Process Tracing for E5’s protocol for question 5

Original protocol broken into semantic chunks

I don’t think it could ever be a huge company

The basic concept is a business simulator.. startup

simulator

(After) successful launch of the first product (for

potential entrepreneurs) with a big marketing sales push

to penetrate as many different markets as we could..

might have a successful second product.. For example,

you could have a product which is how to succeed,

prosper, grow and get promoted within a large

company.

How do I graduate in the top 10% of your class at

Stanford, or Harvard or Yale.

we’re really talking about any learning in an interactive

situation where simulation is a benefit.

next there is negotiation..

so..there is sales.

So I guess you could go on and on and then you could

genericize the thing to any situation which requires

some sort of technical knowledge.. technical

knowledge of negotiating.. technical knowledge of bio-molecules.. which also involves human organization..

people you have to deal with.. both outside the

company to get them to help.. to work with them and

inside the company to get them to understand what is

the company’s methods objectives etc.

So an organization in a learning situation with technical

requirements

And therefore you could see a several hundred million

dollar company coming from it.

Codes for semantic chunks

Initial perception of potential

First Customer Definition

(He is referring to the first customer

identified in question 1 and

developed into a segment through

“gut feeling” in question 4)

Adding Segments

Beginning of Market Definition

Adding Segments Continued

Market Definition Continued

Market Definition

Change of mind of perceived

potential

39

TABLE 3

Process Tracing for N19’s protocol of question 4

Original protocol broken into semantic chunks

I’d want to know about the market as far as the

competitors go and find out how they’re doing in

the markets.

How much of a market there is, potential

customers who’d be interested in this product

anywhere?

Think of all the potential markets out there,

whether it be high school, college, people sitting

around at home. …

To find out about this market, I’d go on the

Internet first of all and look into the types of, just

Google it and see what’s out there. And see if

there is any kind of, see what the competition is…

In anything academic like this - it seems

academic… you’re teaching people things - I

would say the growth possibilities are good for the

company as long as the program works… [O]r you

could just market the hell out of it and it really

doesn’t have to be all that good.

Codes for semantic chunks

Analysis of existing markets by reference to

existing competitors

Market analysis, per Kotler, by starting with

the universe of “potential customers who’d

be interested in the product anywhere.”

Another reference to the universe of

possible customers, and reference to a

rudimentary segmentation.

Method of doing market research: Internet-based data gathering to understand the

nature of the market and competitors in it.

Perception of the prospects for the firm,

based on perceptions about the general

prospects for the education market and

reference to a general strategy of heavy

marketing promotion.

40

FIGURE 1

Contrasting the Textbook Paradigm in Marketing with Effectuation

Classic Causation Model from Marketing TextbooksMarket DefinitionSegmentation(using relevant variablessuch as age, income, etc.)Targeting(based on evaluation criteriasuch as expected return)Positioning(through marketingstrategies)to reachTHE CUSTOMERCustomerIdentification(throughWho am I?

What do I know?Whom do I know?)Customer Definition(Developing Gut Feeling throughstrategic partnerships & “selling”)Adding Segments/Strategic PartnersMarket DefinitionProcess of Effectuation Used by Expert Entrepreneurs41

APPENDIX 1

Instructions to Subjects and Detailed Description of Venturing

Introduction

Please read aloud the following instructions and the product description. Please

think aloud continuously as you solve the problems thereafter. In the following

experiment, you will solve ten decision problems. These problems arise in the context of

building a new company for an imaginary product. A detailed description of the product

follows this introduction.

Although the product is imaginary, it is technically feasible and financially viable.

The data for the problems have been obtained through realistic market research -- the kind

of market research used in developing a real world business plan. So far, the entrepreneurs

who participated in this study found the project both interesting and feasible.

Before you start on the product description and the problems, I do need one act of

creative imagination on your part. I request you to put yourself in the role of the lead

entrepreneur in building this company -- i.e., you have very little money of your own to

start this company, but you have about five years relevant working experience in the area.

Description of the product:

You have created a computer game of entrepreneurship. You believe you can

combine this game with some educational material and profiles of successful entrepreneurs

to make an excellent teaching tool for entrepreneurship. Your inspiration for the product

came from several reports in the newspapers and magazines about increasing demand for

entrepreneurship education; and the fact that a curriculum involving entrepreneurship even

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at the junior high or high school level induces students to learn not only business-related

topics but math and science and communication skills as well.

The game part of the product consists of a simulated environment for starting and

running a company. There are separate sub-simulations of markets, competitors,

regulators, macroeconomic factors and a random factor for "luck". The game has a

sophisticated multi-media interface -- for example, a 3D office where phones ring with

messages from the market, a TV that will provide macroeconomic information when

switched on, and simulated managerial staff with whom the player (CEO) can consult in

making decisions. At the beginning of the game, the player can choose from a variety of

businesses the type of business he/she wants to start (For example: manufacturing,

personal services, software etc.) and has to make decisions such as which market segment

to sell to, how many people to hire, what type of financing to go for, etc. During the game,

the player has to make production decisions such as how much to produce , whether to

build new warehouses or negotiate with trucking companies, etc.; marketing decisions such

as which channels of distribution to use, which media to advertise in and so on;

management decisions involving hiring, training, promoting and firing of employees, and

so on. There is an accounting subroutine that tracks and computes the implications of the

various decisions for the bottom line. The simulation's responses to the player's decisions

permit a range of possible final outcomes -- from bankruptcy to a "hockey stick".

You have taken all possible precautions regarding intellectual property. The name

of your company is Entrepreneurship, Inc. The name of the product is Venturing.

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APPENDIX 2

Original Protocol of Question 5 from E5

“This company could make a few people very rich, but it cannot.. I don’t think it could

ever be a huge company. The basic concept is a business simulator.. startup simulator..

so.. in the same way in a jet simulator you can hop in and fly something electronically and

not blow it up.. so you can hop into a business situation and practice and get a lot of

reflexes built up and thought processes built up up front. So.. a successful launch of the

first product with a big marketing sales push to penetrate as many different markets as we

could.. might have a successful second product. For example, you could have a product

which is how to succeed, prosper, grow and get promoted within a large company.

Making an equivalent product for the quote organization person as opposed to the

entrepreneur would give you market of everybody with aspirations at IBM, AT&T, Exxon

etc. etc. so.. That product could be a follow-on product.. the research would be similar, the

product development would be similar, and so the production part would be equivalent and

some of the same marketing channels would also work. You could make another product,

would be, for students. How do I graduate in the top 10% of your class at Stanford, or

Harvard or Yale. And there.. you could simulate the learning process in the classroom.

and research traits that tend to make you successful or not. study habits that tend to make

you successful or not. and.. a lot of how to be a good student is teachable. A lot. In my

case for example, I took.. I read my material religiously before I went to classroom, I took

rough notes in the classroom, I always sat in the front row, I always asked more questions

than anybody else, to try to understand what’s going on. Before the next class typically

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two days later, I would meticulously copy all of the notes. Comparing my classroom notes

and enhancing them with reference materials in the textbook. I would copy my notes into

my notebook to create a set of notes that was damn near publishable quality and then prior

to.. during finals week.. prior to finals week I would reduce my entire notebook to cue

cards.. index cards. And when I walked into a final, I had it dead cold and.. that’s how I

worked at Dartmouth for example. So there are studying habits that I’m aware of and you

can do research on successful students and you could develop a profile that the.. marketing

pitch of which should be.. students who graduate in the top 10% of a college class aren’t

just smart in an accident. They have different habits and ways of doing business that cause

them to be successful and those are neither genetic nor intelligence related.. they are

learnable. So there’s your.. now you got a product that can.. you can sell to every student

in the country. uhm.. so we talked about entrepreneur business, big business, students, so

we’re really talking about any learning in an interactive situation where simulation is a

benefit. So you got.. next there is negotiation.. there are books on negotiators.. how to

negotiate.. famous books.. here you could.. in reading a book about negotiation would be

less effective than having an interactive 3D game about negotiation. So there you could

practice being a good negotiator. And that would work. There’s not a salesman in the

United States who wouldn’t buy one of those. How to sell you know so you got you know

another learning situation where how you act and how you push people can can help you

sell better. so.. there is sales. So I guess you could go on and on and then you could

genericize the thing to any situation which requires some sort of technical knowledge..

technical knowledge of negotiating.. technical knowledge of bio-molecules.. which also

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involves human organization.. people you have to deal with.. both outside the company to

get them to help.. to work with them and inside the company to get them to understand

what is the company’s methods objectives etc. So an organization in a learning situation

with technical requirements. That simulation that had those traits so now you can.. I gave

four five endeavors.. you can expand that so.. maybe I’m gonna change my opinion about

the growth potential for the company.. The company could.. it is easy to see how within an

hour you could name ten products and the ten products would address huge markets like all

employees in Fortune 500 companies that.. who are rich enough to pay hundred dollars for

it. So now all of a sudden you can see it’s a software that could be a.. could be a hit on the

scale of Lotus.. what Lotus was to the spreadsheet world. And therefore you could see a

several hundred million dollar company coming from it.”

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