An entrepreneur’s prior knowledge and experience play a critical role in his ability to identify and exploit entrepreneurial opportunities. Although entrepreneurship research has acknowledged the role that prior information and prior knowledge play in opportunity recognition, few studies have explored their role in entrepreneurial discovery. We test the role of a particular prior knowledge in entrepreneurial discovery within a laboratory setting. Participants were randomly assigned to one of two treatment groups. Those in the propitious treatment were given prior knowledge that oriented them toward the arbitrage opportunity within the experiment, and those in the unpropitious treatment were given prior knowledge that oriented them away. As hypothesized, those in the propitious treatment were significantly more likely to discover the arbitrage opportunity.
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Kirzner (2005, p. 76) emphasizes a distinction between information and knowledge, which states that the former is an input in a process of learning that results in knowledge (i.e. subjectively perceived and processed information). By implication, any information an individual possesses is necessarily subjectively perceived and processed in one way or another (i.e. there is no such thing as a “raw datum” in the mind of an individual). While our focus is on how differences in information affect the kind of opportunity one may discover, we recognize that ultimately, we are examining how differences in knowledge matter across individuals for the kind of opportunities they discover. Indeed, entrepreneurship in Kirzner’s work is fundamentally a theory of perceived information (i.e. knowledge) that may lead to opportunity recognition through the process of alertness. In other words, some knowledge may lead to entrepreneurial discovery, while some other may not. In all cases, Kirzner focuses on knowledge (i.e. perceived information), not simply on “raw” information. Given the links between information (i.e. “raw data”) and knowledge (i.e. subjectively perceived and processed information) and our goals in this paper, however, we do not need to emphasize the distinction between the two.
Another way of stating it is to say that prior knowledge is about directing one’s gaze not enhancing alertness.
Shepherd and DeTienne (2005, p. 93) give the following example: “For example, Ed Pauls, the creator of NordicTrack, epitomizes how prior knowledge allows individuals to identify opportunities. Ed was a trained mechanical engineer whose passion revolved around cross-country skiing. His passion was unfulfilled when inclement weather prevented him from going skiing. Thus, he invented an indoor cross-country ski machine.”
We should also note that many, if not most, economic experiments involve some degree or component of alertness (entrepreneurship). Unfortunately, since the entrepreneurial process is so rarely studied explicitly, we can infer little by way of causal relationships in those experiments. For example, some of the discovery of specialization and exchange found in Kimbrough et al. (2008) must undoubtedly be entrepreneurial. Additionally, there have been several economic experiments endeavoring to tease out the characteristics of successful entrepreneurs (see, for instance, Burmeister and Schade 2007).
Since the only action of interest to us is discovery, rather than profit maximization or total welfare, details of the cost functions and diminishing marginal valuations employed are not relevant, and have therefore been omitted for the sake of space, focus, and simplicity. Selling fruit in the village did produce the highest profit, but that could not be known until after the discovery had occurred. Similarly, relative earnings depended on how early discovery occurred, if at all. Complete cost and payment schedule details are available by request.
Although we did not screen for differences in K T, our design eliminates systematic effects resulting from differences in K T by randomizing treatment assignments.
We use a t test here simply for its familiarity and robustness to non-normal distributions. For a non-parametric alternative, the Wilcoxen Mann–Whitney yields p = 0.049. Table 2 relies on the WMW because the sample sizes there are not large enough to justify the t test. All statistics were generated by STATA 10; code and data are available on request.
We do not provide formal content analysis here. Nor can we correct for the unfortunate fact that surveys done after the experiment are necessarily soliciting retrospection (Gaglio and Katz 2001). We cannot determine whether participants’ ex post discussions of why they behaved as they did actually map to ex ante motivation for their actions or if they are merely ex post rationalizations. As a matter of future research, we would like to have in-process descriptions of activity, though we expect such invasive observation will have strong effects on the observed. The full list of survey responses is available upon request.
Gender, being binary for our sample, is the only category variable with enough observations and variance for us to comment on. We could have added additional controls on group composition differences, but as we discussed earlier, we are not investigating a type identification algorithm, nor do we have any theoretical impetus for what sorts of secondary observations might matter. With hundreds of additional data points, we might get away with some data mining, but we feel such an enterprise is better suited for a different type of study.
The inference may seem unwarranted, but since K t is a part of K T, all else equal, we believe that it is logically correct to surmise that what is true for K t is also true for K T.
As one reviewer pointed out, neither treatment would be considered a control group in the classic sense, in that both groups are given some sort of prior knowledge. Our experiment is therefore more analogous to a test of health effects from various levels of dietary salt intake. In such an experiment, one might be concerned that forbidding one group from having any salt at all over a prolonged period would likely have adverse effects. Therefore, the difference in salt intake between groups would be the variable of interest. Similarly, after careful consideration, we decided that reading no story at all would confound our results, since both the contextualizing aspect of a story and the prior knowledge itself would be at play. We opted instead for equivalent depths of story emersion, but with opposite vectors of relevant prior knowledge. It’s the difference that matters, though of course the difference is impossible to measure directly.
“Three major dimensions of prior knowledge are important to the process of entrepreneurial discovery: prior knowledge of markets, prior knowledge of ways to serve markets, and prior knowledge of customer problems” (Shane 2000, p. 452). Also Shepherd and DeTienne (2005, pp. 104–105) state that “it is important for entrepreneurship scholars investigating the relationship between prior knowledge and the identification of opportunities to distinguish between types of prior knowledge.”
See Ward (2004, p. 175), for instance, “Sometimes knowledge provides a bridge to the next new development and sometimes it becomes a fence that blocks our path.”
See Dimov (2007a, p. 576) who state that “Individuals’ prior knowledge of the opportunity domain increases their likelihood of acting on their initial opportunity insights only when their style of learning is compatible with the situation at hand.”
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1.1 Instructions and screen shots
Welcome to today’s economics experiment!
You will be taking part in a decision making study. We are interested in your decisions that you make on your own. That means, now that the experiment has started, no talking and no texting, please. Please turn off all electronic devices and place them in your bag, under the desk.
If you have any questions at any time during the experiment, or have any trouble with the computer, please raise your hand, and we will come to you to answer your question.
Please click the “Story” button to continue.
We’ll begin with a short story. In this experiment, you will take the role of Mr. Green.
Mr. Green grew up in a small village called Green Village, where he runs a small factory. Mr. Green has always been very similar to his fellow villagers. They usually like the same drinks, food, clothing and art that Mr. Green likes. On his 25th birthday, Mr. Green travels to Brown Town, a small country town 150 miles away. He spends the day shopping in the local market and sampling the local food and drink. Most of what he finds he is familiar with from his home village, but that night he buys an exotic fruit that he’s never had before, and he absolutely loves it. Mr. Green thinks to himself that the other villagers would probably like this fruit.
1.3 Instructions part 1
You earn money in this experiment by increasing Mr. Green’s wealth and happiness. At the end of the experiment, we will add up Mr. Green’s gold coins and happy points and pay you the total divided by 15.
We will now guide you step by step through a few of the possible actions you can perform.
In this experiment, all actions are performed by dragging and dropping pictures, just like you might drag a file and drop it into a folder on your computer.
Practice dragging the gold coin below, and drop it into the factory (then follow the instructions that appear below).
1.4 Instructions part 2
By spending money in the factory, one gold coin, you have created boxes. Now drag the boxes and drop them in Green Village.
1.5 Instructions part 3
Notice that by selling boxes in Green Village, you earned money. Now try spending money in Brown Town to buy fruit.
1.6 Instructions part 4
Good. Now drag the boxes of fruit to Mr. Green.
1.7 Instructions part 5
Notice that when Mr. Green eats the fruit, his happiness increases. Each week, Mr. Green’s happiness increases by 5 with his first box of fruit, by 3 with his second box, and by 1 with his third box.
Each drag and drop counts as one action. You can perform 1 action per day, and there are 6 days per week. After 15 weeks, the experiment will end, and we will pay you your earning in this experiment plus your on-time bonus of $7. The experiment will also end if you run out of money.
How you earn money: Remember, at the end of the experiment, we will add together Mr. Green’s happy points and gold coins, and pay you in US dollars ($) the total divided by 15.
You may review the story and instructions now by clicking on “Story” below. When you are ready to begin, click “Go to Quiz”. However, once the experiment begins, you will not be able to return to the instructions.
Go to Quiz
Here is a little test. Pass, and the experiment will begin. Otherwise, you will return to the story to try again. Be alert!
If Mr. Green eats 2 boxes of fruit in 1 week, the total number of happy points increases by
How far is Brown Town from Green Village?
Mr. Green is generally
Similar to other people from Green Village
Different from other people from Green Village
This first image is the starting screen for the market.
This second image shows the screen after a few turns and in which both boxes and fruit have been purchased.
The first box of fruit sold in the village earned 6 coins, the second 4, and the third 2. Since payment was based on the linear sum of coins and happiness, the ex-post optimal strategy is to consume one and sell two boxes of fruit per week. We found that most subjects who discovered arbitrage either followed this strategy or simply alternated between selling and consuming fruit, occasionally exploring other possible drag-drop combinations.
Well, that about does it.
Please raise your hand now, and the experimenter will come to check your earnings.
Earnings from the experiment: $12
Ontime show-up bonus: $7
Total Earnings: $19
Please explain why you did what you did in this experiment:
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Arentz, J., Sautet, F. & Storr, V. Prior-knowledge and opportunity identification. Small Bus Econ 41, 461–478 (2013). https://doi.org/10.1007/s11187-012-9437-9
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