Principal Topic

The role of information seeking is a crucial element in discovering valuable venture ideas and how to exploit them. Because the requisite information is often rare and nonobvious, this research addresses how to decide how to search for it. Two alternatives have been foremost in the literature: (1) information search theory from Neoclassical economics, which utilizes an optimal stopping rule by which the marginal cost of future searching will equal the marginal value of acquiring additional signals and (2) Kirznerian, unintentional search, which is characterized by “alertness.” The first approach is unrealistic because it requires that markets approximate equilibrium in the way that they allocate information and resources. The limitation of the second approach is that it offers practically no help to aspiring entrepreneurs other than the prescription to stay alert, which is physiologically impossible. Also, alertness has a more familiar name—luck, which we don’t know how to teach. Advocates of the second approach criticize the first for advocating a mechanical optimization process that arbitrarily constrains thinking, whereas advocates of the first criticize the second for having nothing practical to teach.

This research responds to these criticisms by proposing a third alternative, which is based on the selection of a consideration set of promising search possibilities. The consideration set itself is a reflection of an entrepreneur’s prior knowledge and consequently can generate economies of scope in search costs. Within a consideration set, entrepreneurs can access signals from information services, or channels, to which they can subscribe, based on their prior knowledge. Although the proposed approach does not depict how average entrepreneurs discover valuable venture ideas, it does accommodate an impressive new stream of research findings that provides evidence that successful entrepreneurs limit their environmental scanning to focus on searching for ideas that they know best.

Method

This research combines advances in stochastic decision theory to model optimal search within the proposed consideration set. It derives a model, which it applies to a typical search scenario to demonstrate the viability of the approach. Prior to the conference, we will also attempt to train aspiring entrepreneurs in the use of consideration sets and compare the results with those generated by a Monte Carlo simulation to demonstrate that their effectiveness surpasses the results that can be obtained by chance.

Implications

The use of consideration sets to model successful information search incorporates recent research findings and has clear pedagogical value for aspiring entrepreneurs. Although it does not depict how average entrepreneurs actually search, this seems rather unimportant compared to the guidance that it can potentially provide. Clearly, they need guidance because most of them fail in discovering and exploiting valuable venture ideas.