Understanding the Role of Perceptions in Opportunity Evaluation: A Discrete Choice Experiment

Per L. Bylund; Trey Malone

doi:10.1515/erj-2021-0232

Artikel

Understanding the Role of Perceptions in Opportunity Evaluation: A Discrete Choice Experiment

Per L. Bylund und Trey Malone

Veröffentlicht/Copyright: 15. März 2022

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Aus der Zeitschrift Entrepreneurship Research Journal Band 14 Heft 2

Abstract

We construct a discrete choice experiment to study opportunity evaluation by entrepreneurs. This new method in entrepreneurship studies allows us to measure entrepreneurs’ utility functions and thereby their perceptions of opportunity. Consequently, we produce empirical evidence for how entrepreneurs evaluate opportunities beyond the objective facts and find two types of entrepreneurs with different evaluative behavior that cannot be explained by situational or demographic differences. We contribute to the opportunity evaluation literature by measuring perceptions, provide theoretical argument and present empirical evidence for heterogeneity among entrepreneurs, and introduce new methods that allow for more nuanced empirical analyses of entrepreneurial decision-making.

Keywords: discrete choice experiment; latent class analysis; entrepreneurial decision-making; opportunity evaluation

Corresponding author: Per L. Bylund, School of Entrepreneurship, Oklahoma State University, 424 Business Building, Stillwater, OK 74078-1010, USA, E-mail: per.bylund@okstate.edu

Appendix

We provide the following explanations of our methods in an effort to help entrepreneurship researchers incorporate discrete choice experiments into their experimental program.

Step 1. Specify the Theoretical Model

Because DCE’s represent a unique method for collecting decision-making data, the first step is to identify what decisions are most of interest for the research project. When entrepreneurs choose what business they want to start, they reveal which market characteristics they value. For our purposes, we specify entrepreneur i’s utility of selecting business choice j in choice option s as:

(1) U i j s = β j + α 1 ⋅ Subsidy j + α 2 ⋅ Ocean j + τ 1 ⋅ Govt.Barriers i j + τ 2 ⋅ Riskiness i j + τ 3 ⋅ Nat.Barriers i j + τ 4 ⋅ Growth i j + ϵ i j s

where the alternative-specific constants β j indicate the utility of business venture j not explained by the perceptions or preferences defined in the model, Subsidy_j is the annual subsidy allocated to entrepreneurial venture j, α is the marginal (dis)utility of the subsidy j, Govt.Barriers_ij is entrepreneur i’s perception of government imposed barriers to entry rating for business venture j, Riskiness_ij is entrepreneur i’s perception of the riskiness of business venture j, Nat.Barriers_ij is entrepreneur i’s perception of the natural barriers to entry for business venture j, Growth_ij is entrepreneur i’s perception of the growth potential for business venture j, ϵ i j s is the unobserved portion of the utility function, and all τ h represent the marginal utilities associated with changes in perceptions. Note that the specification of this model includes an error term, which makes this equation random. As such, anything not included in the utility function is captured by the error term.

Step 2. Design the Experiment

The design of the DCE is often the most difficult step in the process. If all attribute combinations were to be included in the survey, the number of questions asked to each participant is likely to exhaust the participant. For example, if we wanted to include four choice options, each with two attributes that were provided at three attribute levels, the total number of questions required would be (4 × 3)², or 144 questions. To reduce the number of required questions, different assumptions can be made regarding the independence of each attribute. The complexity of these designs often depends on the complexity of the question of interest, with the number of choice questions depending on the number of attributes included on the survey and the number of choice options provided within each question.

While a comprehensive discussion of the merits of each design falls outside the scope of this article (see Kuehl 2000; Hensher, Rose, and Greene 2015), we will focus on describing the most basic experimental design – the main effects orthogonal fractional factorial design. The key benefit to this structure is that each choice attribute is uncorrelated with the others, thereby removing any endogeneity concerns across decisions. The key drawback to this structure is that it assumes all attributes are entirely orthogonal (or unrelated) with one another as well, which means that including interactions between attributes is not advised. For our purposes, we focus on three choice options, each with two attributes and two attribute levels. As such, we used the FACTEX procedure in SAS^® to determine how many questions to ask and what attribute levels to include for each choice question.^[5] While the full design would require each participant to answer (3 × 2)² = 36 questions, the orthogonal fractional factorial design only requires each participant to answer eight questions.

Step 3. Collect the Data

A key step for collecting the data is to identify the population of interest. This group consists of all units to which one desires to generalize the study’s results. The sample frame is a subset of this survey population, as it is the list from which a sample is to be drawn in order to represent the survey population. The difference between the sample frame and the population of interest is coverage error, which is likely to exist at some level in all surveys. While the sample frame is often based on convenience, the level of concern surrounding selection bias and coverage error can be reduced by contracting the sample from professional survey lists, such as those provided by SSI^® or Qualtrics^®. Once this list has been established, a sample must be sent surveys. The survey sample can be defined as the number of participants who received the survey, and the completed sample will be a subset of this population. Power tests are commonly used to determine how many participants will be needed to legitimately represent the population of interest.

Step 4. Estimate the Empirical Model

Many models can be utilized to analyze choice data from a DCE. The model most commonly used is the traditional multinomial logit model (MNL), where the dependent variable is the log of the odds ratio between choice alternative j and some predetermined baseline decision. By including a “none” option in each discrete choice, we used the commonly used baseline of “none” for this study. The estimated log odds ratio is commonly interpreted as participant i’s indirect utility of choosing option j (V _ij), or:

(2) V i j = β j + α 1 ⋅ Subsidy j + α 2 ⋅ Ocean j + τ 1 ⋅ Govt.Barriers i j + τ 2 ⋅ Riskiness i j + τ 3 ⋅ Nat.Barriers i j + τ 4 ⋅ Growth i j .

Although a thorough description of all potential models is outside the scope of this article,^[6] two specifications are likely to be especially useful for questions in entrepreneurial research. Both of these can be conducted using “canned” techniques available in many statistical programs. For this study, we conduct our statistical analysis using NLOGIT^®, which is particularly powerful for estimating limited dependent variable models (see Hensher, Rose, and Greene 2015).

We also hypothesize that entrepreneurs will make decisions based on varying decision processes. The one might interpret the ongoing discussion surrounding the precise identification and differentiation of entrepreneurs from others in the population could be interpreted as being about differences in underlying utility functions. One potentially useful expansion of the MNL model is the latent class model, which assumes that there are underlying, latent characteristics that are likely to make decision-makers respond differently to the choice questions. Without imposing any strong assumptions on the model structure, these latent class logit models effectively separate the data based on the underlying choice structure of the data. Instead of estimating a single indirect utility function (equation 2), these models use an endogenous sorting mechanism to estimate multiple indirect utility functions.

Step 5. Generate Policy Estimates

Additional questions can be answered regarding the effect of changes in attributes and perceptions on a participant’s decision-making. Many policy variables can be estimated, including economic welfare changes (Small and Rosen 1981). A common practice in the non-market valuation literature is to estimate “willingness-to-pay” (WTP) for various choice attributes and perceptions. For instance, Malone and Lusk (2017) find that consumers are willing to pay more for increases in taste perceptions relative to increases in health or safety perceptions. To estimate WTP, a monetary variable must be included in the DCE. Then, to convert the attribute/perception into a willingness-to-pay estimate, divide the desired parameter by the monetary parameter. As an example, we included an annual subsidy for this study. To estimate an entrepreneur’s WTP for new market creation, we would divide α 2 by α 1 .^[7]

We can also estimate the relative probabilities that an entrepreneur would start each of the businesses, and the effect of changes in the specified variables on the likelihood the participant will choose to start that business. Empirically, the probability entrepreneur i chooses business j is:

(3) Prob ( j is chosen ) = e V i j ∑ k = 1 J e V i j

where V _ij is the systematic portion of the utility function determined by the attributes of business venture j, individual-specific characteristics, and individual-specific perceptions.

Elasticities are also often used to evaluate changes. While elasticities are some of the most frequently utilized policy tools in economics, few studies in the entrepreneurship have utilities their explanatory power. Stated simply, an elasticity represents the percent change in one variable given a one percent change in another variable. In economics, elasticities are commonly discussed in terms of prices, as in describing the effect a one percent increase in the price of a product on the quantity demanded of that product.^[8] Although many elasticity specifications can be estimated from discrete choice data, the most simple to understand is a point elasticity. Empirically, the point elasticity of the probability of alternative j for participant i with respect to a marginal change in attribute k of the alternative j can be defined as:

(4) E i j k = α j k X i j k ( 1 − P i j k )

where α j k is the estimated parameter for the attribute of interest, X _ijk is the data from the choice, and P _ij is the probability participant i chooses alternative j.

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Received: 2021-05-25

Accepted: 2022-02-08

Published Online: 2022-03-15

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Artikel in diesem Heft

https://doi.org/10.1515/erj-2021-0232

Schlagwörter für diesen Artikel

discrete choice experiment; latent class analysis; entrepreneurial decision-making; opportunity evaluation