Although policy capturing allows real time, unbiased capture of VC decisions, it does have some short-falls. As with any experiment, the issue of reductionism must be considered. The subjects are exposed to a decision situation which does not perfectly mirror the "real life" decision. Such "paper tests" affect the external validity of many lens model experiments (Brehmer & Brehmer, 1988; Strong, 1992). Nevertheless, policy capturing experiments are a valid method for deriving what information decision makers actually use (Stewart, 1993). Although such "paper" experiments have been criticized, Brown (1972) finds that under even the most contrived cases, the decisions reflect actual decisions. Moreover, since the VC decision has a large "paper" component in the real world (i.e. much of the VC's information comes from business plans), correlation between the experimental task and the "real world" decision should be even higher.
The experiment also forces VCs to make decisions based upon the presented cues. In reality, VCs would (1) have access to a multitude of possible information cues and (2) use interactive due diligence and other methods to clarify and assess reliability of chosen cues. A common theme in the follow up interviews is that VCs like to reserve final judgment until they have a chance to meet with the lead entrepreneur. VCs want a chance to see if they can work with the "guy". In essence, meeting with the entrepreneur adds more data points. As such, the real life decision has far more informational noise than the experiment. As the results suggest, more information impedes decision understanding. In the experiment, the VCs had at most eight cues. It is easy to imagine how hundreds of cues would further confuse understanding. Although there are a number of issues to keep in mind when interpreting the results of this study, the results lead to several interesting conclusions.
This paper suggests that VC's do not have a strong grasp on their decision making process, especially as the decision become information laden. Thus, past studies that provide a laundry list of factors may be biased in that they list a multitude of factors that have a relatively small influence on the decision. In fact, Slovic and Lichtenstein (1971) assert, for most decisions, that three information cues typically account for 80 percent of the variance; one cue often explains 40 percent of the variance. Considering that an actual investment decision has hundreds of available information factors, it likely that VC introspection is especially hindered. As such, post hoc studies may not provide as much prescriptive value as they could because they hide the most important factors with others that create noise.
Although the current paper is concerned with decision understanding, the experiment has implications for another aspect covered in earlier studies (Wells, 1974; Poindexter, 1976; Tyebjee & Bruno, 1984; MacMillan, et al. 1985, 1987; Robinson, 1987; Timmons, et al., 1987); the relative importance of information to the decision. Information necessary to the decision is not of equal importance. George Doriot, a pioneer in the VC industry, notes that "a grade-A man with a grade-B idea is better than a grade-B man with a grade-A idea" (as cited in Sandberg, 1986). It is evident from this quote that VCs do not view all cues as equally important; they do not receive equal consideration. Notwithstanding the VC's ex post assessment that the entrepreneur is the most important consideration, this study derives the actual importance of each presented cue in the VC's decision process. Although the VCs do not have complete freedom in choosing which information cues they wish to use, the relative importance of each cue is interesting. Specifically, each VCs' actual decision policy, as represented by a standardized regression equation, indicates how much weight the respective VC places on each information cue.
Table 2 presents the frequency, by experiment treatment, with which each factor is most influential, second most influential, etc. to the VC's decision. Within Treatment 1, proprietary protection is most important, closely followed by entrepreneur/team characteristics (see Table 2). Market factors (market size and market growth) are the least important. Within Treatment 2, market factors (especially competition cues) are the most important with market familiarity and product factors following (see Table 2). Finally, VCs in Treatment 3 exclusively view product superiority as the most important factor. Buyer concentration (a market factor) is least important. In summary, the type of information available influences the VC's decision process. When certain information is available, it causes VCs to shift their attention (i.e. the addition of competitor information in the additional cues treatment causes the focus to shift to market factors from entrepreneur/team factors). In addition, more information seems to shift the importance from the entrepreneur to the market. Such a finding suggests that the entrepreneur is critical when the VC does not have much information about the market. However, if the VC is confident in the market (or vice-versa), the entrepreneur is not too important. Such a finding is congruent with those of other real time experiments (Hall & Hofer, 1993; Zacharakis & Meyer, 1995). Thus, the current results point to biases in post hoc studies which suggest entrepreneur characteristics are typically most critical to the investment decision.
Implications for VCs are numerous. VCs face a plethora of information when making an investment decision (i.e. Business Plan, outside consultants, due diligence, etc.). It may be difficult for VCs to truly understand their intuitive decision because of all the noise caused by this information overload. This lack of systematic understanding impedes learning. VCs cannot make accurate adjustments to their evaluation process if they do not truly understand it. Therefore, VCs may suffer from a systematic bias that impedes the performance of their investment portfolio. The methodology used in this experiment can be modified and used as a training tool for active VCs. In addition, since VC decision making is very consistent (even if they don't have a strong understanding of that process), decision aides can be developed to minimize the danger of salient information (e.g. the lead entrepreneur is a winner) clouding the VC's judgment.
If VCs do not have a strong understanding of their decision process, they cannot systematically work to improve it. Although VCs are experts, 40 percent of their investments (Ruhnka, Feldman, & Dean, 1992) fail to provide a satisfactory return. Thus, even a modest improvement in the VC decision process can have a significant impact. The methods and techniques used in this study provide a basis to systematically study and understand the process; hence, the current paper provides a basis to improve the process.
It is hoped that this study will encourage VCs to step back and re-evaluate their decision making. The techniques used in this experiment can assist VCs in learning about their actual decision policy. Moreover, the results of such techniques can be used to build decision aides which can further improve VC decision making (Zacharakis, 1995). Any improvement in understanding (which ultimately leads to improved decision making) can have a huge economic impact for both the VC community and their funded ventures.
Barr, P.S., Stimpert, J.L., & Huff, A.S. 1992. Cognitive change, strategic action, and organizational renewal. Strategic Management Journal, 13: 15-36.
Berelson, B. 1952. Content Analysis in Communications Research. Glencoe, IL: Free Press.
Brehmer, A., & Brehmer, B. 1988. What have we learned about human judgment from thirty years of policy capturing. In B. Brehmer & C. Joyce (Eds.) Human Judgment: The SJT View. North Holland: Elsevier.
Brown, T.R. 1972. A comparison of judgmental policy equations obtained from human judges under natural and contrived conditions. Mathematics Bioscience, 15: 205-230.
Brunswik, E. 1956. Perception and the representative design of experiments. Berkeley: University of California Press.
Cyert, R.M., & March, J.G. 1963. A behavioral theory of the firm. Engelwood Cliffs: Prentice Hall.
Dawes, R.M. 1988. Rational choice in an uncertain world. Fort Worth: Harcourt Brace Jovanovich.
Dawes, R.M., Faust, D., & Meehl, P.E. 1989. Clinical versus actuarial judgment. Science, 243: 1668-1674.
Fischhoff, B. 1988. Judgment and decision making. In R. Sternberg & E. Smith (Eds.) The Psychology of Human Thought, 155- 187. Cambridge: Cambridge University Press.
Fiske, S.T., & Taylor, S.E. 1991. Social cognition. New York: Random House.
Gupta, A.K., & Sapienza, H.J. 1992. Determinants of venture capital firms' preferences regarding the industry diversity and geographic scope of their investments. Journal of Business Venturing, 7: 347-362.
Hall, J., & Hofer, C.W. 1993. Venture capitalists' decision criteria and new venture evaluation. Journal of Business Venturing, 8(1): 25-42.
Hammond, K.R. 1977. Social judgment theory: Application in policy formation. In M. Kaplan & S. Schwartz (Eds.) Human Judgments and Decision Processes in Applied Settings, 1-29. New York: Academic Press.
Hammond, K.R. 1975. Social judgment theory: Its use in the study of psychoactive drugs. In K. Hammond & C. Joyce (Eds.) Psychoactive Drugs and Social Judgment: Theory and Research, 69- 105. New York: Wiley.
Hitt, M.A., & Tyler, B.B. 1991. Strategic decision models: Integrating different perspectives. Strategic Management Journal, 12: 327-351.
Hogarth, R.M., & Makridakis, S. 1981. Forecasting and planning: An evolution. Management Science, 27(2): 115-138.
Khan, A.M. 1987. Assessing venture capital investments with non compensatory behavioral decision models. Journal of Business Venturing, 2: 193-205.
Kunkel, S.W., & Hofer, C.W. 1990. Why study the determinants of new venture performance: A literature review and rationale. Presented at Academy of Management meetings.
Lewis, B.L., Patton, J.M., & Green, S.L. 1988. The effects of information choice and information use on analysts' predictions of municipal bond rating changes. The Accounting Review, 63(2): 270-282.
MacMillan, I.C., Zeman, L., & Subba Narasimha, P.N. 1987. Criteria distinguishing unsuccessful ventures in the venture screening process. Journal of Business Venturing, 2: 123-137.
MacMillan, I.C., Seigel, R., & Subba Narasimha, P.N. 1985. Criteria used by venture capitalist to evaluate new venture proposals. Journal of Business Venturing, 1: 119- 128.
Newell, A., & Simon, H.A. 1972. Human problem solving. Engelwood Cliffs, NJ: Prentice-Hall.
Perez, R.C. 1986. Inside venture capital: Past. present and future. New York: Praeger.
Poindexter, E.A. 1976. The efficiency of financial markets: The venture capital case. Unpublished doctoral dissertation: New York University, New York.
Pratt, S.E. 1987. Overview and introduction to the venture capital industry. In S. Pratt & J. Morris (Eds.) Pratt's Guide to Venture Capital Sources. 11th edition. Wellesley, MA: Venture Economics.
Robinson, R.B. 1987. Emerging strategies in the venture capital industry. Journal of Business Venturing, 2: 53-77.
Roure, J.B., & Keeley, R.H. 1990. Predictors of success in new technology based ventures. Journal of Business Venturing, 5: 201-220.
Ruhnka, J.C., Feldman, H.D., & Dean, T.J. 1992. The "living dead" phenomena in venture capital investments. Journal of Business Venturing, 7(2): 137-155.
Sandberg, W.R. 1986. New venture performance. Lexington, MA: Lexington.
Sandberg, W.R., Schweiger, D.M., & Hofer, C.W. 1988. The use of verbal protocols in determining venture capitalists' decision processes. Entrepreneurship Theory and Practice, Winter: 8-20.
Simon, H.A. 1955. A behavioral model of rational choice. Quarterly Journal of Economics, 69: 99-118.
Simon, H.A., & Chase, W.G. 1973. Skill in chess. American Scientist, 61(4): 394-403.
Slovic, P., & Lichtenstein, S. 1971. Comparison of bayesian and regression approaches to the study of information procession in judgment. Organizational Behavior and Human Performance, 6: 649-744.
Stewart, T.R., 1993. Notes on the validity of judgment analysis. Working paper.
Stewart, T.R. 1991. Policy PC: Judgment analysis software reference manual. Albany: Executive Decision Services.
Stewart, T.R. 1988. Judgment analysis: procedures. In B. Brehmer & C. Joyce (Eds.) Human Judgment: The SJT View. North Holland: Elsevier.
Strong, K.C. 1992. A cognitive model of downstructuring strategy. Unpublished doctoral dissertation: University of Colorado, Boulder.
Summers, D.A., Taliaferro, J.D., & Fletcher, D.J. 1970. Subjective versus objective description of judgment policy. Psychonomic Science, 18: 249-250.
Timmons, J.A. 1994. New venture creation: Entrepreneurship for the 21st century. Homewood, IL: Irwin.
Timmons, J.A., Muzyka, D.F., Stevenson, H.H., & Bygrave, W.D. 1987. Opportunity recognition: The core of entrepreneurship. Frontiers Of Entrepreneurship Research, : 109- 123.
Tversky, A., & Khaneman, D. 1974. Judgment under uncertainty: Heuristics and biases. Science, 185: 1124-1131.
Tyebjee, T.T., & Bruno, A.V. 1984. A model of venture capitalist investment activity. Management Science, 30(9): 1051-1056.
Wells, W.A. 1974. Venture capital decision-making. Unpublished doctoral dissertation. Carnegie Mellon University, Pittsburgh.
Zacharakis, A.L., & Meyer, G.D. 1995. The venture capitalist decision: Understanding process versus outcome. Forthcoming in Frontiers of Entrepreneurship Research, 1995.
Zacharakis, A.L. 1995. The venture capital investment decision. Unpublished doctoral dissertation, University of Colorado, Boulder.
Top of Page
Previous Page | Main Page
© 1997 Babson College All Rights Reserved
Last Updated 4/2/97 by Cheryl Ann Lopez
To sign-up for the Center for Entrepreneurial
Studies' publication lists,
please register with the Entrepreneurship WebTeam.