Venture Portfolio Research Articles

Agency hazards and alliance portfolios

Prior research over several decades has catalogued many positive motives underlying firms' decisions to engage in joint ventures and other forms of alliances. In this empirical analysis, we investigate whether agency problems brought about by the separation of ownership and control also stimulate the development of firms' joint venture portfolios. By focusing on joint ventures, as opposed to diversification in general or acquisitions, we address the recent debate on agency theory's domain. Results from a sample of U.S. manufacturing firms' alliance portfolios offer supporting evidence, and comparable findings are obtained for international and domestic joint ventures. Agency hazards are also found to bring about extensions of firms' nonequity alliance portfolios in both the international and domestic settings. Copyright © 2005 John Wiley & Sons, Ltd.

A lack of insight: do venture capitalists really understand their own decision process?

What decision criteria do venture capitalists (VCs) use to make their investment decisions? This question has received much attention within entrepreneurship literature (i.e., Wells 1974; Poindexter 1976; Tyebjee and Bruno 1984; MacMillan, Seigel, and Subba Narasimha 1985; MacMillan, Zeman, and Subba Narasimha 1987; Robinson 1987; Timmons et al. 1987; Sandberg, Schweiger, and Hofer 1988; Hall and Hofer 1993; Zacharakis and Meyer 1995) for a number of reasons. First, VC-backed ventures achieve a higher survival rate than non-VC-backed businesses ( Kunkel and Hofer 1990; Sandberg 1986; Timmons 1994). Second, a better understanding of the decision process may lead to even better survival rates. Finally, entrepreneurs seeking venture funding benefit if they understand what factors are most important to the VC. Although past research has greatly contributed to our understanding of the decision, it may be biased and somewhat misleading. The majority of past studies rely on post hoc methodologies (e.g., interviews and surveys) to capture the decision process. Post hoc methods assume that VCs can accurately relate their own decision processes, but studies from cognitive psychology suggest that people, in particular experts, are poor at introspecting. Introspection is subject to rationalization and post hoc recall biases. Using social judgment theory and the associated lens model as a framework, the current study investigates how well VCs introspect about their own decision process and, by extension, whether the past research efforts are biased. The current research uses policy capturing, a real-time method common in cognitive psychology, to capture the VC's “actual theories in use” versus their “espoused theories” ( Hitt and Tyler 1991). Policy capturing requires that VCs make a series of real-time decisions based on various information factors. Regression analysis of each VCs' decision captures how important each of the information factors is to her/his actual decision process. After the VCs make their decisions, they provided a weighting of how they believe they used the information factors. Comparing the captured decision policies to stated decision policies provides a measure of VC insight. The findings suggest that VCs are not good at introspecting about their own decision process. Even within the confines of a controlled experiment, which greatly reduces the amount of information considered, VCs lacked strong understanding of how they made decisions. Most decision-makers would like to have all relevant information available for their decision. However, as more information becomes available, insight diminishes. Finally, this study finds that VCs are very consistent in their decision process, even though they do not necessarily understand how they make their decisions. 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 process 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, the consistent nature of VC decision-making (even if they do not have a strong understanding of that process) is favorable to the development of decision aides. Decision aides can minimize the danger of salient information (e.g., the lead entrepreneur is a winner) clouding the VC's judgment. Past research also needs to be interpreted in a new light. Although VCs undoubtedly use some of the information cited in past studies, the relative importance of that information needs to be reevaluated. VCs may not, for instance, rely most on the background of the entrepreneurial team. In addition, it is likely that the past studies provide more information factors than VCs actually use. People have a tendency to overstate the information they believe they relied upon and to use far less information (typically three to seven factors) to make a decision than they actually think they use. The methodology used in this experiment has the potential to identify the more relevant information factors cited in previous work. Even though VCs are experts in the new venture funding realm, their decision process has room for improvement. Almost 40% of all backed ventures fail to provide a return to the VC. Considering the billions invested each year, a modest improvement in the failure rate can have a substantial impact on venture portfolio returns. That improvement starts by better understanding the decision process. This study is a step in that direction.

New venture planning: The role of technological forecasting

A system for the analysis and planning of new ventures is developed which provides a structure for the application of logical, mathematical, and scientific procedures to decision problems which (1) involve a significant portion of an organization's resources, (2) have long term effects on a firm's future success, and (3) are characterized by uncertainty in many of the factors important to the decision. The system is based on a synthesis of various analytical techniques from the fields of technological forecasting, decision analysis, and system dynamics, and provides a general methodology for rank-ordering new venture candidates and determining the resource allocation level required for new venture portfolios designed to achieve long term growth objectives. The role played by technological forecasting in new venture planning and in the selection of engineering projects is discussed.