Investment Selection Criteria.

This note briefly summarizes the academic literature on venture capital (VC) investment selection processes.

Our focus is on VCs that concentrate a large share of their capital in early-stage startup investments, which have yet to validate the commercial attractiveness of their products and/or services, as well as demonstrate the effectiveness of their business models to generate revenues and profits. While uncertainty abounds, early-stage startups also provide VCs with the highest potential for the greatest investment multiples. 

We define VCs as “those organizations whose predominant mission is to finance the founding or early growth of new companies that do not yet have access to the public securities market or to institutional lenders” (Gupta & Sapienza, 1992). We consider VCs as financial intermediaries between sources of capital and startup ventures.

As VC performance vis-à-vis its investors is largely a function of the quality of investment decisions, understanding the relative importance of startup selection criteria is crucial to improving VC performance. Such insight is equally useful to startup ventures looking to broadcast "strong" signals that attract VC investments.


Before we introduce the stages of the investment decision-making process, it is important to acknowledge that VCs take on significant risk by design. To conclude that VCs employ decision-making stages primarily to reduce risk is to miss a subtle point, as they are typically more motivated to discover the potential upside payoffs offered by an investment opportunity that reflects significant innovation, than by the downside probable risk of failure due to the inherent liabilities of newness and smallness involved. Thus, the overall VC decision-making process is very much about appraising the potential of an investment opportunity in the absence of objective or definitive business/financial metrics. In fact, there is a widespread belief in the VC community that investment opportunities reside precisely within this "gray space", where VCs do not agree on the potential upside payoffs of a particular startup. The literature on information asymmetries and anecdotal evidence support this viewpoint, as many of the most successful startups were often rejected by certain VCs before receiving an offer from a VC that eventually invested.

VC investment decision-making occurs in sequential and clearly delineated stages. According to Hall & Hofer (1993), scholars who studied venture capitalists’ decision-making agree (1) that it consists of multiple stages; and (2) that the startup evaluation process involves at least two distinct stages: (a) screening, and (b) evaluation (i.e., due diligence).

A brief synopsis of the literature on VC investment decision-making stages follows. Wells (1974) identified six distinct stages in the VC investment decision process. These are (1) the search for investment opportunities; (2) the screening of proposals; (3) the evaluation of proposals; (4) once these evaluations are completed, the startup is either funded or rejected, after which the VC spends significant amounts of time with the new firms; (5) in dealing with startup operations; and ultimately (6) in cashing out of the startups. Tyebjee & Bruno (1984) revisited Wells (1974) and condensed the fourth and fifth stages into a single stage, adding a new stage referred to as deal structuring, and dropping the cashing out stage. Those authors proposed a five-stage model of VC investment activity: (1) deal originating, or the search for prospective investments; (2) screening, in which most proposals are rejected based on the VC’s investment criteria; (3) evaluation, during which the proposed startup is examined in detail; (4) deal structuring, during which the VC and the entrepreneur agree to specific financial arrangements; and (5) post-investment activities, which encompasses the VC’s involvement in the management of the new startup. Hall (1989) identified eight stages: (1) deal flow generation; (2) brief proposal screening; (3) proposals that successfully pass this stage are then assessed in more detail; (4) project evaluation, during which the VC meets with the startup team; (5) startups in which the VC is still interested are then subjected to due diligence; (6) once the agreement to make a deal is made in principle a deal structuring meeting is conducted; (7) startup operations begin; (8) the final stage occurs if and when the VC firm cashes out of the startup.

The following section focuses on the first three stages of the venture capital (VC) investment selection process. According to Hall & Hofer (1993), the decision-making process proceeds until there is reason to reject the proposal — that is, those authors found that not all criteria or stages were systematically applied.


The deal flow/origination stage refers to the number of potential startup ventures that VCs consult in the search for suitable investment opportunities (Manigart et al., 2006; Sorenson & Stuart, 2001). The source of the deal flow (e.g., unsolicited proposals, network events, warm referrals) moderates the extent to which an investment opportunity captures a VC's attention. It is important to note that time is a valuable and rare resource across the various decision-making stages. Shane & Cable (2002) found that VC investment is a function of network ties, both direct and indirect.

Accordingly, the more direct the ties between an entrepreneur and potential VC investor, the more likely an investment will occur. Warm referrals in the deal flow/origination stage are more likely to lead to positive action, moving onto the screening stage of the investment decision-making process (Fried & Hisrich, 1994). The reverse also applies, where unsolicited proposals (i.e., indirect ties) infrequently capture a VC’s attention, and even if they do so, the VC is unlikely to interpret them favorably because the proposal has not garnered any previous vetting or endorsements from trusted sources (Stuart et al., 1999). Tyebjee & Bruno (1984) discovered that only 26 percent of investment deals materialized following an unsolicited call from an entrepreneur, whereas 65 percent of deals were recommended to the VC either by other VCs or by sources such as previous investees or personal contacts.


During the screening stage, VCs determine whether a potential startup venture justifies further assessment. Hall & Hofer (1993) found that VCs spend as little as six-minutes screening a deal. According to the academic literature, VCs focus more on market factors than on entrepreneur/team characteristics in the screening stage, despite believing otherwise (Hall & Hofer, 1993; Zacharakis & Meyer, 1998). This finding of a systematic bias is consistent with Levinthal & March’s (1993) theory that predicts that learning through experience is subject to biases and limitations. 


While the screening stage emphasizes market factors, the evaluation/due diligence stage focuses on the entrepreneur/team and product (Smart, 1999). The VC believes there is potential, but the evaluation/due diligence stage looks hard at whether the entrepreneur/team is capable of executing on that promise, as well as whether the product is not only technically feasible but likely to be adopted by the customer.

The evaluation/due diligence stage takes a considerable amount of VCs' time. Smart (1999) estimated that VCs spend on average 120 hours just evaluating the entrepreneur’s human capital. This finding does not include the time VCs spend on due diligence of the market, product, or the financial standing of the startup (Smart, 1999). The level of due diligence is influenced by time constraints, cost of reducing information asymmetries, and any number of situational aspects that can make thorough due diligence more difficult (Harvey & Lusch, 1995).

As such, due diligence is a cost-benefit tradeoff of how much effort and time VCs commit to reducing adverse selection (Sah & Stiglitz, 1986). Kaplan & Stromberg (2004) describes three types of risk. First, internal risk refers to VCs' difficulty in gauging the entrepreneur/team’s human capital. Second, external risk refers to market acceptance and competitive reaction. Third, execution risk refers to the complexity of developing a successful strategy and product. VCs tend to refrain from investing if they foresee an expensive due diligence process. The selection process moves either to the negotiation stage or a polite rejection. 

Overall, there is a dearth of research on the VC evaluation and due diligence stage, as well as limited examination of the hierarchy of decision criteria in this VC investment decision-making stage. 


Early-stage startups with non-existent or minimal revenue are assessed using non-financial metrics — such criteria derived from the scholarship can be organized into four categories (no particular order): entrepreneur & team capabilities, product and/or service attractiveness, market & competitive conditions, and the potential rate of returns. 


According to MacMillan, Siegel, and Narasimha (1986), VCs weigh an entrepreneur’s personality, abilities, and experience more heavily than the startup's business plan (e.g., products, markets, and competition). Entrepreneur/team capabilities are considered to be intangible assets that minimize execution risk (Harvey & Lusch, 1995). Hall & Hofer (1993) stated that such characteristics are decisive at the extreme ends of the distribution of entrepreneur / team talent. 

One such capability, among others, is the extent to which a team possesses relevant experience in management, serving a particular market/industry, and building startups. Previous management experience appears to be largely valued by VCs because it demonstrates a history of having garnered sustained support from collaborators through difficult times, as well as the ability to develop strategy and implement necessary structure and control systems when the startup scales. According to Zacharakis & Shepherd (2005), VCs place greater emphasis on management experience in environments that have a greater number of competitors. A possible interpretation is that elevated competition heightens the VC’s reliance on entrepreneurs to act and react to the many possible, and a priori unforeseeable, competitive interactions.

Besides the tactical ability of an entrepreneur and her team, the literature also cites the importance of market familiarity, and prior startup experience. These characteristics are often measured as either the average number of years or as the actual performance of the team in these specified areas.  Additionally, VCs assess team completeness (i.e., percentage of key positions filled), personal motivation (i.e., ability to sustain effort), and the individual equity stake of team members (i.e., fair/just balance of startup ownership).

B— Product and/or Service Attractiveness

According to Sandberg & Hofer (1988), VCs seek startups that offer unique and compelling products or services that swiftly gain marketplace acceptance and are not easily duplicated by competitors.

As such, different aspects of the product/service are assessed including the importance of the problem it solves, the degree of product differentiation, the superiority of the solution in comparison to competitors, and the level of protection provided because the product/service or process to deliver product/service is unique and difficult to imitate. A VC's appraisal often depends on the thorough inspection of a prototype.

In addition, VCs evaluate a startup's projected trajectory by considering time to development (i.e., refers to the number of months from initiation of development to the initial sale as forecast in the business plan) and growth potential as determinants of product and/or service attractiveness. 


This category captures market dynamics, industry structure, and the competitive landscape.

Market dynamics refers to market size, market growth, distribution, and buyer’s concentration. Industry structure refers to long-term growth and profitability, as well as barriers to entry upon entry as well as after subsequent entry. According to Sandberg & Hofer (1988), industry structure had a greater impact on startup performance than either strategy or the characteristics of the entrepreneur/team. Those authors found that fragmented, growing industries were preferred, but mature industries were acceptable provided that differentiation was possible.

For instance, positioning in a niche or segment was vastly preferred to price competition. Competitive landscape refers to the number of direct competitors and competitor strength (i.e., marketshare and the ability of competitors to retaliate against market entry). MacMillan et al. (1987) found that the extent to which the startup is initially insulated from competition and the degree to which there is demonstrated market acceptance of the product are significant determinants of startup performance.


Tyebjee & Bruno (1984) found expected return to be determined by market attractiveness and product differentiation.  A related hypothetical criterion is "cash-out potential", which refers to the theoretical potential of a startup to achieve capital gains by merger, acquisition, or public offering.

Keep in mind that the absence of financial information in a startup's proposal potentially raises questions about the seriousness of the request for funds and the business capabilities of the entrepreneur and team. However, according to Hall & Hofer (1993), while financial information on the proposed businesses was reviewed and commented on in a majority of proposals, it did not appear to be a major criterion in VCs' decisions.As a startup generates revenue, the more VCs evaluate it using financial metrics.


From the research, we know that not all selection criteria receive the same amount of attention. The literature proposes factors such as VC biases (Gompers & Lerner, 1998; Zacharakis & Shepherd, 2001) and VC demographics (Dimov et al., 2007; Shepherd et al., 2003) that impact what is being attended to, the interpretation of criteria, as well as decision accuracy. 

Few VC biases have received attention in the literature, but some that have been examined include herding (i.e. where VCs invest in similar startups as do the leading VCs), overconfidence (i.e. selective use of information affects VC decision accuracy), and similarity biases (i.e. VCs evaluate entrepreneurs and teams more favorably if they are similar to the VCs themselves). Biased decisions do not necessarily result in errors (Barr et al., 1992), but biases may prevent decision makers from reaching "optimal" solutions by influencing what the VC pays attention to and how information is interpreted. For example, biases may cause VCs to evaluate certain aspects of the startup's proposal less rigorously.

Researchers have also investigated how VC demographics influence the decision process. On the one hand, Shepherd et al. (2003) found that VC experience has an inverted U-shaped effect on decision performance. While more experience is generally better, those authors found that after 14 years of VC experience, decision effectiveness declines, possibly due to over-reliance on intuition (i.e., previous experiences) at the expense of a complete examination of all investment decision-making factors. On the other hand, Walske & Zacharakis (2009) found that VCs with experience as well as past senior management experience were more likely to succeed, while VCs with past entrepreneurship experience were more likely to fail. Those authors suggested that VCs with past experience are better at managing startup portfolios, those with senior management experience are better at guiding startups towards growth, while those with entrepreneurship experience may not fully evaluate the criteria that disconfirms a startup's potential. 


Roure & Keeley (1990) contend that most of the variance in VC success stems from selecting the right investments. The underlying value of these studies is that a better understanding of the VC investment decision-making process may lead to better investment decisions. This research may also provide valuable insights to startups and other types of organizations seeking to establish collaborative ties with startups, such as corporate venturing, accelerators, and incubators. There are also implications for policy makers as startups have become a critical part of economic development strategies across developed and developing countries (Van Stel et al., 2005).

Authored by
Joshua G. Eckblad,
Ph.D. Candidate


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