What you are is what you like—similarity biases in venture capitalists' evaluations of start-up teams

Franke, Nikolaus; Gruber, Marc; Harhoff, Dietmar; Henkel, Joachim

doi:10.1016/j.jbusvent.2005.07.001

Cited by 275 publications

(203 citation statements)

References 37 publications

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“…For example, [7] investigates whether reviewers' scores on peer review submissions are dependent on the reviewers' also being authors; [8] studies whether students tend to assign higher scores to instructors who have given them higher grades; [9] shows that venture capitalists' evaluation of start-up teams is dependent on their similarity to these teams. In contrast, we are concerned with measuring the specific dependency of individual reviewers and objects, as reviewers and objects may exhibit varying dependency with respect to two given rating criteria.…”

Section: Related Workmentioning

confidence: 99%

On Mining Rating Dependencies in Online Collaborative Rating Networks

Lauw

Lim

Wang

2009

Advances in Knowledge Discovery and Data Mining

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Abstract. The trend of social information processing sees e-commerce and social web applications increasingly relying on user-generated content, such as rating, to determine the quality of objects and to generate recommendations for users. In a rating system, a set of reviewers assign to a set of objects different types of scores based on specific evaluation criteria. In this paper, we seek to determine, for each reviewer and for each object, the dependency between scores on any two given criteria. A reviewer is said to have high dependency between a pair of criteria when his or her rating scores on objects based on the two criteria exhibit strong correlation. On the other hand, an object is said to have high dependency between a pair of criteria when the rating scores it receives on the two criteria exhibit strong correlation. Knowing reviewer dependency and object dependency is useful in various applications including recommendation, customization, and score moderation. We propose a model, called Interrelated Dependency , which determines both types of dependency simultaneously, taking into account the interrelatedness between the two types of dependency. We verify the efficacy of this model through experiments on real-life data.

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Section: Related Workmentioning

confidence: 99%

On Mining Rating Dependencies in Online Collaborative Rating Networks

Lauw

Lim

Wang

2009

Advances in Knowledge Discovery and Data Mining

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“…Moreover, venture capitalists obviously act according to this evidence and evaluate the venture team on the basis of their crossdisciplinary character. A recent study demonstrated that when making funding decisions, venture capitalists prefer teams with a mixed background in management and technical fields (Franke, Gruber, Harhoff & Henkel, 2003).…”

Section: Interdisciplinary Cooperation In New Venture Creationmentioning

confidence: 99%

Preparing business students for co-operation in multi-disciplinary new venture teams: empirical insights from a business-planning course

Lüthje

Prügl

2006

Technovation

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“…Bates, 1997) to venture capitalists (VCs) (e.g. Franke, Gruber, Harhoff and Henkel, 2006). In order to find external financing for their nascent venture, entrepreneurs have to favorably impress potential investors.…”

Section: Introductionmentioning

confidence: 99%

“…Signals that can be used as proxies of missing information are important determinants of the outcome of competitive processes (Shapiro, 1983;Podolny, 1993;Rindova, Petkova, Williamson and Sever, 2005). For producers, reputational signals based on past performance (Fombrun and Shanley, 1991;Rindova, Petkova and Kotha, 2007) (Tyebjee and Bruno, 1981;Gorman and Sahlman, 1989;Muzyka et al, 1996;Franke et al, 2006Franke et al, , 2008.A performance-based reputation is a multidimensional construct (Fombrun and Shanley, 1990;Rhee and Valdez, 2009) that besides past market success can be based on many signals from many different sources. These signals include contests 15 (Rao, 1994), awards (Anand and Watson, 2004;Gemser, Leenders and Wijnberg, 2008) reviews (Basuroy, Chatterjee and Ravid, 2003; Shugan, 1997), ratings (Podolny, 1993), and even the mere volume of media attention (Pollock and Rindova, 2003;Rindova, Petkova and Kotha, 2007).…”

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Nascent ventures competing for start-up capital: Matching reputations and investors

Ebbers

Wijnberg

2012

Journal of Business Venturing

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While earlier studies focused on the general effects of corporate or individual reputations on attracting investment capital, a major contribution of this study is to distinguish between different types of reputations and different types of investors, and then analyze how particular types of reputations have an effect on particular types of investors. We do this by applying selection system theory, which is an approach to understanding competitive processes by focusing on the identity of the decision makers whose evaluations determine the outcome of these competitive processes. The three basic types of selectors are: market selectors, peer selectors and expert selectors.This tripartite classification allows us to categorize types of reputations on the basis of their sources. First, if a reputation is based on success in consumer markets, for example (end-) consumers' quality perceptions of a specific brand of shampoo, it can be linked to market selection. Second, a reputation type can be linked to expert selection if it originates from expert judgments. An example is a credit rating agency 4 such as Moody's. Third, if the reputation is based upon success among peers, for example winning an award at the 'Oscars' where the jury largely consists of other filmmakers, it can be linked to peer selection.At the same time types of investors can be categorized by considering both the characteristics and the goals of the investors making the investment decisions. First, if investors back a venture because they primarily hope that many consumers will like and buy the product they can be identified as market selectors. Second, if investors are (industry) peers of the founding members of the new venture they can be identified as peer selectors. Third, if they are neither market nor peer selectors they can be categorized as expert selectors. This tripartite categorization opens the way to the main argument of this paper: nascent organizations will be more successful in attracting investment capital from an investor if their founding members have performance-based reputations of a type that matches the type of the investor.In the empirical section of this paper we test this argument by studying the impact of the reputations of producers and directors on the investment decisions of the three main investors in the Dutch film industry: distributors, television broadcasters and the Film Fund. We find support for our main theoretical argument, IntroductionAn adequate resource base (Brush, Greene, Hart and Haller, 1993;Davidson and Honig, 2003;Parker and van Praag, 2006), and especially adequate financing Earlier studies applied selection system theory to study competition in product markets. For example, how winning or being nominated for an award impacts box office revenues of mainstream or art-house films (Gemser et al., 2008). Employing selection system theory to better understand the financing stage further tests the usefulness of this framework and also extends the theory itself.Our empirical setting is the Dutch film industr...

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What you are is what you like—similarity biases in venture capitalists' evaluations of start-up teams

Cited by 275 publications

References 37 publications

On Mining Rating Dependencies in Online Collaborative Rating Networks

On Mining Rating Dependencies in Online Collaborative Rating Networks

Preparing business students for co-operation in multi-disciplinary new venture teams: empirical insights from a business-planning course

Nascent ventures competing for start-up capital: Matching reputations and investors

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