Valuing high technology growth firms

Klobucnik, Jan; Sievers, Soenke

doi:10.1007/s11573-013-0684-2

Cited by 19 publications

(8 citation statements)

References 52 publications

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“…Moreover, considering the fact that fast growing industries attract more solvent and reputable investors, we controlled for a startup's industry. In so doing, we relied on a dummy variable to determine whether a startup operates in a high-technology industry (see also Antonczyk et al 2007), by using the SIC code classifications of Bhojraj and Charles (2002) and the extended version of Klobucnik and Sievers (2013). 2 We included the geographical location dummy variable because startups headquartered within the three main U.S. VC clusters, California (Silicon Valley), Massachusetts (Route 128) and New York, might benefit from better access to VC funding (Gaba and Meyer 2008;Inderst and Müller 2004;Zheng et al 2010) and a higher level of interorganizational knowledge spillover (Jaffe et al 1993).…”

Section: Measures and Descriptive Statisticsmentioning

confidence: 99%

A world of difference? The impact of corporate venture capitalists’ investment motivation on startup valuation

et al. 2017

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Corporate venture capital (CVC) investors are regularly painted with the same brush, a fact underscored by the often observed belief in the extant literature that corporate venture capitalists (CVCs) form a homogeneous group. In contrast to this simplifying perspective, this paper categorizes CVCs into subgroups by examining their levels of strategic and financial investment motivation using computer-aided text analysis and cluster analysis. To validate the resulting clusters, this paper studies the impact of CVC type on startup valuation from an intra-group perspective by applying hierarchical linear modeling, thus illustrating which particular investment motivation might be preferable to others in the context of negotiating valuations. An empirical analysis of 52 CVC mission statements and 147 startup valuations between January 2009 and January 2016 revealed that first, CVCs with a strategic investment motivation assign lower startup valuations than CVCs with an analytic motivation that have moderate levels of the two scrutinized dimensions, suggesting that entrepreneurs trade off these CVCs' value-adding contributions against a valuation discount; second, CVCs with an unfocused investment motivation pay significantly higher purchase prices, thus supporting the hypothesis that they have a so-called liability of vacillation; and third, the valuations of CVCs with a financial investment motive are not significantly different from JEL Classification C12 Á C30 Á C38 Á G24 Á G32 Á M13

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Section: Measures and Descriptive Statisticsmentioning

confidence: 99%

A world of difference? The impact of corporate venture capitalists’ investment motivation on startup valuation

et al. 2017

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“…Along with the findings on the optimal code combinations assigned to the various industries [25] as well as the succus on sampling employed in prior studies [e. g. 18,23,[38][39][40][41][42][43][44][45][46][47][48][49], I convert SIC codes into NACE Rev.2 codes and assign the respective code combinations to the eight predefined high-tech industry groupings as reported in table 1.…”

Section: Sample Datamentioning

confidence: 99%

Private Firm Valuation in the Technology Sector: Illuminating the Interaction Between Multiple Performance and Peer Pool Setting

Grbenic¹

2021

IJEFM

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Prior research, investigating the absolute performance of multiples as well as the relative superiority of different types of multiples, yields contradictory results that might be attributed to varying peer pool settings. This paper emphasizes on the technology sector and extends existing research, in its entirety being limited to trading multiples on listed companies, to transaction multiples on private firms. Employing a set of 22,967 observations on private market transactions of technology firms collected from 2000 until 2018, I examine the systematic impact of peer pooling on (i) the relative superiority of cross-sectoral multiples, (ii) the absolute superiority of sectoral multiples and, (iii) the absolute superiority of cross-sectoral multiples being segmented by various country-specific high-tech indicators. The multiples employed capture both, enterprise value and equity value multiples. The performance of the multiples in the various peer pool settings is evaluated according to bias as well as accuracy, utilizing the standard holdout routine on the transactions. The results indicate that (i) contradictory results in prior research on multiple's bias may be strongly attributed to the varying peer pools employed, (ii) the enterprise value to total assets multiple clearly dominates across all peer pools on a cross-sectoral basis, indicating that contradictory results on multiple's accuracy may not be attributed to the varying peer pools employed and, (iii) the performance of sectoral multiples depends on the value driver employed, showing only a weak relationship with the peer pool setting. Therefore, valuation analysts are recommended to utilize larger peer pools when employing cross-sectoral multiples, to emphasize on the enterprise value to total assets multiple, to further break down the high-tech sector into sub-sectors and, to employ sectoral multiples or multiples segmented according to country-specific high-tech indicators alternately.

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“…The extended view of brand valuation of online companies shows that from the financial perspective, the assessment of web-based companies is complicated by the high speed of their growth and related ambiguity in variable expenses, opportunistic financial resources (Schosser & Ströbele, 2019) along with impracticality of traditional forecasts about exchange listing and income (Klobucnik & Sievers, 2013).…”

Section: Introductionmentioning

confidence: 99%

Conceptualization of social media brand equity

Sadyk¹

2020

Pressacademia

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Purpose -While brand equity is widely recognized as one of the most important areas of strategic marketing, it is a rather new and complex research topic in social media context. Hitherto, there is no acknowledged concept of social media brand equity or unified approach of its estimation. The purpose of this study is to suggest a conceptualization of social media brand equity (SMBE) through analysis of the main challenges and drivers of extant explorations in the related areas. Methodology -Out of 225 revised scholarly papers about social media and brand valuation, there are scant number of studies that propose a model for SMBE measurement. Based on the literature review a theoretical proposal is elaborated to understand the concept of SMBE. Findings -Results show that consumer-related factors have a significant impact on SMBE building due to the active involvement of users in the value co-creation process, which is facilitated by numerous social media inherent features. Therefore, customer-based brand equity (CBBE) may be the initial platform for elaboration of the SMBE concept and subsequent development of its measurement scale. Conclusion -Conceptualization of SMBE contributes to the development of the reasonable assessment model available for scholars and practitioners for the brand valuation of social media firms.

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Valuing high technology growth firms

Cited by 19 publications

References 52 publications

A world of difference? The impact of corporate venture capitalists’ investment motivation on startup valuation

A world of difference? The impact of corporate venture capitalists’ investment motivation on startup valuation

Private Firm Valuation in the Technology Sector: Illuminating the Interaction Between Multiple Performance and Peer Pool Setting

Conceptualization of social media brand equity

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