Being at the right place at the right time: does the timing within technology waves determine new venture success?

Schlichte, Franziska; Junge, Sebastian; Mammen, Jan

doi:10.1007/s11573-019-00947-0

Cited by 17 publications

(15 citation statements)

References 108 publications

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“…Consequently, the industry in question becomes even more productive until it overheats, and incumbents are repressed from the market while the high productivity shifts to another (possibly new), related market segment. This effect is entirely in line with the empirical study, in which Schlichte et al (2019) show that the timing of entry to highly specified submarkets between two technology waves is crucial for the success of new firms. Moreover, our model supports their finding that there is a first-follower advantage (in our model represented by successful entrants) as opposed to a first-mover advantage (moderately successful incumbents that are nonetheless outperformed by entrants) because of growing consumer acceptance of new technology (Davis 1989).…”

Section: Discussionsupporting

confidence: 87%

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Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms

Schulz

Mayerhoffer

2021

J Bus Econ

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With the advent of platform economies and the increasing availability of online price comparisons, many empirical markets now select on relative rather than absolute performance. This feature might give rise to the ‘winner takes all/most’ phenomenon, where tiny initial productivity differences amount to large differences in market shares. We study the effect of heterogeneous initial productivities arising from locally segregated markets on aggregate outcomes, e.g., regarding revenue distributions. Several of those firm-level characteristics follow distributional regularities or ‘scaling laws’ (Brock in Ind Corp Change 8(3):409–446, 1999). Among the most prominent are Zipf’s law describing the largest firms‘ extremely concentrated size distribution and the robustly fat-tailed nature of firm size growth rates, indicating a high frequency of extreme growth events. Dosi et al. (Ind Corp Change 26(2):187–210, 2017b) recently proposed a model of evolutionary learning that can simultaneously explain many of these regularities. We propose a parsimonious extension to their model to examine the effect for deviations in market structure from global competition, implicitly assumed in Dosi et al. (2017b). This extension makes it possible to disentangle the effects of two modes of competition: the global competition for sales and the localised competition for market power, giving rise to industry-specific entry productivity. We find that the empirically well-established combination of ‘superstar firms’ and Zipf tail is consistent only with a knife-edge scenario in the neighbourhood of most intensive local competition. Our model also contests the conventional wisdom derived from a general equilibrium setting that maximum competition leads to minimum concentration of revenue (Silvestre in J Econ Lit 31(1):105–141, 1993). We find that most intensive local competition leads to the highest concentration, whilst the lowest concentration appears for a mild degree of (local) oligopoly. Paradoxically, a level playing field in initial conditions might induce extreme concentration in market outcomes.

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Section: Discussionsupporting

confidence: 87%

“…Hence, from a management perspective, our findings underline the importance of timing market entrance and thorough search prior to entry. Namely, our model can explain, why Schlichte et al (2019) find the most successful firms to be fast followers in innovative markets rather than the original innovators themselves.…”

Section: Introductionmentioning

confidence: 95%

Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms

Schulz

Mayerhoffer

2021

J Bus Econ

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“…Though the Crunchbase database is comparably young, it becomes increasingly popular in VC research (Werth and Boeert 2013;Homburg et al 2014;Croce et al 2016;Schlichte et al 2019). Further, the coverage of Crunchbase is on a comparable level to other more established data sources like Thomson One Private Equity (Werth and Boeert 2013) or data recorded by the National Venture Capital Association (Block and Sandner 2009;Huang and Zhan 2015).…”

Section: Datasetmentioning

confidence: 99%

Unicorns—what drives multibillion-dollar valuations?

Bock

Hackober

2020

Bus Res

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The number of ventures with a market value of one billion USD or more has considerably increased during the last decade. Driven by new technologies and business models, these ventures became an integral part of our daily life. Particularly, the number of unicorns based in China and other regions outside the US raised during recent years whereas the phenomenon was initially limited to the US region. Existing research has mainly focused on descriptive approaches to examine the rise of these ventures but lacks knowledge on the drivers of this phenomenon. We address these research gaps and investigate the underlying factors that foster the emergence of such high-valued ventures. Our results present several economic environmental as well as investor-related factors that impact the likelihood for a venture to achieve a market valuation of more than one billion USD. Subsequently, we derive theoretical and practical implications that may foster the future emergence of new high-valued ventures, covering regulatory, investor-and venturespecific aspects.

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“…Crunchbase is a free online database which was founded in 2005 and which provides information about technology companies, investors, and funding details majorly focused on the US market. Though the Crunchbase database is comparably young, it becomes increasingly popular in VC research (Croce et al 2016;Homburg et al 2013;Schlichte et al 2019;Werth and Boeert 2013).…”

Section: Samplementioning

confidence: 99%

Which investors’ characteristics are beneficial for initial coin offerings? Evidence from blockchain technology-based firms

Hackober

Bock

2021

J Bus Econ

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Initial coin offerings have recently become one of the most important funding resources for ventures in the blockchain area. However, often ventures do not rely solely on initial coin offerings as funding source but receive also investments from more established investors prior or during their initial coin offering. In particular, blockchain related ventures have drawn the attention of (corporate) venture capitalists but only less is known on the interplay of these different funding sources and their influence on initial coin offerings as well as on ventures’ further development. Based on the signaling theory as well as the resource-based-view our empirical study find that venture capital investors as well as corporate venture capital investors have a significantly positive effect on initial coin offerings. Further, we find that the reputation, the time of treatment as well as the specialization of investors have a positive influence on the initial coin offering. Finally, our results indicate that the positive effect of venture capital investors as well as the specialization of an investor continues to influence blockchain based ventures’ success in the mid-term.

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Being at the right place at the right time: does the timing within technology waves determine new venture success?

Cited by 17 publications

References 108 publications

Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms

Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms

Unicorns—what drives multibillion-dollar valuations?

Which investors’ characteristics are beneficial for initial coin offerings? Evidence from blockchain technology-based firms

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