PRODUCT STRATEGIES AND STARTUPS’ SURVIVAL IN TURBULENT INDUSTRIES: EVIDENCE FROM THE SECURITY SOFTWARE INDUSTRY

This paper seeks to explore the drivers of startups’ survival in turbulent industries, characterized by high rates of entry and exit, fragmented market shares, and a rapid pace of product innovation. Specifically, the paper aims to underscore the role played by post-entry product strategies, along with their interaction, beyond that of pre-entry conditions. Based on a sample of 270 startups that entered the Security Software Industry from 1989 till 1998, we find evidence that surviving entities are those that more aggressively adopt versioning and product portfolio strategies. Interesting enough, strategic learning seems to play a major role: Focusing on one of the two product strategies commands a higher survival probability than adopting a mixed strategy.

Patent collateral, investor commitment, and the market for venture lending

This paper investigates the market for lending to technology startups (i.e., venture lending) and examines two mechanisms that may facilitate trade within it: (1) the ‘salability’ of patent collateral; and (2) the credible commitment of existing equity investors. We find that intensified trading in the secondary patent market is strongly related to the annual rate of startup lending, particularly for startups with more redeployable patent assets. Moreover, we show that the credibility of venture capitalist commitments to reinvest in their startups’ next round of financing can be critical for startup debt provision. Utilizing the crash of 2000 as a severe and unexpected capital supply shock for VCs, we show that lenders continue to finance startups with recently funded investors better able to credibly commit to refinance their portfolio companies, but withdraw from otherwise-promising projects that may have needed their funds the most. The findings are consistent with predictions of incomplete contracting and financial intermediation theory.Accepted manuscrip

A comparative research on Communities of Practice, University Knowledge Exchange and Business Model Changes between the United Kingdom and Vietnamese Agri-tech Startups

This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/The paper explores the expectation that agri-tech startups in the United Kingdom and Vietnam gain benefits of Communities of Practice (CoP), and how CoP help those businesses to tackle the challenges of business model changes for their growth enhancement. The co-creational collaboration between academics and agri-tech startups when working together on knowledge/experience-sharing through CoP is also investigated. The research uses a case-study approach, meetings, interviews, and documentary data to capture the events, actions and changes of attitudes, behaviours, and expectations of eight agri-tech startups towards their use of CoP. The research indicates a significant change in the business models of the startups when integrating into temporal CoP. These agri-tech startups often exploit their existing business models, while exploring a portfolio of new business opportunities that could generate new growth engines. Some of them quickly move into a value creation phase by which they achieve quick-wins and medium-term actions that generate both financial and non-financial business value. The research results may have implications for policymakers and practitioners who want to develop these types of interventions. Businesses in other fields can also better understand the value and potential of temporal CoP and start applying them. Keywords: Agri-tech, business model change (s), university knowledge exchange, communities of practice, startup (s). *Peer reviewedFinal Published versio

The Science of Startups: The Impact of Founder Personalities on Company Success

Startup companies solve many of today's most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy, air pollution, and the development of novel life-saving vaccines. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm's founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors as well as the team's size. The effects of founders' personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm's ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups. We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). However, we do not find one "Founder-type" personality; instead, six different personality types appear, with startups founded by a "Hipster, Hacker and Hustler" being twice as likely to succeed. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which has the potential to be extended through further research into other team settings within business, government and research

Economic Interplay Forecasting Business Success

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years

Economic Interplay Forecasting Business Success

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years