Reputation, VC funding, and innovation: Evidence from the UK Micro and Nanotechnology sector

Startups’ contributions on economic growth have been widely realized. However, the funding gap is often a problem limiting startups’ development. To some extent, VC can be a means to solve this problem. VC is one of the optimal financial intermediaries for startups. Two streams of VC studies are focused in this dissertation: the criteria used by venture capitalists to evaluate startups and the effect of VC on innovation. First, although many criteria have been analyzed, the empirical assessment of the effect of startup reputation on VC funding has not been investigated. However, reputation is usually positively related with firm performance, which may affect VC funding. By analyzing reputation from the generalized visibility dimension and the generalized favorability dimension using a sample of 200 startups founded from 1995 operating in the UK MNT sector, we show that both the two dimensions of reputation have positive influence on the likelihood of receiving VC funding. We also find that management team heterogeneity positively influence the likelihood of receiving VC funding. Second, studies investigating the effect of venture capital on innovation have frequently resorted to patent data. However, innovation is a process leading from invention to successful commercialization, and while patents capture the upstream side of innovative performance, they poorly describe its downstream one. By reflecting the introduction of new products or services trademarks can complete the picture, but empirical studies on trademarking in startups are rare. Analyzing a sample of 192 startups founded from 1996 operating in the UK MNT sector, we find that VC funding has positive effect on the propensity to register trademarks, as well as on the number and breadth of trademarks.

Individuals in the Workplatform. Exploring Implications for Work Identity and Algorithmic Reputation Management

In the new world of work, workers not only change jobs more frequently, but also perform independent work on online labor markets. As they accomplish smaller and shorter jobs at the boundaries of organizations, employment relationships become unstable and career trajectories less linear. These new working conditions question the validity of existing management theories and call for more studies explaining gig workers’ behavior. Aim of this dissertation is contributing to this emerging body of knowledge by (I) exploring how gig workers shape their work identity on online platforms, and (II) investigating how algorithmic reputation changes dynamics of quality signaling and affects gig workers’ behavior. Chapter 1 introduces the debate on gig work, detailing why existing theories and definitions cannot be applied to this emergent workforce. Chapter 2 provides a systematic review of studies on individual work in online labor markets and identifies areas for future research. Chapter 3 describes the exploratory, qualitative methodology applied to collect and analyze data. Chapter 4 presents the first empirical paper investigating how the process of work identity construction unfolds for gig workers. It explores how digital platforms, intended both as providers of technological features and online environments, affect this process. Findings reveal the online environment constrains the action of workers who are pushed to take advantage of platform’s technological features to succeed. This interplay leads workers to develop an entrepreneurial orientation. Drawing on signaling theory, Chapter 5 understands how gig workers interpret algorithmic calculated reputation and with what consequences for their experience. Results show that, after complying to platform’s rules in the first period, freelancers respond to algorithmic management through different strategies – i.e. manipulation, nurturing relationships, and living with it. Although reputation scores standardize information on freelancers’ quality, and, apparently, freelancers’ work, this study shows instead responses to algorithmic control can be diverse.