This paper addresses how weak and strong signals affect venture capital funding acquired by digital startups at their early stage in various industries of China. We also articulate the interaction mechanism of these strong and weak signals by demonstrating their complementary or substitutive effects in alleviating information asymmetry on startup quality, which can help digital startups secure venture capital financing. Drawing on signalling theory and institutional legitimacy theory, we introduce application (app) downloads as a novel strong signal that can reduce market legitimacy concerns, and previous‐round venture capitalist reputation as a traditional strong signal that mitigates regulatory legitimacy concerns. We treat founders' startup and IT experience as weak signals, as they provide rhetorical and indirect information indicating a startup's potential to establish regulatory and market legitimacy. The study empirically investigates our hypotheses using data of 163 digital startups in various industries of China. Results confirm the positive relationships between strong signals and venture capital funding secured by a digital startup. Furthermore, signals of similar strength are found to complement each other's effects in certain situations, while strong signals can reduce the effects of weak signals on a digital startup's financing performance under specific conditions that create these mixed effects. Implications for digital startup research and practice as well as limitations and suggestions for future research are discussed.
Sharing economy platforms are pressed to rapidly grow user bases at the early stage by aggressively targeting potential users through competitive actions. Due to the volatile nature of the sharing economy and its disruption to industry norms, these platforms encounter legitimacy challenges that impede user base growth. This paper integrates competitive repertoire and institutional legitimacy theories to develop a research model that explains early‐stage user base development in the sharing economy. We posit that the early‐stage user base is associated with structural characteristics of the competitive repertoire, whose effects are moderated by a platform's socio‐political legitimation efforts that address stakeholders’ regulatory and normative concerns. Using a comprehensive sample of 4644 monthly observations of 129 sharing economy platforms in China, we find that the volume of two context‐specific competitive actions, offering economic incentives and staging high‐visibility events, along with competitive repertoire complexity, are positively related to the platform's early‐stage user base. We also identify a significant negative relationship between repertoire differentiation and user base. Direct relationships are moderated by socio‐political legitimation, however, such that legitimation weakens the positive impact of context‐specific action volume but enhances those of repertoire complexity and differentiation. Managerial and practical implications are discussed in light of the findings.
This paper offers a holistic review of the role of big data analytics in sharing economy (SE). Academic literature in this field is analyzed to show the theoretical foundation, important papers, and key themes underlying the field by using various bibliometric analysis tools. We conduct a citation and co-citation analysis on literature concerning big data analytics in sharing economy, which published in the 12-year period from 2010–2021. A total of 205 papers were screened from Web of Science (WoS) database for our analysis. In the citation analysis, we depend on the degree centrality and betweenness centrality to identify 48 important papers. In the co-citation analysis, four major research themes are identified: sustainable business model, efficient match-making, trust building and innovation and value cocreation. The research also highlights future research directions and critical areas for the application of big data analytics in the SE context, which may help to produce in-depth studies.
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