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The Influence Mechanism of Investors on the Value-Added Service Effect of Startups

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Recent Advancements in Computational Finance and Business Analytics (CFBA 2023)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 32))

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Abstract

Value-added services exert a substantial impact on the interaction between startups and investors, leading to a win–win situation. Exploring the mechanisms through which investors exert their influence on the value-added services offered to startups can facilitate this goal. Such an investigation can aid startups in increasing their value, while also enabling investors to reap benefits through investment appreciation. Additionally, it can facilitate the growth of the equity investment industry in a healthy manner. As part of this study, six startups that have received value-added services from investors have been selected as typical cases. A SETC theoretical model has been constructed using grounded theory to extract the main influencing mechanisms of the value-added services’ effects. The research results show that value-added services, in addition to directly affecting the value-added service effect, may also instigate principal-agent conflicts between investors and startups, driven by the external environment and the both parties’ traits, which ultimately influence the value-added service effect. Value-added service is an internal direct driver of the value-added service effect; the external environment is a external direct driver, both parties’ traits are situational drivers, and principal-agent conflict is an intermediary driver.

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Acknowledgements

The work was supported by the General project of Shanghai philosophy and social science planning of China (Grant No. 2018BGL006).

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Correspondence to NaQing Mo .

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Mo, N. (2023). The Influence Mechanism of Investors on the Value-Added Service Effect of Startups. In: Gupta, R., Bartolucci, F., Katsikis, V.N., Patnaik, S. (eds) Recent Advancements in Computational Finance and Business Analytics. CFBA 2023. Learning and Analytics in Intelligent Systems, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-38074-7_13

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