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This paper explores the use of initial coin offerings (ICOs) as a means of fundraising for companies through the issuance of blockchain‐based tokens. Specifically, we investigate how a company can implement ICOs and issue tokens supported by its products while taking into account the presence of network effects. We examine several aspects of ICOs, including the impacts of network effect on the company's optimal token sales design, such as the optimal token issuance price, the ICO cap (which refers to the number of tokens to be issued), and the amount of funds raised. We also consider the cases of deterministic and uncertain network effects, the impacts of limited speculators purchasing tokens during the ICO period, the comparison between ICOs and traditional bank financing, and the impact of ICO cost. The research unfolds in five key parts. Firstly, leveraging a baseline model, we derive equilibrium outcomes and the optimal token sales design, discerning the impact of deterministic and uncertain network effects. Notably, network effect uncertainty consistently benefits companies, ensuring profit resilience despite potential challenges, although in the face of such uncertainty, the ICO company may decrease the raised fund. Secondly, we investigate the consequences of a limited number of speculators in the ICO landscape, finding that adjustments on the token sales design are not required under low expected network effect. Thirdly, a comparative study between ICOs and bank financing reveals distinctive advantages of ICOs. ICOs prove capable of decreasing profit volatility while maintaining an equivalent profit compared to traditional bank financing. This insight offers valuable guidance for companies seeking optimal financing methods aligned with their risk preferences. Lastly, the impact of ICO cost on outcomes and token sales design is scrutinized. Contrary to expectations, ICO cost does not uniformly lead to an increase in the ICO cap, underscoring the nuanced relationship between costs and fundraising strategies. [ABSTRACT FROM AUTHOR]

Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices. [ABSTRACT FROM AUTHOR]