团队

In the highly competitive mobile market, third-party vendors located outside the purview of hosting mobile platforms are becoming major suppliers of functional tool kits for mobile app development and innovation. Mobile app developers, however, face the uncertainty of whether and how to use third-party software development kits (SDKs) from these external vendors to create more appealing and engaging mobile apps. This study examines the extent to and conditions under which third-party SDK utilization affects mobile app market performance. Drawing on the platform ecosystem literature and boundary object theory, we contextualize the boundary-spanning practice in mobile app development as the extent to which developers utilize third-party SDKs and theorize the performance impact of third-party SDKs. Moreover, the boundary-spanning perspective leads us to examine how the performance impact of third-party SDKs varies across tool types versus platform types, the evolution of platform boundaries, and levels of app developers’ platform-specific experience. By conducting difference-in-differences-style analyses on a longitudinal data set of 335,958 multihoming mobile apps released on Apple App Store and Google Play Store, our study reveals that utilizing more third-party SDKs is positively associated with daily active users of mobile apps. This positive impact is limited to tool-type third-party SDKs, however, and is attenuated by platform updates and app developers’ platform-specific experience. This study contributes to the platform-based software innovation and platform governance literature and provides managerial implications for app developers, platform managers, and third-party SDK providers.

Our paper examines analyst reports and online stock opinion articles which recommend buying stocks that, based on the literature, trade at high prices and earn low future returns ("short-leg securities"). Using a textual analysis, we test whether the justifications primarily (1) emphasize safe-haven qualities, (2) indicate exuberance, or (3) highlight lottery-like features. Our results strongly point to (3). We subsequently validate our text-based inferences through a survey of institutional and retail investors with long positions in short-leg securities. Overall, perceived upside potential appears to play a material role in driving investor demand for stocks in the short legs of anomalies.

近年，类似于ChatGPT的大语言模型（Large Language Model，LLM）在全球迅速普及，展示出巨大的应用潜力。通过海量数据的训练，这些模型能够生成连贯且语义合理的文本，并具有卓越的问答能力。在政务服务领域，公众一般通过政府官网、移动应用查询政策法规、办事流程等信息，或者前往政务服务大厅求助。

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

神经科学和社会心理学的大量文献指出，人类天生对别人如何看待自己很在意。在本文中，我们提出投资者的印象管理策略最终亦可以主导他们以口碑相传方式所传递的内容，并可能不经意间造成噪声的传播。我们分析来自美国最大的投资相关网站之一的伺服器日志档数据，发现结果与我们的见解一致，即投资者会更积极地分享适用于印象管理的文章，即使这些文章不太准确地预测回报。其他分析亦指出，高层次的此类分享会导致定价过高。