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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.

In the digital age, traditional public services face significant challenges, including inefficiency and outdated information. Large language models (LLMs), while impressive, struggle with "hallucinations" (generating fluent but incorrect responses) and a lack of domain-specific data, making them inadequate for high-accuracy demands. To address these issues, the Retrieval-Augmented Generation (RAG) framework has emerged as a transformative solution, offering greater accuracy and efficiency.

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.

A large literature in neuroscience and social psychology shows that humans are wired to be meticulous about how they are perceived by others. In this paper, we propose that impression management considerations can also end up guiding the content that investors transmit via word of mouth and inadvertently lead to the propagation of noise. We analyze server log data from one of the largest investment-related websites in the United States. Consistent with our proposition, we find that investors more frequently share articles that are more suitable for impression management despite such articles less accurately predicting returns. Additional analyses suggest that high levels of sharing can lead to overpricing.