Xiaomeng Chen, Assistant Professor in ISTM
PhD, Cornell University
Why did you decide to set out on this research topic?
I have been interested in open innovation platforms, where value is created by communities of volunteers contributing their knowledge and skills freely. As these platforms rely on crowd-based activities and serve as valuable knowledge repositories for the public, a key theme is to understand the strategies that motivate user contributions. And my work focuses on how organizational choices reshape the interrelationships among subcommunities and motivate users’ knowledge contribution and exchange.
What were your key findings?
We examine how the introduction of secondary spaces—such as Discord servers for Hugging Face or Reddit chat rooms for Reddit—affects knowledge creation on the main open innovation platform.
Platforms like Stack Overflow and Quora are typically structured around asynchronous communication, threaded conversations, and gamification mechanisms, which prioritize content quality and expert-driven contributions. However, these features often discourage casual user-to-user interaction and offer limited opportunities for off-topic discussions or social bonding.
We show that adding a secondary channel facilitates more informal and social interactions between users. It changes the organizational structure and subcommunity interrelationship, as users begin to form social subgroups in the secondary channel, developing interpersonal bonds that are largely absent in the main platform. These newly formed subgroups have a mixed impact on knowledge creation.
Specifically, the introduction of the secondary channel does not significantly improve the likelihood of successful knowledge exchange for the average user. Instead, a relatively ‘closed’ subgroup emerges within the secondary channel—comprised of users who primarily assist one another. While members of this subgroup exhibit a substantial increase (71percent) in their individual knowledge contributions, these gains are concentrated within the subgroup and do not translate into broader benefits for the wider community of users who remain outside of it.
We further show that introducing a bridging device—a feed that surfaces related forum postings to members of the subgroup—expanded the benefits of the secondary channel to the broader platform community. This mechanism helps break the ‘closed’ subgroup, enhancing the diffusion of knowledge beyond the subgroup itself.
How do the findings of your research contribute to your field?
This study provides empirical evidence on how secondary communication channels foster subgroups based on bond-based attachments in open innovation platforms, and how such organizational changes reshape the relationship between subgroups and the wider community. We also contribute to the emerging body of research on bridging mechanisms in online platforms, demonstrating how bridging tools can broaden the distribution of benefits across the community.
How do your research findings contribute to society (locally, regionally, nationally, globally)?
This study provides important implications for the design and governance of open innovation platforms: creating new interaction spaces can activate participation among engaged users but may also lead to the emergence of exclusive “insider” circles. A “content push” mechanism that nudges content can help counteract exclusive circles. Given the valuable knowledge created by these communities for the public, we believe better regulation and design in these platforms would enhance knowledge creation in society as a whole.
Aside from your research, what is the most exciting advancement in your field right now?
One of the most exciting developments in the field is the rapid advancement of artificial intelligence (AI) and algorithmic technologies, which raise important questions for both practitioners and policymakers.
On open innovation platforms, generative AI has transformed knowledge creation and content generation. Recent studies suggest that the rise of large language models has reduced user engagement and contributions on platforms historically driven by user-generated knowledge (e.g., Burtch et al., 2024; del Rio-Chanona et al., 2024).
This trend prompts critical questions: How might declining crowd participation affect the generation of new knowledge and, in turn, the quality and diversity of content and knowledge used to train future AI models? Moreover, as concerns about AI hallucination and misinformation grow, user trust in AI-generated content has become a pressing issue. What new framework should govern the use of generative AI in content creation? Should governments mandate disclosure of AI involvement to ensure transparency and accountability?
At the same time, regulators are increasingly focused on the impact of algorithmic pricing on market competition and structure (Calvano et al., 2020; Assad et al., 2024; Qiu et al., 2025). Key questions include whether algorithmic pricing fosters collusion or discriminatory practices in digital markets, and whether restrictions on user data collection could mitigate harm, such as unfair pricing or algorithmic collusion.
Is your research published anywhere yet?
It has been accepted by Information Systems Research, but has not been published yet.