Chenshuo Sun

I am excited to share that my first solo-authored paper has been accepted for publication at @Mrktng_Science 😀😀 A big thank you to the editors and the referees for their constructive feedback throughout the process!!

📷 𝐃𝐞𝐚𝐝𝐥𝐢𝐧𝐞: June 12, 2026 (11:59 PM PT) We welcome innovative work across all areas of Information Systems. Join us in San Francisco for an exciting in-person conference experience and engaging discussions with the IS community.

𝐂𝐚𝐥𝐥 𝐟𝐨𝐫 𝐏𝐚𝐩𝐞𝐫𝐬: 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐨𝐧 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 (𝐂𝐈𝐒𝐓 𝟐𝟎𝟐𝟔) 📷 𝐖𝐞𝐛𝐬𝐢𝐭𝐞: cist2026.github.io/cfp/ 📷 Oct 31 – Nov 1, 2026 | San Francisco, CA 📷 𝐒𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧 𝐎𝐩𝐞𝐧𝐬: May 15, 2026

@Ananya_Sen_ @Mrktng_Science Sorry I missed this reply 😂😂

@ChenshuoSun @Mrktng_Science Seems like a really nice paper! Congratulations! Do you have a manuscript available? Would love to read in more detail. @ChenshuoSun

I am excited to share that my first solo-authored paper has been accepted for publication at @Mrktng_Science 😀😀 A big thank you to the editors and the referees for their constructive feedback throughout the process!!

My new study finds prepopulated search bars can significantly boost online sales👏 @Mrktng_Science @INFORMS thanks much for the media promotion

The November-December 2025 issue of the journal is now available online! pubsonline.informs.org/toc/mksc/44/6

@johnjhorton Here is my recent paper, not leveraging the full capability of GenAI yet: x.com/mrktng_science…

If I were a new marketing PhD student, this is what I would work on:

Happy to share our new publication! We often think of voice AI as a convenience feature. But is it also rewiring our shopping brains? Our paper explores this question: When consumers adopt voice AI for shopping, how does it affect their behavior? pubsonline.informs.org/doi/10.1287/is…

@vtodri @Mrktng_Science I was truly encouraged by you at CIST 2023:)

@ChenshuoSun @Mrktng_Science Congratulations Chenshuo! 🎉 👏

@XiaoxiaLei @Mrktng_Science Congrat too!!

@ChenshuoSun @Mrktng_Science Congrats! 👏👏👏

This paper, as its title shows, focuses on the impact of prepopulating e-commerce platform search bars with keywords on online consumer journey. You can learn more about the paper here: papers.ssrn.com/sol3/papers.cf…

@Mrktng_Science Thanks a lot for promoting the paper!

Newly Accepted Articles 11/24 (3 of 4) "How Does Pre-Populating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment" by Chenshuo Sun

I am thrilled to share that our paper on Voice Shopping (with Zijun, Xiao, and @aghose), after 5 years of dedicated work, has been accepted for publication in Information Systems Research!

@lylengyan @aghose Xie Xie Yan!

@ChenshuoSun @aghose Great work! Congrats, Chenshuo and the team!

Thrilled to attend the 22nd Conference on the Economics of ICT at @ZEW! Presented a paper we've been developing for over 5 years and discussed the latest in AI algorithmic pricing. #ZEW #AcademicTwitter

@_ToFeWe @ZalandoTech @VJascisens @LarsRoemheld Nice paper! I enjoyed reading it.

How do humans interact with pricing algorithms within firms❓ We explore this question using two large-scale field experiments in collaboration with @ZalandoTech in a new WP. This work is a collaboration with Tobias Huelden, @VJascisens and @LarsRoemheld. 🧵Thread below.

M2UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models paper page: huggingface.co/papers/2311.11… The current landscape of research leveraging large language models (LLMs) is experiencing a surge. Many works harness the powerful reasoning capabilities of these models to comprehend various modalities, such as text, speech, images, videos, etc. They also utilize LLMs to understand human intention and generate desired outputs like images, videos, and music. However, research that combines both understanding and generation using LLMs is still limited and in its nascent stage. To address this gap, we introduce a Multi-modal Music Understanding and Generation (M^{2}UGen) framework that integrates LLM's abilities to comprehend and generate music for different modalities. The M^{2}UGen framework is purpose-built to unlock creative potential from diverse sources of inspiration, encompassing music, image, and video through the use of pretrained MERT, ViT, and ViViT models, respectively. To enable music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging multi-modal understanding and music generation is accomplished through the integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA model to generate extensive datasets that support text/image/video-to-music generation, facilitating the training of our M^{2}UGen framework. We conduct a thorough evaluation of our proposed framework. The experimental results demonstrate that our model achieves or surpasses the performance of the current state-of-the-art models.

Landed in Phoenix for #CIST2023 & #informs2023, where my students are presenting 3 papers. It’s been a while (6.5 years) to be back in AZ🥵

Congratulations to @ChenshuoSun for winning the NYU University-wide Outstanding Dissertation Award for his dissertation on Emerging Technologies and the Digital Future. For obvious reasons, the selection process was very competitive given the numerous PhD candidates within all the schools at NYU! #Proudadvisor