Tag Archives: information systems

Computational Design Science and AI Research in Information Systems

First published: December 4, 2025

In this post, I will discuss the current status of computational design science and AI research in the Information Systems community, focusing on the papers that I have recently handled as an editor in different IS journals (ISR, MISQ, DSS, I&M, etc.) and those on which I worked as an author. I will also share my thoughts on the future research agenda.

AI is the topic of our time. The reality is that many disciplines are trying to own the AI agenda in business schools (Caro et al. 2025). In my view, the IS community has a unique advantage to lead this AI agenda. To do that, we need to double down on our IS competitive advantage from a socio-technical perspective (Sarkar et al. 2019). While many neighboring disciplines (e.g., operations, marketing, strategy) can bring useful societal perspectives on the AI topic, I believe the IS research community has unique strengths in opening up the AI black box. I don’t think treating AI as one variable is enough. At the same time, if we solely focus on the technical aspects, the differentiation from computer science may not be clear. Therefore, our AI research in IS should focus on the consequential problems in business and society.

I had the great fortune to handle many IS papers that fall into this category. Since I started my AE role at ISR in 2024, I have accepted two papers (Xie et al. 2025; Gao et al. 2025). Xie et al. (2025) proposed a novel topic model approach to study the mental impact (e.g., suicidal thoughts) of short videos. The authors carefully analyzed the limitations of existing neural topic models (across IS and CS) and designed a novel topic model that can leverage a medical knowledge base. They then evaluated the artifact on two platforms and identified medically relevant topics from the short videos. Gao et al. (2025) investigated whether LLM-based social bots can create socializing values on social media platforms. They conducted extensive analyses on comments generated by LLM bots and found that the bot comment characteristics impact user engagement. Finally, the authors proposed a social bot targeting algorithm to optimize engagement and tested it with extensive simulations. Besides these two papers, there are more computational and AI papers in the pipeline.

Moving forward, I think our IS research community should lead the AI agenda in the business school and beyond. In the near future, I hope to see more research in the following areas:

  1. AI impact studies: As a general-purpose technology, AI is making changes across different industry sectors. I hope to see more AI impact studies, but in specific industry sectors such as online videos (Zhang et al. 2025a), journalism (Zhang et al. 2025b), online dating (Kwon et al. 2022), and more.
  2. Physical AI: Many AI papers are focusing on knowledge work in the digital space. With the advances of embodied AI, I hope the IS community can also study robots (Lee et al. 2025), drones, and rovers.
  3. Quantum AI: I predict that quantum computing will be the next computing paradigm. The IS community can conduct forward-looking research on the topics of quantum computing, quantum communication, and more (https://www3.fox.temple.edu/discover/events-conferences/from-qubits-to-business-value/).

References

Caro, Felipe, Jean-Edouard Colliard, Elena Katok, Axel Ockenfels, Nicolas Stier-Moses, Catherine Tucker, D. J. Wu (2025) Introduction to the Special Issue on the Human-Algorithm Connection. Management Science. https://doi.org/10.1287/mnsc.2023.intro.v72.n1 

Gao, Yang, Maggie Mengqing Zhang, Mikhail Lysyakov (2025) Does Social Bot Help Socialize? Evidence from a Microblogging Platform. Information Systems Research. https://doi.org/10.1287/isre.2024.1089

Kwon, Soonjae, Sunghyuk Park, Gene Moo Lee, Dongwon Lee (2022) Learning Faces to Predict Matching Probability in an Online Matching Platform. In Proceedings of International Conference on Information Systems. https://aisel.aisnet.org/icis2022/digital_commerce/digital_commerce/9/ 

Lee, Myunghwan, Lee, Gene Moo, Shin, Donghyuk, Cho, Wooje, Han, Sang Pil (2025) Service Robots and Workforce Transformation: Evidence from Restaurant Operations. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.5288874

Sarker, Suprateek; Chatterjee, Sutirtha; Xiao, Xiao; and Elbanna, Amany. 2018. “The Sociotechnical Axis of Cohesion for the IS Discipline: Its Historical Legacy and its Continued Relevance,” MIS Quarterly, (43: 3) pp.695-719. https://doi.org/10.25300/MISQ/2019/13747

Xie, Jiaheng, Yidong Chai, Ruicheng Liang, Yang Liu, Daniel Dajun Zeng (2025) Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model. Information Systems Research. https://doi.org/10.1287/isre.2024.1071 

Zhang, Xiaoke, Zhou, Mi, Lee, Gene Moo (2025a) AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.4676705 

Zhang, Xiaoke, Lee, Myunghwan, Zhou, Mi, Lee, Gene Moo (2025b) Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.5357471

IS Papers on Big Data, Analytics, and AI

First published: Feb 25, 2020, Last update: Dec 4, 2025.

My research involves Big Data Analytics and AI in Information Systems literature. This post tries to keep track of the editorial and seminal articles on the topic of Big Data, Data Science, Analytics, and AI in the Information Systems and Management literature. The papers are listed in chronological order:

  1. Bapna, Goes, Gopal, Marsden (2006) Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data, Statistical Science 21(2): 116-130.
  2. Shmueli and Koppius (2011) Predictive Analytics in Information Systems Research, MIS Quarterly 35(3): 553-572
  3. Chen, Chiang, Storey, (2012) Business Intelligence and Analytics: From Big Data to Big Impact, MIS Quarterly 36(4): 1164-1188
  4. Lin, Lucas Jr., Shmueli (2013) Research Commentary: Too Big to Fail: Large Samples and the p-Value Problem, Information Systems Research 24(4): 906-917.
  5. Agarwal, Dhar (2014) Editorial – Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research, Information Systems Research 25(3): 443-448
  6. Varian (2014) Big Data: New Tricks for Econometrics, Journal of Economic Perspectives 28(2): 3-28
  7. Goes (2014) Editor’s Comments: Big Data and IS Research, MIS Quarterly 38(3): iii-viii
  8. AMJ Editors (2016) From the Editors: Big Data and Data Science Methods for Management Research, Academy of Management Journal 59(5): 1493-1507
  9. Abbasi, Sarker, Chiang (2016) Big Data Research in Information Systems: Toward an Inclusive Research Agenda, Journal of the Association for Information Systems 17(2): i-xxxii
  10. Rai (2016) Editor’s Comments: Synergies Between Big Data and Theory, MIS Quarterly 40(2): iii-ix
  11. Baesens, Bapna, Marsden, Vanthienen, Zhao (2016) Transformational Issues of Big Data and Analytics in Networked Business, MIS Quarterly 40(4): 807-818
  12. Athey (2017) Beyond Prediction: Using Big Data for Policy Problems, Science 355(6324): 483-485
  13. Chiang, Grover, Liang, Zhang (2018) Special Issue: Strategic Value of Big Data and Business Analytics, Journal of Management Information Systems 35(2): 383-387
  14. Delen, Ram (2018) Research challenges and opportunities in business analytics, Journal of Business Analytics 1(1): 2-12.
  15. Maass, Parsons, Puraro, Storey, Woo (2018) Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research, Journal of the Association for Information Systems 19(12): 1253-1273
  16. Yang, Adomavicius, Burtch, Ren (2018) Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining, Information Systems Research 29(1): 4-24.
  17. Berente, Seidel, Safadi (2019) Research Commentary: Data-Driven Computationally Intensive Theory Development, Information Systems Research 30(1), 50-64.
  18. Johnson, Gray, Sarker (2019) Revisiting IS Research Practice in the Era of Big Data, Information and Organization 29(1): 41-56
  19. Grover, Lindberg, Benbasat, Lyytinen (2020) The Perils and Promises of Big Data Research in Information Systems, Journal of the Association for Information Systems 21(2): 268-291.
  20. Shmueli (2021) INFORMS Journal of Data Science (IJDS) Editorial #1: What is an IJDS paper?, INFORMS Journal of Data Science.
  21. Ram, Goes (2021) Focusing on Programmatic High Impact Information Systems Research, not Theory, to Address Grand Challenges, MIS Quarterly 45(1): 479-483.
  22. Burton-Jones, Boh, Oborn, Padmanabhan (2021) Editor’s Comments: Advancing Research Transparency at MIS Quarterly: A Pluralistic Approach, MIS Quarterly 45(2): iii-xviii.
  23. Berente, Gu, Recker, Santhanam (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence, MIS Quarterly 45(3): 1433-1450.
  24. Jain, Padmanabhan, Pavlou, Raghu (2021) Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society, Information Systems Research 32(3): 675-687.
  25. Padmanabhan, Fang, Sahoo, Burton-Junes (2022) Editor’s Comments: Machine Learning in Information Systems Research, MIS Quarterly 46(1): iii-xix.
  26. Abbasi, Parsons, Pant, Sheng, Sarker (2024) Pathways for Design Research on Artificial Intelligence. Information Systems Research 35(2):441-459.
  27. Gopal, Ram D., Jingjing Li, Kai Riemer, Suprateek Sarker, Param Vir Singh, Anjana Susarla, Martin Bichler, Jason Bennett Thatcher (2025) Inventing with Machines: Generative AI and the Evolving Landscape of IS Research. Information Systems Research.
  28. Caro, Felipe, Jean-Edouard Colliard, Elena Katok, Axel Ockenfels, Nicolas Stier-Moses, Catherine Tucker, D. J. Wu (2025) Introduction to the Special Issue on the Human-Algorithm Connection. Management Science. https://doi.org/10.1287/mnsc.2023.intro.v72.n1