this IS research Nick Berente and Jan Recker

Behavioral research is alive and well ... online Some time ago, we wondered whether survey research is dead. Today, we speak with , who argues the exact opposite. He gives us plenty of advice on how to design online experiments, sample rigorously on platforms like Prolific, build reliable psychometric measurements, and embed surveys in robust research designs. And because Jason is not only a prolific scholar and senior editor in our field but also very active on LinkedIn, we also talk about reviewing practices and how social media can help communities address topics that need to be spoken about. . 

is with us today. She has done some amazing theory construct research using computational methods before this was really an accepted thing. We discuss which work she built her research around to give it legitimacy, what good stopping rules are for authors or reviewers to know when enough is enough, and how we can engage in humble generalizations of interesting and general regularities. References Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with Social Media: How Cognitive Structuring of Organizing Vision Facets Affects IT Innovation Diffusion. MIS Quarterly, 39(3), 591-614. Walsh, I., Holton, J. A., Bailyn, L., Fernandez, W. D., Levina, N., & Glaser, B. G. (2015). What Grounded Theory Is ... A Critically Reflective  Conversation Among Scholars. Organizational Research Methods, 18(4), 581-599. Levina, N., & Vaast, E. (2015). Leveraging Archival Data from Online Communities for Grounded Process Theorizing. In K. D. Elsbach & R. M. Kramer (Eds.), Handbook of Qualitative Organizational Research: Innovative Pathways and Methods (pp. 215-224). Routledge. Berente, N., Seidel, S., & Safadi, H. (2019). Data-Driven Computationally-Intensive Theory Development. Information Systems Research, 30(1), 50-64. Miranda, S. M., Wang, D., & Tian, C. (2022). Discursive Fields and the Diversity-Coherence Paradox: An Ecological Perspective on the Blockchain Community Discourse. MIS Quarterly, 46(3), 1421-1452. Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI. MIS Quarterly, 45(3), 1527-1556. Lindberg, A., Schecter, A., Berente, N., Hennel, P., & Lyytinen, K. (2024). The Entrainment of Task Allocation and Release Cycles in Open Source Software Development. MIS Quarterly, 48(1), 67-94. Sahaym, A., Vithayathil, J., Sarker, S., Sarker, S., & Bjørn-Andersen, N. (2023). Value Destruction in Information Technology Ecosystems: A Mixed-Method Investigation with Interpretive Case Study and Analytical Modeling. Information Systems Research, 34(2), 508-531. Miranda, S. M., Berente, N., Seidel, S., Safadi, H., & Burton-Jones, A. (2022). Computationally Intensive Theory Construction: A Primer for Authors and Reviewers. MIS Quarterly, 46(2), i-xvi. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75-105. Adamic, L. A., & Glance, N. (2005). The Political Blogosphere and the 2004 U.S. Election: Divided They Blog. Paper presented at the 3rd International Workshop on Link Discovery, Chicago, Illinois. Pentland, B. T., Vaast, E., & Ryan Wolf, J. (2021). Theorizing Process Dynamics with Directed Graphs: A Diachronic Analysis of Digital Trace Data. MIS Quarterly, 45(2), 967-984. Sarker, S., Xiao, X., Beaulieu, T., & Lee, A. S. (2018). Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2). Journal of the Association for Information Systems, 19(8), 752-774. Lee, A. S., & Baskerville, R. (2003). Generalizing Generalizability in Information Systems Research. Information Systems Research, 14(3), 221-243. Tsang, E. W. K., & Williams, J. N. (2012). Generalization and Induction: Misconceptions, Clarifications, and a Classification of Induction. MIS Quarterly, 36(3), 729-748. Hume, D. (1748/1998). An Enquiry Concerning Human Understanding [Reprint]. In J. Perry & M. E. Bratman (Eds.), Introduction to Philosophy: Classical and Contemporary Readings (3rd ed., pp. 190-220). Oxford University Press.   Exemplar Computationally-intensive Theory Construction Papers Bachura, E., Valecha, R., Chen, R., & Rao, H. R. (2022). The OPM Data Breach: An Investigation of Shared Emotional Reactions on Twitter. MIS Quarterly, 46(2), 881-910. Gal, U., Berente, N., & Chasin, F. (2022). Technology Lifecycles and Digital Innovation: Patterns of Discourse Across Levels of Abstraction: A Study of W

According to the internet, Elon Musk is often praised for his visionary mindset, innovation, risk-taking attitude, and energy. is just like that, we think. With the positivity he brings into every project and meeting, Jan has been right at the center of many seminal developments in our field over the past twenty years, from the rise of design science to the inception of NeuroIS, the development of literature reviews, and more recently the creation of process science. We take the opportunity to reflect with him on his work, the way he builds and steers highly successful research groups, and how he manages to do research that is both impactful and engaging to many different audiences. As usual, the references to readings we mention are listed on .

In science, citations are used to give credit to sources that are relevant to the topic that is being discussed where the citation appears.  They are a key vehicle through which we establish a cumulative knowledge tradition – we use them to acknowledge material that informs our arguments. But citations are much more than that. They have become a key metric of academic success in their own right, providing a quantifiable basis to measure a scholar’s impact, reputation, and fame. And as any metrics-based systems, also the citation system can be gamed, and is being gamed. Time to unpack the role that citations play and discuss which citations are legit – and which may just be a bit flunky. As usual, the references to readings we mention are listed on .

Research is a conversation. Every scholar must become a professional writer. But how do we learn these things? Most graduate school programs do not include a writing course and books on how to write are read even less than other types of books. Is good writing maybe all either genetics or just experience? Or does it depend on how we approach research, either phenomena- or theory-driven? We think both things matter – but there are also some practical steps people can take to get their writing going and maintain the flow of writing. As usual, the references to readings we mention are listed on .

The thing is, special issues are special. Hence the name. But what is it that makes them special? We look at some of the hottest special issues out there for information systems researchers and we discuss three key aspects of special issues – topical fit, competition, and process – that provide both advantages and disadvantages to researchers thinking about submitting to them. And for some weird reason we end up discussing our experiences at doctoral and junior faculty consortia and why everyone should attend them, always. As usual, the references to readings we mention are listed on .