PhD Courses in Denmark

Course coordinator: Michel Avital, Department of Digitalization (DIGI)

Faculty

Professor Ioanna Constantiou
Department of Digitalization, CBS
Associate Professor Christiane Lehrer
Department of Digitalization, CBS
Tenure Track Adjunkt Anand Bhardwaj
Department of Digitalization, CBS
Tenure Track Assistant Professor Olivia Benfeldt
Department of Digitalization, CBS
Tenure Track Adjunkt Travis Greene
Department of Digitalization, CBS
Professor Carsten Sorensen 
Department of Digitalization, CBS

Aims and Objectives

This introductory course highlights a selection of contemporary central topics in information systems and digital technology management research. It is designed to familiarize doctoral students with the main research streams and contributing scholars in IS research, introduce common research approaches, and provide a safe environment for stepping into lifelong research endeavors. In addition to reviewing a rich subset of the IS research literature, the course seeks to prepare the foundations that will assist the participants in developing and writing their theses. Each annual round of the course covers a new selection of topics that are prevalent in the IS research discourse.

Learning Objectives

At the end of the course, students should be able to:

• Identify and discuss the main research streams in IS research
• Identify the key scholars in IS research and discuss their contributions to the body of knowledge 
• Identify and discuss key disciplinary controversies and debates  
• Discuss the development of the IS research discipline and its community of scholars
• Apply IS research theory to own thesis project
• Position own thesis project in the context of the IS body of knowledge

Structure and Format

The course surveys a broad literature base to provide students with a grasp of the main issues in the IS discipline while developing a deeper understanding of its development and conceptual turns. The course emphasizes breadth and is geared toward the socio-technical aspects of information technologies in an organizational context. Specifically, participants will have an opportunity to examine the main strands of IS research and their interrelationships in the overall context of the discipline and management studies. 

The course is designed as a sequence of 6 distinct three-hour meetings, each covering a central topic in information systems research. The meetings are designed in a research seminar format that includes guided discussions, mini-workshops, and teacher and student class presentations. In addition to a critical and appreciative review of existing work, the seminar emphasizes constructive discussion aimed at helping students design state-of-the-art research that builds on and extends the current body of knowledge.  

Considering the underlying objective, the readings, class preparation and class participation are essential. In preparation for each seminar, each student will review the assigned articles and subsequently should prepare and upload to CANVAS a “conversation starter” that discusses and integrates the readings as well as offers personal insights and suggested topics for further discussion in the seminar. Upload or post your conversation starter to the designated Assignment section in Canvas at least 48 hours prior to the class (Sunday and Wednesday at noon, respectively).

Evaluation

A Pass/Fail grade will be based on participation and timely submission of all six 2-page (max) conversation starters.    

Retake exam: students who attended at least 50% of the sessions but do not fulfill the passing requirements may submit a 25-page literature review theory development paper that is based on all the required readings. The paper is due 30 days after the last class. 

Workload

*1 ECTS = 28 hrs

Course Plan

Required Readings

(1) Decision Making from DSS to Algorithms (Ioanna Constantiou)

Overall Focus

This session provides an overview of how IS evolution contributes to organizational decision-making and the implications for the organization. We will reflect on the challenges and opportunities related to introducing algorithms into decision-making processes.

Required readings

Arnott, D., & Pervan, G. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20(2), 67–87. https://doi.org/10.1057/palgrave.jit.2000035

Arnott, D., & Pervan, G. (2008). Eight key issues for the decision support systems discipline. Decision Support Systems, 44(3), 657–672. https://doi.org/10.1016/j.dss.2007.09.003

Shollo, A., Constantiou, I., & Kreiner, K. (2015). The interplay between evidence and judgment in the IT project prioritization process. The Journal of Strategic Information Systems, 24(3), 171–188. https://doi.org/10.1016/j.jsis.2015.06.001

Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research, 32(3), 713–735. https://doi.org/10.1287/isre.2020.0980

Recommended readings

Lebovitz, S., Levina, N., & Lifshitz-Assaf, H. (2021). Is AI ground truth really “true”? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quarterly, 45(3), 1501–1526. https://doi.org/10.25300/MISQ/2021/16564

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Balasubramanian, N., Ye, Y., & Xu, M. (2022). Substituting human decision-making with machine learning: Implications for organizational learning. Academy of Management Review, 47(3), 448–465. https://doi.org/10.5465/amr.2019.0470

Raisch, S., & Fomina, K. (2025). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421

(2) Use and Users as Core Constructs in IS Research (Christiane Lehrer)

Overall Focus

In this session, we focus on the construct of information systems (IS) use, one of the most central constructs studied in the IS discipline. It is arguably the most consequential construct in our field because the nature, modalities, and extent of IS use have significant implications for individual-, group-, organizational-, and societal-level outcomes. Specifically, we will discuss the conceptualization of IS use and selected contemporary theories of IS acceptance and use. We will then discuss how IS use can be conceptualized in your research context and how a deeper understanding of users and IS use can inform your PhD research. The class will be primarily discussion-based and will focus on your research projects.

Required readings

Burton-Jones, A., & Straub Jr., D.W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246.

Burton-Jones, A., & Volkoff, O. (2017). How can we develop contextualized theories of effective use? A demonstration in the context of community-care electronic health records. Information Systems Research, 28(3), 468-489.

Lehrer, C., Constantiou, I., Matt, C., & Hess, T. (2023). How ephemerality features affect user engagement with social media platforms. MIS Quarterly, 47(4), 1663-1678.

Lehrer, C., Eseryel, Y., Rieder, A., & Jung, R. (2021). Behavior change through wearables: The interplay between self-leadership and IT-based leadership. Electronic Markets, 31(4), 747-764.

Schuetz, S., & Venkatesh, V. (2020). The rise of human machines: How cognitive computing systems challenge assumptions of user-system interaction. Journal of the Association for Information Systems, 21(2), 460-482.

Recommended readings

Bhattacherjee, A., & Lin, C. P. (2015). A unified model of IT continuance: three complementary perspectives and crossover effects. European Journal of Information Systems, 24(4), 364-373.

Burton-Jones, A., & Grange, C. (2013). From use to effective use: A representation theory perspective. Information Systems Research, 24(3), 632-658.

Burton-Jones, A., & Gallivan, M. J. (2007). Toward a deeper understanding of system usage in organizations: a multilevel perspective. MIS Quarterly, 657-679.

Burton-Jones, A., Stein, M. K., & Mishra, A. (2020). MISQ Research Curation on IS Use Research. MIS Quarterly Research Curations, 1(1), 1-24. https://www.misqresearchcurations.org/blog/2017/12/1/is-use

Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.

(3) Knowledge and Knowing in the Age of AI (Anand Bhardwaj)

Overall Focus

This session traces changing understandings of knowledge and knowing in information systems and organization research, from early approaches to organizational knowledge creation and knowledge management systems to practice-based perspectives on knowing and contemporary debates about expertise and artificial intelligence. We will examine a foundational tension between treating knowledge as something that can be articulated, stored, and transferred, and understanding knowing as situated, relational, and enacted in practice. We then consider expertise as a parallel conversation that raises related questions about who is recognized as knowing, how expertise is constituted, and how it becomes embedded in organizational arrangements. Finally, we turn to artificial intelligence, asking what happens when organizations attempt to translate expert knowledge into computational systems, and when those systems increasingly participate in producing knowledge themselves. We will close by discussing how these tensions bear on your own thesis projects, particularly where they touch AI, expertise, or organizational knowledge.

Required readings

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. https://doi.org/10.1287/orsc.5.1.14

Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961

Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249–273. https://doi.org/10.1287/orsc.13.3.249.2776

Heimstädt, M., Koljonen, T., & Elmholdt, K. T. (2024). Expertise in management research: A review and agenda for future research. Academy of Management Annals, 18(1), 121–156. https://doi.org/10.5465/annals.2022.0078

van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580. https://doi.org/10.25300/MISQ/2021/16559

Recommended readings

Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005

Lebovitz, S., Levina, N., & Lifshitz-Assaf, H. (2021). Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quarterly, 45(3), 1501–1526. https://doi.org/10.25300/MISQ/2021/16564

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. https://doi.org/10.25300/MISQ/2021/16553

Faraj, S., Perez-Torrents, J., Mantere, S., & Bhardwaj, A. (2026). A time for monsters: Organizational knowing after large language models. Strategic Organization, 24(2), 343–356. https://doi.org/10.1177/14761270251410675

Hadjimichael, D., & Tsoukas, H. (2019). Toward a better understanding of tacit knowledge in organizations: Taking stock and moving forward. Academy of Management Annals, 13(2), 672–703. https://doi.org/10.5465/annals.2017.0084

(4) Theorizing Data: From IT Artifact to Governance (Olivia Benfeldt)

Overall Focus

Data are implicated in every contemporary digital phenomenon, yet information systems research often treats data as innocuous byproducts of technologies, platforms or algorithms. In this session, we reverse the emphasis and ask: What becomes theoretically visible when we put data rather than technology at the centre of our explanations? We will examine how data are brought into being; what kind of work data require before they can be used; how the production of data can reshape the activities and phenomena they appear to record; and how their meaning and value are mediated through governance in practice. During the session, we will experiment with applying a data lens to your own research and discuss what aspects of your empirical phenomena become newly visible, problematic, or theoretically consequential when data themselves become the focal object of inquiry. Instead of assuming every IS phenomenon can be understood as a data phenomenon, we will close the session with a discussion about when foregrounding data changes an explanation -- and when it does not.

Required readings

Jones, M. R. (2019). What we talk about when we talk about (big) data. The Journal of Strategic Information Systems, 28(1), 3–16. https://doi.org/10.1016/j.jsis.2018.10.005

Beynon-Davies, P. (2016). Instituting facts: Data structures and institutional order. Information and Organization, 26(1–2), 28–44. https://doi.org/10.1016/j.infoandorg.2016.04.001

Parmiggiani, E., Østerlie, T., & Almklov, P. G. (2022). In the backrooms of data science. Journal of the Association for Information Systems, 23(1), 139–164. https://doi.org/10.17705/1jais.00718

Aaltonen, A., & Stelmaszak, M. (2024). The performative production of trace data in knowledge work. Information Systems Research, 35(3), 1448–1462. https://doi.org/10.1287/isre.2019.0357

Benfeldt, O., & Persson, J. S. (2025). Semiotic mediation in data governance: Towards valuing data as assets. Information and Organization, 35(3), https://doi.org/10.1016/j.infoandorg.2025.100588

Recommended readings

Aaltonen, A., Alaimo, C., Parmiggiani, E., Stelmaszak, M., Jarvenpaa, S. L., Kallinikos, J., & Monteiro, E. (2023). What is missing from research on data in information systems? Insights from the inaugural workshop on data research. Communications of the Association for Information Systems, 53, 475–490. https://doi.org/10.17705/1CAIS.05320

Aaltonen, A., Alaimo, C., & Kallinikos, J. (2021). The making of data commodities: Data analytics as an embedded process. Journal of Management Information Systems, 38(2), 401–429.  https://doi.org/10.1080/07421222.2021.1912928

Benfeldt, O., Zambach, S., & Gierlich-Joas, M. (2025). Modalities of Data Diplomacy: How Negotiations Shape Data Governance in Practice. Scandinavian Journal of Information Systems, 37(2), 17–58. https://doi.org/10.17705/3SJIS/037.12

Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., Deken, F., & Feldberg, F. (2022). Resourcing with data: Unpacking the process of creating data-driven value propositions. The Journal of Strategic Information Systems, 31(4).  https://doi.org/10.1016/j.jsis.2022.101744

Østerlie, T., & Monteiro, E. (2020). Digital sand: The becoming of digital representations. Information and Organization, 30(1). https://10.1016/j.infoandorg.2019.100275

(5) Ethics and Philosophy of Information Systems (Travis Greene)

Overall Focus

Philosopher Wilfrid Sellars memorably described the aim of philosophy as the attempt “to understand how things in the broadest sense of the term hang together in the broadest way possible.” In this seminar, we will discuss different, influential visions for how philosophy is and ought to be done. Not only will we consider how these different conceptions of philosophy impact theorizing about ethics (one of the major branches of philosophy), but special attention will be paid to how these different conceptions relate to IS research on philosophical and ethical topics. More specifically, this session provides an overview of the most common ethical theories used in IS ethics, and critically examines similarities and differences between philosophical methodology and IS research methodologies. The goal of this seminar is to help you develop a more nuanced, reflective understanding of the underlying philosophical orientation of your own research.

Required readings

Mason, R. O. (1986). Four Ethical Issues of the Information Age. MIS Quarterly, 10(1), 5–12. https://doi.org/10.2307/248873

Stahl, B. C. (2012). Morality, ethics, and reflection: a categorization of normative IS research. Journal of the Association for Information Systems, 13(8), 1. https://aisel.aisnet.org/jais/vol13/iss8/1/

Hassan, N. R., Mingers, J., & Stahl, B. (2018). Philosophy and information systems: where are we and where should we go?. European Journal of Information Systems, 27(3), 263-277. https://doi.org/10.1080/0960085X.2018.1470776

Mingers, J., & Standing, C. (2020). A Framework for Validating Information Systems Research Based on a Pluralist Account of Truth and Correctness. Journal of the Association for Information Systems, 21(1), 117–151. https://aisel.aisnet.org/jais/vol21/iss1/6

 Gal, U., Hansen, S., & Lee, A. S. (2022). Research perspectives: Toward theoretical rigor in ethical analysis: The case of algorithmic decision-making systems. Journal of the Association for Information Systems, 23(6), 1634-1661. https://aisel.aisnet.org/jais/vol23/iss6/1

Schlagwein, D. (2021). Natural sciences, philosophy of science and the orientation of the social sciences. Journal of Information Technology, 36(1), 85-89. https://doi.org/10.1177/0268396220951

Recommended readings

Anscombe, G. E. M. (1958). Modern moral philosophy. Philosophy, 33(124), 1-19. https://doi.org/10.1017/S0031819100037943

Darwall, S., Gibbard, A., & Railton, P. (1992). Toward fin de siecle ethics: some trends. The Philosophical Review, 115-189. https://doi.org/10.2307/2185045

Aguinis, H., Beltran, J. R., Archibold, E. E., Jean, E. L., & Rice, D. B. (2023). Thought experiments: Review and recommendations. Journal of Organizational Behavior, 44(3), 544-560. https://doi.org/10.1002/job.2658

Rowe, F. (2018). Being critical is good, but better with philosophy! From digital transformation and values to the future of IS research. European Journal of Information Systems, 27(3), 380-393. https://doi.org/10.1080/0960085X.2018.1471789

Mingers, J., & Walsham, G. (2010). Toward Ethical Information Systems: The Contribution of Discourse Ethics. MIS Quarterly, 34(4), 833-854. https://doi.org/10.2307/25750707

Ma, C., Chong, A. Y. L., Thatcher, J. B., & Phang, C. W. (2026). Rethinking Artificial Intelligence and Ethics: Uncovering Unexamined Assumptions and Assessing Their Implications. Information Systems Journal. https://doi.org/10.1111/isj.70042

(6) Artificial Intelligence: From Cybernetics to GenAI (Carsten Sorensen)

Overall Focus

Generative AI is certainly capturing almost the entire digital zeitgeist. Immense capital investments are being made based on assumptions that the technology represents the next step in the development of a general-purpose technology, heralding the next industrial revolution. The aim of this session is to place the technology in a historical context of both past AI summers and winters, as well as within its contemporary context. This entails investigating the computational fundamentals and historical background of the technology. The session also aims to direct a critical perspective towards some of the core assumptions within the broader AI debate and to review the contemporary AI debate within Information Systems.

Required readings

Baird, A. and L. M. Maruping (2021). "The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts." Management Information Systems Quarterly 45(1): 315-341.

Berente, N., B. Gu, J. Recker and R. Santhanam (2021). "Managing Artificial Intelligence." MIS Quarterly 45(3): 1433-1450.

Dell’Acqua, F., C. Ayoubi, H. Lifshitz, R. Sadun, E. Mollick, L. Mollick, Y. Han, J. Goldman, H. Nair and S. Taub (2026). "The Cybernetic Teammate: A field experiment on generative AI and teamwork." Organization Science 37(4): 1217-1242.

Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126-148.

Stelmaszak, M., M. Möhlmann and C. Sørensen (2025). "When Algorithms Delegate to Humans: Exploring Human-Algorithm Interaction at Uber." MIS Quarterly 49(1): 305-330.

Turing, A. M. (1950). "Computing machinery and intelligence." Mind 59(236): 33-60.

Wegner, P. (1997). "Why Interaction is More Powerful Than Algorithms." Communications of the ACM 40(5): 80-91.

Recommended readings

Benbya, H., S. Pachidi and S. L. Jarvenpaa (2021). "Artificial Intelligence in Organizations: Implications for Information Systems Research." Journal of the Association for Information Systems (JAIS) 22(2).

Grønsund, T. and M. Aanestad (2020). "Augmenting the algorithm: Emerging human-in-the-loop work configurations." The Journal of Strategic Information Systems 29(2): 101614.

Jain, H., Padmanabhan, B., Pavlou, P. A., & Raghu, T. S. (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.

McCarthy, J., M. Minsky, N. Rochester and C. Shannon (2006 (orig. 1950)). "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence." AI Magazine 27(4): 12-14.

Rosenblatt, F. (1958). "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain." Psychological Review: 386-408.

Schneiderman, B. and P. Maes (1997). "Direct manipulation vs Software Agents: Excerpts from debates at IUI 97 and CHI 97." Interactions(November-December): 42-61.

Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser and I. Polosukhin (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA.

Zhang, Z., Y. Yoo, K. Lyytinen and A. Lindberg (2021). "The unknowability of autonomous tools and the liminal experience of their use." Information Systems Research 32(4): 1192-1213.

Esoteric

Chopra, A., S. Bhattacharya, D. Salvador, A. Paul, T. Wright, A. Garg, F. Ahmad, A. C. Schwarze, R. Raskar and P. Balaprakash (2025). "The Iceberg Index: Measuring Skills-centered Exposure in the AI Economy." arXiv preprint arXiv:2510.25137.

Turing, A. M. (1936). "On computable numbers, with an application to the Entscheidungsproblem." J. of Math 58(345-363): 5.

Books

Bostrom, N. (2014). Superintelligence: Paths, Dangers. Oxford, OUP.

Carr, N. G. (2014). The Glass Cage: Automation and Us, W. W. Norton & Co.

Charalabidis, Y., R. Medaglia and C. v. Noordt, Eds. (2024). Research Handbook on Public Management and Artificial Intelligence, Edward Elgar.

Constantiou, I., M. P. Joshi and M. Stelmaszak, Eds. (2024). Research Handbook on Artificial Intelligence and Decision Making in Organizations, Edward Elgar.

Damsgaard, J. (2025). AI Mellem Fornuft og Følelse. Copenhagen, DJØF Forlag.

Dreyfus, H. L. & Dreyfus, S. E. (1986). Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York: Free Press.

Ekbia, H., & Nardi, B. (2017). Heteromation, and other stories of computing and capitalism. MIT Press.

Frey, C. B. (2019): The Technology Trap: Capital, Labor, and Power in the Age of Automation. Princeton University Press.

Graeber, D. (2018): Bullshit Work: A Theory. London: Pinguin.

Hodges, A. (1983). Alan Turing: The Enigma. London, Burnett Books.

Hutchins, E. (1995). Cognition in the wild. MIT Press.

Kurzweil, R. (2006). The Singularity is Near: When Humans Trascend Biology. New York, Viking Penguin.

Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control, Penguin.

Russell, S. J. and Norvig (2021). Artificial intelligence a modern approach, Pearson Education, Inc.

Susskind, D. (2020): A World Without Work: Technology, Automation, and How We Should Respond. Henry Holt and Company.

Susskind, R. E. & D. Susskind (2015): The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford: Oxford University Press.

Suzman, J. (2020). Work: A History of how We Spend Our Time, Bloomsbury Publishing.

Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence, Knopf.

Weizenbaum, J. (1976). Computer Power and Human Reason. UK, Penguin Books.

GO TO COURSE REGISTRATION