(in Polish) Nowe technologie w marketingu 2600-DMz2NTM

The seminar takes as its subject not technologies themselves but claims about their effectiveness in marketing, and teaches students to assess those claims. The reason is practical: a substantial share of technologies announced as breakthroughs has no documented effect on customer behaviour, and a manager who does not make that distinction takes investment decisions on the basis of a vendor's sales materials.
The organising frame is the question of why beliefs about the effectiveness of technologies rise and fall in a manner weakly connected to evidence. The tool for describing an individual technology is the notion of affordances: the course asks not what a technology is, but what actions it makes possible and for whom. The blocks covering the technologies named in the programme of studies, and the closing block on measuring the effect of an implementation, are set within that frame. Materials are selected so that each block contains items that resolve in opposite directions. The detailed range of content is given in part B.
Estimated student workload: 75 hours, including 18 hours of classes and approximately 57 hours of independent work. The smaller number of class hours in part-time studies does not mean a narrower scope or lower requirements – the same outcomes are achieved with a greater share of independent work.

Course coordinators

Piotr Nawrocki

Type of course

obligatory courses

Mode

Classroom

Learning outcomes

On completion of the course the student:

knowledge:
– distinguishes three layers of evidence on the effectiveness of a technology: peer-reviewed research, market data with a stated measurement date, and vendor materials (K_W03)
– discusses the mechanism by which beliefs about the effectiveness of technologies rise and fall independently of evidence (K_W06)
– explains the disclosure obligations applying to machine-generated content and the question of rights to such content (K_W07)

skills:
– describes a technology through the actions it affords rather than through its technical properties (K_U01)
– determines whether a cited measurement warrants a conclusion about the effect of an implementation and, if it does not, indicates which measurement is missing (K_U01)
– leads the work of a team on a written study and documents the agreed division of responsibility (K_U08)
– reaches sources beyond the materials provided in class and assesses their credibility (K_U09)

social competences:
– identifies claims presented without evidential support in someone else's text (K_K02)
– assesses the consequences of a technology implementation for people and groups outside the implementing organisation (K_K03)
– formulates a decision recommendation together with an option considered and rejected (K_K04)
– recognises the ethical dilemma involved in implementing a technology (K_K05)

Assessment criteria

The final grade is based on a team project (50%) and a written examination (50%).

The project is prepared in teams of three to five people, in stages during classes; detailed requirements, assessment criteria and deadlines are given in the first class. The written examination covers the material of the whole course.

Work is submitted in the form and by the deadline set in class; work not submitted on time means the loss of points for that element. Passing the course requires submitting the project and obtaining at least 50% of the points overall. The resit takes the form of a written examination covering the whole course.

Artificial intelligence tools may be used to organise one's own argument and to edit language, not to generate substantive content or to cite sources without verifying them; the team includes a note on their use at the end of the study.

Practical placement

Bibliography

Required reading:
Abrahamson, E., Fairchild, G. (1999), Management fashion: Lifecycles, triggers, and collective learning processes, Administrative Science Quarterly, 44(4), 708–740. https://doi.org/10.2307/2667053
Treem, J. W., Leonardi, P. M. (2013), Social media use in organizations: Exploring the affordances of visibility, editability, persistence, and association, Annals of the International Communication Association, 36(1), 143–189. https://doi.org/10.1080/23808985.2013.11679130
Tan, Y.-C., Chandukala, S. R., Reddy, S. K. (2021), Augmented reality in retail and its impact on sales, Journal of Marketing, 86(1), 48–66. https://doi.org/10.1177/0022242921995449
Hennig-Thurau, T., Aliman, D. N., Herting, A. M., Cziehso, G. P., Linder, M., Kübler, R. V. (2022), Social interactions in the metaverse: Framework, initial evidence, and research roadmap, Journal of the Academy of Marketing Science, 51(4), 889–913. https://doi.org/10.1007/s11747-022-00908-0
Hofstetter, R., de Bellis, E., Brandes, L. (2022), Crypto-marketing: How non-fungible tokens (NFTs) challenge traditional marketing, Marketing Letters, 33(4), 705–711. https://doi.org/10.1007/s11002-022-09639-2
Zhang, Y., Gosline, R. (2023), Human favoritism, not AI aversion: People's perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation, Judgment and Decision Making, 18, e41. https://doi.org/10.1017/jdm.2023.37
Luo, X., Tong, S., Fang, Z., Qu, Z. (2019), Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases, Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192
Doshi, A. R., Hauser, O. P. (2024), Generative AI enhances individual creativity but reduces the collective diversity of novel content, Science Advances, 10(28). https://doi.org/10.1126/sciadv.adn5290
Lewis, R. A., Rao, J. M. (2015), The unfavorable economics of measuring the returns to advertising, The Quarterly Journal of Economics, 130(4), 1941–1973. https://doi.org/10.1093/qje/qjv023
Goldberg, S. G., Johnson, G. A., Shriver, S. K. (2024), Regulating privacy online: An economic evaluation of the GDPR, American Economic Journal: Economic Policy, 16(1), 325–358. https://doi.org/10.1257/pol.20210309
Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B., Larson, J. M. (2024), Synthetic replacements for human survey data? The perils of large language models, Political Analysis, 32(4), 401–416. https://doi.org/10.1017/pan.2024.5
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., Kumar, A. (2026), What is your AI agent buying? Evaluation, biases, model dependence, and emerging implications of agentic e-commerce, Companion Proceedings of the ACM Web Conference 2026, 8697–8700. https://doi.org/10.1145/3774904.3792943

Supplementary reading and current materials – implementation cases, market data with a stated measurement date and regulatory announcements – are provided by the instructor and posted on the course page before each meeting. Given the pace of change in this area, this set is updated in every edition of the course.

Notes

Term 2026Z:

Classes are held with a large group in a lecture hall. Project teams work during classes, seated together; the results of selected teams are discussed with the whole group.
The course is delivered in six meetings. Because of the smaller number of organised class hours, part of the material covered in class in full-time studies – the analysis of pairs of contrasting cases and predicting the outcome before it is revealed – is moved to preparatory tasks completed before the meeting. The scope of the course and its requirements are identical to those in full-time studies.