Course Manual
Digital Technologies and Marketing
6013B0506Y
BSc Business Administration
2022-2023 Semester 2
Credits: 6 ECTS (168 hours)
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1. Aims of the course
Digital technologies affect virtually every facet of our daily life. You may shop on Amazon, use
your voice assistant to dim the lights in your living room, share the pictures taken on your
smartphone with friends, and let a Fitbit smartwatch track your physical activity. Digital
technology is not just omnipresent in the lives of consumers but also continues to radically
reshape and transform companies’ operations and marketing – both at the tactical and the
strategic level.
Developments in digital technologies have generated new approaches in the planning, design,
execution, and measurement of companies’ marketing efforts. These technologies present
marketers with novel opportunities for creating value for the firm itself and its key constituents
and stakeholders, such as customers. Yet, fully appreciating how digital technologies affect
consumers and how firms can effectively leverage these technologies for strategic value creation
is challenging.

Given the prevalence of these technologies in our daily discourse it is important to keep in mind
that “digital” and “technology” are no ends in themselves. Instead, digital technologies are better
conceived as a means to an end. They are valuable because they enable firms to do business
differently. Therefore, marketers need to develop an understanding of existing and emerging
technologies in order to formulate compelling value-creation strategies enabled by digital
technologies. This course grapples with these contemporary issues and offers insights into the
effects of prevalent and emerging digital technologies on consumers and their behavior. Building
on this understanding, it examines their implications for marketing and how marketers can
leverage digital technologies to generate novel consumer insights and unlock value for firms.
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2. Learning outcomes
By successfully completing the Digital Technologies and Marketing course, students will
develop an understanding of the effect of digital technologies on consumers and their behavior.
They will be well-versed in analyzing how these technologies continue to transform how
businesses operate and market their offerings. Students should be able to leverage these insights
to develop marketing actions and strategies enabled by digital technologies that create and
enhance value. Specifically, upon successful completion of this course, students are able to:
• understand and appreciate the transformative impact of digital and technological
developments on consumers and firms;
• explore the impact of technological developments on marketing research techniques and
outcomes;
• analyze how marketers and firms can leverage such developments to formulate and
execute marketing strategies;
• analyze scientific literature from the marketing field to solve real world business
questions;
• present and discuss their analyses (formally and informally) in a manner that benefits
fellow students' understanding and learning experience.
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3. Course format and structure

Digital Technologies and Marketing (DTM) is based on a curation of contemporary academic
articles about these topics.
The course consists of the following:
1. Weekly lectures (Tuesday, 13:00-15:00).
2. Weekly seminars in groups of ~30 students each (Wednesday-Thursday).
In the lectures, we will cover and illustrate the core concepts and literature for specific topics.
The full articles (i.e., exam requirement) provide more detailed information about the content
discussed in class. Reading academic articles (vs. a textbook) might be new and challenging, but
useful to stay up to date on the latest trends.
The seminars directly build on the materials covered in the weekly lectures. The central goal of
the seminars is to bring the materials to life by focusing on specific applications or cases of
marketing actions and/or strategies enabled or influenced by digital technologies.
We will discuss a mini challenge in each seminar. These challenges are aligned with the content
of that specific week. The goal of this format is to create an interactive classroom in which
everybody has active participation in the course content. In the second part of the seminar, you
will have time to work on the DTM project (in weeks 2-5). Practically, both the mini challenges
and group assignment will involve R (programming language for statistics).
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4. Assessment
The requirements for passing this course are the following:
Examination component Responsible Requirement Weight
Mini challenges
DTM project
Written exam
Individual
Team
Individual
Submitted
Submitted
Minimum 5.5
10%
30%
60%
Final Grade Individual Minimum 5.5 100%

Mini challenges
• During the seminars in weeks 1-5, we will work on several mini challenges. You submit
your solution to Canvas at the end of the seminar.
• Mini challenges will be marked as follows.
o 0 points = not submitted / submitted but content not sufficient
o 2 points = submitted and approved (i.e., pass)
• You need to submit 80% (4 out of 5) on time to pass the course
• There is no minimum passing grade for the mini-challenges.
DTM project
• The DTM project is a team effort, which counts for 30% of your final grade. You will
typically work in teams of 5 students. In this project, you will adapt a scraper, and
analyze this data to generate insights for marketing managers. The final output of your
project is a presentation in week 6 as well as a supporting slide deck (e.g., PowerPoint).
• There is no minimum passing grade for the DTM project, but submission is required.
• Additional details & information about the assignment, and the deliverables will be
provided on Canvas.
Written exam
• The written exam is an individual assignment, which counts for 60% of your final grade.
• Programming/coding will not be a part of the exam
• The minimum grade to pass is a 5.5.
• Failing this requirement implies you will have to participate in the resit exam. It is your
most recent exam grade that counts – not the highest one.
• The exam will consist of open-ended (essay) questions, mini-cases, and calculations. The
exam covers the literature (i.e., the articles from academic journals), lecture slides and
seminar materials.
• More details will be published on Canvas and discussed during the course.
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5. Teaching team
Dr. Nick Bombaij
Course coordinator and instructor
n.j.f.bombaij@uva.nl
Georgia Kirilova
Seminar instructor
g.kirilova@uva.nl

Policy on questions:
• First, ask fellow students or group members. Also check out this manual, which coves
many general course questions.
• Second, ask me during the lecture or tutorial. We will see each other twice a week.
• Emailing me with questions is a last resort. This is simply not a manageable way of
communication for a class of 300 students. Last-minute emailing (e.g., a few hours before
a deadline or the exam) will not get a response.
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6. Course outline and timetable
Week Calendar
Week
Topic Seminar activity /
Assignments
1 6 Introduction, big data & web scraping Mini challenge 1:
Introduction to R via Datacamp
Install R + RStudio
2 7 Website design & customer journey Mini challenge 2:
Attribution
Start DTM assignment
3 8 Social media & recommendation systems
Mini challenge:
Recommendations
Continue DTM assignment
9 Reading + assignment break
4 10 User generated content Mini challenge:
Text analysis
Continue DTM assignment
5 11 The technological forefront

Mini challenge:
ChatGPT
Finish DTM assignment
6 12 Summary, Q&A, assignment support DTM assignment presentation
13 Exam: 29-03-2022 (19:00-21:00)
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Week 1 | Introduction to Digital Technologies and Marketing
Overview
• Introduction to the course and details on the assessment
• Important definitions of data
• Information on web scraping
Readings & materials for the lecture
Boegershausen, J., Datta, H., Borah, A., & Stephen, A. T. (2022). Fields of gold: Scraping
web data for marketing insights. Journal of Marketing, 86(5), 1-20.
Seminar activity
• Mini challenge: Introduction to R
Week 2 | Consumers and digital technologies
Overview
• A/B testing
• Website design
• Customer journey & attribution
Readings for the lecture
Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2019). Creating effective online
customer experiences. Journal of marketing, 83(2), 98-119.
OPTIONAL: Anderl, E., Becker, I., Von Wangenheim, F., & Schumann, J. H. (2016).
Mapping the customer journey: Lessons learned from graph-based online attribution
modeling. International Journal of Research in Marketing, 33(3), 457-474.
Seminar activity
• Mini challenge: attribution in R
• Group assignment: collect data
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Week 3 | Social media & recommendation systems
Overview
• Improvised marketing intervention
• Influencer marketing
• Recommendations
Readings for the lecture
Borah, A., Banerjee, S., Lin, Y. T., Jain, A., & Eisingerich, A. B. (2020). Improvised
marketing interventions in social media. Journal of Marketing, 84(2), 69-91.
Gai, P. J., & Klesse, A. K. (2019). Making recommendations more effective through
framings: Impacts of user-versus item-based framings on recommendation click-throughs.
Journal of Marketing, 83(6), 61-75.
Karagür, Z., Becker, J. M., Klein, K., & Edeling, A. (2022). How, why, and when
disclosure type matters for influencer marketing. International Journal of Research in
Marketing, 39(2), 313-335.
Seminar activity
• Mini-challenge: recommendation systems
• DTM assignment
Week 4 | User generated content
Overview
• Topic analyses
• Language use
• Responding to reviews

Readings for the lecture
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business:
Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
Packard, G., & Berger, J. (2017). How language shapes word of mouth's impact. Journal of
Marketing Research, 54(4), 572-588.
Wang, Y., & Chaudhry, A. (2018). When and how managers' responses to online reviews
affect subsequent reviews. Journal of Marketing Research, 55(2), 163-177.
Seminar activity
• Mini-challenge: text analyses of reviews
• DTM assignment
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Week 5 | The technological forefront
Overview
• Transparency
• Automation
• Chatbots

Readings for the lecture
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion.
Journal of Marketing Research, 56(5), 809-825.
Kim, T., Barasz, K., & John, L. K. (2019). Why am I seeing this ad? The effect of ad
transparency on ad effectiveness. Journal of Consumer Research, 45(5), 906-932.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Machines vs. humans: The impact of
artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6),
937-947.
Seminar activity
• Mini-challenge: ChatGPT
• DTM assignment
Week 6 | Course summary lecture
Overview
• Course summary and review
• Exam preparation / Exam Q&A
• DTM project support
Readings for the lecture
N/A
Seminar activity
• DTM project presentations [30%]