The course aims to provide the exposure of current topics in digital businesses and tools required to pursue research in the domains of digitalization at large and consumer-firm interactions. At the organizational level, the impact of digitalization is largely fostered by the adoption of Intelligent Automation (IA) and Artificial Intelligence (AI) by customers and employees. Hence, the current course builds on theoretical, conceptual, and methodological advances from the following research streams: digital strategy, innovation and ecosystems, digital marketing, and consumer behavior.
The course format integrates seminar-based readings, lectures, hands-on exercises, and presentations. Students are expected to learn not only the diverse applications of digitalization in various consumer spaces including advertising but also gain exposure to the various types of data sources available to propose new research ideas.
During the course, the students will gain an overview of the front lines of IA & AI adoption and the impact of digitalization in businesses and public policy. Students will be exposed to an in-depth understanding of digitalization when creating and capturing value. The sessions will cover lectures from diverse research areas such as behavioral economics, marketing finance, consumer online search, new products, services adoption, etc.
One of the biggest areas where IA & AI has affected businesses is in the consumer-firm service delivery and communication space. The adoption of image recognition, language technology, and predictive modeling are three areas where IA & AI have made a difference. Consequently, the next part of the course focuses on the macro as well as the micro aspects of adopting IA & AI-based services in businesses. Students will read and understand ethics, personalization, privacy, and other related topics.
The course also provides the required impetus for the students to become aware of various data sources available for analysis, and the students will get an overview of methods and tools for analyzing text data and audio data.
The course concludes with students presenting their research proposal ideas that will be the first part of the term paper for the course.
Argue for theories and constructs about implications of AI to Businesses
Apply these theories and constructs to Marketing & Consumer Behavior
Argue for key theories and constructs specific to Marketing & Consumer Behavior
Account for key patterns in empirical findings regarding this relationship
Understand the key methodologies employed in the context of AI & Marketing
Understand and discuss the degree to which different theories and constructs are complements or substitutes
Relate new empirical findings to underlying theoretical concepts
Formulate relevant and interesting research questions at the intersection of Marketing & AI
Absorb, communicate, discuss, and evaluate research at the intersection of AI & Marketing/Consumer Behavior at the research frontier
Contribute to AI discussions in marketing strategies of the firm
Teaching will be physical. Some sessions will be through Teams/Zoom.
Teaching from 09:00 to 17:00. (Teaching time for Sunday will be announced later)
Week 43, 2022
Introduction/overview of IA & AI
Tor W. Andreassen, Jim Spohrer
Creating and capturing value in a digital era; digital strategies and consumer trends
Tina Saebi, Magne Angelshaug,
Front lines in adopting IA & AI in service
Helge Thorbjørnsen, Aruna D. Tatavarthy, Darius Frank, Roland Rust.
Trends in digital communication, personalization, regulation, and ethics
Aruna D. Tatavarthy, + 1 from innovation index
Overview of AI-based methods for research
Ivan Belik, Nhat Q Le, + 1 one from innovation index (?)
Presentations of and feed-back on research proposals
Tor W. Andreassen. We will allocate participants incl innovation index faculty to faculty for feedback.
A more detailed schedule will be presented during the introduction. Students are expected to prepare the course literature before the class.
Maximum 20 students (pedagogical reasons).
Ph.D. candidates from NHH and other Norwegian institutions and Ph.D. candidates from institutions, which are part of NHH’s Innovation Index Research Partnership, can attend the course. DIG partners can also participate.
Priority: Ph.D. candidates from NHH.
Second priority: Ph.D. candidates from other Norwegian institutions.
Third priority: Ph.D. candidates associated with institutions are part of NHH’s Innovation Index Research Partnership.
Fourth priority: DIG partners.
Master-level marketing knowledge.
REQUIREMENTS FOR COURSE APPROVAL
Students must present at least one article in class.
Students are expected to participate actively in class discussions.
An individual term paper is written in English. Electronic hand-in of term paper in Wiseflow by December 20 at 14:00.
Bring your computer.
RESEARCH FACULTY ASSOCIATED WITH THE PH.D. COURSE
Professor Tor W Andreassen, NHH (marketing & innovation strategies & course responsible)
Professor Roland T. Rust, Smith School of Business, University of Maryland
Dr. Jim Spohrer, Retired IBM Director, Cognitive OpenTech (2017 – 2021), Member Board of Directors, International Society of Service Innovation Professionals (ISSIP)
Professor Helge Thorbjørnsen, NHH (CB & adoption)
Professor Lasse Lien, NHH (strategy and digital ecosystems)
Associate professor Nhat Q. Le, Norwegian Business School, Adjunct professor NHH, SOL (AI-based methods for research)
Associate professor Aruna Divya Tatavarthy (CB & adoption)
Associate professor Tina Saebi, NHH (digital business models innovation)
Associate professor Ivan Belik, NHH (AI methods for research)
Associate professor Darius Frank, Aarhus University, business school
Compendium of articles and book chapters.
The course schedule with a complete list of readings will be available in due time before the seminar.
AI & Business models
Foss, N.J, Saebi, T. 2017. Fifteen years of research on business model innovation. Journal of Management, 43(1), 200-227.
Trischler & Li-Ying 2021 Digital business model innovation: toward construct clarity and future research direction. Review of Managerial Science.
Sjödin et al 2021 How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research 134.574-587.
1. Vaska et al 2021 The Digital Transformation of Business Model Innovation: A Structured Literature Review. Frontiers in Psychololgy
2. Christensen, C.M., Bartman, T., Van Bever, D. 2016. The hard truth about business model innovation. MIT Sloan Management Review, 58(1), 30-40.
Longoni, C., & Cian, L. (2022). Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect. Journal of Marketing, 86(1), 91–108.
Frank D.-A., Elbæk C.T., Børsting C.K., Mitkidis P., Otterbring T. & Borau S. (2021). Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic. PLoS ONE, 16(11). e0259928.
Yun, J.H., Lee, E.-J. & Kim, D.H. (2021). Behavioral and neural evidence on consumer responses to human doctors and medical artificial intelligence. Psychology & Marketing, 38, 610– 625.
Adoption an AI
New perspectives on consumer adoption and diffusion of innovations, JOURNAL OF BUSINESS RESEARCH, 116 (2020) 522-525
Understanding explaining and utilizing medical artificial intelligence (Dec 2021, pp 1636-42), Nature Human behavior
Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “word-of-machine” effect, Journal of Marketing, 2020, vol 86, pp 91-108
Trends in digital communication, personalization, regulation, and ethics
List of Journal Articles:
Social Networks, Personalized Advertising, and Privacy Controls
Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?
Secrets and Likes: The Drive for Privacy and the Difficulty of Achieving It in the Digital Age
Consumer privacy and the future of data-based innovation and marketing
Tucker, Catherine E. “Social networks, personalized advertising, and privacy controls.” Journal of marketing research 51, no. 5 (2014): 546-562.
Johnson, Garrett A., Scott K. Shriver, and Shaoyin Du. “Consumer privacy choice in online advertising: Who opts out and at what cost to industry?.” Marketing Science 39, no. 1 (2020): 33-51.
Acquisti, Alessandro, Laura Brandimarte, and George Loewenstein. “Secrets and likes: The drive for privacy and the difficulty of achieving it in the digital age.” Journal of Consumer Psychology 30, no. 4 (2020): 736-758.
Bleier, Alexander, Avi Goldfarb, and Catherine Tucker. “Consumer privacy and the future of data-based innovation and marketing.” International Journal of Research in Marketing37, no. 3 (2020): 466-480.