Front Lines On Adoption Of Digital And Ai-Based Services – MAR529

Course Responsible: Professor Tor W. Andreassen

Time and place: 24th- 28th of October 2022, NHH Norwegian School Of Economics in Bergen

Grading Scale: Pass – fail.

Application: https://www.nhh.no/en/courses/front-lines-on-adoption-of-digital-and-ai-based-services/?displayNextTerm=True
(Application Deadline 1st of September 2022)

TOPICS

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.

LEARNING OUTCOME

Knowledge

• 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

Skills

• 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

General competence

• 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

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	Topics	Teachers (tentative)
Sunday	Introduction/overview of IA & AI	Tor W. Andreassen, Jim Spohrer
Monday	Creating and capturing value in a digital era; digital strategies and consumer trends	Tina Saebi, Magne Angelshaug,
Tuesday	Front lines in adopting IA & AI in service	Helge Thorbjørnsen, Aruna D. Tatavarthy, Darius Frank, Roland Rust.
Wednesday	Trends in digital communication, personalization, regulation, and ethics	Aruna D. Tatavarthy, + 1 from innovation index
Thursday	Overview of AI-based methods for research	Ivan Belik, Nhat Q Le, + 1 one from innovation index (?)
Friday	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.

RESTRICTED ACCESS

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.

RECOMMENDED PREREQUISITES

Master-level marketing knowledge.

REQUIRED PREREQUISITES

REQUIREMENTS FOR COURSE APPROVAL

• Students must present at least one article in class.
• Students are expected to participate actively in class discussions.

ASSESSMENT

• An individual term paper is written in English. Electronic hand-in of term paper in Wiseflow by December 20 at 14:00.

COMPUTER TOOLS

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

Literature

• Compendium of articles and book chapters.
• The course schedule with a complete list of readings will be available in due time before the seminar.

Reading list

AI & Business models

1. Foss, N.J, Saebi, T. 2017. Fifteen years of research on business model innovation. Journal of Management, 43(1), 200-227.
2. Trischler & Li-Ying 2021 Digital business model innovation: toward construct clarity and future research direction. Review of Managerial Science.
3. 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.

Optional:

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.

 

 

AI in service

1. Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
2. Longoni, C., & Cian, L. (2022). Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect. Journal of Marketing, 86(1), 91–108.
3. 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.
4. 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

1. New perspectives on consumer adoption and diffusion of innovations, JOURNAL OF BUSINESS RESEARCH, 116 (2020) 522-525
2. Understanding explaining and utilizing medical artificial intelligence (Dec 2021, pp 1636-42), Nature Human behavior
3. 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: 

1. Social Networks, Personalized Advertising, and Privacy Controls
2. Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?
3. Secrets and Likes: The Drive for Privacy and the Difficulty of Achieving It in the Digital Age
4. Consumer privacy and the future of data-based innovation and marketing

References: 

1. Tucker, Catherine E. “Social networks, personalized advertising, and privacy controls.” Journal of marketing research 51, no. 5 (2014): 546-562.
2. 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.
3. 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.
4. 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.