Course Name: Text and Image Mining for Business Research
Time and place: April 27 -30, 2026 @ Hanken School of Economics
The first edition of this course took place in April 2022 and received an average course grade 4.70 (1= lowest grade – 5= highest grade) with a response rate of 85% (11 participants). The second edition of this course took place in June 2024 and received an average course grade 5 (1= lowest grade – 5= highest grade) with a response rate of 39% (7 participants).
Learning goal and objectives: The objective of this course is to enhance students’ knowledge on how to use text mining methods, processes, and tools for business research with a focus in marketing and management. In addition to text, the last part of the course will involve work with image mining. The learning goal is to prepare students for the conceptual and empirical challenges involved in the use of unstructured data (i.e., text and images) for business research. The course will cover topics such as theories of language and aesthetics, measurement error, validation, machine learning, and endogeneity issues.
Instruction and examination: The course is built on prior reading of recent guides and empirical applications of text and image mining, and hands-on work on class examples based on recent datasets from Twitter, Instagram, TripAdvisor, and Yelp.
The course will cover five main units:
1) An Introduction to Methods and Theories for Unstructured Data
2) Dictionary Methods for Text Analysis
3) Machine/Deep Learning (supervised and unsupervised)
4) Large Language Models and Generative AI
5) Beyond text data (focus on Image Analysis)
The course involves theory sessions, hands-on examples guide (carried out in parallel with the student), individual applications and post discussion/consultations. The course will be helpful for doctoral students’ decision-making concerning data gathering, measurement, modelling, and manuscript positioning.
Credits: 4 ECTS
Grading: The course will be graded as Pass or Fail. There will be an individual project that will be carried out through the course.
Prerequisites: Programing and basic statistical knowledge is recommended. The course will use Knime Analytics as the main analytics software, together with limited integrations of R and Python. Students attending the course will need to install all required software and extension before attending (instructions will be provided). A laptop computer with at least 8GB RAM is recommended to run the analyses (16 GB is ideal).
Admittance:
• Max. 25 students are admitted to the course.
• Application guidelines: PhD. students in all fields of economics and business studies, (e.g., in marketing, management, accounting, etc.). As most of the literature and examples relate to marketing and management, priority will be given to doctoral students who are majoring in those disciplines. Participants can apply by sending their CV and a motivation letter to Robert Ciuchita (robert.ciuchita@hanken.fi).
• Last application date: 01.02.2026
• Notification of acceptance: 15.02.2026
Instructor:
Francisco Villarroel Ordenes, Full Professor of Marketing, University of Bologna (https://www.unibo.it/sitoweb/francisco.villarroel/en).
• Francisco.villarroel@unibo.it; +39.0541.434151
• https://scholar.google.co.uk/citations?user=MKmmBLIAAAAJ&hl=en&oi=sra
Dr. Francisco Villarroel Ordenes (PhD. in Marketing, Maastricht University) is a Full Professor of Marketing at the University of Bologna. Francisco has experience teaching the courses Branding, Social Media Marketing, Performance Marketing, Analytics, and Text Mining, at the Bachelor, MSc. and PhD. level. His research revolves around the themes of branding, customer experience, and digital marketing, and it has been published in leading journals including the Journal of Consumer Research, Journal of Marketing, Journal of Service Research and Journal of Retailing, among others. He currently serves on the editorial review boards of the Journal of Consumer Research, Journal of Retailing, the Journal of Service Research, and the Journal of Business Research.
Course coordinator and contact information:
Robert Ciuchita, Associate Professor, Department of Marketing, Hanken School of Economics (https://harisportal.hanken.fi/en/persons/robert-ciuchita)
• robert.ciuchita@hanken.fi; +358 (0)50 414 3557
• https://scholar.google.co.uk/citations?user=s6QZALAAAAAJ&hl=en&oi=ao
Dr. Robert Ciuchita (Ph.D. in Marketing from Maastricht University) is an Assistant Professor of Marketing at Hanken School of Economics (Finland). His research and teaching interests lie in the areas of service management, service innovation, digital marketing, and customer experience management. His research has appeared in, for example, the Journal of Service Research, Journal of Interactive Marketing, Journal of Business Research, Journal of Service Management and Journal of the Association for Consumer Research. He currently serves as an associate editor (Innovation & Technology) for the Journal of Business Research and on the editorial review boards of the Journal of Service Research, the Journal of Interactive Marketing, Industrial Marketing Management and the Journal of Service Management.