About this course

  • Drivers of Consumer Preferences and Food Choice
    • Intrinsic product characteristics (e.g., sensory attributes, ingredients), search attributes (e.g., price, package size), credence attributes (e.g., sustainability and health)
    • Expectations and acceptance (liking)
    • Sensory sensitivity, attitudes, emotions
    • Situation and context
  • Drivers of Consumer Behavior and Behavior Change

    • Behavioral theory
    • Individual differences
    • Attitudes and personality segments
  • Quantitative Methods in Consumer Research

    • Recruitment and segmentation of consumers
    • How to measure consumer preferences and attitudes, validated scales
    • Rapid methods (Check-all-that-apply)
    • Study design: surveys, conjoint analysis, choice experiments, experimental auctions, acceptance testing, blind and informed testing, intervention studies and nudging, longitudinal studies
    • Analysis of consumer data (analysis of variance, multivariate analysis, preference mapping)
  • Qualitative Methods in Consumer Research

    • Interviews, focus groups, social media insights
    • Thematic analysis
  • Current Issues in Research, Industry, and Society (Note: Topics may vary)

    • Consumer science for health (food processing, satiety, healthy eating habits)
    • Consumer science for sustainability (alternative proteins, food waste, source separation of packaging)
    • Consumer science with specific groups (e.g., children, older adults)
  • Principles in Responsible Research and Innovation (RRI), GDPR and Ethics
  • How to read and discuss scientific articles, Literature search (optional seminar)

Learning outcome

Knowledge: Students will become familiar with concepts, theories, methods, applications, and established knowledge in the field of consumer research and consumer behavior in relation to food.

Skills: Students will be able to:

  • Plan and conduct consumer studies using the most established approaches in the field
  • Use tools and methodologies to process data, interpret and communicate results from consumer tests
  • Use literature in the discipline

General competence: Students will be able to solve research or industry-related problems within the field of consumer insight. They will be able to interpret and discuss relevant literature through independent reflection and critical thinking. Students will also practice collaboration, group presentations, and data analysis techniques that can be applied in other subjects.

The knowledge, skills, and competencies incorporated in this course may contribute to achieving the UN Sustainable Development Goals related to food‑related consumer behavior and consumption, especially Goal 3: Good Health and Well-being and Goal 12: Responsible Consumption and Production.

  • Learning activities

    Lectures, seminars, group work and independent study. An introduction to the topics will be given through lectures and group work, as well as presentations/discussions in seminars with the course responsibles present.
  • Teaching support

    Canvas.

    valerie.almli@nofima.no

    Mob. 91166405

  • Syllabus

    Ares, G., & Varela, P. (Eds.). (2018). Methods in consumer research, volume 1: new approaches to classic methods (Vol. 1). Woodhead Publishing.
  • Prerequisites

    No specific knowledge is required. The student must be interested in the subject.
  • Recommended prerequisites

    Raw material and technology knowledge within food science, Product development.

    Statistics and analysis of variance (e.g., STAT100 Statistics and STAT210 Experimental Design and Analysis of Variance).

    Knowledge in sensory science (e.g., MVI240 Sensory analysis)

  • Assessment method

    School exam of 3,5 hours on a given topic. Counts 100%

    Written exam Karakterregel: Letter grades Hjelpemiddelkode: A1 No calculator, no other aids
  • About use of AI

    K3: The use of AI tools is authorised for deliverables from the group work during the course, unless specified otherwise by the teachers.

    The use of AI is not permitted in the exam.

    Descriptions of AI-category codes.

  • Examiner scheme

    An external examiner approves the exam text and the assessment plan. Grading of school exams by teachers and external examiner.
  • Mandatory activity

    Group work, group presentations and short reports, practical exercises. Attendance to minimum 70 % of all lectures and guest lectures.
  • Teaching hours

    Three weeks intensive course. About 40 hours lectures and 20 hours seminars/groupwork over three weeks.
  • Preferential right

    M-MAT, M-MATVIT
  • Admission requirements

    General university entrance qualification