Course description

Course description

This course applies a hands-on approach to market research and will enable you to conduct a market research project from A to Z. The course comprises all steps from the generation of research questions to the analysis of self-collected data to the deductions of implications for marketing. We will look into implicit as well as explicit attitude measures as well as psychological covariates. We will apply segmentation techniques as well as causal-effects models.

A current cross-cultural study of consumer behavior during the COVID19 pandemics serves as application case for our course. This setting provides the unique opportunity to compare consumer behavior in UK, France, Germany, Indonesia and UK.  The focus of our research will be consumers´ acceptance of protection measures while shopping, specifically the willingness to wear face masks. We will look into drivers of adoption and derive from our findings ideas for the social marketing of protective gear.

Course objectives and learning outcomes

The course enables students to handle and solve complex research questions in marketing and social science. Based on examples of (business) strategy students get to know complex multivariate analysis methods and how to apply them on their own. By obtaining knowledge and the ability to use statistical software packages, students also qualify to perform operational empirical analysis in a research project, consultancy, and professional practice.

By the end of the course, it is expected that students will be able to:

1. Demonstrate the ability to transform marketing problems into research hypotheses and proposal.
2. Display the ability to develop proper measurement to collect data on the variables that are relevant to address the research hypotheses.
3. Demonstrate the ability to select and utilize proper statistic tools to assess the dataset and the research hypotheses.

The skills mentioned above are key for a prospective student becoming an empirical researcher in the fields of social science, especially in market research.

Grading information

The course is designed as an interactive real-life research experience: Accordingly, the process of conducting high-quality quantitative research and its careful documentation is assessed, while positive or negative empirical findings do not influence grading. Course deliverables include a written research report (60%), presence and active participation in exercises as well as participation in a market research project including a work-in-progress presentation of the teams´ project work (40%). When presenting, NO final results are expected, instead, presentations should encourage a critical discussion both about methods and contents. Presentations and obtained feedback should also be used as guidance for the final written research report (flexible length).

To pass the course, passing the research report and taking part in the exercises and the presentations is a precondition. Both parts have to be passed at least with a 4.0 in order to pass this course. Exceptions like an exam are not possible.

Course organization

The course will be hold entirely online. Lectures and exercises will take place at the times communicated in STiNE. Microsoft teams will be used as platform for lectures, exercises and group works. Students are asked to install the needed course software in before of the first lecture:

VPN: https://www.rrz.uni-hamburg.de/services/netz/vpn.html

MS-Teams: https://www.rrz.uni-hamburg.de/services/kollaboration/microsoft-office-365/teams.htm

STATA: https://www.rrz.uni-hamburg.de/services/software/software-thematisch/statistik/stata.html

Schedule

Integrated lecture and exercise: 

1. Course introduction and overview  <CW 45: November 3rd 2020 >

• Course organization & software
• Deriving hypotheses
• Primary and secondary data
• Our topic: “Mask usage during COVID19 pandemics”
• Students built teams & (pre)select topics

Preparation: Students gain own questionnaire experience

2. Measurement instruments <CW 46: November 10th 2020 >

•  Students reflect on their personal questionnaire experience
• Explicit measurement instruments
• Implicit measurement instruments (IAT)
• Vignettes for experimental research

3. Sampling <CW 47: November 17th 2020 >

• Students choose their topics
• Measurement, errors and data for business research
• Sampling techniques

Preparation: Students conduct an exemplary data collection

4. Data handling <CW48: November 24th 2020 >

• Introduction into Stata
• Students present insights from their exemplary data collection

5. Descriptive statistics   <CW49: December 1st 2020 >

• Visualization: Scatter and Box-Plots
• Outlier treatment
• Students derive descriptive statistics for their topics

6. Data reduction < CW50: December 8th 2020 >

• Scoring techniques (e.g. IAT-score)
• Factor analysis
• Students implement data reduction for their topics

7. Basic methods for hypothesis testing <CW51: December 15th 2020 >

• Mean comparisons
• Analysis of variance
• Students perform initial hypotheses tests
• Outlook: Methods for hypothesis testing

Preparation: Students specify research idea(s) for their group project

8. Specification of research questions <CW 1: January 5th 2020 >

• Group discussions on their research topics & possible research questions

9. Basic causal models <CW 2: January 12th 2020>

• Correlation and regression
• Students perform initial analyses

10. Basic segmentation and positioning techniques <CW 3: January 19th 2020 >

• Target segment strategy
• Cluster analysis
• Students perform initial analyses

Preparation: Students prepare their method presentations as “WIP”=work-in-progress

11. Advanced scaling techniques <CW 4: January 26th 2020 >

• Conformatory factor analysis

<Student presentations on basic scaling techniques>

12. Advanced causal models <CW 5: February 2nd 2020 >

• Structural equation modeling

<Student presentations on basic causal models>

13. Advanced positioning techniques <CW 6: February 9th 2020 >

• Multidimensional scaling
• Correspondence analysis

<Student presentations on basic segmentation techniques>

14. Complex causal-effects models <CW 7: February 16th 2020 >

• Outlook
• Wrap-Up

 Literature:

• Malhotra, N. K., & Dash, S. (2016). Marketing research: An applied orientation. Pearson.
• Mazzocchi, M. (2008). Statistics for Marketing and Consumer Research (6th edition), Pearson, SAGE Publications
• Saunders, M. N. K., Lewis, P., & Thornhill, A. (2017). Research Methods for Business Students (7th edition). Pearson Education Limited