Deductive management studies require sound theory building and correct statistical testing. However, researchers are also challenged to solve important problems in between: a most frequent concern raised by reviewers is the disconnect between theory and empirics. This course centers on the troublesome link between theory and empirics and addresses questions like these:

• How do I define my constructs in the trade-off between high generalizability of theoretical predictions and high credibility of empirical measurements?
• How do I identify dependent variables that are theoretically interesting, which of them reflect practically relevant decisions?
• How do I implement the logical structure of constructs derived from theory (non-linearity, moderation, mediation, multi-level, dynamics, etc.) in regression models?
• How do I find a degree of model complexity that appears ambitious but still digestible to readers?

The course is suited to all PhD students who pursue a deductive research approach. In particular, it may attract students from the following departments:

• Global Business and Trade
• Management
• Marketing
• Strategy and Innovation

Throughout the course, participants will make first-hand experience in linking theory and empirics using their own research (dissertation project, or one specific paper out of it) as application examples. It is therefore important that course participants have already an idea about what their dissertation topic will be about, the theory they want to use in their work, the main theoretical concepts they are interested in, and how these concepts could be measured empirically.

After completion this course, students will be able to realize a fit between theory and empirics in their dissertation projects.

Sessions will take place in the classroom if possible but be held online if necessary. In all cases, minimum attendance is 80% of course hours. Giving short presentations on their work progress is compulsory for every participant.

For a common state of knowledge, every participant prepares the introduction session by reading the following articles:

Narula et al., 2019, Applying and advancing internalization theory: the multinational enterprise in the twenty-first century, Journal of International Business Studies, 50, 1231-1252.

Saylors, R. & Trafimow, D., 2020, Why the increasing use of complex causal models is a problem: on the danger sophisticated theoretical narratives pose to truth, Organizational Research Methods, DOI: 10.1177/1094428119893452.

In the introduction section, the course instructor will give an overview of the challenges and the basic options to take when crafting and testing models in management studies, using the example of new internalization theory (cf. the article above). In the following, students form groups to produce a draft of the hypotheses and methods sections of a scholarly manuscript on a topic of their choice. We will assist their progress towards this goal in two rounds of presentations and discussions.

Prior to the tentative presentations, groups prepare a slide deck and make it accessible to the other groups (upload to Learn@WU). In the tentative presentations, groups introduce the theory used for their paper, describe the data supposed be used to test this theory (can be fictive), and propose a testable model of hypotheses and a concept of measuring constructs. Both fellow groups give reviewer comments on the model’s fit in the intersection of theory and empirics. The course instructor will spur and moderate a discussion that leads to a working plan for revising the model.

In the final presentations, groups report on the way they implemented the changes suggested in the tentative presentations session and present the revised version of the hypotheses and measurements. A discussion will assess the progress made and indicate further room for improvement. On this basis, groups write and submit a paper including the hypotheses and methods sections of a scholarly manuscript.

Students can implement the model in a software package such as STATA or R and test it if suitable data are available. If so, the paper would include a results section, but this part is not mandatory for the course.

Tentative presentation: 25%

Reviews of the other groups’ tentative presentations: 25%

Final presentation: 25%

Write-up: 25%

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bjoern.schmeisser@wu.ac.at