You work for a research project funded by the Deutsche Forschungsgemeinschaft (DFG). Your duties include academic services in this project. You will also have the opportunity for visits of research partners located in the US and Sweden. You may also pursue independent research and further academic qualifications including PhD studies (Promotion). As PhD candidate you become member of our Doctoral Program in Business Research (https://www.wiwiss.fu-berlin.de/en/forschung/dpbr/index.html), which aims to help young academics thrive through all stages of their training. You will also become a full member of the Marketing Department at FU Berlin (https://www.wiwiss.fu-berlin.de/en/fachbereich/bwl/marketing/index.html).
Job requirements:
Product failures can have serious consequences for consumer welfare and firm performance. For example, in the U.S. alone, a child dies every two weeks from a furniture or TV tip-over. When firms become aware of such product failures, they either voluntarily recall the product or the authorities order the recall. But too frequently product recalls are not effective. For example, a survey by the U.S. Consumer Product and Safety Commission (CPSC) in 2018 found that the overall recall effectiveness rate for consumer product recalls is rather low: more than 80% of products have not been returned, repaired, replaced, or disposed. Most buyers are still using the dangerous products which poses a threat to consumer health and to firm performance if further incidents occur, cause litigation costs, require re-announcements of the recall, and lead to further reputation damage. This project therefore seeks to find answers to the following overarching research question: How can managers and regulators optimize product recall procedures and notifications to protect both consumer health and firm performance? To answer this research question, this project is designed to build an extensive database and quantify a prediction model including the antecedents, consequences, and boundary conditions of recall effectiveness. The results of this prediction model are going to be used to create a product recall effectiveness classification and impact prediction system (PRE-CLIPS). In the future, PRE-CLIPS can enable decision makers to plan and execute more successful product recalls.

Requirements:
Master degree (or equivalent) in business studies, economics, or information systems

Desirable:
- Experience in marketing and/or supply chain management desirable
- Experience with quantitative methods and statistical software packages and/or natural
  language processing, text mining, machine learning
- Capacity for teamwork in an international environment
- Willingness to work with quantitative data
- Very good command of English language
- German language skills desirable