In progress Doctoral Research in Business Analytics in Information Systems
- ERIM PhD RSM 2017 LIS BIM
Researchers in the field of information systems combine information management and data analytics in order to generate new scientific knowledge. The Business Information Management (BIM) section in the Department of Technology of Operation Management at Rotterdam School of Management (Erasmus University) is seeking a highly motivated PhD student who desires to pursue an academic research career in this field.
Applicants must have strong quantitative training, with preference given to candidates who have earned an MSc, MPhil or Research Master in econometrics, statistics, or microeconomics, and who have proficiency with R, SQL, python, or other data-oriented programming languages.
The PhD student will gain the training and experience necessary to conduct independent research through course work and seminars in IS, economics and quantitative marketing; workshops and research seminars; and through applied and theoretical research conducted in concert with the BIM faculty. The PhD thesis will be defined by the student with input from the supervisory group, and thus requires creativity, self-direction, and a passion for scientific inquiry.
Business Analytics, Information Strategy, Digital Innovation, Social Media, Mobile Commerce, Economics of IS, Digital Platform
Time frame2018 - 2021
You will develop your research topic together with the advisors.
The PhD will be completed under the daily supervision of Dr. Ting Li (http://www.rsm.nl/people/ting-li/). Ting Li is an expert in digital strategy, ecommerce, social media analytics, mobile marketing, business analytics, and pricing and revenue management. The supervisory team also includes Prof. Eric van Heck (http://www.rsm.nl/people/eric-van-heck/). Eric van Heck is an expert in digital business and architecture, big data and analytics, digital work, and energy markets.
As a PhD student, you will conduct research within the domains of information strategy, digital commerce, social media analytics, mobile analytics, digital platforms; or related topics. Your topic will be of your own choosing, but should leverage the expertise of the supervisory team.
Additionally, as a PhD student, you have the opportunity to collaborate with other BIM faculty in the department. This group works on a wide-range of interesting topics, including digital marketing (Dr. Dimitrios Tsekouras), social networks (Dr. Rodrigo Belo), digital platforms (Dr. Mark Boons), digital piracy (Dr. Zike Cao), business networks (Dr. Otto Koppius). You will also be able to take advantage of our strong industry ties, institutional databases, and software development support, to gain access to data sets and/or conduct field experiments.
To strengthen your international profile and complement your time at RSM, you will have the chance to go abroad for a research visit at a top North American University (3 to 6 months; past destinations include MIT, Maryland, Minnesota, and CMU).
The Rotterdam School of Management (RSM) is the dynamic business school of the Erasmus University. It is ranked as a top business school within and outside of Europe due to its members´ research productivity and sophisticated study programs. The BIM section at RSM is recognized worldwide and is multi- and interdisciplinary, publishing their research in top IS and management journals. This makes the BIM section a lively, creative, and intellectually stimulating place to conduct research. For further information about the Rotterdam School of Management, you can visit www.rsm.nl; for information concerning the department, see https://www.rsm.nl/research/departments/technology-and-operations-management/; for more information about the research institute and further information about the PhD program, please visit www.erim.eur.nl.
The PhD student will work in close collaboration with the supervisor and other faculty on tasks that include:
- Identify a consequential phenomenon that is relevant to managers or policy makers, and which has not been fully addressed in prior research
- Obtain data, primary or secondary, that are needed to better understand the phenomenon
- Use the scientific literature to understand and explicate the theoretical foundations of the phenomenon
- Identify the fundamental variables and relationships that are most important to the phenomenon of interest, and formalize these mathematically while relating them to data
- Identify the main assumptions that need to be made in order to solve or estimate the model, and understand their implications
- Develop the computer code necessary to extract research results from data
- Present research findings at national and international conferences
Document findings for publication in leading scientific journals, and ultimately, your PhD thesis