Dear colleagues,
In April 2025, Hakan Ozalp (University of Amsterdam) and I will teach a 5 ECTS PhD course on the use of large datasets and various forms of machine learning in management research. The course is open to research master and PhD students, as well as postdocs and junior faculty. The course centers around a single three-day module that is taught on-site at the campus of Vrije Universiteit Amsterdam, complemented by a couple of online sessions. More information about the course can be found here. Contact me should you have any questions about the course.
Analyzing Digital Data in Business and Management Research
April 2025
Joey van Angeren (Vrije Universiteit Amsterdam), Hakan Ozalp (University of Amsterdam)
ECTs
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5 ECTS
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Tuition fee and registration deadline
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Refer to the course website; register before 17 March 2025 (24 February 2025 for early bird registration)
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Target groups
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The Analyzing Digital Data in Business and Management Research course is open to research master students, PhD candidates, postdocs, and junior faculty engaging in research in business and management, broadly defined. This course is an advanced methods course that assumes prior knowledge of business and management or organization studies as well as a basic understanding of quantitative and/or quantitative research methods. Familiarity with programming in Python or R is also required, although full proficiency in those programming languages is not necessary.
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Course content
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Many phenomena that are of interest to management and organization scholars are captured in the form of rich digital data. Detailed work and navigation processes are captured in information systems and activity logs, product characteristics are described in textual product descriptions, gig platforms capture a wealth of data about workers, and CEOs manage the impression of their firms in video-recorded press conferences. The availability of such rich digital data provides novel opportunities for theorization and analysis. To this purpose, scholars of management and organization have in recent years increasingly turned to methods originally developed in computer science, most notably forms of machine learning, to collect and work with digital data. A distinct feature of those methods is that they are applied in both quantitative and qualitative research, for purposes that range from data exploration, to theory development, and onto hypothesis testing.
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The course Analyzing Digital Data in Business and Management Research provides an introduction to a variety of machine learning methods that can be used to analyze digital data in management and organization research, be it for the purpose of quantitative or qualitative analyses. The focus of the course is on the end-to-end research process. The course introduces website scraping and application programming interfaces (APIs) as ways to collect digital data. It surveys and develops hands-on experience with the main research methods to analyze large-scale numerical, text, and audiovisual data, and explores how others have applied those methods in both quantitative and qualitatve research. The publication process of papers based on those methods is also covered in the course.
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Learning goals
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- Understand the main methods for analyzing large-scale, text, and audiovisual data as well as how those methods can be applied in quantitative and/or qualitative research.
- Reflect on the application of methods for the analysis of digital data in research in business and management or related disciplines
- Collect digital data using website scraping or application programming interfaces (APIs)
- Apply methods to analyze digital data in in the context of a quantitative and/or qualitative research study
- Navigate the publication process for empirical papers that are based on digital data
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Course design
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This course is centered around a three-day module on the VU campus in Amsterdam, with smaller online sessions before and after. Each course component is dedicated to a specific aspect of conducting research using digital data (e.g., data collection, data analysis, publication process). The three-day module that is taught on campus provides theoretical, reflective, and hands-on experience with specific methods for data analysis. All sessions consist of a mixture of interactive lectures, paper discussions, and hands-on coding sessions. Hence, it is expected that students come well prepared.
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Travel and accommodation
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Participants are requested to make their own travel and accommodation arrangements. |
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Joey Van Angeren
Associate Professor
KIN Center for Digital Innovation
School of Business and Economics
Vrije Universiteit Amsterdam
joey.van.angeren@vu.nl------------------------------