The Amsterdam People Analytics Center (APAC) helps facilitate partnerships between industry and academia in the area of people analytics. People analytics is a data-driven approach to people management and focuses on answering people related business questions using a variety of data sources collected and stored in organizations.
It combines different disciplines, such as Human Resource Management (HRM), Organizational Behavior, Management, statistics, and data science. The center's aim is to promote high-quality scientific research in the field of people analytics at the interface of theory and practice.
Its members are grounded both in the fields of (Human Resource) Management and Organizational Behavior, and have specific expertise in research methods. Topics we cover include careers, learning analytics, person-organization fit, strategic HRM, management practices, leadership, and applicant selection. We have experience with a broad range of approaches to people analytics, including combining and analyzing different data sources collected at and/or stored in organizations using new analytic techniques, psychometric assessment, and text mining.
APAC offers collaboration opportunities on a broad range of topics related to people analytics, in order to investigate issues which are relevant to practitioners and add to the body of knowledge on HRM and Organizational Behavior. Organizations can approach us for collaborative research projects aimed at improving people related decision making in general or answering specific people related questions.
Please contact Corine Boon (E: email@example.com) if you have any questions or are interested in collaborating with us!
Our team focuses on the following key themes. If you would like to know more, take a look at examples of current projects we are working on, and examples of relevant recent publications of the team on past projects.
We study the socioeconomic and psychometric match in the supply and demand of labor, with the aim of moving closer to the optimal use of human capital.
We leveraging synthetic data and research ethical ramifications of algorithm based decision making.
We focus on the characteristics, and consequences of (in)effective leaders over time, using a combination of different data sources collected or stored in organizations as well as leadership diagnostics.
We focus on improving matchmaking processes between individuals (e.g., students), education and the labor market.
We study the added value of HR investments using a combination of different data sources collected or stored in organizations (over time), in order to be able to make data driven HR decisions.
We focus on informing theory and practice in the broad field of HRM and Organizational Behavior using text mining.
We use interoperable data sources to create and elaborate turnover models and turnover theory.