Organizations often find it difficult to identify talented employees. A research group led by Prof. Dr. Bruno Staffelbach evaluates what kind of biases are relevant in talent identification, how they influence decision making in talent identification and how to deal with biases that are discriminating.

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Talent management is crucial for organizational sustainability and competitive advantage. It includes the identification, development and retention of talented employees. However, little is known about what defines an effective talent identification process and how accurate decisions in the talent identification process are made such that individuals who are chosen as talents are indeed those who demonstrate abilities and skills in line with organization’s talent definition. There are different reasons for inaccurate decisions. For example, inaccurate decisions may result from biases. Biases are inclinations towards something, or a predisposition, prejudice or preference.

The focus of the research project is on the similarity bias. The similarity bias refers to the phenomenon of favoring or evaluating those who are similar to oneself as better than others who are not similar. Furthermore, the project examines the influence of contextual factors such as volatility, uncertainty, complexity, and ambiguity also known as VUCA context.

The world is continuously changing – new technologies, globalization – and with it the societal expectations towards organizations. The quantitative research project aims to contribute to meeting the changing societal expectations, namely of having an effective and non-discriminatory talent identification.

 

  • Original title of the project: "Biases in Talent Identification. A Quantitative Investigation of Contextual Influence"
  • Lead: Prof. Dr. Bruno Staffelbach, Professor for Business Administration
  • Project team: Dr. Lea Rutishauser, Sandra Furrer (Doctorand)
  • Project duration: 34 months
  • Approved funding amount of the Swiss National Science Foundation (SNSF): CHF 420'000 (rounded) 

 

20th November 2019