Biases and trust in data-driven decision-making: insights from ethnographic research


Biases and trust in data-driven decision-making: insights from ethnographic research


Objectives. This research aims at investigating cognitive and data biases and how they influence trust in data when data-driven decision-making is first introduced in the firm. Indeed, totally data-driven decision-making is still very far from expectations since managerial cognition can affect not only strategic decisions but also data and algorithms.

Methodology. We adopted an ethnographic approach to explore the case of a traditional transport authority in the North of the UK. We conducted a qualitative content analysis using an inductive approach and a quantitative content analysis applying the sentiment analysis. 

Findings. Our findings showed granular details on how trust in data changes as managers fall into one of the three traps zone, namely, cognition, data technology, and traps recognition zones. The study sheds light on a better understanding of the mechanism underlying the interaction between cognitive and data biases that influence data trust and suggest intriguing dynamics and synergy between cognition and data.

Research limits. Considering the study’s qualitative nature, the findings might be case-specific and lack generalizability.

Practical implications. Managers should be aware that the first steps of data-driven decision-making can fall into cognition and technology traps characterized by low technology awareness and the absence of domain-specific technology experience.

Originality of the study. To the best of our knowledge, the present study is the first research that comprehensively explored the dual nature of biases, cognitive and logical, of data-driven decision-making with the focus on trust in data.

#cognitive bias #data bias #data-driven decision-making #ethnographic research #managerial cognition