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Beyond Positive Response Rates: Capturing Information Richness in Workplace AI Acceptance Using Belief Structure
Ewa Roszkowska1, Tomasz Wachowicz2
1Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland.
Entropy (Basel, Switzerland)
|July 28, 2026
Summary
Cross-country acceptance of AI in the workplace varies significantly. Safety applications are favored, while monitoring and dismissal tools face low public acceptance across the EU27.
Area of Science:
- Social Sciences
- Computer Science
- Decision Sciences
Background:
- Public acceptance of artificial intelligence (AI) in the workplace is crucial for its adoption.
- Existing methods often oversimplify complex survey data, masking nuanced attitudes.
- Understanding cross-country variations in AI acceptance is vital for policy and implementation.
Purpose of the Study:
- To analyze EU27 citizens' attitudes towards AI-driven workplace practices using a novel method.
- To evaluate acceptance levels for specific AI applications and overall AI integration.
- To compare the proposed method with traditional analysis techniques.
Main Methods:
- Application of the Belief Structure Technique for Order Preference by Similarity to Ideal Solution (B-TOPSIS) method.
- Utilizing data from Special Eurobarometer 554 on AI and the future of work.
- Construction of individual and aggregated B-TOPSIS indexes for AI workplace applications.
Main Results:
- Significant cross-country differences in AI workplace practice acceptance were observed.
- AI for information gathering, work allocation, and data processing showed moderate acceptance.
- Safety-focused AI applications received high support, while monitoring and automated dismissal faced low acceptance.
Conclusions:
- The B-TOPSIS method effectively captures the full distribution of survey responses and uncertainty.
- AI acceptance in the EU workplace is multidimensional and context-dependent.
- The proposed belief-structure-based approach offers a more robust analysis than conventional summary measures.
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