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Piotr Czarnecki

Publications and source records attributed to Piotr Czarnecki.

2 recordsLinked to original sources

The Role of Employment Flexibility in Enhancing the Competitiveness of Temporary Staffing Service Providers in Poland

This paper examines the role of employment flexibility in enhancing the competitiveness of firms using temporary staffing services, with empirical evidence from Poland. The study focuses on how flexible employment arrangements influence operational efficiency, cost reduction, workforce scalability, market responsiveness, and client satisfaction. A quantitative survey was conducted among managers and owners of Polish enterprises that cooperate with temporary staffing agencies, using purposeful sampling to capture informed managerial perspectives. The findings show that employment flexibility significantly reduces downtime, accelerates onboarding processes, and lowers personnel and recruitment costs. Flexible staffing enables rapid workforce scaling during demand fluctuations and facilitates access to specialized skills without long-term commitments. The results also indicate that employment flexibility enhances organizational responsiveness, improves profitability in short-term projects, and strengthens resilience to seasonal and market volatility. Additionally, flexibility is identified as a key determinant of client satisfaction and loyalty toward staffing service providers. The study demonstrates that employment flexibility is not merely a cost-control mechanism but a strategic human resource capability that supports competitiveness, operational adaptability, and sustainable performance in dynamic labor markets.

econ.GN

Prevention is better than cure: a case study of the abnormalities detection in the chest

Prevention is better than cure. This old truth applies not only to the prevention of diseases but also to the prevention of issues with AI models used in medicine. The source of malfunctioning of predictive models often lies not in the training process but reaches the data acquisition phase or design of the experiment phase. In this paper, we analyze in detail a single use case - a Kaggle competition related to the detection of abnormalities in X-ray lung images. We demonstrate how a series of simple tests for data imbalance exposes faults in the data acquisition and annotation process. Complex models are able to learn such artifacts and it is difficult to remove this bias during or after the training. Errors made at the data collection stage make it difficult to validate the model correctly. Based on this use case, we show how to monitor data and model balance (fairness) throughout the life cycle of a predictive model, from data acquisition to parity analysis of model scores.

cs.AI