Categoryissue 5-2025, publication

dr inż. Katarzyna WŁODARSKA* ORCID 0000-0001-7611-001X Instytut Nauk o Jakości, Uniwersytet Ekonomiczny w Poznaniu, Al. Niepodległości 10, 61-875 Poznań, Polska, e-mail: Katarzyna.Wlodarska@ue.poznan.pl

Received 1.10.2025. Accepted 10.10.2025

Abstract

Purpose: Assessment of the applicability of NIR technology for monitoring selected quality attributes of plant-based milk alternatives available on the Polish market.
Design/methodology/approach: Near infrared (NIR) spectroscopy was used to analyze plant-based milk alternatives made from different raw materials, such as coconut, oat, almond, rice, soy, and hazelnut. Using conventional laboratory methods, the total soluble solids content, dry matter, density, and turbidity were determined. Predictive models were developed using partial least squares (PLS) regression.
Findings/conclusions: The best predictive performance was observed for dry matter content model (R² = 0.93; RMSECV = 0.62%). Comparable predictive abilities were demonstrated by the models for the soluble solids content (R² = 0.84; RMSECV = 1.06°Bx), turbidity (R² = 0.88; RMSECV = 7.42 NTU) and density (R2 = 0,84; RMSECV = 0,04 g/ml).
Research limitations: Due to the limited number of samples, the full variability typical of this product category may not have been fully represented. The findings, however, demonstrate the potential of NIR technology for quality evaluation. For practical applications in the food and agricultural sectors, a larger and more diverse sample set is necessary.
Practical implications: NIR sensors, integrated with information systems, can be used for on-line and in-line analyses to monitor the production process with minimal environmental impact through resource savings and waste reduction.
Originality/value: The obtained results indicate the feasibility of simultaneous rapid and non-destructive determination of multiple quality parameters of milk alternatives based on direct NIR spectral measurements. NIR technology represents an attractive alternative to traditional analytical methods.

Keywords

NIR technology, machine learning, quality, food, plant-based milk alternatives

mgr inż. Małgorzata KONIUSZY – Prezes Koniuszy Happy Work Culture, Lębork, Polska
prof. dr hab. Małgorzata Z. WIŚNIEWSKA – Uniwersytet Gdański, Wydział Zarządzania, Zakład Zrównoważonego Rozwoju i Nauk o Jakości, ul. Armii Krajowej 101, 81-824 Sopot, Polska, e-mail: malgorzata.wisniewska@ug.edu.pl

Received 5.08.2025. Accepted 18.08.2025

Abstract

Purpose: The article aims to present and critically analyze the Happy Work Culture (HWC) model. The research problem is to answer the question: What are the advantages and potential benefits of using HWC?
Design/methodology/approach: Critical literature review, case study, content analysis, and logical synthesis and reasoning were used.
Findings/conclusions: The HWC model can significantly support work culture diagnostics by providing data that allows for informed decision-making to shape the appropriate quality of work. It enables the identification of areas requiring improvement, the development of a targeted human resources management strategy, and monitoring progress in implementing cultural changes and process optimization. Systematic use of the HWC model can contribute to improved employee engagement, reduced tur over, and increased organizational competitiveness.
Research limitations: The model is time-and labor-consuming, and its implementation requires the participation of numerous employees at various levels. The model uses specific language and formulations that may be somewhat difficult to understand. Content validation did not include operational employees; therefore, the model should also be validated at the operational level, encompassing a representative sample of employees and using appropriate statistics to assess the reliability and validity of the statements.
Practical implications: The model provides the basis for developing personalized strategies and recovery plans tailored to the organization’s actual needs. This article contributes to understanding the impact of happiness, well-being, and work culture on business performance.
Originality/value: HWC is a pioneering solution that allows for an in-depth analysis of six key aspects of the workplace, identifying strengths and areas requiring development. It allows for the identification of bottlenecks and gaps in workplace culture.

Keywords

organizational culture, work culture, work quality, happiness, Happy Work Culture

Prof. Emer. Jan FRICK – University of Stavanger, Kjell Arholms gate 41, 4021 Stavanger, Norway, e-mail: Jan.Frick@uis.no

Received 2.10.2025. Accepted 13.10.2025

Abstract

Purpose: The intention was to investigate the context of Quality 5.0 as emphasis on sustainability is rapidly increasing, and the use of possibilities with AI systems is changing even faster. In the face of escalating global challenges, Quality 5.0 emerges as a transformative framework that redefines organizational excellence by integrating sustainability, advanced technology, and human-centered values. Building on the foundations of Quality 4.0 and aligning with the broader vision of Industry 5.0, Quality 5.0 moves beyond efficiency and defect reduction to embed environmental stewardship, social responsibility,
and economic resilience directly into the definition of quality.
Design/methodology/approach: This paper explores how organizations can leverage cutting-edge technologies such as Artificial Intelligence (AI), digital twins, blockchain, and the Internet of Things (IoT) to foster predictive, transparent, and sustainable operations. Central to this evolution are concepts like circular economy principles, life-cycle thinking, and strategic Asset Management, which together drive operational excellence, risk resilience, and long-term value creation. Through a detailed case study of the SFI LEO initiative in offshore asset management, the paper demonstrates the practical application of Quality 5.0 principles.
Findings/conclusions: Ultimately, the adoption of Quality 5.0 offers a roadmap for organizations seeking to thrive in an increasingly complex and sustainability-driven global landscape, where success is measured not only by performance but also by lasting, positive impact on people and the planet.
Research limitations: The work is limited by how Quality 5.0 is seen from the authors’ work with Industrial Asset Management context and international projects.
Practical implications: Quality 5.0 offers possibilities to augment Industry 4.0 technologies in an Industry 5.0 context where humans make sustainable use of the technologies. One such possibility is the extension of the life of buildings, equipment, and systems.
Originality/value: This paper tries to set an industrial framework for Quality 5.0 with its sustainable possibilities.

Keywords

Quality 5.0, Sustainability, Artificial Intelligence (AI), Asset Management , Industry 5.0