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Firm size as a moderator of stakeholder pressure and circular economy practices : Implications for economic and sustainability performance in SMEs
(2025) Ahmadov, Tarlan; Durst, Susanne; Gerstlberger, Wolfgang; Nguyen, Quang M.; Department of Business and Economics
PURPOSE: This study examines the interplay between stakeholder pressure (internal and external), circular economy (CE) practices, firm size, and their impact on the sustainability and economic performance of Small and Medium sized Enterprises. This research underscores firm size as a key moderator in the relationship between stakeholder pressures and CE adoption, aiming to provide a comprehensive understanding of this dynamic in SMEs. METHODOLOGY: Based on a cross-sectional survey of 124 SMEs in Estonia, Latvia, and Lithuania, with respondents primarily being owners and managers of firms, a three-step approach tested the proposed model for CE practices. First, Confirmatory Factor Analysis (CFA) was used to ensure that the observed variables represented latent constructs. Second, Ordinary Least Squares (OLS) and Weighted Least Squares (WLS) regression methods were used to control for factors influencing CE adoption. Finally, the interaction terms assessed the moderating role of firm size. FINDINGS: The research shows that firm size moderates these effects, with external stakeholder pressure significantly influencing CE adoption more than internal pressure. These finding underscores how firm size shapes SMEs’ responses to stakeholder pressure when adopting CE practices. IMPLICATIONS: This study provides empirical evidence that stakeholder pressure significantly influences SMEs in the Baltic States to adopt CE practices, thus impacting economic and sustainability performance. Smaller firms can enhance CE practices by strategically managing stakeholders, whereas larger SMEs should align with external stakeholder expectations for more effective CE initiatives, leading to improved organizational performance. ORIGINALITY AND VALUE: This study demonstrates how stakeholder pressures drive CE practices and impact organizational sustainability and economic performance. Firm size plays a crucial role as a moderator amplifying the influence of external stakeholder pressure on CE practices.
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Proteomic characterization of Lysinibacillus reveals early-stage PET biodegradation potential
(2026-03-24) Dudziak, Radoslaw B.; Muñoz-Hisado, Víctor; Hidalgo-Arias, Andrea; Martínez-Carrancho, María; Garcia-Lopez, Eva; Fakhouri, Farayde Matta; Martinez-Alonso, Emma; Alcázar, Alberto; Sigurbjörnsdóttir, Margrét Auður; Fonseca, Gustavo Graciano; Cid, Cristina; Faculty of Natural Resource Sciences
Plastic pollution is a global challenge due to the persistence of synthetic polymers such as polyethylene terephthalate (PET) and the limited efficiency of current recycling strategies. While microbial biodegradation is a promising alternative, the relatively recent introduction of plastics has constrained microbial evolutionary adaptation and enzymatic efficiency. In this study, a PET-associated bacterial strain was isolated from Icelandic soil, identified as Lysinibacillus sp. via 16S rRNA sequencing, and evaluated through growth assays, proteomics, in silico screening, and surface imaging. In mineral medium with PET as the sole carbon source, Lysinibacillus sp. exhibited a shorter lag phase and higher early-stage growth than the reference strain Ideonella sakaiensis (p < 0.05 at Weeks 1, 2, 4, and 6) over six weeks. FE-SEM revealed microbial colonization, surface erosion, fissures, and delamination, indicating polymer surface alteration. MALDI-TOF MS proteomic analysis did not detect canonical PET-degrading enzymes such as PETase or MHETase. The in silico genome-wide screen similarly failed to identify PETases or other known polyester hydrolases carrying the conserved GXSXG motif, the Ser-His-Asp catalytic triad, or compatible α/β-hydrolase domain architecture. Instead, PET exposure triggered metabolic reprogramming dominated by oxidative-stress response proteins (peroxiredoxins, superoxide dismutases, thiol peroxidases) and central metabolic enzymes. Although several proteins annotated as hydrolases were expressed, these lacked the catalytic signatures and structural features characteristic of validated PET-degrading polyesterases. Taken together, the proteomic and in silico results indicate that Lysinibacillus sp. responds to PET through broad metabolic and oxidative-stress adaptation rather than through expression of dedicated PET-hydrolyzing enzymes. This stress-driven remodeling may support limited transformation of PET-associated compounds but does not constitute evidence of direct PET depolymerization. The rapid adaptive response of Lysinibacillus sp. complements the slower, enzyme-driven strategy of I. sakaiensis, supporting the potential of microbial consortia for multi-stage plastic biodegradation.
Verk
Collaborative GenAI : Humanized Interaction Fields for Knowledge Creation
(Academic Conferences and Publishing International Limited, 2025) Böhm, Karsten; Durst, Susanne; Kianto, Aino; Toth, Ilona; Department of Business and Economics
Generative AI (GenAI) is increasingly becoming part of our habits, both in our professional and private lives. The use of this is a way to shape, change and influence knowledge creation and utilisation, and thus a very interesting phenomenon for the field of Knowledge Management (KM). In a previous work, the authors of this paper focused on the bidirectional effects of KM processes due to the interaction between humans and machines using natural language as a medium. The result of this work was the generative and responsive artificial intelligence (GRAI) model, which not only generates content on demand, but also adapts and modifies knowledge-related interactions. This research focusses on the concept of interaction fields and investigates the collaborative nature of those interaction fields to develop the conceptual model even further. This is achieved by relating to the characteristics of collaborative robotics (COBOTs) as an established form of human-machine interaction and the main differences of human-machine interactions to derive the concept of Humanized Interaction Fields (HIF) that describe relevant aspects of interaction in the field of Human-centered AI (HCAI). The research contributes to the understanding for the co-creation of knowledge between human and machine.
Verk
AI Evaluations of Sarcoma From Magnetic Resonance Imaging
(2025-11-18) Gunnarsson, Arnar Evgení; Gargiulo, Paolo; Department of Engineering
Background: Sarcomas are a rare and heterogeneous group of malignant tu- mors, making early detection and diagnosis a high priority. Diagnosis tradition- ally relies on expert interpretation of radiological imaging and histopathological biopsies, processes that are time-consuming, subjective, prone to inter-observer variability and are invasive. Methods: This research investigates the potential of artificial intelligence (AI), specifically radiomics and machine learning (ML), to support sarcoma diagnosis, characterization and grading based on MRI scans. Quantitative features were ex- tracted from multiple image transforms, including original, wavelet, Laplacian of Gaussian, square, square root, logarithm, exponential, gradient, and local binary pattern transforms. From these images, first-order statistics and texture descrip- tors (GLCM, GLSZM, GLRLM, NGTDM) were computed. A diverse set of ML models were evaluated, including Random Forest, Logistic Regression, SGDClas- sifier, Ridge Classifier, LightGBM, XGBoost, and CatBoost. Models were trained on three primary tasks: (1) binary classification of healthy vs. sarcoma tissue, (2) sarcoma grading based on the FNCLCC system, and (3) differentiation between bone and soft tissue sarcomas. In addition, a habitat generation framework was introduced, clustering radiomic segments into biologically meaningful subregions to enhance grade and sub-group classification. Model performance was assessed using AUC-ROC, accuracy, and macro- and micro-averaged F1-score, precision, and recall. Results: For binary classification of healthy vs. sarcoma tissue, models per- formed strongly, achieving AUC-ROC scores ranging from 0.829 to 0.999 depend- ing on data preprocessing, with consistently high metric values overall. This demon- strates that ML methods are well suited to distinguishing healthy from patholog- ical tissue. Grade classification achieved AUC-ROC scores ranging from 0.618 to 0.690 using radiomics and between 0.520-0.591 using habitats, with metrics gen- erally above baseline for three-class classification. While performance remains modest, results indicate meaningful correlation between radiomic inputs and FN- CLCC grade. Sub-group classification of bone vs. soft tissue sarcomas achieved AUC-ROC scores of 0.750-0.863 using radiomics and 0.585-0.707 using habitats, indicating that ML methods can effectively aid in sarcoma sub-type differentia- tion. Conclusions: The findings demonstrate that combining multiple image trans- forms with radiomics for diagnostic, characterization and grading classification feasible and effective. Advanced ML models substantially improve performance across diagnostic and prognostic tasks. Furthermore, the habitat generation ap- proach offers additional biological interpretability and can enhance grade and sub- group prediction. These results highlight the promise of AI-driven radiomics pipelines to support clinical decision making in sarcoma diagnosis and hopefully earlier management and extended survival. Keywords: Artificial Intelligence, Machine Learning, Sarcoma, Diagnostics, Sarcoma Grading, Radiomics, Habitats.
Verk
The development, implementation and validation of an objective match scoring system for elite netball
(2025) Butterworth, Andrew; Hamblen, Andrew; O’Donoghue, Peter; Department of Sport Science
This study created and empirically validated an objective match scoring system for the dynamic and tactically rich sport of elite netball. Vast data was captured from all matches (488) played over five-seasons in the UK Netball Superleague. This was ordered into evenly weighted segments for each performance indicator, with each segment assigned a 0 to 10 value on a sliding scale, with the lowest segmented performances awarded a 0, and the highest a 10. Next, individual performance indicators for both teams in all 308 matches from seasons 2021, 2022 and 2023 were calculated an out of ten score, based on which segment they fell within. A resultant out of ten score for the overall match performance of each team was then computed, showing its objective strength. Use of this system was then implemented within elite coaches pre- and post-match analysis processes, with mixed methods utilised to ascertain the empirical validity in practice. Quantitative analysis concludes that the system accurately scores the outcome of matches with statistical significance, whilst qualitative data confirms the usefulness for elite coaching practice, specifically as an aid to previewing and reviewing performances. We conclude that the tool is an objective, trustworthy addition to the coaching process.

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