bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phylogenetic and taxonomic insights intoBetula: low-coverage whole genome sequencing and plastome analysis with focus on the rare Ukrainian endemic speciesBetula klokoviiZaverucha
Authors: Tarieiev Andrii , Karbstein Kevin , Gailing Oliver 
Published: January 2025

AbstractBetula klokoviiZaverucha is a rare endemic species of Ukraine that is still not well taxonomically studied. In the current pilot study, we performed low-coverage whole genome sequencing forB. klokovii, related species (B. pendulaRoth andB. pubescensEhrh.) and the assumed hybridB. klokovii × B. pendula,assessed the genomic structure of taxa with different mapping settings using UMAP non-linear dimension reduction algorithm, extracted and assembled whole plastomes.Single Nucleotide Polymorphism (SNP) analysis based on low-coverage whole genome sequencing (LC-WGS) followed by UMAP visualization reveals the separation ofB. klokoviifrom the other analysed taxa. The best taxonomic resolution was achieved with reads filtered for contamination. In contrast, the best result in obtaining complete plastome assemblies was achieved via NOVOPlasty pipeline and raw reads.The size for the eight newly assembled plastid genomes ranges between 160,535 and 160,625bp, GC content is 36,1%. We annotated 130 genes (113 unique) for all eight assemblies. In addition, we investigatedB. klokovii’srelationships with 20 other birch species and two intraspecific taxa by reconstructing plastome-based Bayesian inference and maximum likelihood phylogenies. Overall, plastome phylogeny provides a better resolution in comparison to phylogenies based on a few plastid or nuclear molecular markers. However, it could be affected by chloroplast capture, and some other factors like quality of sequencing and assembly, and is not suitable to detect hybrids when used alone.In particular, we found thatB. klokoviiis likely a separate taxon that is closely related toB. pubescensbut morphologically and genetically distinct. The study shows that genome-wide SNP data and plastome phylogenies have a certain potential for addressing issues with specific taxa within the genusBetulaL. However, to fully leverage the potential of this approach, we suggest collecting a much larger number of birch plastome sequences to be sequenced and assembled. For a better understanding of birch phylogeny there is a need for reference-grade chromosome scale genome assemblies for polyploid species.
Damping autoregressive grey model and its application to the prediction of losses caused by meteorological disasters
Authors: Yan Shuli , Gong Xiaoyu , Zeng Xiangyan 
Published: January 2025

PurposeMeteorological disasters pose a significant risk to people’s lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is proposed.Design/methodology/approachFirstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization algorithm.FindingsThe properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was verified.Originality/valueThe dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.
Self-supervised learning fabric defect segmentation using anomaly generation
Authors: Qi Kankan , Guo Yanchi , Zhou Jian 
Published: January 2025

PurposeThe problem of fabric defect detection is a particularly challenging task, as the fabric defects occupy only a small portion of the image pixels and it is difficult to collect sufficient samples for training deep learning-based models. The purpose of this work is to present a novel self-supervised learning method to address this problem.Design/methodology/approachIn order to solve the problem of lack of defect samples, based on the fabric-specific degree of texture regularity, we propose an anomaly generation method to create synthetic fabric defects by destroying the normal fabric texture. To improve the detection of defects of different sizes, a global–local parallel detection mechanism is proposed. A self-supervised model including an anomaly generation module, a reconstruction subnetwork and a discriminative subnetwork is established to achieve model training without prior anomaly information.FindingsThe proposed method features self-supervised training, does not require no labelled anomaly data and detects anomalies by distinguishing their distance from normal samples at both global and local levels. When tested on four fabric datasets, our approach outperforms state-of-the-art unsupervised and self-supervised methods and achieves significantly higher localization accuracy.Originality/valueA high-fidelity fabric defects synthesis method is presented to alleviate the problem of collecting numerous fabric defects, providing a reference for other surface anomaly detection problems. The proposed global–local parallel detection mechanism can serve as a reference for other methods dedicated to detecting particular anomalies. The proposed self-supervised network model can effectively locate fabric anomalies without prior labelling information, which could be used as a framework for other model designs.
Innovativeness and start-up performance: a configurational analysis of relational factors
Authors: Donaldson Colin , Kraus Sascha , Kallmuenzer Andreas , Cheng Cheng-Feng 
Published: January 2025

PurposeThis study aims to explore which relational factors are crucial for accelerator-based start-ups to achieve high financial performance and whether innovation levels influence this relationship. Utilizing fsQCA and drawing from the resource-based view (RBV), we analyze 128 start-ups in a Spanish accelerator, split by innovativeness, to understand the impact of relational and human capital factors on performance.Design/methodology/approachThe study uses fuzzy-set qualitative comparative analysis (fsQCA) to investigate conditions leading to high financial performance among 128 start-ups in a Spanish accelerator, divided by innovativeness. Four key factors are analyzed: social capital, social competence, resource mobilization and entrepreneurial ecosystem support. fsQCA examines complex relationships between these factors and financial performance.FindingsRelational and human capital factors significantly impact start-up financial performance, varying with innovativeness. Highly innovative start-ups benefit from social competence and networked support, while less innovative but profitable start-ups rely on resource mobilization skills. The study highlights the contingent value of these factors, showing that unique configurations drive financial success.Research limitations/implicationsThe paper enhances the RBV in entrepreneurial contexts by highlighting the critical role of relational resources and their configurations. It suggests social competence and networked support are crucial for highly innovative start-ups, while resource mobilization is key for less innovative ones. These findings encourage nuanced theorizing of start-up success strategies, considering varying innovativeness levels and their impact on performance.Originality/valueThis study enhances understanding of the relationship between relational factors and financial performance in accelerator-based start-ups, considering innovation levels. It provides insights into how different configurations of social capital, competence, resource mobilization and ecosystem support lead to success. It underscores the importance of considering the contingent value of relational factors for start-up growth.
Digital transformation, dynamic capability and total factor productivity of manufacturing enterprises
Authors: Luo Taiye , Qu Juanjuan , Cheng Shuo 
Published: January 2025

PurposeEnhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.Design/methodology/approachUsing the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.FindingsIt is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.Originality/valueThis study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.
An integrative perspective on inter-organisational collaboration in healthcare: a modified total interpretive structural modelling approach
Authors: Yadav Sunil Kumar , Singh Shiwangi , Prusty Santosh Kumar 
Published: January 2025

PurposeThis study develops a model for understanding the relationships and interactions between the antecedents influencing inter-organisational collaboration in the healthcare sector.Design/methodology/approachA comprehensive and systematized search was conducted on Scopus to identify all relevant studies investigating the antecedents of inter-organisational collaboration in the healthcare sector. Antecedents were identified based on insights from experts and a systematised search method. A modified total interpretive structural model (m-TISM) was used to determine the hierarchical relationships between the identified antecedents. Finally, the Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis was employed to categorise the identified antecedents into clusters based on their driving or dependence influence.FindingsEight antecedents of inter-organisational collaboration in the healthcare sector were identified. The results revealed that having a shared vision and goals, digital infrastructure and proximity are the most crucial antecedents of inter-organisational collaboration in healthcare, along with leadership, shared resources and communication.Research limitations/implicationsFuture research on inter-organisational collaboration in the healthcare sector can include additional factors that may influence collaboration beyond those currently studied. Structural equation modelling can be employed to validate the proposed model.Originality/valueThe study proposes a hierarchical model for inter-organisational collaboration in the healthcare sector. The framework will help healthcare executives and academicians identify key antecedents that are most critical to enabling effective collaboration.
Leveraging social media data in knowledge management to identify noncompliance: insights from the foodservice industry
Authors: Lee Carmen Kar Hang 
Published: January 2025

Purpose
Social media data contains a wealth of content related to customers’ reactions to, and comments on, firms’ performance. Through the lens of signaling theory, this paper aims to investigate the use of social media data as a knowledge resource in communicating firms’ noncompliance risk to regulatory agencies.

Design/methodology/approach
This paper proposes a two-step social media analytics framework to detect noncompliant firms. First, it creates a context-specific dictionary that contains keywords relevant to firms’ noncompliant behaviors. Next, it extracts those keywords from customer reviews, customer sentiment and emotions to predict firm noncompliance. It tests these ideas in the context of food safety regulations.

Findings
It identified over 100 words that are related to restaurants’ hygiene deficiencies. Using the occurrence of these words in customer reviews, as well as sentiments and emotions expressed within them, the author’s best-performing model can identify nearly 90% of the restaurants that severely violated regulations.

Practical implications
After being processed by appropriate machine learning algorithms, customer reviews serve as valuable knowledge resources, enabling regulatory agencies to identify noncompliant firms. Regulatory agencies can use this model to complement the current compliance monitoring scheme.

Originality/value
This research contributes a novel methodology for creating a context-specific dictionary that keeps only the relevant words customers use when discussing firms’ noncompliant acts. In the absence of such an approach, numerous irrelevant signals would be included in the modeling process, thereby increasing the cost of social media analytics.
Entrepreneurial orientation and sustainable business model innovation through technology transfer. A study of SMEs leadership in knowledge-based economies
Authors: Korayim Diana , Shaik Aqueeb Sohail , Agarwal Reeti , Nijjer Shivinder , Sasso Pasquale 
Published: January 2025

Purpose
The purpose of this study is to examine the connections between knowledge-based economies, entrepreneurial orientation (EO), sustainable business model innovation and technology transfer (TT). This study specifically investigates whether TT might foster EO and long-term business model innovation in small and medium-sized enterprises (SMEs). This study focuses on economies that are knowledge-based, where information is viewed as a vital resource for economic growth.

Design/methodology/approach
This study uses structural equation modelling method and a quantitative research strategy to analyse data gathered from 309 SMEs in knowledge-based economies. A survey questionnaire created to examine the relevant factors was used to gather the sample data from PROLIFIC platform using judgemental sampling technique.

Findings
This study’s conclusions point to an association between TT and EO that is favourable, SMEs’ ability to use TT to strengthen their EO and the significance of EO in fostering innovation in SMEs. This study offers empirical proof of the role that TT may play in fostering innovation in sustainable business models and EO in SMEs.

Research limitations/implications
Policymakers, business professionals and academic researchers who are interested in the function of TT in knowledge-based economies will find it to be a helpful source of information. According to this study’s results, TT can assist SMEs in using outside expertise and assets to grow their entrepreneurial capacities, promote innovation and build long-term business strategies.

Originality/value
This study provides empirical evidence of the role of TT in fostering innovation within sustainable business models and cultivating EO within SMEs, contributing to the literature on these critical topics.
Accelerating Benders Decomposition for sustainable food closed-loop supply chain network under uncertainty: a case study
Authors: Heidari Ali , Khalilzadeh Mohammad , Pamucar Dragan , Ecer Fatih 
Published: January 2025

PurposeThe purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.Design/methodology/approachA robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.FindingsThe proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.Practical implicationsThe study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.Originality/valueThis research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.
Examining the role of green marketing mix elements on green purchase behavior
Authors: Irshad Madeeha 
Published: January 2025

PurposeThis study examines the impact of green promotions and green packaging on the green purchase behavior through the mediating role of environmental knowledge and green satisfaction in the context of fast-food chains.Design/methodology/approachThe study follows a quantitative approach for data collection. The data were collected from 400 consumers of fast food chains operating in Pakistan through questionnaires.FindingsThe results showed that green promotions and green packaging had a strong positive impact on consumers’ green satisfaction, environmental knowledge and green purchase behavior. Green satisfaction and environmental knowledge mediated the relationship between green promotions and green purchase behavior and green packaging and green purchase behavior.Originality/valueThe study is the first of its kind to determine the mediating roles of green satisfaction and environmental knowledge in the context of green marketing. The research offers significant theoretical and managerial insights.
Effect of silicone monomers on the properties of fluorinated acrylate pressure-sensitive adhesive for ePTFE bonding
Authors: Ma Xiao-Yan , Ren Yi-Wen , Li Hui , Li Wei , Liang Yanli , Zheng Wenjiang 
Published: January 2025

Purpose
Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded polytetrafluoroethylene (ePTFE) bonding.

Design/methodology/approach
A series of silicon-containing fluorinated acrylic copolymers were synthesized through free radical solution polymerization with vinyloxy trimethylsilane, allyltrimethylsilane, 3-(trimethoxysilyl)propyl methacrylate or 1,3,5-tris(3,3,3-trifluoropropyl) methylcyclotrisiloxane as silicon monomers, and comprehensive performance of the copolymers was evaluated based on Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography, glass transition temperatures (Tg), differential scanning calorimetry, thermogravimetric analysis, water contact angle, the track, 180° peel strength, and shear holding power.

Findings
Based on the FTIR and XPS results, it is confirmed that the silicon monomers were successfully introduced into the fluorinated acrylate copolymer. XPS analysis indicated that the silicon groups had the tendency to enrich on the surface of the film, thereby reducing the F content on the film surface. The glass transition temperatures (Tg) of the PSAs increased when silicon monomers were introduced, while the thermal stability declined. The contact angles of the acrylic PSA films were increased with the introduction of silicon monomers. From the perspective of bonding performance, the track, 180° peel strength and shear holding power decreased to varying degrees compared to silicon-free PSA, except significantly elevated holding power with MPS as the silicon monomer.

Originality/value
Silicon-containing fluorinated acrylic copolymers were synthesized, and the comprehensive performance was evaluated as PSAs of ePTFE for the first time.
Emerging trends and critical insights in international business research: a hybrid review
Authors: Bui Huynh Nguyen , Phung Nam Phuong , Le My Linh , Nguyen Tan Hai Dang 
Published: January 2025

Purpose
This paper aims to present a hybrid review combining the theory, context, characteristics and methodology (TCCM) framework and bibliometric analysis of international business research from 1991 to 2023, shedding light on the field’s growth, trends and key contributors.

Design/methodology/approach
Relevant papers were selected using the preferred reporting items for systematic reviews and meta-analyses model. Initially, this paper conducted a descriptive analysis to identify prolific institutions, countries and journals. Using bibliometric techniques and the TCCM framework, this paper analyzed theory, context and method visualized through word clouds and keyword co-occurrence. The characteristic aspect was analyzed using bibliographic coupling to identify major themes, providing a comprehensive understanding of the most significant factors in international business research.

Findings
The analysis of a data set comprising 5,644 documents reveals a steady increase in the annual growth rate of publications, highlighting the growing significance of international business in the global economy. First, this paper noticed a significant increase in publications in leading international business journals, with the Journal of International Business Studies being the most prolific. Second, using the TCCM framework, this paper discovered that the resource-based view, institutional theory, transaction cost theory and internalization theory are predominant in international business research. Most studies have concentrated on firm or enterprise-level entities, followed by country-level analyses. This paper also identified six main themes: (1) innovation and strategy, (2) market, (3) HR impact and leadership, (4) marketing, (5) internationalization and (6) entrepreneurship. Quantitative methods have been the most frequently used research design, followed by qualitative and mixed methods.

Research limitations/implications
The study offers researchers and practitioners a roadmap for future investigations, collaboration and innovation in international business, thereby advancing the knowledge and understanding of this complex field within a globally interconnected economy.

Originality/value
This study provides insights into growth and trends, identifying journals, clustering research topics and enhancing theoretical and methodological understanding in the field of international business.