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The purpose of this paper is to prepare microencapsulated phase change materials (PCMs) and apply them to cotton and wool fabrics for developing thermo-regulating fabrics.

Microencapsulated n-hexadecane and n-octadecane with poly(methylmethacrylate-co-2-hydroxy ethyl methacrylate) shell was prepared. Microcapsules were fabricated using oil-in-water emulsion polymerization method. Their chemical structure, microstructure, thermal energy storage properties and thermal stability were analyzed by Fourier-transform infrared spectroscopy, polarized light microscope, differential scanning calorimeter and thermogravimetric analyzer, respectively. The mean particle size was tested by a particle sized instrument. The microcapsules were applied to the wool and cotton fabrics using pad-dry-cure method. The thermo-regulating property of the fabrics was evaluated using the T-History test. The distribution and durability of the microcapsules on the fabrics was investigated with scanning electron microscopy.

Spherical microcapsules with p(MMA-co-HEMA) shell and n-alkane core have been produced successfully. n-hexadecane in microcapsule solidifies at 14.8−15.6°C with the latent heat of 65.6−129.8 J/g and melts at 16.7−16.9°C with the latent heat of 67.6−136.9 J/g. Microencapsulated n-octadecane solidifies at 25.8−26.3°C with the latent heat of 74.1−106.2 J/g and melts at 26.8−27.4°C with the latent heat of 80.3−113.4 J/g. The microcapsules have enough thermal stability to the temperature of 150°C that was applied during the fixation of microcapsules on the fabric. The thermo-regulating effect of the microcapsule-incorporated fabrics has been proved by the T-history test.

PCM microcapsules with p(MMA-co-HEMA) shell and n-hexadecane and n-octadecane core have been produced and their usage to produce thermo-regulating textiles have been proved. To determine the thermo-regulating property of the fabrics treated with these new PCM microcapsules, a T-History system has been designed.

In this study, fabrication of polymer and cotton fabric exhibiting stimuli-responsive wetting and water vapor permeability features together with antibacterial activity was aimed.

Temperature and pH-responsive poly(N-isopropyl acrylamide-graft-chitosan) (PNIPAM-g-CS) copolymer were produced via the free radical addition polymerization method and fixed to the cotton fabric using butane tetracarboxylic acid (BTCA) cross-linker by double-bath impregnation method. The chemical structure of the graft copolymer was characterized by Fourier-transform infrared spectroscopy (FT-IR) spectroscopy and H-Nuclear magnetic resonance (1H NMR) analyses. Thermo-responsive behavior of the fabric was investigated by wetting time and water uptake tests, contact angle measurement and surface energy calculation. Additionally, antibacterial activity of the fabric treated with copolymer was studied against S. aureus bacterium.

PNIPAM-g-CS graft copolymer was synthesized successfully, which had lower critical solution temperature (LCST) value of 32 °C and exhibited thermo-responsive property. The treated fabrics exhibited hydrophilic character at temperatures below the LCST and hydrophobic character at temperatures above the LCST. It was found that polymer-coated fabric could have regulated the water vapor permeability by the change in its pore size and hydrophilicity depending on the temperature. Additionally, treated fabric displayed a pH-responsive water absorption behavior and strong antibacterial activity against S.aureus bacterium.

In the study, it has been shown that the cotton fabrics can be fabricated which have antibacterial activity and capable of pH and temperature responsive smart moisture/water management by application of copolymer. It is thought that the fabric structures developed in the study will be promising in the production of medical textile structures where antibacterial activity and thermophysiological comfort are important.

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.

Drawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.

The results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.

The conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

The main aim of this study was to examine gender differences in job satisfaction in Europe.

For the empirical analysis, data from the Sixth European Working Conditions Survey were used. Oaxaca–Blinder decomposition with a principal component analysis (PCA) aggregated variable, after unconditional quantile regressions in a multiple imputation background, was implemented.

Women report higher job satisfaction than men do. Women were significantly more satisfied than men for the middle levels of the job satisfaction distribution.

This study expands the evidence on the determinants of job satisfaction in the European labour market by applying a recent form of decomposition that invests in unconditional quantile regression (UQR). To the best of this study knowledge, this is the first time that the Oaxaca–Blinder decomposition with a PCA aggregated variable after unconditional quantile regression has been employed to study gender-based differences in job satisfaction.

This paper aims to investigate the internal and external determinants of firms’ efficiency and develop optimal corporate governance risk benchmarks for the manufacturing sector across different countries.

Corporate governance risk data were acquired from Institutional Shareholder Services Europe SA. Data on firms’ efficiency and for explanatory and control variables were taken from the DataStream database. The generalised directional distance function data envelopment analysis (GDDF-DEA) model and its stochastic extension provided corporate efficiency measures and optimal corporate governance benchmarks. The authors used ordinary least squares multiple regression analysis with wild bootstrapping to test the study hypotheses.

The authors found significant differences between firms’ optimal and actual efficiency input/output variables and corporate governance risks in the manufacturing sector across countries. Internal firm characteristics such as group affiliations, product market competition and insider ownership and external institutional factors such as the legal system, the rule of law, control of corruption, law enforcement and cultural values are vital determinants of firms’ efficiency.

This paper provides valuable guidance to enable corporate managers, regulators and policymakers to enhance firms’ efficiency and corporate governance practices.

This paper develops optimal corporate governance risk benchmarks and identifies the most critical internal and external factors affecting firms’ efficiency in the manufacturing sector in various countries. It also used a novel GDDF-DEA model, with the multi-parametric model for bias correction of efficiency estimator.

This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

The research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.

The results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.

The proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.

The contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

This study aims to scrutinize the relationship between the perception of big data (BD) features and the primary outcomes of financial accounting. Likewise, it explores whether financial accounting practices moderate the relationship between BD features and firm sustainability.

The study used a questionnaire survey based on the Likert scale for two distinct groups of participants: academic scholars and industry practitioners operating in the BD era within the energy sector.

The results reveal significant positive associations between BD features and firm performance, reporting quality, earnings determinants, fair value measurements, risk management, firm value, the efficiency of the decision-making process, narrative disclosure and firm sustainability. Besides, the path analysis indicates an indirect impact of BD on firm sustainability via financial accounting practices. The results suggest that energy firms should consider incorporating BD analysis into their financial accounting processes to improve their sustainability performance and create long-term value for their stakeholders.

The findings are particularly interesting to academics in accounting and business to improve the accounting curriculums to fit the technological revolution, especially in the field of BD analytics. Practitioners within energy industries must also refine their skills and knowledge to meet the challenges of BD in the foreseeable future. The results provide important implications for policy setters to revise current financial accounting standards to cope with technological innovation.

The study makes a valuable contribution by critically examining the impact of BD on various financial accounting practices neglected in prior research. It highlights the transformative power of BD in the domain of financial accounting and provides insights into its potential implications for energy firms.

The purpose of this paper is to investigate whether the financial resources invested in advanced manufacturing technologies (AMTs) and social capital (SC), in terms of the set of internal and external relationships a firm holds, have a positive effect on the adoption of Industry 4.0 (I4.0) technologies. Furthermore, it tests whether the organizational context strengthens these relationships.

The authors used regression models to analyze data collected through an international survey carried out within the scope of a European project involving six Central European regions.

Results show that small- and medium-sized enterprises (SMEs) having stronger internal and external SC have a higher propensity to adopt I4.0 technologies, and both management support and absorptive capacity (AC) strengthen these relationships, whereas investments in AMTs within the manufacturing area and internal SC have a positive association with the intensity of I4.0 adoption. However, in presence of a high level of management support and AC, the relationship between external SC and I4.0 adoption becomes positive and significant. Management support also moderates the impact that investments in AMTs in the manufacturing area and internal SC have on the intensity of adoption of I4.0 technologies.

This paper is one of the first to investigate the context of SMEs that, having fewer resources, face some difficulties in exploiting the potential of I4.0 revolution. Moreover, it adopts a broad perspective on the factors that facilitate the adoption of I4.0.

The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on organizational performance.

The authors used socio-technical and dynamic capabilities theory to propose the notable research model. The authors explored the factors driving the use of SHRM 4.0 practices and their contribution to organizational performance through the development of dynamic capabilities. The authors collected data from 383 senior HR managers using a structured questionnaire, and PLS-SEM was used to analyze the data.

The results show that socio-technical factors such as top management support, HR readiness, competitive pressure, technology readiness and perceived usefulness influence the use of SHRM 4.0 practices, whereas security and privacy concerns negatively influence them. Furthermore, the authors also found the use of SHRM 4.0 practices influencing the dynamic capacities (build (learning), integration and reconfiguration) and, subsequently, its impact on organizational performance.

Its novelty lies in developing a model using dynamic capabilities and socio-technical theory to explore how SHRM 4.0 practices influence organizational performance through dynamic capabilities. This study extends the literature on SHRM 4.0 practices, HR technology use, HR and dynamic capabilities by contributing to socio-technical theory and dynamic capabilities and expanding the scope of these theories in the area of HRM. It provides crucial insights into HR and top managers to benchmark SHRM 4.0 practices for improved organizational performance.