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The purpose of this paper is to generate nitrogen-containing groups in the cotton fabric surface via low-temperature nitrogen plasma as an eco-friendly physical/zero-effluent process. This was done for rendering cotton dye-able with Acid Blue 284, which in fact does not have any direct affinity to fix on it.

Dyeing characteristics of the samples such as color strength (K/S), fastness properties to light, rubbing and perspiration and durability, as well as tensile strength, elongation at break, whiteness, weight loss and wettability in addition to zeta potential of the dyed samples, were determined and compared with untreated fabric. Confirmation and characterization of the plasma-treated samples via chemical modifications and zeta potential was also studied using Fourier transform infrared spectroscopy (FTIR) and Malvern Zetasizer instrumental analysis.

The obtained results of the plasma-treated fabric reflect the following findings: FTIR results indicate the formation of nitrogen-containing groups on cotton fabrics; notable enhancement in the fabric wettability, zeta potential to more positive values and improvement in the dyeability and overall fastness properties of treated cotton fabrics in comparison with untreated fabric; the tensile strength, elongation at break, whiteness and weight % of the plasma treated fabrics are lower than that untreated one; and the durability of the plasma treated fabric decreased with increasing the number of washing cycles.

The novelty addressed here is rendering cotton fabrics dye-able with acid dye via the creation of new cationic nitrogen-containing groups on their surface via nitrogen plasma treatment as an eco-friendly and efficient tool with a physical/zero-effluent process.

This paper aims to comprehensively analyze the factors influencing the adoption intentions of the digital rupee, a digital currency, among users in India.

Drawing upon the Technology Acceptance Model (TAM), the study examines the relationships between cognitive beliefs (perceived usefulness, perceived ease of use, perceived trust, perceived self-efficacy, perceived cost and awareness), affective belief (attitude) and adoption intention of the digital rupee. The study uses a structured questionnaire to collect primary data from 1,707 respondents, which are then analyzed using structural equation modeling.

The results indicate that perceived usefulness and perceived ease of use significantly impact users' attitudes toward the digital rupee, as well as their adoption intentions. The findings further reveal that perceived trust, perceived self-efficacy, and awareness positively influence attitude and adoption intention. On the other hand, perceived cost exhibits a negative effect on attitude and adoption intention. These results provide empirical evidence on the factors that shape users' attitudes and intentions toward adopting the digital rupee.

The research methodology used in this study ensures rigorous data collection and analysis. The structured questionnaire enabled the collection of detailed information from a large sample of respondents, allowing for robust statistical analysis. The utilization of structural equation modeling facilitated the examination of complex relationships among variables, enhancing the reliability and validity of the findings.

The study's findings offer practical guidance for policymakers, financial institutions and researchers in shaping digital currency regulatory frameworks, tailored financial services and further exploration of adoption dynamics.

The research has social implications by potentially influencing the way individuals and communities in India engage with digital currencies, impacting financial inclusion and digital economic participation.

This research contributes to the understanding of the adoption of digital currencies in India and provides valuable insights for policymakers, financial institutions and researchers in the field of digital finance and technology adoption.

The purpose of this research is to examine, from the self-efficacy standpoint, the factors that influence medical tourists' use of social media for travel planning. To that end, this study presents a conceptual framework for evaluating medical tourists' online behaviour and empirically tests the model's validity by examining various dimensions of self-efficacy.

A sample of 224 people who searched for medical tourism information online was used in this study. Partial least squares structural equation modelling (PLS-SEM) was employed to test the proposed model with data from an online questionnaire survey.

The findings suggest that sources of self-efficacy reinforce one's belief in the ability to use social media for medical travel planning purposes. According to the structural model analysis results, with the exception of social persuasion (SP), all of the proposed factors were significant sources of self-efficacy.

The findings can assist medical tourism providers in optimising online searches for medical travel information, as well as medical tourism destination marketers in directing the tourism providers' marketing efforts towards the use of social media to target potential medical tourists more efficiently and on a larger scale.

This study is being conducted in response to a significant knowledge gap in identifying the factors that influence medical tourists' online behaviour. This information can help medical tourism destination marketers gain a competitive advantage by using social media to target potential medical tourists more effectively and on a larger scale.

The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.

Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.

This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.

The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.

The main aim of this study is to highlight the significance of fostering social capital and improving the quality of work life (QWL) for the well-being of healthcare workers. The second objective of this research is to address a notable gap in the current knowledge by examining the mediating influence of QWL on the relationship between work-related social capital and life satisfaction within the healthcare profession.

This study used a cross-sectional research methodology to examine the complex relationships among the variables and included a sample of 330 individuals who are employed full-time in the healthcare profession in the North Indian Region.

The study confirms all research hypotheses, showing that social capital improves work life. Thus, work-life quality improves life satisfaction significantly. The mediation analysis in this study used bootstrapping to show that work-life quality mediates the association between social capital and life satisfaction.

Addressing social support issues and using effective human resource management tactics can improve employees’ work life and satisfaction. The findings are essential in collectivistic cultures because strong workplace relationships improve professional welfare.

This study differentiates itself by analysing social capital and QWL as multi-dimensional constructs inside the workplace, ensuring the results’ correctness and validity. This study provides a distinct viewpoint for scholars and practitioners, enhancing comprehension of the correlation between life satisfaction and work-related social capital within the healthcare industry.

The main objective of this study is to examine the relationship between person-organization fit (P-O fit), job satisfaction (JS), workplace spirituality (WS) and turnover intentions in small and medium-sized enterprises (SMEs) and the moderate role of WS in these relationships.

Research data were collected by cross-sectional data collection methods. The sample contains 242 employees of SMEs in Indonesia. The analytical method used to test research hypotheses is moderate regression analysis.

The results show that P-O fit is an antecedent that can reduce turnover intention. JS has a negative effect on turnover intention and WS has a negative effect on turnover intention. WS moderates the relationship between P-O fit and turnover intention and the relationship between JS and turnover intention.

This study enhances the understanding of the importance of WS in reducing employee turnover intentions. This research also contributes to the research model based on fit theory and social exchange theory.

The practical implication of this research is to improve P-O fit in employees. Managers can manage the right people for the environment and the right environment for the individual. In other words, the organization must choose both. The most basic way to create a P-O fit for employees is to recruit employees suitable to organizational goals because, in general, SMEs rarely have a written vision and mission.

Through the of spirituality in the workplace, employees will experience a sense of meaning and purpose in their work, foster a sense of unity through positive relationships and achieve alignment between personal and organizational values. Consequently, this will diminish the inclination to seek employment elsewhere.

Although turnover intention in employees is considered necessary, there is still minimal research that discusses turnover intention and WS in SMEs. To the authors' knowledge, no one has tested WS on the relationship between PO fit and JS with turnover intention in SMEs.

Corrosion is a major concern for many industries that use metals as structural or functional materials, and the use of corrosion inhibitors is a widely accepted strategy to protect metals from deterioration in corrosive environments. Moreover, the toxic nature, non-biodegradability and price of most conventional corrosion inhibitors have encouraged the application of greener and more sustainable options, with natural and synthetic drugs being major actors. Hence, this paper aims to stress the capability of natural and synthetic drugs as manageable and sustainable, environmentally friendly solutions to the problem of metal corrosion.

In this review, the recent developments in the use of natural and synthetic drugs as corrosion inhibitors are explored in detail to highlight the key advancements and drawbacks towards the advantageous utilization of drugs as corrosion inhibitors.

Corrosion is a critical issue in numerous modern applications, and conventional strategies of corrosion inhibition include the use of toxic and environmentally harmful chemicals. As greener alternatives, natural compounds like plant extracts, essential oils and biopolymers, as well as synthetic drugs, are highlighted in this review. In addition, the advantages and disadvantages of these compounds, as well as their effectiveness in preventing corrosion, are discussed in the review.

This survey stresses on the most recent abilities of natural and synthetic drugs as viable and sustainable, environmentally friendly solutions to the problem of metal corrosion, thus expanding the general knowledge of green corrosion inhibitors.

The study explores the current research trends within the responsible leadership (RL) domain and proposes a future research agenda by conducting an extensive review of past research. The study aims to understand recent developments in theories, constructs and contexts in RL literature.

Scopus database is used for the data collection on RL and patterns from 1998–2022. In total, 138 articles were covered for a systematic literature review (SLR) of RL behaviors. Further, the search was extended, and 109 more articles were included for bibliometric analysis of RL using R software. In total, 247 papers were reviewed.

The results present the consequences and antecedents of RL behaviors with external and internal stakeholders. Literature also indicates that researchers are more attentive to empirical studies with internal stakeholders, such as responsible leaders' impact on employee outcomes. Among theories, stakeholder theory's normative integrative and instrumental perspectives are used with RL.

The first limitation of the study is that this study collected data only from the Scopus database and the choice of language was English. Future studies may use other databases, languages and keywords. Instrumental and integrative RL behavioral styles would help balance organizations' financial and social goals.

This research enhances the literature on RL by combining content and bibliometric analysis to develop a more systematic and comprehensive understanding of integrative and instrumental leadership behaviors.

The study principally aims to investigate the relationship among work–life balance (WLB), job satisfaction (JS) and employee commitment (EC) among the railway staff by integrating the social exchange theory (SET). The study also explores JS as a mediator in the context of the new normal.

A mixed-method sequential explanatory design was utilized for this study. The research instrument was administered to 533 railway employees, using purposive sampling to ensure reliability and validity. Structural equation modeling (SEM) was employed to analyze the quantitative data and measure mediation effects. Additionally, 15 interviews were conducted with employees from three job positioning groups (A, B and C) to gain further insights into commitment-related concerns.

The study found that WLB and JS positively influenced EC, with JS acting as a mediator between WLB and EC. Furthermore, factors such as fostering friendships among colleagues, effectively managing work–life integration and recognizing the importance of job roles were identified as crucial in enhancing the relationship between WLB and EC.

The study includes SET to examine the social exchange process while considering WLB benefits as a reward from the employer and EC as the outcome of this reward. This study contributes by examining the effects of COVID-19 on the railway industry and EC. The mixed-methods sequential explanatory design gave a comprehensive understanding of the relationships between WLB, JS and EC. The study’s implications highlight the importance of implementing supportive policies, such as flexible work schedules and a supportive organizational culture, to enhance employee commitment and reduce attrition rates. The study emphasizes the significance of prioritizing employee well-being to achieve organizational goals and enhance organizational commitment.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.