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Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.

In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.

Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.

This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.

The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.

This study aims to assess the impact of participation in self-help group (SHG) on the psychological empowerment of rural tribal women.

Primary data was collected using multistage random sampling from the rural women of Jharkhand, India. The propensity score matching method was adopted using the psmatch2 command in STATA.

The results show a significant positive change in women’s self-esteem, self-confidence, self-efficacy, autonomy, knowledge and skills, reduction in agony and quality of life after participation in SHG. This reveals that participation in SHG has a significant positive impact on the psychological empowerment of rural tribal women.

Despite the numerous studies on rural women’s empowerment, there is little evidence of literature focusing on the impact of participation in SHG on psychological empowerment, specifically in the tribal context. This study primarily focuses on women belonging to the scheduled tribe category of Jharkhand, one of the poorest states of India.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

Green HRM practices in the hospitality sector are now receiving growing interest. However, the extent to which these practices contribute towards employee non-green workplace outcomes remains largely unknown. This study explores the relationships among green HRM practices, happiness at work, employee resilience, and feedback-seeking behaviour.

The study employs two-wave data from a sample of 306 five-star hotel employees in India. Using partial least square-structural equation modelling, the relationships are tested.

The study’s results demonstrate that green HRM practices positively impact happiness at work, employee resilience, and feedback-seeking behaviour. Additionally, the relationship between green HRM practices and feedback-seeking behaviour and employee resilience is mediated by happiness at work.

Drawing on the Job Demands-Resources Theory, Social Exchange Theory, and Broaden and Build theory, this paper proposes that green HRM practices can contribute to happiness at work, employee resilience, and feedback-seeking behaviour.

To establish a positive connection between green HRM practices and employee outcomes, organizations must recognize the vital role played by happiness at work as a mediator. This means that organizations must implement green HRM practices and ensure their positive impact on employee happiness at work.

The originality of this research lies in its holistic approach to green HRM outcomes, suggesting that the benefits of these practices extend beyond environmental impacts to influence the psychological and behavioural dimensions of employees.