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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.

The literature that is presently available on sustainable supply chain management (SSCM) combining optimization and industry 4.0 techniques falls short in its depictions of the recent developments, budding pertinent areas and the importance of SSCM in the growth of industrial economies around the world. This article's main objective is to analyze current trends, highlight the latest initiatives and perform a meta-analysis of the literature that is currently accessible in the SSCM area with a special focus on optimization and industry 4.0 techniques. The paper also proposes a conceptual framework that will assist in illuminating how the ideas of optimization and industry 4.0 may contribute to realizing sustainability in supply chains.

The proposed study systematically reviews 85 research publications published between 2010 and 2022 in referenced peer-reviewed journals in diverse fields, including engineering, business and management, services and healthcare. Numerous categories are considered throughout the examination of the literature, including year-wise publications, prominent journals, type of research design, concerned industry and research technique used.

The study demonstrates a deeper comprehension of the literature in the field and its evolution throughout numerous industry sectors, which is helpful for both practitioners and academics. The results from the content analysis highlight various future research opportunities in the domain.

This is one of the first research articles that have attempted to establish, analyze and highlight the current trends and initiatives in the SSCM domain from an optimization and industry 4.0 techniques viewpoint. The cluster-based future research propositions also enhance the novelty of the study.

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.

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.

The current work aims to understand the consumers’ adoption of electric vehicles (two-wheelers) from their value perspective by utilizing the value-based adoption model.

The study considered data from 302 potential electric two-wheeler customers and tested the hypotheses using structural equation modeling.

The outcomes showed that perceived economic benefits, social image, enjoyable acceleration and enhanced fun and perceived environment (positively) and perceived physical safety risk, perceived cost of ownership and range and charging risk (negatively) influenced the customers’ perceived value linked with electric two-wheeler (ETW) adoption. Only low engine noise emission and infrastructure issues did not affect perceived value.

Most of the respondents considered in the study were less than 35 years old. Hence, the model can be tested for other age groups.

The study’s outcomes will help ETW marketers, manufacturing companies and governments (state and central) to provide a more convenient environment for electric two-wheelers' adoption and help them curate appropriate strategies.

The current work offers a better understanding of potential customers' ETW adoption by employing a value-based trade-off.

The present study apotheosises on the relationship between blockchain and fintech and its impact on the financial services sector. It then gauges into the various aspects that have been included in the published literature by the authors all across the world.

Post-pandemic, technology has led to tremendous opportunities in the financial sector, and the customers have started assessing financial services in online mode, thus enabling companies to innovate their business strategies. The present chapter aims to explore the areas where blockchain is benefitting the financial sector.

The objectives of this study were achieved through systematic review of literature performed with the help of bibliographic analysis. Further, a PRISMA model was developed, and the networks were derived with the help of Scopus analyser software and VOSviewer Version 1.6.15.

It is observed that since the introduction of the term and development of the first blockchain, a lot of work has been performed in the said domain, but it is still at a nascent stage. It is thus discovered that the string specific research documents got published in Scopus database from 2016 onwards, and a considerable amount is being performed by authors from China, the United States and India.

The present chapter exhibits the various dimensions in which a lot has to be yet explored and needs to devise innovative and full proof strategies which will enable to overcome the challenges, thus strengthening the position and working of the companies in Industry 5.0.

This study aims to investigate how indigenous socio-cultural (ISC) practices within informal networks, such as guanxi and wasta, provide benefits beyond mere access. Specifically, the authors explore their global impact on B2B relationships, focusing on reciprocity. A multiregional sample extends research on ISC practices in B2B relationships by examining contingency effects of informal network ties.

The authors surveyed 404 buyers in two developed (Hong Kong and Portugal) and two emerging economies (Kuwait and Colombia), and also categorized by strong and weak informal network ties. Using structural equation modeling, the authors examined a relationship marketing (RM) model from a typology (Clubs, Sociocracies, Clans and Compadres) for assessing contingency effects.

The study reveals that in developed economies with strong formal institutions, negative aspects of favor reciprocity norms intensify unless informal networks are driven by strict sociomoral obligations. This supports research indicating that contractual governance competes rather than complements relational governance. Moreover, trust-building processes are crucial in regions with low uncertainty tolerance to mitigate adverse effects.

Suppliers from developed nations need more than cultural communication insights when engaging in business in emerging markets. We acknowledge changes buyers may expect adherence to reciprocity rules embedded in the ISC practices of their informal networks.

This study pioneers a typology of social structures to analyze ISC practices across buyers with varying formal institutional strength and informal network ties. It sheds light on institutional dynamics, trust-building processes, and nuances surrounding both positive and negative aspects of reciprocity practices. Furthermore, it broadens the scope of RM to Asia, Europe, Latin America and the Middle East.

This paper aims to develop a specific type of mobile nonrigid support friction stir welding (FSW) robot, which can adapt to aluminum alloy trucks for rapid online repair.

The friction stir welding robot is designed to complete online repair according to the surface damage of large aluminum alloy trucks. A rotatable telescopic arm unit and a structure for a cutting board in the shape of a petal that was optimized by finite element analysis are designed to give enough top forging force for welding to address the issues of inadequate support and significant deformation in the repair process.

The experimental results indicate that the welding robot is capable of performing online surface repairs for large aluminum alloy trucks without rigid support on the backside, and the welding joint exhibits satisfactory performance.

Compared with other heavy-duty robotic arms and gantry-type friction stir welding robots, this robot can achieve online welding without disassembling the vehicle body, and it requires less axial force. This lays the foundation for the future promotion of lightweight equipment.

The designed friction stir welding robot is capable of performing online repairs without dismantling the aluminum alloy truck body, even in situations where sufficient upset force is unavailable. It ensures welding quality and exhibits high efficiency. This approach is considered novel in the field of lightweight online welding repairs, both domestically and internationally.