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This research aimed to identify the optimal plasma cutting parameters for enhancing the quality and efficiency of steel alloy processing. Systematic experiments, supported by microstructure analyses and the Taguchi method, were conducted to determine the ideal settings for minimizing defects and resource consumption while ensuring high-quality cuts.

The study employed a systematic approach to determine optimal plasma cutting parameters for enhancing steel alloy processing. Experiments were conducted using a Kawasaki RS-010L robotic manipulator, powered by a DS 120P.33 inverter power supply. A range of plasma cutting parameters, including current, voltage, cutting speed and airflow, were carefully examined to achieve high-quality cuts while minimizing resource consumption. The Taguchi method was applied to optimize key responses – cutting angle, cutting width and burr formation – providing deeper insights into the influence of each parameter and identifying conditions for further refinement.

The study successfully identified the optimal plasma cutting parameters for steel alloy processing. Practical experimentation determined that a current of 50 A, voltage of 118 V, cutting speed of 3,200 mm/min and airflow of 1.9 × 10^–3 m³/s produced a smooth cutting surface, minimal burr formation and a cutting angle within ±4° relative to the surface. The Taguchi method determined optimal settings to minimize angle and cutting width: 126 V, 4,400 mm/min and airflow of 2.3 × 10^–3m³/s for angle and 118 V, 4,400 mm/min and 1.9 × 10^–3m³/s for cutting width. Microstructure analysis confirmed uniformity and conformed to International Organization for Standardization 9013:2002 standards for cut quality.

This research advances steel alloy processing by combining experimental methods and the Taguchi method to determine optimal plasma cutting parameters for 6 mm thick steel 09G2S, addressing a gap in studies focused on specific materials. Unlike general studies, these results are tailored to low-carbon, low-alloy steels, providing actionable guidance for achieving efficient, high-quality cuts while minimizing waste and improving weldability and ductility. This work contributes to cost-effective and sustainable practices in the metal fabrication industry.

The purpose of this study is to analyse the impact of corporate attributes like a company’s profitability, size, age, leverage and board size on companies’ sustainability reporting as measured through India’s new business responsibility and sustainability reporting (BRSR) framework.

A random sample of 130 companies was taken from the top 1,000 listed companies on the National Stock Exchange. Sequential mixed methods research approach was used to prepare a sustainability quality index. Then, a hierarchical multiple regression analysis was performed to examine the impact on the quality of reporting by Indian companies.

Interestingly, the analysis revealed that traditional metrics like age, profitability, board size and leverage did not have significant associations with reporting quality. Rather, the size of a company in terms of market capitalisation was found to have a strong positive impact on sustainability reporting.

This was a cross-sectional study, as time series data for BRSR reporting is not yet available. Also, only five parameters were taken for analysis. Lastly, subjective judgment in content analysis may be involved.

This suggests that only larger companies in India are prioritising sustainability reporting over smaller ones. It affirms the legitimacy and stakeholder theory in the Indian context.

To the best of the authors’ knowledge, this study is one of the first endeavours to assess the efficacy of the new Indian BRSR framework and test its primary objectives. Furthermore, significant implications have been given for managers to catalyse and reinforce the sustainability momentum down the lane across companies of all sizes in India.

In a rapidly evolving recruitment landscape, virtual reality (VR) emerges as a transformative tool for human resource management (HRM). This chapter delves into the profound impact VR can have on attracting, assessing and onboarding talent. Furthermore, VR services have been already included in other areas where it has been shown its essence to be useful for those industries.

The current study explores how VR simulations can provide immersive experiences, allowing candidates to virtually step into the workplace and interact with potential colleagues and tasks. This fosters a deeper understanding of the role and company culture, leading to better-matched hires and improved candidate experience.

This chapter also examines the potential of VR for skills assessments, offering a more realistic and engaging evaluation process compared to traditional methods.

Finally, this study discusses the challenges and considerations for integrating VR into HRM practices, ensuring a successful implementation of this innovative technology in the realm of recruitment.

Purpose: This study investigates the influence of inflation and oil price fluctuations on stock prices and returns. A thorough bibliometric analysis of research articles published in the last 22 years has been conducted to achieve this objective.

Need for the Study: Inflation and oil prices are critical macroeconomic variables that significantly impact the economy, including the stock market. It is crucial for investors and policymakers to understand the complex relationship between these economic variables and the stock market.

Methodology: The study employed a bibliometric analysis to identify the most influential authors, institutions, and research themes related to the impact of inflation and oil prices on stock prices.

Findings: The study identified that the fluctuations of the stock market are significantly influenced by both inflation and oil prices. This relationship has been extensively studied in the literature, with several key research themes identified. These themes include the effects of inflation and oil prices on various sectors and industries, the role played by monetary and fiscal policy, and the impact of international trade and global economic conditions.

Practical Implications: The insights that have emerged have significant implications for investors and policymakers. It highlights the need for risk management strategies and mitigation of macroeconomic factors’ impact on stock prices.

The aim of this study reported in this paper was to explore the application of operational excellence methodologies in a global context.

A qualitative interview approach was used to understand the current state, benefits, challenges, success factors, tools and techniques of operational excellence methodology implementation with relevance to logistics companies worldwide. About 16 interviews were undertaken with practitioners working in leading companies and with leading academics in Asia, Europe, Africa, North America, South America and Australia.

The findings show that operational excellence methodologies including Lean, Six Sigma, Lean Six Sigma and Agile can apply in logistics firms to improve operations and productivity and save costs. Top management support and involvement play an important role in the success of operational excellence projects in the logistics service.

The findings will be of interest to top and middle managers and logistics practitioners, with a dual aim of improving logistics performance and saving costs.

The present study has been one of the first global study attempts to explore the implementation of operational excellence methodologies in the logistics sectors.

The study aims to assess the impact of disruptive frugal digital technology and small and medium-sized enterprises' (SMEs') innovation performance on the food sector in Masvingo urban, Zimbabwe. A descriptive research design was used, and quantitative data were obtained using a questionnaire survey on 50 restaurant employees and 100 customers. The empirical findings demonstrated that the performance of SMEs was significantly improved by social media, cloud computing, virtual reality (VR), augmented reality (AR), and robotics. The study concludes that disruptive digital technologies (social media, cloud computing, AR, VR and robots affect the performance of SMEs by improving food production, streamlining information transfer, and utilizing cutting-edge technical applications. This chapter recommends that the government, through those in charge of formulating policy, educate and encourage the adoption of disruptive digital technology. The application of social media, cloud computing, VR, AR, and robotics will increase local food producers' access to the market. Digitalization will have a significant effect on Zimbabwe's local food system in the future.

This study aims to understand the kind of content and context that effectively create higher fan social media engagement (SME) through pre-match content posted by sports teams.

This research examines the effect of inspirational, informational, entertaining and warmth content appeal on affective and cognitive responses from fans in the form of likes and shares. Messages on X (previously Twitter), chosen as a representative social media platform, from the teams participating in the Indian Premier League, were analysed using regression models to validate the proposed model empirically.

For sports clubs, entertaining, warmth and inspirational content is more effective than information content in generating likes on social media. Content with high vividness is effective only for sports teams with high performance. Fans of low-performance teams exhibit higher responsiveness to content with inspirational appeal.

This research contributes to the sports marketing literature by examining the influential role of warmth and inspirational content in generating higher SME in the pre-match context.

This study provides prescriptions to sports clubs for leveraging social media platforms to engage their fans through appropriate content. Given the growth of sports leagues in developing and developed countries, this study provides guidelines to sports clubs for effective social media marketing.

To the best of the authors’ knowledge, this study is among the first to integrate social identity theory and elaboration likelihood model theoretical frameworks to study fan engagement with social media content posted by sports clubs.

This research aims to uncover how the adoption process of a mobile payment solution unfolds in its local use. We examined micro-enterprises’ adoption of UPI-based payment system in India.

We employed an ethnographic approach to study the micro-entrepreneurs’ daily practice and adopted a relational ontology to conduct an “in-practice” enquiry of the UPI payment adoption process.

Applying the practice perspective of Technology Affordances and Constraints Theory, we found that UPI adoption was shaped through a dynamic interplay of emerging affordances and constraints. New features, such as All-in-one QR, enabled actions like cross-platform money transfer but also introduced challenges like fraud. Advancements in the technology addressed previous challenges but also created new hurdles. This cycle of evolving affordances and constraints within the contextual use of UPI payment technology shaped its adoption process.

The findings contribute to the existing body of mobile payment literature by elucidating the role of localized and ongoing enactment of affordances and constraints. Unlike existing adoption theories, using relational concepts of affordances and constraints revealed how the integration of mobile payment into practice leads to emergent use patterns and widespread adoption.

We emphasize the need for solutions that address localized challenges and contextual needs. The study advises policymakers to consider technology’s benefits and limitations in interventions.

The study offers a unique view on mobile payment adoption, highlighting the role of technological materiality and temporality. It reveals how the changing materiality of technology shapes the adoption process through localized affordances and constraints.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.