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2: Aminobenzoic acids as inhibitors in nitric acid Ortho, meta and para aminobenzoic acids have been studied as corrosion inhibitors for 60/40 brass in nitric acid. Ortho and para aminobenzoic acids are cathodic in action whereas meta aminobenzoic acid acts by influencing both the cathodic and anodic reactions.

Organic compounds containing sulphur, nitrogen and oxygen atoms are capable of retarding metallic corrosion. As the thiourea molecule contains one sulphur and two nitrogen atoms, thiourea and its derivatives are potential corrosion inhibitors. While extensive investigations have been carried out on inhibitor properties of thiourea, due attention has not yet been paid to a systematic study of inhibitor action of thiourea derivatives. However, several substituted thioureas have been investigated as corrosion inhibitors. The applications of thiourea and its derivatives as corrosion inhibitors reported in literature up to 1967 are narrated in this article.

Thiourea and nicotinic acid are good inhibitors for the corrosion of brass in nitric acid solutions. Polarisation studies indicate that the anode polarisation with nicotinic acid is less than that on the cathode side. Both the inhibitors increase the cathode and anode polarisation considerably.

Lacan et. al. studied oleic acid, naphthenic acid, stannous chloride, potassium dihydrogen phosphate and cobalt linolate for the prevention of corrosion of brass in sodium hydroxide solutions.

The recent economic changes in India and the gender discrimination practices of the patriarchal society have forced Indian women to turn to the financial sector as an essential means of generating returns. This study aims to identify the factors influencing investors’ investment frequency in India’s two most recognized metropolitan areas.

The authors applied structural equation modeling to augment Allport’s consumer behavior model and the social influence theory for assessing the frequency of investments made by 690 investors. The direct and indirect linkages in the proposed model were evaluated using moderation and mediation techniques.

The study’s findings show that investors’ perceptions of gender discrimination practices and social influence considerably increase investors’ involvement, magnifying their investment frequency. In addition, access to reliable information reinforces the relationship between investors’ involvement and their frequency of investments, whereas the low-risk tolerance weakens this association.

The findings could help policymakers, investors, financial media outlets, financial experts, educational institutions and society strengthen India’s financial sector by leveraging the linkage between the underlying constructs and investors’ behavior.

The aspects of involvement and gender inequality have not garnered enough attention in the previous studies on behavioral finance. The study delves deeper into investor behavior by establishing a link between the underlying constructs and broadening the horizons of prominent consumer behavior models. It also unfurls the moderating role of access to information and risk tolerance to comprehend the association better.

Improved performance in operational (lean) and environmental (green) dimensions has been extremely critical to the global competitiveness of organizations. As the performance of small- and medium-sized enterprises (SMEs) is influenced by various external and internal factors, the purpose of this paper is to analyze the lean–green performance of Indian manufacturing SMEs by investigating the influential relationships of various factors along with the set of lean and green practices adopted by the firms.

The study employs a holistic approach by integrating multiple case study and data envelopment analysis (DEA) in eight manufacturing SMEs to verify a set of five propositions relating issues such as organizational factors, quality and environmental management certifications, implementation of lean and green practices with operational and environmental performance in Indian SMEs. Within-case analysis and cross-case analysis are used for a qualitative investigation of cases while DEA with four input variables, two desirable output variables and one undesirable output variable, is used for quantitative investigation with returns to scale (RTS) and damages to scale (DTS) analysis.

The RTS/DTS results suggest that Indian SMEs exhibit decreasing RTS and increasing DTS, implying that they need to decrease their operational sizes in order to improve the operational and environmental performance. The possible alternative and more practical strategy could be to introduce new technology innovation and holistic adoption of manufacturing excellence initiatives such as lean and green.

The research findings provide insights into the lean and green performance enhancement approach in the context of SMEs. The study extends key managerial implications and policy-related guidelines.

The purpose of this paper is to propose a balanced scorecard (BSC)- and strategy map-based quantitative framework for assessing the lean and green performance of the supply chain (SC). As the SC competitiveness demands efficient and effective utilization of resources throughout the value chain, not only adoption of lean and green SC paradigms but simultaneously its performance evaluation is also vital.

The lean and green SC performance measures are classified into four categories of BSC. A fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology combined with analytical network process is proposed for examining the causal relationships between BSC perspectives and respective assessment criteria. The application of the proposed assessment framework is demonstrated for the case of Indian textile SC.

The research delivers a quantitative assessment framework for evaluating lean and green performance of the SC. The results obtained for a typical case of Indian textile SC revealed that “delivery performance,” “profitability” and “operational cost” are the most crucial performance measures. The perspective of “internal processes” is the most significant of all BSC perspectives while “learning and growth” perspective acts as the driving force to improve lean and green SC performance.

The paper makes two contributions in the domain of lean and green assessment of SC performance. First, it proposes an evaluation framework to investigate into the causal relationships among the BSC perspectives and related factors. Second, it undertakes an empirical investigation for Indian textile SC to develop key managerial insights and provide policy-related recommendations.

Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain (SC) activities and achieve sustainable growth through dynamic capabilities (DCs). This approach equips businesses with the necessary tools to optimise their operations and remain competitive in a dynamic business environment. The value proposition of a business encompasses a wide range of activities that add value at each stage. By leveraging DCs, a firm can achieve innovation, gain a competitive advantage and enhance its adaptability. Conversely, effective value chain management can amplify the influence of a firm's DCs on SC sustainability, by reducing waste, optimising resource utilisation and fostering strategic partnerships. This mutually beneficial connection takes the form of a dynamic interaction in which I4.0 technologies act as a catalyst to help organisations become more resilient, adaptive and responsive. The adoption of these technologies denotes a comprehensive approach to business shift, not merely technical integration. I4.0 has an impact on several organisational disciplines outside of manufacturing, from automation and efficiency advantages to quality enhancements. This chapter offers an extensive literature review to explore the level of SC sustainability that a business can achieve by combining its DCs and implementing strategic I4.0 adoption. The function of value chain management in moderating the effects of I4.0 and DCs on SC sustainability is also assessed. This study proposes a theoretical model that is grounded in the insights extracted from the literature review.

Due to increasing socio-ecological concerns, manufacturers are paying ample attention to the strategic decision-making for enhancing customer satisfaction considering Industry 4.0 requirements. Customers' preferences are being considered vital into the decisions related to sustainable supplier selection for building competitive gain. Thus, the objective of this study is to develop a real-case-based empirical approach for evaluating performance of suppliers based on customers' feedback, an area not explored much in literature. The novelty of current study lies in the development of an integrated supplier evaluation and selection model involving three phases: (1) identifying sustainable criteria according to Industry 4.0 requirement through customers' feedback, (2) calculating relative scores of criteria using Z-numbers and (3) determining evaluation weights of suppliers using fuzzy-TOPSIS. The contribution of the study lies in effective validation of the model by considering the case of a manufacturing firm, which aids the firm in evaluating performance of suppliers based on customers' socio-ecological expectations while considering reliable information provided by decision-makers (DMs).

In a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.

The study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.

The results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.

The prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.

The study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.