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This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

This paper aims at studying numerically the entropy generation of nanofluid flowing over an inclined sheet in the presence of external magnetic field, heat source/sink, chemical reaction along with slip boundary conditions imposed on an impermeable wall.

A suitable similarity transformation technique has been used to convert the coupled nonlinear partial differential equations to ordinary differential equations (ODEs). The ODEs are then solved simultaneously using the finite difference method implemented through an in-house computer program. The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.

The relative strengths of the irreversibilities due to heat transfer, fluid friction and the mass diffusion arising due to the change in each of the controlling variables have been delineated both in the near-wall and far-away-wall regions, which may be helpful for a better understanding of the thermo-fluid dynamics of nanofluid in boundary layer flows. The numerical results obtained from the present study have also been validated with results published in open literature.

The effects of different controlling parameters such as magnetic parameter, radiation parameter, Brownian motion parameter, thermophoresis parameter, chemical reaction parameter, Reynolds number, Brinkmann number, Prandtl number, velocity slip parameter, temperature slip parameter and the concentration slip parameter on the entropy generation and Bejan number have been discussed comprehensively through the relevant physical insights for the first time.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

This paper aims to explore the interlinkage and association of executive isolation at the workplace faced by Chief Executive Officer (CEO) of a not-for-profit organizations (NPOs) and its impact on the attrition at the C-Suite Professionals (CXO), Direct reports of CEO levels.

Executive isolation at top management with reference to the CEO level has emerged as a major challenge that is faced by NPOs with the effect being multiplied by the pandemic and remote working. This paper intends to examine the relevance of the impact of executive isolation experienced by top management leading to increase in the attrition at the CXO levels in NPOs due to their increasing dissatisfaction. To make a thorough study, a detailed literature review has been done followed by qualitative research methods of individual interviews, group interviews and surveys to ascertain the implications of CXO-level executive isolation on the CXOs attrition in NPOs.

The executive isolation experienced by CEOs makes them develop certain preconceived set of beliefs. By being isolated from the direct report CXOs and action on the ground and working from a remote location, they tend to inculcate their own decisions into the direct reports, thereby depriving them of authority and autonomy. This starts leading to the high level of CXO attrition.

This paper has tried to study the linkage of the executive isolation at top management with the levels of CXO dissatisfaction leading to attrition at NPOs. This topic appears to be much-needed to be understood, especially when the new normal of work is being redefined.

The paper enumerates that the NPOs can attempt to deal with the challenges of engaging CXOs through virtual working; however, the mindfulness can be impacted by the experiences of executive isolation at management levels. This, in turn, can lead to lower morale, compromised performance resulting in CXO-level dissatisfaction and attritions.

With the limited awareness about executive isolation and its multiplier effect due to the pandemic, NPOs, like other enterprises, had to resort to virtual working. However, executive isolation at management levels apparently leads to reduction in the CXO-level engagement with the teams under them and with the CEO to which they report. This aspect can lead to the NPOs not being able to achieve their impact objectives during the outward turbulence and inward challenges of CXO-level attritions because of the CXO-level dissatisfaction.

The already-strained vaccine supply chain (VSC) of the expanded program for immunization (EPI) require a more robust and structured distribution network for pandemic/outbreak vaccination due to huge volume demand and time constraint. In this paper, a lean-agile-green (LAG) practices approach is proposed to improve the operational, economic and environmental efficiency of the VSC.

A fuzzy decision framework of importance performance analysis (IPA)–analytical hierarchy process (AHP)–technique for order for preference by similarity in ideal solution (TOPSIS) has been presented in this paper to prioritize the LAG practices on the basis of the influence on performance indicators. Sensitivity analysis is carried out to check the robustness of the presented model.

The derived result indicates that sustainable packaging, coordination among supply chain stakeholders and cold chain technology improvement are among the top practices affecting most of the performance parameters of VSC. The sensitivity analysis reveals that the priority of practices is highly dependent on the weightage of performance indicators.

This study's finding will help policymakers reframe strategies for sustainable VSC (SVSC) by including new management practices that can handle regular immunization programs as well as emergency mass vaccination.

To the best of the authors' knowledge, this is the first study that proposes the LAG framework for SVSC. The IPA–Fuzzy AHP (FAHP)–Fuzyy TOPSIS (FTOPSIS) is also a novel combination in decision-making.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

The current study aims to deliver a consolidated view of environmental sustainability in cold supply chain performance systems (CSCPS), incorporating theoretical and empirical analysis for improving environmental standards. For this purpose, this study firstly aims to explore and analyze the various crucial challenging factors for environmental sustainability in the cold supply chain (CSC). Secondly, it discovers the most effective sustainable strategies for improving the environmental sustainability of CSCPS.

The exploration of the crucial challenging factors and the proposed sustainable strategies have been done using a systematic literature review relevant to the sustainable performance of CSC. At the same time, semi-structured brainstorming sessions were conducted with the domain professionals having an industrial and academic background to finalize the strategies. Empirical analysis has been performed using an intuitionistic fuzzy (IF) based hybrid approach of SWARA and COPRAS methods.

The key findings of the study address that “higher energy consumption during refrigerated transportation and storage” is the most crucial challenge for environmental sustainability in CSC. In addition, “managerial refrain to profit decline due to sustainability implementation” is the second most crucial challenge that hinders the adoption of sustainable practices in CSCs. Meanwhile, the governmental attention to motivating organizations for green adoption and implementation of solar energy-driven refrigeration technologies are the two most important discoveries of the study that might help in improving CSC's environmental performance.

From the implications side, the study enriches and extends the current literature content on CSC sustainability. In addition, it offers sound managerial implications by identifying the challenges that create threats among the management for sustainability adoption and suggesting the most suitable sustainable strategies, which may help the management to raise the environmental performance of their CSC. Besides having various important theoretical and managerial implications for the study, contemplation of only environmental sustainability traits as a broader perspective limits the scope of the study.

The study's main contribution is the exploration of the most crucial challenges imparting obstructions in sustainable development and sustainable strategies, which may get the interest of the CSC players, market leaders, and industrial and academic practitioners working in the domain of CSC sustainability. In addition, this study offers structured theoretical and empirical evidence for CSC's environmental sustainability, thus playing a bridging role between theoretical sustainability concepts and its practical implications in CSC industries.

This study aims to deal with developing composite filaments and investigating the tribological behavior of additively manufactured syntactic foam composites. The primary objective is to examine the suitability of the cenosphere (CS; 0–30 Wt.%) to develop a high-quality lightweight composite structure with improved abrasion strength.

CS/polyethylene terephthalate glycol (PETG) composite feedstock filaments under optimized extrusion conditions were developed, and a fused filament fabrication process was used to prepare CS-filled PETG composite structures under optimal printing conditions. Significant parameters such as CS (0–30 Wt.%), sliding speed (200–800 rpm) and typical load (10–40 N) were used to minimize the dry sliding wear rate and coefficient of friction for developed composites.

The friction coefficient and specific wear rate (SWR) are most affected by the CS weight percentage and applied load, respectively. However, nozzle temperature has the least effect on the friction coefficient and SWR. A mathematical model predicts the composite material’s SWR and coefficient of friction with 87.5% and 95.2% accuracy, respectively.

Because of their tailorable physical and mechanical properties, CS/PETG lightweight composite structures can be used in low-density and damage-tolerance applications.

CS, an industrial waste material, is used to develop lightweight syntactic foam composites for advanced engineering applications.

CS-reinforced PETG composite filaments were developed to fabricate ultra-light composite structures through a 3D printing routine.