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The process of conveyance of solid–liquid mixtures poses a significant challenge due to the considerable wear and tear experienced by critical components. This issue not only affects the lifespan of the system but also jeopardizes its safe operation. The purpose of this study is to numerically and experimentally investigate the erosion wear behavior of impeller steels (SS-410 and S-317) using Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques, aiming to address the significant challenges posed by wear in slurry transportation systems.

In this study, a robust two-phase solid-liquid model combining CFD with Discrete Phase Modeling (DPM) was applied to simulate the effects of coal-ash slurries on impeller steel. Additionally, an experimental evaluation was conducted using the DOE approach to analyze the impact of various parameters on impeller steel. This integrated methodology enabled a comprehensive analysis of erosion wear behavior and the influence of multiple factors on impeller durability by leveraging CFD for fluid flow dynamics and DPM to model particle interactions with the steel surface.

Simulation results highlight a strong link between particle size and the wear life of impeller steel. Through simulations and experiments on SS-410 and SS-317 under varied conditions, it’s evident that SS-410 outperforms SS-317 due to its higher hardness and density. This is supported by Taguchi’s method, with SS-410 showing a higher Signal-to-Noise ratio. Notably, particle size emerges as the most influential parameter compared to others.

Current research primarily focuses on either CFD or experimentation to predict pump impeller steel erosion wear, lacking relevant erosion mechanism insights and experimental data. This study bridges this gap by employing both CFD and DPM methods to comprehensively investigate particle effects on pump impeller steel and elucidate erosion mechanisms.

Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up.

This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it.

The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented.

Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact.

This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood.

In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

This study aims to investigate the impact of corporate climate change disclosure (CCD) on the financial performance of Indian firms.

The study is grounded in the principles of signalling theory, legitimacy theory and the cost-benefit analysis approach. The sample for the study includes 77 Indian firms from 2018–2019 to 2021–2022. Required data are collected from published annual reports, sustainability reports and the Ace Equity Database. The explanatory variable CCD is measured using content analysis based on the Task Force on Climate-related Financial Disclosures (TCFD) framework. The panel fixed-effects or random-effects models have been considered for hypotheses testing.

The disclosure level of CCD and its different components is found to be moderate with an average score of 0.364 among top Indian firms. Regression results reveal a significant positive association between CCD on firms’ market-based performance, suggesting its long-term benefits. Besides, additional analysis indicates the differential impact of CCD on financial performance based on firms’ CEO duality status, industry affiliation and pre-COVID and post-COVID period, thus establishing their moderating role in the observed relationship.

The study highlights the necessity of enhancing climate-related disclosure by Indian firms and strategically leveraging the same to boost their financial performance.

Few studies have examined the implications of CCD (based on the TCFD framework) on firm performance. Moreover, exploring the moderating role of CEO duality, industry type and COVID-19 in the CCD and firm performance relationship is a novel empirical contribution.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

India’s rapid economic growth has triggered a significant transformation in its logistics sector, fueled by comprehensive reforms and digital initiatives outlined in the National Logistics Policy. Smart warehouses, equipped with cutting-edge technologies such as IoT, AI and automation, have taken center stage in this evolution. They play a pivotal role in India’s digital journey, revolutionizing supply chains, reducing costs and boosting productivity. This AI-driven transformation, in alignment with the “Digital India” campaign, positions India as a global logistics leader poised for success in the industry 4.0 era. In this context, this study highlights the significance of smart warehouses and their enablers in the broader context of supply chain and logistics.

This paper utilized the ISM technique to suggest a multi-tiered model for smart warehouse ecosystem enablers in India. Enablers are also graphically categorized by their influence and dependence via MICMAC analysis.

The study not only identifies the 17 key enablers fostering a viable ecosystem for smart warehouses in India but also categorizes them as linkage, autonomous, dependent and independent enablers.

This research provides valuable insights for practitioners aiming to enhance technological infrastructure, reduce costs, minimize wastage and enhance productivity. Moreover, it addresses critical academic and research gaps contributing to the advancement of knowledge in this domain, thus paving the way forward for more research and learning in the field of smart warehouses.

The qualitative modeling is done by collecting experts' opinions using the ISM technique solicits substantial value to this research.

In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots.

For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk

The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria.

Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study.

The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly.

Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

In the current era, the focus on circular economy (CE) has gained substantial momentum among the research communities across the manufacturing world. It has become the need of the hour to act fast due to the alarming issues of unsustainability such as climate change, global warming, waste generation, environmental pollution, resource scarceness and ecological degradations. This research aims to investigate and model the CE enablers in the product development process related to the moderating effect of net zero.

The significant CE enablers are identified through literature review and expert brainstorming. The Pythagorean fuzzy decision-making trial and evaluation laboratory (PF-DEMATEL) technique has been used to investigate and evaluate the significant CE enablers in product development process. PF-DEMATEL determines the inter-relationship and casual dependency among the selected CE enablers. Indian automobile small and medium enterprises (SMEs) have been considered as a case organization to demonstrate the effectiveness of the proposed method.

The findings reveal that “Top management support and clear vision towards CE adoption” is the most important enabler and “Artificial intelligence in product value chain” is recognized as the least vital enabler. This research aids the managers, decision-makers, policy planners and workforce to develop and formulating efficient blueprints for the effective adoption of CE in Indian SMEs.

This is the first kind of research that explores CE enablers in product development process for Indian SMEs.

The study aims to present a systematic literature review (SLR) to understand the current status of research on social media usage among the bottom of the pyramid (BOP). The purpose of this study is to identify the research gaps in this domain and review future research agendas by using theory, context, characteristics and methods [TCCM] framework.

An SLR, keywords co-occurrence and TCCM analysis were used to analyse and synthesize insights from 44 studies gained from Web of Science and Scopus databases.

The findings suggest that the USA and India are popular contexts for studying BOP. The BOP population uses social media to gain utilitarian, hedonic and social values. Further, social media can help BOP explore “entrepreneurship” opportunities, value co-creation and bring innovations.

This study expands the intellectual boundaries of social media at BOP and suggests multidisciplinary research. Additionally, adopting novel theoretical lenses helped determine social media's impact on BOP.

Considering the growing emphasis on sustainability, companies are developing green innovation strategies for creating new products and processes that reduce environmental effects. The impact of green innovation on firm performance is well established in the literature; however, the relationship between a firm’s adoption of green innovation and its social behaviour has not yet been explored. This study aimed to fill this gap by analysing the impact of green innovation on companies’ social behaviour, at both the overall and sub-dimensions levels.

This study was conducted on a sample of 191 companies worldwide between 2016 and 2019. Company data were extracted from the Joint Research Centre database established by the European Commission and the Organisation for Economic Cooperation and Development. In contrast, data on corporate social behaviour was taken from the LSEG Workspace database. We applied a panel regression using a fixed effects model to test the research hypotheses.

The results support the positive impact of green innovations on corporate social behaviour in the immediate and subsequent periods. However, the empirical results do not provide significant evidence for some dimensions of corporate social behaviour, such as respect for human rights and product responsibility.

The study’s novelty lies in its emphasis on how green innovation shapes corporate social behaviour and enhances stakeholder relationships. Green innovation is introduced as a strategic instrument for meeting social duties and increasing trust, loyalty and ethical engagement with important stakeholders.

The purpose of this study is to examine the impact of data analytics, collaboration and flexibility on supply chain resilience (SCR) performance in the current dynamic global market.

This study uses a partial least squares modeling approach to analyze the relationships defined in the conceptual model. This data was organized through a survey questionnaire shared with the professionals working in different industries and belonging to supply chain functions. This survey was designed to measure data analytics capability (DAC), supply chain collaboration (SCC), supply chain flexibility (SCF), industry dynamism (INY) and SCRP, consisting of 29 items. This analysis included involved assessing measurement model for reliability and validity and a structural model for hypothesis testing.

This research empirically examines that collaboration and flexibility are significantly enhanced by advanced DACs, generating superior SCRP. Furthermore, the findings validate that cooperation and adaptability among the supply chain are necessary to reinforce this inherent resilience. The relation of SCC, SCF and the SCRP was significantly moderated with the INY.

This study complements the extant literature by providing empirical evidence of the tangible effects of data analytics on SCR. The study demonstrates the need for the alignment of supply chain strategies with the INY, giving some directions on how businesses can tailor their practices to specific market environments for enhanced resilience.