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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.

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.

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.

This paper aims to investigate the impact of board gender diversity (BGD) on firm financial stability during the COVID-19 pandemic compared to the pre-pandemic period.

Difference-in-differences method was used for a sample of 891 US companies observed from 2018 to 2021.

The results indicate significant negative relationships between BGD and financial stability. The authors put in evidence a nonlinear relationship between BGD and financial stability. Also, the authors found that internal women directors as well as external ones decrease financial stability.

The results emphasize the beneficial effect of having more women on corporate boards during health crises and suggest that policymakers should take measures to promote BGD.

This paper highlights the impact of BGD on financial stability and provides additional evidence on the usefulness of BGD as an effective tool for crisis management.

This study explores the intricate relationship between social media communication, consumer attitude and purchase intention within the context of lifestyle category products. With the rapid proliferation of social media platforms, businesses have turned to these platforms to connect with consumers and influence their purchasing decisions. This study aims to provide an in-depth analysis of how social media communication strategies impact consumer attitudes and, in turn, influence purchase intentions.

The study employs partial least squares structural equation modeling (PLS-SEM) to analyze the data collected from a sample of consumers.

The results of this study present that lack of visibility (LV), low-efficiency levels (LEL) and unpredictable elements (UE) are ranked as the top three major risk hurdles whereas real-time information on a package’s location (LV1), putting a GPS tracking system to track last-mile journey (OT3) and users wants on time location of their package (LV2) are ranked as top three most significant criteria affecting the practices of modern last-mile logistics in e-commerce businesses.

The results of this study contribute to our understanding of how social media influences consumer behavior in the lifestyle product sector, shedding light on the underlying mechanisms that drive consumer purchasing decisions.

By constructing and testing experimentally a research model that reveals a thorough analysis of pertinent literature and identifies multiple important elements influencing consumer behavior in the lifestyle category, this paper adds to the body of knowledge on marketing. Practical ramifications for lifestyle firms are examined, along with suggestions for improving their social media tactics, in light of the findings.

This study aims to examine the various internal and external factors affecting the financial stability of Indian Commercial Banks. The aim is to improve the effectiveness of the Indian banking system in facilitating the transmission of monetary policy and to strengthen its resilience in the event of a banking crisis.

A panel data regression analysis is employed on unbalanced panel data of Indian commercial banks including public sector, private sector and foreign sector banks for the period of 2005–2022.

This study revealed that Indian banks with higher profits and high capitalization are more stable than others. However, banks with large bank size and high management costs are less stable as compared to other banks. In the case of macroeconomic variables, foreign exchange reserves have a significant positive impact on banking stability. Moreover, the unemployment rate has a significant negative impact on the banking stability of India.

Research identifies relevant micro and macroeconomic drivers pertaining to India’s banking stability, a developing economy. These findings have significant implications and can attract the attention of analysts, regulators, bankers and academicians in this area. Nevertheless, the scope of the study is limited to the variables chosen to evaluate their contribution to banking stability, but other variables may influence Indian banking conditions.

Indian banks are advised by the research to place a high priority on profitability, capitalization and effective risk management. Customers and investors should choose banks with strong metrics. The priorities for policymakers should be preserving robust reserves and tackling unemployment with focused initiatives. Adopting digitalization can improve banks’ customer service and operational effectiveness, which is important for overcoming economic obstacles. These tactics provide doable measures to improve the resilience and stability of the banking industry in India and other emerging nations.

This research differentiates from the rest by focusing solely on the Indian banking system, in contrast to previous ones that often treated India as part of a bigger part like the BRICS or South Asia continent. It acknowledges the need to comprehending the unique traits and difficulties faced by the Indian banking system. Moreover, the current study distinguishes itself by focusing on the combined impact of microeconomic and macroeconomic indicators in the Indian context, unlike earlier research that concentrated on assessing the effects of individual variables. The current study also investigated new variables like corporate governance and foreign exchange reserves in the context of Indian banking which have not been explored by existing literature. Research is also crucial in the context of the analysis’s time frame, since it captures the period of economic transformation that included demonization, implementation of GST, major mergers and global COVID-19 pandemic.

This study aims to explore blockchain potential for digital marketing (BlkChn-Mk-KAP) by developing and validating a measurement model for assessing the constructs of knowledge, attitude and practice (KAP) related to blockchain technology in digital marketing.

A four-study process was used. The first study reviewed the literature to develop a pool of possible measurement items. Using exploratory factor analysis and reliability assessments, Study 2 (n = 162) investigated the dimensionality of the items developed in Study 1. The factorial structure from Study 2 was validated in Study 3 (n = 204), and the measurement model invariance was assessed using covariance-based structural equation modelling (CB-SEM). Finally, in Study 4 (n = 203), the predictive validity of the BlkChn-Mk-KAP was tested using a CB-SEM approach, testing its constructs correlations with the perceived usefulness of blockchain for digital marketing.

The findings indicate that the BlkChn-Mk-KAP measurement model comprises three-dimensional multi-item scales: knowledge, attitude and practice.

This study introduces a promising BlkChn-Mk-KAP model to examine blockchain’s role in digital marketing. The authors acknowledge the sampling limitation in this research. To enhance the generalisability of the findings, future research should expand to different groups, including generation, gender and age. In addition, further exploration of the explicit links between blockchain knowledge, attitudes and subsequent digital marketing performance is warranted.

Educating employees about blockchain technology’s unique features can shape favourable attitudes and stimulate the utilisation of blockchain-enabled technologies in digital marketing practice. BlkChn-Mk-KAP can offer a reliable and valid instrument to benchmark marketers’ KAP of blockchain-powered digital marketing as they implement blockchain technology to gain a competitive advantage.

This study helps to adopt sustainable practices ensuring the wellbeing of the key stakeholders.

This research introduces the first validated conceptualisation and measurement model, BlkChn-Mk-KAP, to evaluate blockchain KAPs among digital marketing professionals.

Sequel advertisements, although used often in practice, are not well studied in literature. This study aims to define sequel advertisements, investigate their effectiveness on customer attitudes towards ads/brands and identify key elements leading to their effectiveness.

The authors first conduct a field study using Facebook data to show that sequels are more effective than non-sequels and repeat ads. Study 2 experimentally tests recall and brand attitudes of sequels over new advertisements, Study 3 tests the effectiveness of different character retention and plot extension variations in sequels and Study 4 experimentally establishes the cognitive path that leads to greater effectiveness of sequel advertisements.

Sequel advertisements are more effective on consumer recall and brand attitudes as compared to new or repeat advertisements, with message format of the first advertisement moderating the effectiveness of the sequel. Consumers’ recognition of the prior-sequel connection mediates the effectiveness of the sequel advertisement. Plot extension with character retention is the most effective sequel advertising strategy.

This research opens opportunities for research on the effectiveness of sequels across different media formats/product categories and sequential ads.

The findings suggest that managers can profitably adopt a strategy of narrative ad followed by a sequel that extends the plot while retaining the characters.

The authors define sequel advertisements, describe the process mechanisms leading to greater effectiveness of sequels and identify superior sequel advertising strategies.

Recognizing the transformative impact of financial technology (Fintech) and mobile wallets (m-wallets) on modern financial practices, this study aims to explore the complex dynamics of m-wallet adoption in Bangladesh, focusing on user perceptions and financial autonomy. By integrating self-determination theory (SDT) and diffusion of innovation (DOI) theory, the authors propose a model incorporating constructs such as perceived security (PS), perceived trust (PT), compatibility, ease of use (EOU), perceived financial autonomy (PFA), perceived financial competence (PFC), relative advantage (RA) and intention-to-use (IU).

This study used a survey-based methodology to gather data from m-wallets users in Bangladesh. In this survey, 445 individuals participated; 393 of those were deemed legitimate and were chosen for study. Partial least squares-structural equation modeling was used in the investigation. This method made sure that the relationships between the proposed model’s constructs were thoroughly examined.

The research findings confirm that PFC significantly impacts the IU m-wallets and PFA. PFA also positively influences the IU. PS impacts the IU and PT, which further affects the IU. EOU influences RA and the IU. The study highlights the importance of PFC, PS, PT and EOU in driving the adoption of m-wallets.

This study integrates SDT and DOI theory to explore m-wallet adoption. It highlights financial competence and autonomy as key factors, offering insights and practical strategies for Fintech stakeholders in emerging markets to enhance adoption through financial literacy, security measures and user-friendly designs.

Under a rapidly changing and disruptive environment, the need for resilient, sustainable and survival-oriented performances is currently urgent. To deal with these challenges, the reconfigurable capabilities of blockchain technology (BT) can support supply chains (SC) to be resilient and survive during disruptions while maintaining sustainability goals. Through the lens of the dynamic capability view (DCV), this study aims to investigate the impact of adopting the dynamic capabilities of BT to achieve the resilient–sustainable–survival (RSS) paradigm under a disruptive context.

This study follows the DCV to develop theoretical hypotheses and adopts structural equation modeling to examine and analyze the impact of BT’s capabilities on the triple aspects of the RSS paradigm. It uses the partial least squares technique to evaluate the weight of the correlations among the constructs, test the dependencies of its relationships and assess the model’s power for predicting the outcome variables.

Based on the empirical evidence from the manufacturing and assembling SCs, this study provides insights into the interplay of the sensing, seizing and transforming capabilities of BT, and the most needed ones to fit the expectations of the RSS model. It shows that such capabilities can have a significant impact and can be a powerful tool that contributes to the enhancement of the RSS’s aspects in SC networks.

This study represents one of the initial queries to conceptualize the RSS model of the SC within a disruptive context. For researchers, it adds more understanding of the deployment of the DCV in the technology adoption area. For practitioners, it helps identify the capabilities of BT and their potential to fit the expectations for the RSS paradigm.