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This study examines the relationship between green entrepreneurship and environmental quality in selected Asian economies. The impact of green entrepreneurship dimensions, i.e. renewable energy utilization, innovation orientation, green funding and resource efficiency is empirically assessed on environmental quality. The research assists in opening the “black box” mechanism of how green entrepreneurship affects the environmental pillar of sustainability.

An analysis of 11 Asian countries is conducted over the period from 2000 to 2019, using panel regression techniques to examine the link between green entrepreneurship dimensions and environmental quality indicators.

The findings reveal that all four dimensions of green entrepreneurship have an inverse relationship with carbon emissions and ecological footprint while showing a positive relationship with the load capacity factor. This implies that green entrepreneurship contributes positively to environmental sustainability by enhancing the ecosystem’s resilience and capacity to support life.

We advocate for policymakers to prioritize green entrepreneurship to stimulate innovation for energy transition and environmental sustainability. Furthermore, creating a conducive atmosphere for green entrepreneurs can spur job creation, economic growth and societal well-being, thus fostering a more resilient and sustainable future for all.

In this study, we adopted a multidimensional approach for measuring green entrepreneurship, which represents an advancement over existing literature that predominantly relied on renewable energy consumption to gauge the green entrepreneurship phenomenon at the macro level.

This study aims to examine the effect of e-customer relationship management (e-CRM) on customer e-loyalty through e-service quality and e-satisfaction. This study also examines how customers’ e-loyalty affects their willingness to recommend a banking service.

A total of 372 private bank customers from Chattogram, the second largest and only port city of Bangladesh, were chosen using a convenience sampling technique. Structured equation modelling was used to analyse the data.

E-CRM positively impacts e-service quality, customer e-satisfaction and customer e-loyalty. The association between e-CRM and customer e-loyalty is sequentially mediated by e-service quality and e-satisfaction. E-loyalty has a significant influence on willingness to recommend a banking service.

The findings will help Bangladeshi banks boost the number of prospective customers implementing e-CRM. In addition, mediators between e-CRM and e-loyalty provides managers a new insight on willingness to recommend a banking service.

The sequential mediation effect of e-service quality and customer e-satisfaction on the connection between e-CRM and e-loyalty represents the unique contribution and enriches the present e-CRM literature, particularly in the Bangladeshi private banking sector.

As internet banking service marketing platforms continue to advance, customers exhibit distinct behaviors. Given the extensive array of options and minimal barriers to switching to competitors, the concept of customer churn behavior has emerged as a subject of considerable debate. This study aims to delineate the scope of feature optimization methods for elucidating customer churn behavior within the context of internet banking service marketing. To achieve this goal, the author aims to predict the attrition and migration of customers who use internet banking services using tree-based classifiers.

The author used various feature optimization methods in tree-based classifiers to predict customer churn behavior using transaction data from customers who use internet banking services. First, the authors conducted feature reduction to eliminate ineffective features and project the data set onto a lower-dimensional space. Next, the author used Recursive Feature Elimination with Cross-Validation (RFECV) to extract the most practical features. Then, the author applied feature importance to assign a score to each input feature. Following this, the author selected C5.0 Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost and LightGBM as the six tree-based classifier structures.

This study acclaimed that transaction data is a reliable resource for elucidating customer churn behavior within the context of internet banking service marketing. Experimental findings highlight the operational benefits and enhanced customer retention afforded by implementing feature optimization and leveraging a variety of tree-based classifiers. The results indicate the significance of feature reduction, feature selection and feature importance as the three feature optimization methods in comprehending customer churn prediction. This study demonstrated that feature optimization can improve this prediction by increasing the accuracy and precision of tree-based classifiers and decreasing their error rates.

This research aims to enhance the understanding of customer behavior on internet banking service platforms by predicting churn intentions. This study demonstrates how feature optimization methods influence customer churn prediction performance. This approach included feature reduction, feature selection and assessing feature importance to optimize transaction data analysis. Additionally, the author performed feature optimization within tree-based classifiers to improve performance. The novelty of this approach lies in combining feature optimization methods with tree-based classifiers to effectively capture and articulate customer churn experience in internet banking service marketing.

Metaverse technology has attracted much attention in many contexts, including industry, education, marketing and business. Some recent studies have focused on qualitative studies based on the actual definition of the metaverse. However, practical research related to metaverse platforms remains in its infancy. This study provides actionable insights into the determinants of metaverse adoption by using perceived fluidity.

A two-stage structural equation modeling (SEM) approach and Hayes’ Macro approach are used to examine the proposed hypotheses.

Results show that technology features (e.g. real-time rendering, interactivity and immersion) increase users’ perceived fluidity, which in turn leads to positive intentions to use the metaverse. A high level of perceived realism is not an advantage for metaverse technology and plays a negative moderating role in this mechanism. The interaction of awe with technological features can enhance the negative moderating effects of realism.

This study pioneers the examination of perceived fluidity as a key determinant of metaverse adoption, offering a novel perspective beyond traditional factors. It uniquely identifies the paradoxical role of perceived realism, demonstrating its potential negative impact on user experience. In addition, the research highlights the reinforcing effect of awe on this relationship.