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This paper aims to explore the intention to adopt the Metaverse in Islamic banks, with a particular focus on evaluating perceived usefulness, ease of use, user satisfaction and the influence of religiosity. Integrating the technology adoption model (TAM) and religiosity intention model, this study will dissect the multidimensional aspects influencing the acceptance of Metaverse technologies.

Surveying Islamic bank professionals in Jordan, this study used a structured questionnaire and data augmentation to analyze Metaverse adoption factors. Using partial least squares-structural equation modeling, the relationships between ease of use, usefulness, religiosity and satisfaction were explored.

The study identifies pivotal relationships among perceived usefulness, ease of use, user satisfaction and religiosity in the context of adopting metaverse technologies in Islamic banks in Jordan. Evidence highlights the dominant role of perceived usefulness and ease in influencing both intention to use and satisfaction levels. Religiosity, while not a direct influencer, plays a collaborative role, underscoring a balanced mix of technological and religious elements that will potentially shape the adoption trajectory of metaverse technologies within this specific banking sector.

Integrating metaverse technologies in Islamic banks necessitates balancing technological advances with Sharia compliance. The study underscores the importance of aligning user experience with religious values and fostering innovation within Islamic ethical guidelines.

This study uniquely integrates the TAM and religiosity-intention model to explore metaverse adoption in Islamic banks, unveiling a nuanced interplay between technology and religious values. It offers practical insights for tailoring innovations in the Islamic financial ecosystem.

The purpose of this study is threefold: firstly, to compare Pakistani and French consumers’ perceptions of well-being; secondly, to investigate how consumers in both countries relate to food; and thirdly, to assess whether they associate food with well-being.

Thirty participants (15 French and 15 Pakistani) between the ages of 24 and 35 were interviewed, using convenience and snow bowling sampling. Data triangulation was performed by combining three qualitative techniques, word association, photo-elicitation-based interviewing and open-ended questions to explore consumer perceptions of well-being, food and food well-being.

The study’s findings suggest that well-being is a broad concept in which food is an ingredient. Food and well-being share common elements, and food well-being can be defined as an individual’s psychological, physical, social and societal relationship with food ascribed by affordability and food literacy.

Pleasure, sharing and respect emerged as dimensions of food well-being that can be applied to transfigure consumer behaviour and reduce over-consumption, food waste and hunger. The dimensions of well-being and food were explored for both countries to understand their cultural nuances and determine the influence of food on well-being. This comparative analysis will help researchers understand consumers’ preferences for food in various aspects from two regions. This study can potentially contribute to scale development in food and well-being, which can help researchers measure the effects of food and well-being in different sectors of the economy, particularly in health care. The most aspiring aspect of the current research is the insights unveiled during interactions with research participants, which will help develop consumer baseline feelings.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

This paper aims to prompt ideas amongst readers (especially librarians) about how they can become active partners in knowledge dissemination amongst concerned user groups by implementing 3D printing technology under the “Makerspace.”

The paper provides a brief account of various tools and techniques used by veterinary and animal sciences institutions for information dissemination amongst the stakeholders and associated challenges with a focus on the use of 3D printing technology to overcome the bottlenecks. An overview of the 3D printing technology has been provided following the instances of use of this novel technology in veterinary and animal sciences. An initiative of the University Library, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, to harness the potential of this technology in disseminating information amongst livestock stakeholders has been discussed.

3D printing has the potential to enhance learning in veterinary and animal sciences by providing hands-on exposure to various anatomical structures, such as bones, organs and blood vessels, without the need for a cadaver. This approach enhances students’ spatial understanding and helps them better understand anatomical concepts. Libraries can enhance their visibility and can contribute actively to knowledge dissemination beyond traditional library services.

The ideas about how to harness the potential of 3D printing in knowledge dissemination amongst livestock sector stakeholders have been elaborated. This promotes creativity amongst librarians enabling them to think how they can engage in knowledge dissemination thinking out of the box.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

“Idol pilgrimage tour” is a popular trend among young Chinese fans who travel for idol-related purposes, engage in interactive events and have co-created experiences at destinations. With the growing market size of fan economy, fan tourists generate significant revenue for the local. However, many destinations have not fully utilised this opportunity, and there is a lack of research on this niche form of tourism. This research was undertaken to address this research gap.

This study adopted an idol worship–motivation–co-created experience–tour satisfaction–destination loyalty framework in the context of idol pilgrimage tours. In addition, this study investigated the direct influence of idol worship on the other four constructs. Data were collected from 354 Chinese fans who had such experience through online questionnaires. The partial least squares–structural equation modelling technique was used to examine the research model.

It was demonstrated that idol worship has a direct influence on motivation, co-created experience, satisfaction and loyalty and that there is a positive relationship between motivation, co-created experience, satisfaction and loyalty. The results advance the brand sacralisation literature by studying worship in a tourism context and contribute to interactive marketing literature by clarifying the interactive mechanism between relationships among the idol, fans and destinations. The study suggests some practical implications for destination management organisations attempting to target tourist fans.

This is the first study introducing the concept of the idol pilgrimage tour. Empirical results of this study reveal the underlying mechanism of how idols influence fans' travel-related psychology and behaviour.

This study aims to analyze the factors affecting tea-producing farmers' intention to use e-commerce exchanges (ECEs) to sell their products, combining the technology acceptance model (TAM) theory and barrier factors.

The authors use the generalized structural equation modeling (GSEM) to analyze the intermediate model that is built on TAM.

Research results show that perceived usefulness (PU) and perceived ease of use (PEU) significantly influence farmers' intention to use ECEs to sell their products. However, knowledge and information barriers hinder farmers' intention to use such ECEs.

Encouraging farmers to use ECEs is the most helpful solution for agricultural economic development in the context of the Covid-19 pandemic.

From an academic perspective, this is the first study that combines the TAM theory of Davis (1989) and barrier factors to analyze farmers' intention to use ECEs. The findings are valuable references for policymakers to propose strategies for agricultural economic development during the current pandemic. At the same time, the empirical results obtained from this study provide good orientations for agricultural economic development 4.0 in the future.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

The primary aim of this study is to gain insight into the entrepreneurial marketing strategy (EMS) decisions made by microenterprises in the craft sector and draw comparisons between the marketing strategy formulation and implementation of conventional businesses and those of craft-based microenterprises with a specific focus on the context of emerging markets, particularly India.

This paper follows a qualitative interpretivist paradigm using a multiple-case methodology approach. It tracks craft-based microenterprises that make furniture or home décor products in India. The study participants were the founders, principal designers, studio managers or craftspersons.

This study’s findings reveal that craft-based microenterprises implement an EMS that adopts a hybrid form of market orientation strategy. In this approach, the product or creative concept is at the centre of the decision-making, and the customer needs are factored in at a later stage for customisation. These microenterprises prioritise product positioning over segmentation and targeting strategies.

This study tries to understand marketing strategy decision-making processes among craft-based microenterprises in India. Given that study participants came from only two-product-based craft businesses, this limits the generalisability of the findings to similar or related contexts. This study provides a framework and methodology for replication in other contexts and industries to formulate a nuanced understanding of micro, context-specific, craft-based businesses.

This study uses qualitative analysis to understand EMS in craft-based businesses in India. This study contributes to this fledgling stream of literature at the interface of marketing and entrepreneurship to understand entrepreneurial marketing. This study analyses the marketing strategy of craft-based businesses using the framework of Morgan et al. (2019).