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In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

Metaverse, that is extended reality (XR)-based technologies such as augmented reality (AR) and virtual reality (VR), are increasingly believed to facilitate fundamental human practice in the future. One of the vanguards of this development has been the consumption domain, where the multi-modal and multi-sensory technology-mediated immersion is expected to enrich consumers' experience. However, it remains unclear whether these expectations have been warranted in reality and whether, rather than enhancing the experience, metaverse technologies inhibit the functioning and experience, such as cognitive functioning and experience.

This study utilizes a 2 (VR: yes vs no) × 2 (AR: yes vs no) between-subjects laboratory experiment. A total of 159 student participants are randomly assigned to one condition — a brick-and-mortar store, a VR store, an AR store and an augmented virtuality (AV) store — to complete a typical shopping task. Four spatial attention indicators — visit shift, duration shift, visit variation and duration variation — are compared based on attention allocation data converted from head movements extracted from recorded videos during the experiments.

This study identifies three essential effects of XR technologies on consumers' spatial attention allocation: the inattention effect, acceleration effect and imbalance effect. Specifically, the inattention effect (the attentional visit shift from showcased products to the environmental periphery) appears when VR or AR technology is applied to virtualize the store and disappears when AR and VR are used together. The acceleration effect (the attentional duration shift from showcased products to the environmental periphery) exists in the VR store. Additionally, AR causes an imbalance effect (the attentional duration variation increases horizontally among the showcased products).

This study provides valuable empirical evidence of how VR and AR influence consumers' spatial bias in attention allocation, filling the research gap on cognitive function in the metaverse. This study also provides practical guidelines for retailers and XR designers and developers.

The Mediterranean diet is one of the healthiest eating patterns and relies much on the regular consumption of fish, which is a source of unsaturated fatty acids, protein, vitamins and minerals. The purpose of this study was to describe and compare the macronutrients and micronutrients profile of 24 commercial fish species available in the Portuguese market.

A comparative study was undertaken based on nutritional datasheets provided by laboratories and based on the database of the Portuguese National Health Institute Doutor Ricardo Jorge.

The results obtained showed that while most of the fish species are low-fat, gilthead and salmon are considered high in fat, with salmon presenting significant amounts of unsaturated fatty acids. The blue shark shows the highest amounts of protein, whereas codfish shows significantly high levels of salt content. Sardine is much richer in terms of vitamins than the other species. With respect to minerals, there is a lot of variation among species. It is worth noticing the skate, with high amounts of sodium, calcium and phosphorous. Statistical analysis evidenced some relations between the properties evaluated, and it was possible to establish two distinct groups of fish through cluster analysis.

The species evaluated revealed a highly important nutritional value that should be an incentive to their regular consumption. Moreover, a lack of nutritional information about some micronutrients was found in several fish species, which should be studied in future studies.

This paper fulfils an identified need to compare nutritional composition of fish available in Portugal.

Competitiveness has been the objective of investigation in tourism in general, and particularly in hospitality, by many researchers in the past decades. The purpose of this study is to identify which main factors are considered crucial for the increase of competitiveness in hospitality, by applying the Porter’s diamond model to this sector.

The methodology used in the study required the use of primary data from which the authors obtained 285 responses from companies related to hospitality in Portugal. In the processing of data, the authors used a structural equation model.

The results showed that “government policies”, “factor conditions”, “related and supporting industries” and “cooperation and innovation” were determinant factors of the competitiveness of the sector.

Despite the importance of competitiveness in hospitality, the number of studies focused on the empirical validation models of competitiveness, applied to the hospitality industry, is still incipient. This study contributes to fill this gap.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Given the growing use of virtual reality (VR) technology in marketing, our research focuses on the development trajectory of research in the marketing field from 2012 to 2022 to identify essential phases and sub-trends within this topic.

This study employs a main path analysis (MPA) methodology to analyze academic articles related to VR in marketing from the Web of Science database.

The research on VR in marketing has experienced significant growth over the past 10 years and is projected to continue thriving in the future. During the past decade, research in this field has transitioned from exploring VR affordances in marketing to realizing the potential of VR in marketing. From the information systems perspective, the three primary research trends that have garnered the most attention from researchers are VR technology as an artifact, marketers’ motivational approach and consumers’ motivational approach. With the continual advancement of VR technology, the research trend of Metaverse marketing will gradually displace VR in marketing.

To the best of our knowledge, this is the first research using MPA to explore the development trajectory of VR in marketing and provide a comprehensive picture of it under the Affordance-Actualization theory.

Given the potential for authorial fashion to lead the way in the field of sustainable fashion and digital platforms to be a powerful tool for spreading sustainable messages, this study seeks to explore the emphasis given to sustainability in the digital communication of Portuguese and Spanish’s authorial fashion.

A thematic analysis of all the posts published on the feeds of the 63 designers who presented their collections at the Madrid and Lisbon fashion weeks in September/October 2022 (Spring/Summer 2023) or February/March 2023 (Fall/Winter 2023/2024) editions was carried out. The information was collected over a six-month period between the two fashion weeks. This study is based on the categories identified in the reflexive thematic analysis developed by Testa et al. (2020). After adapting the categories to our analysis, 14 emerging themes were identified as observation criteria.

A total of 4,699 posts were examined. The analysis reveals a general lack of interest in mentioning sustainability – a subject evident in just around 6% of the content – as well as a high emphasis on the visual aspect of the fashion product. Several similarities between the Portuguese and Spanish markets were observed.

This study is important since there are few cross-cultural studies in the field of fashion between the two countries, particularly on sustainable fashion. Furthermore, it establishes a structure that is easily replicable in various markets and geographical areas.

The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.