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It has been previously demonstrated that stochastic volatility emerges as the gauge field necessary to restore local symmetry under changes in stock prices in the Black–Scholes (BS) equation. When this occurs, a Merton–Garman-like equation emerges. From the perspective of manifolds, this means that the Black–Scholes and Merton–Garman (MG) equations can be considered locally equivalent. In this scenario, the MG Hamiltonian is a special case of a more general Hamiltonian, here referred to as the gauge Hamiltonian. We then show that the gauge character of volatility implies a specific functional relationship between stock prices and volatility. The connection between stock prices and volatility is a powerful tool for improving volatility estimations in the stock market, which is a key ingredient for investors to make good decisions. Finally, we define an extended version of the martingale condition, defined for the gauge Hamiltonian.

The information paradox suggests that the black hole loses information when it emits radiation. In this way, the spectrum of radiation corresponds to a mixed (non-pure) quantum state even if the internal state generating the black hole is expected to be pure in essence. In this paper we propose an argument solving this paradox by developing an understanding of the process by which spontaneous symmetry breaks when a black hole selects one of the many possible ground states and emits radiation as a consequence of it. Here, the particle operator number is the order parameter. This mechanism explains the connection between the density matrix, corresponding to the pure state describing the black hole state, and the density matrix describing the spectrum of radiation (mixed quantum state). From this perspective, we can recover black hole information from the superposition principle, applied to the different possible order parameters (particle number operators).

<jats:p>Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.</jats:p>

<jats:p>Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. We explore a novel concept called Double Machine Learning that embraces causal machine learning in this research. The core goal is to select independent variables from a gesture identification problem that are causally related to final gesture results. This selection allows us to classify and analyze gestures more efficiently, thereby improving models’ performance and interpretability. Compared to commonly used feature selection methods such as Variance Threshold, Select From Model, Principal Component Analysis, Least Absolute Shrinkage and Selection Operator, Artificial Neural Network, and TabNet, Double Machine Learning methods focus more on causal relationships between variables rather than correlations. Our research shows that variables selected using the Double Machine Learning method perform well under different classification models, with final results significantly better than those of traditional methods. This novel Double Machine Learning-based approach offers researchers a valuable perspective for feature selection and model construction. It enhances the model’s ability to uncover causal relationships within complex data. Variables with causal significance can be more informative than those with only correlative significance, thus improving overall prediction performance and reliability.</jats:p>

Purpose Research on battery electric vehicles (BEVs) has typically considered environmental concern a key determinant of behavioral intention that leads individuals to prefer electric vehicles. This paper challenges this assumption and argues that technology frameworks may require new variables to capture consumers' preferences. A UTAUT2-based study has been developed to assess the role of environmental concern in the BEVs context and put forward the technology show-off (TS) concept to explain the technology's acceptance. Design/methodology/approach A quantitative and cross-sectional look at behavioral intention is adopted. The study uses structural equation modeling to analyze a sample of 236 Macau residents to determine the relevance of the factors behind the choice to adopt BEVs. Findings The findings indicate that environmental concern and price may be relevant to explain behavioral intention to adopt the BEVs technology. Furthermore, the UTAUT2 framework seems to benefit from adding new variables, with TS playing a pertinent role in explaining technology acceptance. Social implications The findings show that environmental concern fails to build an argument for the shift to full electric mobility and promote the desired behavioral change toward adopting BEVs. Herein lies the necessity to consider new variables that can better describe the characteristics of modern society. Originality/value This paper proposes the TS construct, combining visibility and trialability as significant determinants of behavioral intention to use technology. The study also stresses the need to reconsider the role of environmental concerns' impact on consumer decision-making.

This paper aims to investigate the factors influencing men’s purchase intentions for skincare products, particularly focusing on the evolving attitudes toward masculinity, grooming and self-care. The study seeks to identify dimensions such as self-image, health concerns, masculinity and perceptions regarding skincare, along with the impact of social media use on men’s skincare purchase intentions.,The research uses an online questionnaire to gather data from 178 valid responses. The collected data is analyzed using partial least squares structural equation modeling.,The results reveal that men’s skin health concerns significantly impact their purchase intention for skincare products. Self-image concerns and perceptions regarding skincare also emerge as influential determinants in shaping men’s purchasing decisions. Conversely, health concerns and social media platform use do not directly influence skincare purchase intention. Notably, self-image completely mediates the relationship between men’s social media usage and their intention to purchase skincare products.,The data is based on responses from an online questionnaire, which may introduce biases. In addition, the research focuses on specific personal variables and social media use, potentially overlooking other influential factors.,By recognizing the importance of men’s skin health concerns, self-image and perceptions regarding skincare, cosmetic companies can tailor marketing strategies to effectively target key dimensions to enhance sales of skincare products among men.,In a broader societal context, this research contributes to the ongoing evolution of attitudes. By identifying influential factors in men’s skincare purchase intention, the study sheds light on changing societal norms and perceptions. Acknowledging these shifts can lead to a more inclusive understanding of masculinity and contribute to breaking traditional stereotypes related to men’s grooming practices.,This research contributes to the understanding of men’s skincare purchase intention by exploring dimensions such as self-image, health concerns, masculinity and perceptions regarding skincare, in conjunction with the impact of social media use. The findings provide valuable insights, expanding on previous studies on men’s attitudes toward skincare products. The identification of self-image as a complete mediator is a novel contribution.

Purpose This research focuses on common misconceptions about the factors driving women to purchase footwear impulsively. Its primary objective is to explore how emotional and social triggers specifically influence women's purchasing decisions, contrasting with the traditionally rational consumer models. Design/methodology/approach An online questionnaire was administered to a sample of women, yielding 199 useable responses. Findings The findings reveal the key determinants of women's impulsive retail footwear purchases, which include self-regulation, hedonic motivations and the influence of the retail store environment. This research challenges the prevailing assumption that women's passion for shopping is driven solely by inherent characteristics and suggests that external factors substantially shape their impulsive buying behaviour. In summary, the stereotypical portrayal of women as compulsive retail footwear shoppers may result more from external stimuli and environmental factors rather than an intrinsic trait. Originality/value This study improves the existing knowledge of women’s impulsive buying behaviour by unveiling the determinants of women's impulsive footwear purchases and assessing whether prevailing stereotypes hold true.

This paper examines the evolving trends in Chinese student mobility to Thailand, highlighting three distinct phases shaped by changes in the higher education: the dominance of Thai language programmes (1990–2010), the rise of business and international programmes (2010–2020), and the increasing preference for graduate studies (2020 onwards). By analysing the economic, cultural, and institutional factors facilitating these shifts, this paper positions Thailand as an emerging alternative study destination for Chinese students. It highlights the significance of this migration within the context of Thailand’s declining fertility rate and labour shortages, focusing on how Thai universities have adapted through active recruitment strategies targeting Chinese students. This paper also addresses the push and pull factors underpinning this migration and the pursuit of alternative educational pathways among Chinese youth. Additionally, it explores the strategic role of Sino-Thai collaborations under the BRI and their broader implications for educational mobility and economic ties.

Purpose The aim of this study is to explore the role and impact of action research in the adoption of circular economy strategies by a fashion retail brand. This exploration is motivated by the need to address the underutilization of action research in management studies, despite its potential to foster a deep understanding of organizational processes and to drive positive transformations. The study seeks to illustrate how action research can contribute to the practical implementation of sustainability initiatives, specifically within the context of new environmental legislation and growing demands for sustainable practices in retailing. Design/methodology/approach This research employs an action research methodology, particularly suited to the retail field, where understanding and influencing organizational processes are key. Through a detailed case study of a fashion retail brand, the study illustrates how action research facilitates the adoption of circular economy strategies. Findings The findings of this study underscore the effectiveness of action research in implementing circular economy strategies within the fashion retail industry. Specifically, it highlights how this approach has led to the successful reduction of waste and reintegration of products into their lifecycle. Originality/value The originality of this study lies in its thorough application of action research to measure and refine the outcomes of circular economy strategies in retailing. This novel approach provides substantial insights into the potential of the circular economy to drive practical innovations in business practices within retail.

This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.

In the wave of digital transformation, Chinese banks have prioritized digital banking services as key strategic goals, aiming to revolutionize the mobile banking experience. This study aims to assess the factors influencing the willingness to use the various financial and contextual services offered through digital banking. Specifically, it is proposed a model based on users' perceptions of mobile banking scenarios and examines how the development of digital banking services influences users' willingness to use them. The study involved qualitative in-depth interviews with 12 mobile banking users, with the interview content analyzed using Nvivo qualitative analysis software. The data analysis identified 9 core coding categories: Financial Professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived Usefulness, and Willingness to Use. These categories were further refined to construct a theoretical model of user willingness in digital banking services, drawing from the optimized Technology Acceptance Model (TAM). The findings provide valuable insights for the banking industry in Macau, aiding in understanding customer needs and supporting the positive development of mobile finance and contextual digital banking services in the region.

Purpose Retail omnichannel implementation faces barriers hindering accurate and efficient integration across marketing channels. Our desk examination identified a need for a broader perspective in investigating these barriers, moving away from a dominant, narrow approach. This research aims to develop a comprehensive set of items to measure retail omnichannel obstacles, refine the scale and assess its reliability and validity for a robust measurement tool. Design/methodology/approach Our approach combines quantitative and qualitative methods, using data from primary and secondary sources to create and validate the omnichannel obstacles scale. Findings This study emphasises the inclusive nature of retail functional areas, departing from prior literature that examined them in isolation. Instead of focussing on separate domains where retail omnichannel obstacles may arise, we adopt a holistic perspective by integrating previously disconnected elements. Originality/value We assert that challenges in retail omnichannel operations encompass three distinct dimensions: operational efficiency, channel inefficiency, and strategy and organisational culture within retailing. In our final validated measurement model, we consolidate the channel inefficiency dimension and refine the omnichannel obstacles scale to emphasise two areas of consideration.

Purpose Retail omnichannel implementation faces barriers hindering accurate and efficient integration across marketing channels. Our desk examination identified a need for a broader perspective in investigating these barriers, moving away from a dominant, narrow approach. This research aims to develop a comprehensive set of items to measure retail omnichannel obstacles, refine the scale and assess its reliability and validity for a robust measurement tool. Design/methodology/approach Our approach combines quantitative and qualitative methods, using data from primary and secondary sources to create and validate the omnichannel obstacles scale. Findings This study emphasises the inclusive nature of retail functional areas, departing from prior literature that examined them in isolation. Instead of focussing on separate domains where retail omnichannel obstacles may arise, we adopt a holistic perspective by integrating previously disconnected elements. Originality/value We assert that challenges in retail omnichannel operations encompass three distinct dimensions: operational efficiency, channel inefficiency, and strategy and organisational culture within retailing. In our final validated measurement model, we consolidate the channel inefficiency dimension and refine the omnichannel obstacles scale to emphasise two areas of consideration.

Purpose: This study explores the emotional impact of post-purchase guilt on younger consumers in the Chinese luxury retail market, with a specific focus on the role of Cause-related Marketing (CrM) in mitigating negative emotions across luxury and non-luxury product categories.Design/Methodology/Approach: A quantitative experimental design was utilized, involving 326 respondents exposed to different advertising scenarios. The study tested the impact of CrM on post-purchase guilt in both luxury (high-priced) and non-luxury (moderately priced) product conditions, using a 2 × 2 factorial design. The data were analyzed using ANCOVA to assess the effects of CrM campaigns across conditions.Findings: The results demonstrate that CrM effectively reduces post-purchase guilt across both luxury and non-luxury product categories, providing a moral justification for purchases by linking them to a positive social cause. However, contrary to expectations, the impact of CrM was not significantly stronger in the luxury context compared to non-luxury. This suggests that CrM's influence on post-purchase guilt operates uniformly, regardless of product type.Originality: This research enhances understanding Millennial and Gen Z consumer behavior in the Chinese luxury market. The findings offer actionable insights for luxury brands, highlighting the effectiveness of CrM in addressing guilt-related concerns, thereby informing marketing strategies aimed at younger generations.Keywords: post-purchase guilt, Millennials, Gen Z, Chinese luxury retail industry, cause-related marketing.Acknowledgments: The first author would like to thank CEGE – Research Centre in Management and Economics, funded by The Multiannual Funding Programme of R&D Centres of FCT – Fundação para a Ciência e Tecnologia under the project UIDB/00731/2020. The fourth author would like to thank COMEGI funded by FCT – Fundação para a Ciência e Tecnologia under the project UIDB/04005/2020.DOI: https://doi.org/10.58869/EJABM10(3)/06

Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.