Examining How "Technology Show-off" Affects the Acceptance of Innovations in the Battery Electric Vehicle Sector. - Lampo, Alessandro (Proceedings of the 2024 6th International Conference on Management Science and Industrial Engineering, 2024)
Theories and models try to describe the complexity of how people embrace and make use of innovations. The determinants of behavior concerning battery electric vehicles (BEVs) have traditionally revolved around factors such as vehicles’ price and range. Surprisingly, despite being small and one of the wealthiest territories in the world, Macau SAR faces challenges in BEV market penetration, underscoring the need to explore alternative causes of behavioral intention. To explain the adoption of technologies, this paper focuses on technology show-off (TS), which combines Rogers’ (2003) concepts of visibility and trialability of technology in a single construct as a determinant of behavioral intention. Therefore, when individuals perceive technology as more visible and available for trial, their behavioral intention to adopt it strengthens. Using structural equation modeling for quantitative analysis, this study examines the significance of TS in terms of the intention to adopt battery electric vehicles. The findings highlight the salience of the technology show-off construct in explaining behavioral intention, underscoring its significance in the context of modern society’s characteristics. This study contributes to advances in understanding technology acceptance and highlights the importance of incorporating experiential aspects (such as TS) into the traditional technology acceptance models.

Comparing Online Learning Experiences Between University Students With and Without Special Educational Needs During COVID-19 - Lim, Yun Ting; Nah, Yong-Hwee; Chen, Mo (ISSN: 2758-0962 The Paris Conference on Education 2024: Official Conference Proceedings, 2024)
The aim of this study is to examine the online learning experiences of university students with Special Educational Needs (SEN), and how their experiences might differ from their typically developing peers. Fifty typically developing students (mean age = 22; 29 females) and 31 students with SEN (mean age = 22; 15 females) from a local

Order parameter conditions from mutual information and symmetry conditions - Arraut, Ivan; Yu, Wing Chi (International Journal of Modern Physics B, 2024)
The mutual information method has demonstrated to be very useful for deriving the potential order parameter of a system. Although the method suggests some constraints which help to define this quantity, there is still some freedom in the definition. The method then results inefficient for cases where we have order parameters with a large number of constants in the expansion, which happens when we have many degenerate vacuums. Here, we introduce some additional constraints based on the existence of broken symmetries, which help us to reduce the arbitrariness in the definitions of the order parameter in the proposed mutual information method.
Publisher: World Scientific Publishing Co.
Keywords: Mutual information; order parameter; quantum phase transitions; spontaneous symmetry breaking

Understanding Policy Acceptance Through UTAUT-2: The Case of the Northbound Travel Policy for Macau Vehicles - Lampo, Alessandro; Diakite, Ansoumane D.; Ip, Emil C. H. (Journal of Global Business Research and Practice, 2024)

The cosmological constant emerging from a symmetry invariant - Arraut, Ivan (Modern Physics Letters A, 2024)
The cosmological constant is normally introduced as an additional term entering the Einstein–Hilbert (EH) action. In this letter, we demonstrate that, instead, it appears naturally from the standard EH action as an invariant term emerging from spacetime symmetries. We then demonstrate that the same constraint emerging from this invariant suppresses the short wavelength modes and it favors the long wavelength ones. In this way, inside the proposed formulation, the observed value for the vacuum energy density is obtained naturally from the zero-point quantum fluctuations.
Publisher: World Scientific Publishing Co.
Keywords: Dark energy; cosmological constant; zero point quantum fluctuations

Changing professional identities: a qualitative study of how Singapore allied educators managed their own resistance to change - Kit, Phey Ling; Ow Yeong, Wai Mang Elizabeth; Chen, Mo (Teacher Development, 2024)

Neutrino oscillations from the perspective of the quantum Yang-Baxter equations - Arraut, Ivan; Arrieta-Diaz, Enrique (arXiv, 2024)
The origins of neutrino masses is one of the biggest mysteries in modern physics since they are beyond the realm of the Standard Model. As massive particles, neutrinos undergo flavor oscillations throughout their propagation. In this paper we show that when a neutrino oscillates from a flavor state {\alpha} to a flavor state \b{eta}, it follows three possible paths consistent with the Quantum Yang- Baxter Equations. These trajectories define the transition probabilities of the oscillations. Moreover, we define a probability matrix for flavor transitions consistent with the Quantum Yang-Baxter Equations, and estimate the values of the three neutrino mass eigenvalues within the framework of the triangular formulation.
arXiv:2409.00560 [hep-ph]
Keywords: High Energy Physics - Phenomenology

Determining the relationships of employee resilience and organizational culture with levels of employee work engagement - Kuok, Angus; Taormina, Robert (2024)
The objective is to assess whether the extent to which employee resilience and organizational culture would be significantly related to and statistically predict the three facets of employee work engagement. Resilience was measured by four facets (Determination, Endurance, Adaptability, and Recuperability); and Organization Culture was measured for three types (Bureaucratic, Innovative, and Supportive). The dependent measures were the three facets of Work Engagement (Cognitive, Emotional, and Physical). This research by questionnaire was conducted in 2023. The questionnaires completed by 316 full-time workers revealed that all four facets of employee resilience had significant positive correlations with all three types of work engagement. Also, all three facets of work engagement were significantly higher in Innovative and Supportive cultures compared to Bureaucratic cultures. The regression analyses performed showed that the resilience factors of Determination and Adaptability were strong positive predictors of all three facets of work engagement. Furthermore, Innovative culture had additional positive effects on all three facets of work engagement; while Supportive culture had an additional positive effect on Emotional Work Engagement. The implications of the results for management are also discussed in this paper.

Predicting consumer ad preferences: Leveraging a machinelearning approach for EDA and FEA neurophysiologicalmetrics - Lobo Marques, Joao Alexandre; Neto, Andreia C.; Silva, Susana C.; Bigne, Enrique (Psychology & Marketing, 2024)
Abstract 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.
Publisher: Wiley

Trust in AI and Top Management Support in Generative-AI Adoption - Korzyński, Paweł; Silva, Susana Costa E; Górska, Anna Maria; Mazurek, Grzegorz (Journal of Computer Information Systems, 2024)

Analysis of deep learning algorithms for emotion classification based on facial expression recognition - Qian, Cheng; Lobo Marques, Joao Alexandre; Fong, Simon James (Proceedings of the 2024 8th International Conference on Big Data and Internet of Things, 2024)
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 paper presents an empirical study that evaluates four existing deep learning models—VGG16, DenseNet, ResNet50, and GoogLeNet—utilizing the Facial Expression Recognition 2013 (FER2013) dataset. The dataset contains seven distinct emotional expressions: angry, disgust, fear, happy, neutral, sad, and surprise. Each model underwent rigorous assessment based on metrics including test accuracy, training duration, and weight file size to test their effectiveness in FER tasks. ResNet50 emerged as the top performer with a test accuracy of 69.46%, leveraging its residual learning architecture to effectively address challenges inherent in training deep neural networks. Conversely, GoogLeNet exhibited the lowest test accuracy among the models, suggesting potential architectural constraints in FER applications. VGG16, while competitive in accuracy, demonstrated lengthier training times and a larger weight file size (512MB), highlighting the inherent balance between model complexity and computational efficiency.
Publisher: ACM

Enhancing Dynamic Hand Gesture Recognition through Optimized Feature Selection using Double Machine Learning - Yan, Keyue; Lam, Chi Fai; Fong, Simon; Lobo Marques, Joao Alexandre; Song, Qun; Qin, Huafeng (Proceedings of the 2024 8th International Conference on Big Data and Internet of Things, 2024)
Publisher: ACM

Post-Covid-19 Gender Inequality in Global Employment: A Difference-In-Difference Analysis - Lei, Weng Chi (Atlantis Press, 2024)
The fifth Sustainable Development Goal of the United Nations targets achieving gender equality by 2030, but recent progress has been sluggish. Gender inequalities in the labor markets may have been exacerbated by the Covid-19 recession. This paper aims to investigate the effects of the Covid-19 pandemic on gender inequalities in the labor markets by...
ISSN: 2352-5398

Understanding the dynamics of customer satisfaction and repurchase intentions in the era of platform service supply chain – a case study of Foodpanda in Pakistan - Choudhary, Waliha; Waqar Haider, Syed; Raza, Aamir; Silva, Susana C.; Carmo Dias, Joana (Journal of Foodservice Business Research, 2024)

Assessment of the post-acute COVID-19 syndrome cardiovascular effect through ECG analysis - Ribeiro, Pedro; de Souza, Clarice Cristina Cunha; Camerino, Cristine Mayara Cavalcante; Pordeus, Daniel; Leite, Camila Ferreira; Lobo Marques, Joao Alexandre; Madeiro, João Paulo; Rodrigues, Pedro Miguel (2024)
Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.

Sharing towards cohesive community: the role of social capital in sharing economy - Lai, Michael (ResearchGate, 2024)
PDF | Purpose Whilst the majority of academic studies have focused on the for-profit business-to-consumer type of sharing economy, the community-based... | Find, read and cite all the research you need on ResearchGate

Impact of Education on Green Fintech Adoption: Evidence From the New Sub-Center of Beijing - Lei, Weng Chi; An, Boyang (Journal of Global Business Research and Practice, 2024)

Habermas, Honneth, Critical Theory, and Ideology Critique for Transformative Mixed Methods Research - Morrison, Keith (Journal of Mixed Methods Studies, 2024)
This article sets a theoretical foundation to transformative mixed methods research that is rooted in the critical theory of Habermas and Honneth. This addresses Habermas’s knowledge-constitutive interests and communicative action for redressing societal pathologies, and Honneth’s work on (mis)recognition, (dis)respect, and social justice. In doing so, the article argues for broadening the scope and embrace of mixed methods research, to go beyond being empirical research only or largely, and to include theorisation, critical theoretical discourse and its analysis, and ideology critique, as legitimate methods for (transformative) mixed methods research. The article makes a case for these methods as constituting important research methods in themselves in the portfolio of mixed methods research, moving the boundaries of mixed methods research beyond solely empirical studies, and providing emancipatory lenses and consciousness-raising in recognising that transformation takes many forms.

Beyond the myth: understanding women's impulsive retail footwear shopping - Duarte, Paulo; Silva, Susana C.; Magro, Margarida; Dias, Joana Carmo (International Journal of Retail & Distribution Management, 2024)
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.,An online questionnaire was administered to a sample of women, yielding 199 useable responses.,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.,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.
Publisher: Emerald Publishing Limited

Using Machine Learning CART Decision Trees for Predicting the Causes of Delays in Projects from the Construction Industry - Lobo Marques, Joao Alexandre; Chun, Fung Ka; Riccelli, Bruno (2024)
Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.

Artificial Intelligence and Deep Learning in Stock Prediction: A Bibliometric Review - Chin, Yang Lin; Lobo Marques, Joao Alexandre; Lin, Kun Chan (2024)
Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.
Issue: 1

Optimization of Energy Storage Systems with Renewable Energy Generation and Consumption Data - Filho, Edmilson Moreira Lima; Silveira, Aêdo Braga; Ferreira, Alexandre Marques; Lobo Marques, Joao Alexandre; Batista, Josias Guimarães; Guimarães, Glendo De Freitas; Alexandria, Auzuir Ripardo De; Rodrigues, Joel José Puga Coelho (2024 IEEE 7th Student Conference on Electric Machines and Systems (SCEMS), 2024)
This work provides a comprehensive systematic review of optimization techniques using artificial intelligence (AI) for energy storage systems within renewable energy setups. The primary goals are to evaluate the latest technologies employed in forecasting models for renewable energy generation, load forecasting, and energy storage systems, alongside their construction parameters and optimization methods. The review highlights the progress achieved, identifies current challenges, and explores future research directions. Despite the extensive application of machine learning (ML) and deep learning (DL) in renewable energy generation, consumption patterns, and storage optimization, few studies integrate these three aspects simultaneously, underscoring the significance of this work. The review encompasses studies from Web of Science, Scopus, and Science Direct up to December 2023, including works scheduled for publication in 2024. Each study related to renewable energy storage was individually analyzed to assess its objectives, methodology, and results. The findings reveal useful insights for developing AI models aimed at optimizing storage systems. However, critical areas need further exploration, such as real-time forecasting, long-term storage predictions, hybrid neural networks for demand-based generation forecasting, and the evaluation of various storage scales and battery technologies. The review also notes a significant gap in research on large-scale storage systems in Brazil and Latin America. In conclusion, the study emphasizes the need for continued research and the development of new algorithms to address existing limitations in the field.

Doctor Bone: treinamento de redes neurais para auxílio na identificação da idade óssea - Barbosa, Rodrigo Lages; Martins, Heitor de Castro Teixeira e; Barbosa, Felipe Cassiano; da Costa, Beatriz Torres; Matela, Rolf Freitas; Neto, José Fernando Rodrigues Ferreira; Fontes, Yuri Nekan Soares; Lobo Marques, Joao Alexandre; Duarte, João Batista Furlan; Neto, Joel Sotero da Cunha (2024)
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.

Empowering Luxury: How Cause-Related Marketing Influences Gen Z and Millennials' Post-Purchase Guilt in China's High-End Retail Landscape - Dias, Joana Carmo; Silva, Susana; Martins, Carla; Casais, Beatriz (European Journal of Applied Business and Management, 2024)
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
Number: 3

Consumers' Perception of Electric Aviation in Macao: An Exploratory Study Based on the Theory of Planned Behavior - Lampo, Alessandro (Proceedings of the 10th International Conference on Industrial and Business Engineering, 2024)
Battery Electric Aircraft (BEA) technology is gaining attention due to the potential to reduce carbon emissions and noise pollution, contributing to global environmental sustainability. Grounded in the Theory of Planned Behavior (TPB), this paper explores the determinants of attitude, social norm, behavioural control, and perceived risks related to the intention of Macau residents to use electric aeroplanes within the Greater Bay Area (GBA). This research uses a quantitative approach. Data is collected through structured surveys distributed to potential adopters. To assess the relationships between the determinants in our model, Structural Equation Modeling (SEM) is employed. The findings reveal that a favourable attitude and perceived behavioural control positively influence individuals’ intentions to adopt electric aeroplanes. However, perceived risks strongly impact adoption intentions, suggesting that addressing safety and reliability concerns is essential for promoting the technology within the region. The implications of this research extend beyond academic interests, as Macau’s unique position within the GBA offers the opportunity for electric aeroplanes’ adoption. Further, the reduced carbon emissions and noise pollution align with the city’s objectives and create a harmonious balance between economic prosperity and environmental preservation for future generations. This study offers important insights for integrating advanced computing technologies into BEA systems to enhance electric aeroplanes’ operational efficiency and safety to support their adoption. It also provides a path for policymakers and industry stakeholders toward sustainable economic development and integration of Macau within the GBA.

Macau Cultural Tapestry: Unravelling a City’s Visual Identity Through Its Patterns and Textures—Digital Archive and Mapping - Martins, Filipa; Seck, Mamadú; Pinheiro, Francisco; Estadieu, Gerald (Springer Series in Design and Innovation, 2024)
ISBN: 9783031761553

The Future of Digital Banking in Macau: Developing a Theoretical Model of User Willingness for Digital Banking Services - Lin, Jing-ying; Lobo Marques, Joao Alexandre; Neto, Andreia (Proceedings of the 2024 15th International Conference on E-business, Management and Economics, 2024)
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.
Publisher: ACM

Unveiling retail omnichannel challenges: developing an omnichannel obstacles scale - Radomska, Joanna; Kawa, Arkadiusz; Hajdas, Monika; Klimas, Patrycja; Silva, Susana C. (International Journal of Retail & Distribution Management, 2024)
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.

Unveiling the power of supplier-country image on B2B customers' perceptions: Beyond the product’s beauty - Crespo, Cátia Fernandes; Crespo, Nuno Fernandes; Silva, Susana C. (International Business Review, 2025)

是來時路，亦是前行燈：澳門教師的使命與歸屬 - Ge, Rochelle (2025)

Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics - Marques, João Alexandre Lobo; Neto, Andreia C.; Silva, Susana C.; Bigne, Enrique (Psychology & Marketing, 2025)
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.
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mar.22118
Keywords: advertising; consumer ad preferences; consumer neuroscience; emotions; explainable artificial intelligence; machine learning

Beyond the myth: understanding women's impulsive retail footwear shopping - Duarte, Paulo; Silva, Susana C.; Magro, Margarida; Dias, Joana Carmo (International Journal of Retail & Distribution Management, 2025)
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.

The shifting landscape of Chinese student mobility to Thailand - Lertpusit, Sivarin; Ge, Rochelle; Ho, Kong Chong (Asian Population Studies, 2025)
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.
Publisher: Informa UK Limited

Action research on circular economy strategies in fashion retail - Silva, Susana C.; Neiva, Carolina Rocha; Dias, Joana Carmo (International Journal of Retail & Distribution Management, 2025)
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.

Is masculinity ruining the male skincare market? Comprehensive insights on consumer behavior in cosmetics - Duarte, Paulo; Silva, Susana; Carvalho, Maria Joana (Journal of Consumer Marketing, 2025)
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.
Publisher: Emerald Publishing Limited

A grounded theory study of Chinese female spouses of problem gamblers in Hong Kong: emergence of a multiple-paradigm coping model - Kwan, Edward Wood Kai; Jackson, Alun C.; Tse, Samson (Asia Pacific Journal of Counselling and Psychotherapy, 2025)

The role of environmental concern and technology show-off on electric vehicles adoption: the case of Macau - Lampo, Alessandro; Silva, Susana C.; Duarte, Paulo (International Journal of Emerging Markets, 2025)
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.

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning - Yan, Keyue; Lam, Chi-Fai; Fong, Simon; Lobo Marques, Joao Alexandre; Millham, Richard Charles; Mohammed, Sabah (Sensors, 2025)
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.
Publisher: MDPI AG

Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions - Qian, Cheng; Lobo Marques, Joao Alexandre; Alexandria, Auzuir Ripardo de; Fong, Simon James (Sensors, 2025)
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.
Publisher: MDPI AG

A Possible Solution to the Black Hole Information Paradox - Arraut, Ivan (AppliedMath, 2025)
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).
Number: 1 Publisher: Multidisciplinary Digital Publishing Institute
Keywords: Hawking radiation; black holes; information paradox; spontaneous symmetry breaking

Local Equivalence of the Black–Scholes and Merton–Garman Equations - Arraut, Ivan (2025)
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.

Real-time emotion recognition based on facial expressions using artificial intelligence techniques - A review and future directions - Cheng, Qian; Lobo Marques, Joao Alexandre; de Alexandria, Auzuir Ripardo (2025)

Solving the dark energy problem via symmetry constraints - Arraut, Ivan (The European Physical Journal C, 2025)
We derive the vacuum energy from the zero-point quantum fluctuations after imposing a natural constraint emerging from the rotational symmetry inside the de-Sitter metric. The constraint imposes a maximum azimuthal angle for each frequency mode emerging from the vacuum. In this way, the shorter the wavelength of the mode, the larger will be its suppression. The same result is derived subsequently by using the Friedmann–Lemaitre–Robertson–Walker (FLRW) metric. We then make a physical interpretation of the physical effects from the perspective of pair creations over the vacuum, where the mentioned constraint emerges, limiting then the maximum angle which each pair generated from the vacuum can rotate with respect to each other during their short existence.
Publisher: Springer Science and Business Media LLC

Non-thermodynamic Black Hole Evaporation - Arraut, Ivan; Mehta, Abhishek Kumar (arXiv, 2025)
In this paper, we investigate black hole evaporation from the path integral perspective. We demonstrate that besides the standard thermodynamic modes, there are non-thermodynamic modes of black hole evaporation which contain remnants. The pure thermodynamic process is recovered when the Gauss-Bonnet action is involved. This scenario opens a new window for analyzing the process of black-hole evaporation.
arXiv:2505.06859 [hep-th]
Keywords: High Energy Physics - Theory

Assessing critical thinking disposition through text analysis in traditional Chinese: An empirical study of tourism and gaming undergraduates in Macao - Siu Lam, Carlos; Liu, Yong; Zhang, Yinghong; Wu, Hao (Frontiers in Education, 2025)
Critical thinking disposition (CTD) is increasingly recognized as an important trait in education, reflecting the inclination and habits necessary for addressing complex challenges in today's world. This study assessed the CTD of students enrolled in a tourism and gaming management programme, focusing on two key dimensions: Analyticity and Open-Mindedness. This study was conducted at a university in Macao and involved 65 participants. The students were presented with an article relevant to their major, written in Traditional Chinese, and were asked to provide their opinions on each statement in the article. A rubric was designed to analyze their responses and assess their Analyticity and Open-Mindedness within the CTD framework. The results demonstrated high reliability (Cronbach's α = 0.91) and revealed an association between Analyticity and Open-Mindedness. Using Python programming, the study analyzed the frequency of parts of speech (POS) in students' responses, introducing a novel approach for evaluating CTD in Traditional Chinese. Regression modeling showed that parallel and adversative conjunctions significantly predicted Analyticity, while the frequency of conjunction use varied across Open-Mindedness classifications. These findings highlighted an innovative and objective method for assessing CTD through text analysis, offering promising applications for educational research in Traditional Chinese-speaking contexts.
Publisher: Frontiers
Keywords: critical thinking disposition; educational assessment; text analysis; tourism and gaming education; traditional Chinese