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ARTeFACTo is a transdisciplinary studies journal and conference centered around the concept of digital media and computer artifacts, which were the genesis of its name. It aims to give visibility to all digital media and technology-based artifacts, creators, or contributors from a transdisciplinary, humanistic, and holistic perspective of integration and composition. The main objective is constructing and disseminating knowledge through descriptions of artistic practice, authorship or co-authorship (including the narration and description of other works), and graphic data (images, diagrams, animations, drawings, sketches). The Journal is open to international contributions from all those studying digital media/computer artifacts, embracing their conceptualization, design, creative and research processes, computational implementation, exhibition and fruition, and role in today’s information and knowledge society. Also welcomed are articles fostering the establishment of the conceptual foundations of an artifact theory in the digital media world. Accepted articles shall be original (in compliance with the standards of conduct of the COPE – Code of Conduct and Good Practices) in Portuguese and English, with a mandatory abstract in English. The Conference has the same goals as the Journal but provides a specific floor for the concrete exhibition of artifacts along with their presentation and discussion. All submissions will be evaluated by peer review, including two advisors from the Scientific Council. All feedback will be sent to the authors, indicating their approval, refusal of publication, or requests for correction or recast. The authors will be notified of the publication once the entire selection process has been completed. ARTeFACTo is a joint project between Artech International and Universidade Aberta.

The pandemic exposed weaknesses in the global trade system, making it clear that climate actions are the priority in the recovery. International organizations are urging countries to seize this opportunity and integrate climate-friendly trade and investment rules to promote sustainable development. Trade is recognized as a powerful tool for tackling climate change, offering economies ways to both reduce emissions and adapt to environmental changes. In this paper, we investigate the digital and sustainable trade facilitation measures implemented in ASEAN countries, namely Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. We use a well-established trade model, the gravity model, to assess the impacts of trade facilitation efforts, particularly those that leverage digital technologies and promote sustainability. The data for this analysis comes from the UN Global Survey on digital and sustainable trade facilitation in 2017, 2019, and 2021. The results show that trade facilitation measures are crucial to increasing trade among the ASEAN countries. Measures of transparency of trade procedures, trade formality alleviation, and cross-border paperless trade have significant positive impacts on bilateral trade between ASEAN countries.

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.

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.

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.

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...

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.

The understanding of how people accept and embrace new policies is vital in today's world. This paper introduces an original way of looking at this by adapting the widely recognized Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2). The goal is to provide a foundational model for assessing policy acceptance. More specifically, we adapted the UTAUT-2 framework to study how Macau residents perceive the "Northbound Travel for Macau Vehicles" policy, which allows cars with Macau registration plates to enter China. Using structural equation modeling software (SmartPLS), we analyze data collected from 136 respondents who experienced the policy.Our findings reveal that Performance Expectancy (PE) and Habit (HB) significantly influence individuals' intention to take advantage of the policy. In other words, people are more likely to embrace policies they perceive as beneficial and that align with their existing habits. Effort Expectancy (EE) and Facilitating Conditions (FC) do not significantly impact acceptance, perhaps as a result of participants' familiarity with the policy and their resource availability. Surprisingly, while not directly tied to usage, Social Influence (SI) shows a high mean value, suggesting its potential role in policy acceptance when influential individuals adopt the policy. This pioneering research contributes to the field by bridging the gap between technology acceptance models and policy studies. Most importantly, it validates the use of the UTAUT-2 as a technology framework that is adapted for assessing policy acceptance.

In a world where sustainable food choices are becoming increasingly important, this study explores the connection between food neophilia — the desire to experiment with new foods — and people's willingness to include insects in their diets. Using the Theory of Planned Behavior (TPB), our research delves into how neophilia (NP) moderates respondents’ attitude (ATT), social norms (SN), and perceived behavioral control (PBC) concerning the adoption of insects as a food source. The analysis draws from 160 self-administered surveys and employs structural equation modeling. Conducted in the context of Macau SAR (China), our study reveals the pivotal role that neophilia plays in shaping consumer attitudes and intentions. Notably, respondents generally expressed a willingness to explore novel culinary experiences. A positive moderating effect of neophilia on attitudes toward insect consumption is observed, suggesting that individuals with higher neophilia scores are more inclined to hold favorable intentions regarding insects as food. However, neophilia's influence on moderating SN and PBC exhibits contrasting effects, implying that a strong inclination for food neophilia may not necessarily leadto increased social pressure or perceived control in adopting insect-based diets. In light of these findings, this study recommends that practitioners and policymakers promote insect consumptionas an innovative and adventurous means of achieving sustainable nutrition. While the primary focus is on the impact of food neophilia on people's intention to consume insects as food, the study underscores the urgent need for diversified and sustainable dietary choices to address escalating environmental concerns and secure a resilient food supply for future generations.

Integrating financial technologies with green initiatives is critical to the sustainable development agenda. This is particularly true for newly developed smart cities like Tongzhou, the sub-city center of Beijing. To assess the adoption of green fintech in Tongzhou, this paper extends the EnergyAugmented Technology Acceptance Model (EA-TAM) to incorporate two green factors – environmental awareness and green knowledge. This paper applies structural equation modeling techniques to analyze data from 403 respondents who live, work, or study in Tongzhou and finds allhypothesized constructs significant. Since green knowledge is significant to the adoption of green fintech, this paper further divides the sample into a high-education group (162 respondents with university-or-above degrees) and a low-education group (251 respondents with post-secondary-orlower degrees) to evaluate the impact of education. All the hypothesized factors are significant to the high-education group,but environmental awareness and perceived usefulness are insignificant to the low- education group. Hence, the results provide evidence that people in the newly developed smart city adopt green fintech due to their environmental sensitivity. The adoption of green fintech is more environmentally sensitive for people with high education levels.

As environmental awareness grows, sustainable modes of transportation have garnered increasing importance. With the rising popularity of electric vehicles (EVs), a broader community now acknowledges their numerous advantages, such as lower noise levels, enhanced efficiency, and cost-effectiveness in comparison to traditional internal combustion engine vehicles. Furthermore, EVs significantly contribute to a more sustainable future by emitting fewer pollutants and reducing overall environmental impact. However, consumers' perceptions and expectations about the vehicle's country of origin - the nation where the vehicle is manufactured - remain unclear, particularly in the case of Chinese-manufactured EVs due to China's reputation for pollution and product safety concerns. This study employsa qualitative framework based on the Unified Theory of Acceptance and Use of Technology 2(UTAUT-2) to assess the acceptance of Chinese electric vehicles, and the influence of the Country-of-Origin Effect (COE) on a sample of Portuguese residents. Data was gathered through semi-structured interviews and analyzed using qualitative methods. The study's results shed light on the significance of the country of origin in shaping consumers' behavioral intentions to purchase Chinese electric vehicles, indicating a positive influence. This suggests that country of origin is a crucial factor when considering an individual's intention to adopt electric vehicles. Additionally, the research highlights the importance of various other factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, price value, hedonic motivation, and habit in shaping consumers' attitudes and intentions. Our results underscore the complexity of consumer behavior toward electric vehicles, suggesting the need for a multifaceted approach to understanding andpromoting EV adoption. Research is needed to examine the COE in different cultural and geographic contexts to develop effective strategies to enhance the global adoption of electric vehicles, particularly from countries with varying environmental reputations

The UTAUT-2 offers the most comprehensive assessment of individual acceptance and use of technology to date. In particular, the theoretical additions of “hedonic motivation”, “price value”, and “habit”, made the model suitable for studying technology in a consumer context. However, a review of the literature revealed that the construct of habit has been dropped from a large number of studies. There are several reasons for this, including that the technologies examined were relatively new for the respondents to form a routine behavior. Therefore, this study aims to explore whether the construct can be used as a key predictor of future intention to use an innovation rather than an acquired practice among technology users. For this purpose, a conceptual model based on the theoretical additions to the UTAUT-2 is proposed and analyzed with structural equation modeling (SmartPLS). Our results showed significant relationships between the predictors and the behavioral intention to use battery electric vehicles (BEV) technology, and, in particular, depicted the construct of habit as the strongest factor in the decision to adopt the technology. In light of our findings, the construct of habit (HT) should be used in research together with the other UTAUT-2 predictors to assess individuals’ perceptions of possible future habitual behaviors.

Digital inclusion in Macao is in the very beginning stage, and disability inclusion practice on social media in producing and promoting accessible social media content and needs is hard to find. This study aims to analyse the factors that influence communication staff's practice of disability inclusion on social media by using the combined employee behaviour and communication process model to provide suggestions to management who wants to promote disability inclusion practice on social media.The Service Centre for the Deaf of the Macau Deaf Association (MDA) was selected as the object for this case study. The online social media used for MDA's communication was analysed, and semi-structured in-depth interviews with members of the Association were conducted. The research findings showed that, except for the reward structure, factors examined from the work environment in terms of organisation, supervision and co-workers; communication staff themselves; outcomes of accessible social media communication; audience and feedbacks show relations with disability inclusion practices on social media. The delivery of inclusive culture, the influential power of disability stakeholders and the positioning of social media platforms are the key influencing factors.The interesting part of this study is that people without disabilities seem to be excluded from the disability inclusion practice carried out on MDA's Facebook. Their social media content is highly accessible to deaf and hard-of-hearing audiences yet seems not to involve the general public. The study object is a good model for producing accessible content, yet the optimisation of promoting social media accessibility needs further exploration.

Small and medium-sized enterprises (SMEs) can benefit significantly from open innovation by gaining access to a broader range of resources and expertise using absorptive capacitive, and increasing their visibility and reputation. Nevertheless, multiple barriers impact their capacity to absorb new technologies or adapt to develop them. This paper aims to perform an analysis of relevant topics and trends in Open Innovation (OI) and Absorptive Capacity (AC) in SMEs based on a bibliometric review identifying relevant authors and countries, and highlighting significant research themes and trends. The defined string query is submitted to the Web of Science database, and the bibliometric analysis using VOSviewer software. The results indicate that the number of scientific publications has consistently increased during the past decade, indicating a growing interest of the scientific community, reflecting the industry interest and possibly adoption of OI, considering Absorptive. This bibliometric analysis can provide insights on the most relevant regions the research areas are under intensive development.

A growing number of people are becoming aware of the environmental footprint that our actions have on the environment. Research indicates that a key factor leading to the adoption of an electric vehicle is consumers’ high environmental concern. Indeed, the environmental concern (EC) construct is commonly associated with the purchase of sustainable and eco-friendly products in literature. Our study challenges this assumption. We examined how the environmental factor influenced the behavioral intention of Macau residents to adopt battery-electric vehicle (BEV) technology. For this purpose, we conducted a study based on the UTAUT-2 framework and used structural equation modeling (SmartPLS) to analyze the data. As a result, the choice of vehicles did not depend on the consumers’ level of concern. It appeared that consumers strongly perceived the benefits of a cleaner environment, however, when it comes to technology, environmental benefits are nice to have, rather than the primary incentive to purchase BEVs. Researchers should consider the role of environmental concern as a background factor in technology acceptance models, rather than a direct predictor of behavior. It is also recommended that marketers correctly consider this element when developing their product communications strategies, to appeal to the desired segment of customers.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.