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Social Media Influencer (SMI) marketing represents a contemporary addition to the arsenal of digital advertising tools. Digital Content Creators are individuals who regularly share a variety of content, including visuals, audio recordings, and updates, across multiple social media platforms to shape consumers' perceptions of a brand and its products. The focus of this study is to examine how the credibility aspects of social media influencers (expertise, attractiveness, and trustworthiness) influence purchase intention and brand intimacy while also considering the mediating role of consumer engagement. This study used a quantitative, cross-sectional design with convenience sampling targeting social media-active individuals. Data were collected via a questionnaire distributed through email and social media, selecting participants who followed influencers. To gather data, 250 participants were engaged in an online questionnaire distributed via Google Forms. The findings indicate that the credibility dimensions of SMIs, particularly their attractiveness and trustworthiness, positively influence brand intimacy and purchase intention. Furthermore, consumer engagement serves as a critical mediator, connecting the authenticity of social media influencers with purchase intention and brand intimacy. In line with these results, it becomes evident that consumer engagement indirectly influences influencer credibility (trustworthiness and attractiveness), purchase intention, and brand intimacy. Notably, expertise does not exert any discernible impact on either brand intimacy or purchase intention. This study's outcomes provide valuable insights for marketing managers, underscoring the significance of partnering with influencers who possess a high level of trust within their respective marketing niches.

Predicting stock prices is difficult because of their multiple input variables, volatility, and unpredictable nature. To provide a suitable model for forecasting the global stock market, this study conducts an exploratory analysis comparing two models based on Artificial Intelligence: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Networks. The work considers a publicly accessible dataset and uses feature engineering to extract time-series features. Stock price predictions are made using the SVM and LSTM algorithms. For this purpose, Accuracy (ACC) and Root Mean Squared Error (RMSE) are considered accuracy and performance measures. According to the results, LSTM with mean accuracy (ACC) = 0.9061 achieved better accuracy than SVM with mean accuracy (ACC) = 0.881. SVM with mean RMSE = 0.729 achieved better performance and the degree of fit to the data than LSTM with mean RMSE = 427.1. According to the results, the study demonstrates the effectiveness and applicability of machine learning methods for estimating the values of the global stock market and providing valuable models for researchers, analysts, and investors.

<jats:title>Abstract</jats:title> <jats:p>Under the agreement signed with Portugal, which defined the terms of the handover to China, Macau became a Special Administrative Region on 20 December 1999. China undertook to maintain the way of life, the rights and freedoms of the residents and the essence of the laws previously in force, and guarantee the inapplicability of the socialist system. Events in Hong Kong since 2019 and the concerns of the Central Government have led to changes in the national security law and electoral laws which, among other things, have imposed political screening on candidates for the Legislative Assembly and Chief Executive, which can lead to their exclusion without appeal, while criminalising calls for blank votes, null votes, and abstentions. This article answers the question of whether these changes are compatible with the guarantees provided, the Luso-Chinese Joint Declaration and Macau’s Basic Law.</jats:p>

Environmental concerns drive corporate and consumer focus on sustainable packaging. Research explores key factors influencing consumer intent, emphasizing the importance of strategic integration for enhanced purchase intentions and environmental goals. A comprehensive literature review identifies factors such as perceived value, willingness to pay, environmental concern, and attitude toward sustainable packaging. Empirical validation using survey data demonstrates the statistical significance of these factors on consumer purchase intentions, with the willingness to pay to emerge as the most influential determinant. Stakeholders are urged to incorporate these findings into strategies for sustainable packaging, fostering positive environmental impact, and informing academic and managerial discussions.

<jats:p>This study aims to understand how companies address and integrate sustainability challenges in packaging design, as well as the motivations and processes that influence managers’ decisions when adopting sustainable practices. Semi-structured interviews were conducted with managers from five major Portuguese companies to gather qualitative data on the motivations and processes related to sustainable packaging strategies and actions. The list of questions was developed based on the literature review, from which the dimensions to be analyzed were identified. The results indicate that several factors influence companies’ decisions regarding sustainability in packaging. Despite some factors being beyond the control of companies, the interviews reveal that companies possess the necessary knowledge and are committed to adopting more sustainable packaging.</jats:p>

Background: Maternal infections are linked to neurodevelopmental impairments, highlighting the need to investigate SARS-CoV-2-induced immune activation. Objective: This study aimed to evaluate the impact of maternal infection on neurodevelopment and investigate whether cytokine and chemokine profiles predict delays at 24 months. Methods: Conducted in Brazil (January 2021–March 2022), this follow-up study included 18 SARS-CoV-2 positive pregnant women at 35–37 weeks’ gestation, 15 umbilical cord blood samples, and blood samples from 15 children at 6 months and 14 at 24 months. Developmental delay was defined using the Bayley Scales of Infant and Toddler Development, Third Edition, with scores below 90 in cognitive, communication, or motor domains. Results: At 6 months, 33.3% of infants exhibited cognitive delays, 20% communication delays, and 40% motor delays, increasing to 35.71%, 64.29%, and 57.14% at 24 months, respectively. Elevated interferon-gamma and tumor necrosis factor-alpha in cord blood correlated with cognitive delays, while interleukin (IL)-6, IL-8, IL-17, and IL-1β were associated with motor delays. Increased C-X-C motif chemokine ligand 10 and other cytokines were associated with communication delays. Conclusion: Maternal SARS-CoV-2 may impact infant neurodevelopment, as early cytokine elevations correlate with delays, highlighting the importance of early monitoring and interventions to reduce long-term effects. Impact: Prenatal SARS-COV-2 infection in pregnant women is linked to developmental delays in toddlers, with cytokine and chemokine changes associated with neurodevelopmental outcomes at 24 months. This study shows the long-term impact of maternal SARS-COV-2 infection on child development, highlighting inflammatory markers like IFN-γ, TNFα, IL-6, IL-8, IL-17, IL-1β, and CXCL10. Identifying specific cytokines correlating with cognitive, communication, and motor delays suggests potential biomarkers for early intervention. Conducted in Fortaleza, Brazil, the study emphasizes understanding local epidemiological impacts on child development, especially in regions with high infection rates. (Figure presented.)

The ongoing Russia–Ukraine conflict has had significant repercussions for businesses, with many scaling back operations in Russia due to international sanctions. However, some companies continue operating there while making superficial gestures to appear supportive of the oppressed side (a practice known as ‘warwashing’). These actions conflict with profit motives and contribute to consumer skepticism and potential boycotts. This study examines how Portuguese and Danish consumers respond to warwashing, aiming to assess if cultural differences influence reactions. A quantitative survey, including nine questions based on literature and key differences between the two countries, was conducted using a deductive approach. Results were analyzed via JMP statistical software, with paired t-tests applied. Findings reveal a significant difference in reactions between Portuguese and Danish consumers, with Danish consumers showing a heightened response, engaging more frequently in impactful actions. This aligns with Hofstede’s cultural model, which portrays Danes as more open to change and expecting transparency. Boycott theory is also supported, suggesting that Danes are more inclined to boycott products and services, while Portuguese consumers show less faith in the effectiveness of such actions. This cross-country comparison reaffirms Hofstede’s Cultural Value Dimensions, providing insight into real-world cultural differences. Additionally, the study highlights the concept of collective action, where individuals avoid certain products or services as a form of protest, revealing variations in the prevalence of this behaviour across different societies.

Purpose – This work investigates how different strategies for providing cues about the non-human identity of a sales agent influence consumers’ perceptions and purchase-related outcomes, and how a social interaction style shapes these responses. Additionally, the authors explore the role of consumers’speciesism against non-human entities in eliciting unfavourable responses to the disclosure of the agent’s artificial nature. Design/methodology/approach – Three experimental studies were conducted using real chatbot interactions. Study 1 investigates how non-human identity cues impact consumer trust and, subsequently, attitude towards the firm and intention to purchase the product offered. Study 2 tests these effects across different levels of social presence. Study 3 examines consumer responses to different non-human identity disclosure strategies, considering speciesism’s moderating role. Findings – Study 1 proves that disclosing (vs not disclosing) the artificial nature of a sales agent leads to a decline in trust towards the firm, which in turn negatively influences both attitude towards the firm and purchase intention. This finding reveals discrimination against disclosed (vs non-disclosed) artificial sales agents despite identical, flawless performance. However, Study 2 proves that the negative effects vanish when perceived social presence is high. Study 3 underlines that high speciesism leads to a trust decline if non-human identity cues are presented during the interaction but not if presented earlier in the journey before the interaction. Research limitations/implications – The study highlights the negative effects of disclosure on important, firm-related outcomes. These insights advance current literature by showing that disclosing cues about the non-human nature of a sales agent can undermine psychological and behavioural responses–even when the disclosed agent performs just as effectively as its undisclosed counterpart. This result is noteworthy, as most prior research has linked aversive reactions to artificial agents with situations in which algorithms underperform, whereas this study examines agents that function flawlessly. Furthermore, the study reveals that these adverse effects are driven by speciesism–prejudices against non-human entities–offering a novel explanation for consumers’ negative responses. Practical implications – The findings stress that transparency about the artificial nature of sales agents is penalised by customers and comes at a high cost for business-relevant outcomes. However, by transforming an artificial agent into a social actor through subtle design modifications, firms can overcome the unfavourable prejudice against artificial agents. By creating a social appearance, firms can harness the potential of automated sales services–even when disclosure of the agent’s artificial identity is required. As firms may soon be obliged to disclose the artificial identity of their sales agents, the critical question shifts from whether to disclose to how to disclose in order to mitigate negative consequences. Finally, we offer guidance on targeting the right consumers with artificial agents–specifically, those with lower levels of speciesism-related prejudices. Originality/value – This work addresses pressing issues for managers concerned with the implementation of artificial sales agents. Results extend knowledge on speciesism towards digital agents, inform which consumers are particularly prone to respond negatively to such agents, and present levers for designing chat-based social interactions that prevent non-human-related prejudices that could undermine the effectiveness of conversational technologies.

<jats:p xml:lang="en">Over the last few years, brands have increasingly looked to influencer marketing to promote their products. More recently, a new approach has emerged, leveraging artificial intelligence to create virtual influencers. Despite the growing importance of virtual brand ambassadors, academic research on virtual influencers remains fragmented, with limited discussion regarding the ideal characteristics of such agents. This paper addresses this gap in the literature and identifies the conditions necessary for virtual influencers to deliver positive outcomes. Based on existing literature, we identify eight essential attributes that significantly influence the effectiveness of virtual influencers. We also propose an agenda for future research and present a conceptual model to elucidate virtual influencer dynamics. This research enhances our understanding of virtual influencers’ role and impact in contemporary brand promotion, providing valuable insights for scholars and practitioners.</jats:p>

In this paper, we demonstrate that the effects of Dark-Matter, partially in the way proposed by the Modified Newtonian Dynamics (MOND), emerge naturally from the standard theory of General Relativity (without any modification) under a new proposed vacuum solution. There is a family of metric solutions able to reproduce the galaxy rotation curves and the relevant scales where the Dark-Matter effects are supposed to appear in a galaxy. This family of solutions deviate from the standard spherically symmetric solution. The proposed formulation, being relativistic by nature, opens the scenario where we can test the relativistic effects attributed to Dark-Matter and having relevance in cosmology. Among such effects, we have gravitational lensing, effects on the CMB scenario and effects on the formation of galaxies.

本書由36 位不同界別的領袖、專家和學者，分享與人口老齡化相關的精闢觀察與洞見，探索創新永續的生活和經濟模式，包括相關的政策、黃金時代經濟的發展、中國安老服務的新視野、醫康養老新發展、智齡科技的應用、永續人才和社區發展等議題，為業界提供參考，亦為45 歲以上的黃金一代應對未來退休生活提供啟發。 「我們的生活越來越受創新技術的影響，我們的社會也更加重視綠色生活和可持續發展。科技和綠色生活方式必須融入智齡產品和服務中。」 —— 陳茂波 香港特別行政區財政司司長 「我們的共同目標是在老齡化世界中不讓任何人掉隊。」 —— 威廉•史密斯博士 聯合國紐約總部老年事務非政府組織委員會主席 「我們深信人口老化為全球帶來嶄新的機遇。中、老年人是唯一正在不斷增長的人力資源，也是創新產品和服務的龐大消費群體。」 —— 容蔡美碧 黃金時代基金會創會主席

By using both the weak-value formulation as well as the standard probabilistic approach, we analyze Hardy’s experiment introducing a complex and dimensionless parameter (ϵ), which eliminates the assumption of complete annihilation when both the electron and the positron departing from a common origin cross the intersection point P. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when ϵ=1, which is just a singular value. In this paper we demonstrate that this particular value is forbidden inside the scenario proposed by the experiment.

The aviation sector is transforming as electrification emerges as a promising technology. Adopting battery-electric aircraft (BEA) - aircraft that solely rely on rechargeable onboard batteries - is a sustainable alternative to conventional aviation that could change short-haul regional travel habits for business and leisure travellers. This study examines the factors influencing individuals’ public acceptance in China's Greater Bay Area (GBA) context. Given the limited research, a qualitative methodology grounded in the Theory of Planned Behaviour (TPB) examines the underlying factors influencing behavioural intentions (attitudes, subjective norms, perceived behavioural control, and perceived risks). The findings indicate that participants recognise the technology's environmental benefits and potential to enhance regional connectivity; however, they still have concerns about safety, infrastructure, and operations. The respondents’ perceived ease of access, information available, and endorsements from reputable sources also have essential roles in influencing broader acceptance. Addressing these factors with appropriate communication efforts is vital for promoting trust and accelerating technology acceptance and use. Although exploratory, this study offers insights to develop strategies for infrastructure readiness, build public confidence, and endorse sustainable aviation. The research is conducted within the GBA context. Still, the findings also apply to regions with fragmented geographies or developing transportation networks, thus contributing to global environmental sustainability and advancing regional integration goals.

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.

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.

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.