Search Results (1,120)

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

A reliable geothermal risk assessment methodology is key to any business decision. To be effective, it must be based on widely accepted principles, be easy to apply, be auditable, and produce consistent results. In this paper, we review the key characteristics of a geothermal project and propose a novel approach derived from risk and uncertainty definitions used in the hydrocarbon industry. According to the proposed methodology, the probability of success is assessed by estimating three different components. The first is the geological probability of success, which is the likelihood that the geological model on which the geothermal project is based is correct and that the key fundamental geological elements are present. The second, the temperature threshold, is defined as the probability that the fluid is above a certain reference value. Such a reference value is the one used to design the development. Such a component, therefore, depends on the end use of the geothermal resource. The third component is the commercial probability of success and estimates the chance of a project being commercially viable using the Reverse Enthalpy Methodology. Geothermal projects do not have a single parameter that represents their monetary value. Therefore, in order to estimate it, it is necessary to make an initial assumption that can be revisited later in an iterative manner. The proposed methodology works with either the capital expenditure of the geothermal facility (power plant or direct thermal use) or the drilling cost as the initial assumption. Varying the other parameter, it estimates the probability of having a net present value (NPV) higher than zero. Full article

The adoption of technological innovations in the maritime industry is of interest to business, policy, and academic communities. In the last group, this interest has translated into the publication of a large but scattered literature, making it difficult to compare findings and identify the dynamics, structures, and patterns that might inform future research. A comprehensive review of past research on this topic might help achieve this. To date, no such review has been carried out, which is an important gap in the literature that this paper contributes to bridging. Two bibliometric review techniques—co-citation analysis of cited references and bibliographic coupling of documents—are applied to 171 journal articles published between 1999 and February 2025 to answer the following questions: 1. What is the knowledge base of this literature? 2. What are the recent research trends (research fronts) in this literature? The analysis reveals that research on “shore power” dominates both the knowledge base and research fronts. Other key research themes centre on “autonomous shipping”, “blockchain”, and “alternative fuels”. Based on these results, implications for future research are drawn. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

This paper introduces the concept of degenerative affordances to explain how social media can unintentionally destabilise family-run influencer businesses. While affordance theory typically highlights the enabling features of technology, the researchers shift the focus to its unintended, risk-laden consequences, particularly within family enterprises where professional and personal identities are deeply entangled. Drawing on platform capitalism, family business research, and intersectional feminist critiques, the researchers develop a theoretical model to examine how social media affordances contribute to role confusion, privacy breaches, and trust erosion. Using a mixed-methods design, the researchers combine narrative interviews (n = 20) with partial least squares structural equation modelling (PLS-SEM) on survey data (n = 320) from family-based influencers. This study’s findings reveal a high explanatory power (R² = 0.934) for how digital platforms mediate entrepreneurial legitimacy through interpersonal trust and role dynamics. Notably, trust emerges as a key mediating mechanism linking social media engagement to perceptions of business legitimacy. This paper advances three core contributions: (1) introducing degenerative affordance as a novel extension of affordance theory; (2) unpacking how digitally mediated role confusion and privacy breaches function as internal threats to legitimacy in family businesses; and (3) problematising the epistemic assumptions embedded in entrepreneurial legitimacy itself. This study’s results call for a rethinking of how digital platforms, family roles, and entrepreneurial identities co-constitute each other under the pressures of visibility, intimacy, and algorithmic governance. The paper concludes with implications for influencer labour regulation, platform accountability, and the ethics of digital family entrepreneurship. Full article

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation. Full article

This study investigates the interconnected roles of financial planning, environmental consciousness, and artificial intelligence (AI) in fostering sustainable entrepreneurship among merchants in Machala, Ecuador. Through structural equation modeling analysis of data from 300 entrepreneurs, we found that financial planning positively influences both sustainable entrepreneurship (β = 0.508, p < 0.001) and environmental consciousness (β = 0.421, p < 0.001). Environmental consciousness demonstrates a significant impact on sustainable business development (β = 0.504, p < 0.001), while AI integration emerges as a powerful enabler of both financial planning (β = 0.345, p < 0.001) and sustainable entrepreneurship (β = 0.664, p < 0.001). The findings reveal how AI technologies can democratize access to sophisticated sustainability planning tools in resource-constrained environments, potentially transforming how emerging market entrepreneurs approach environmental challenges. This research advances our understanding of sustainable entrepreneurship by demonstrating that successful environmental business practices in developing economies require an integrated approach combining financial literacy, ecological awareness, and technological adoption. The results suggest that policy interventions supporting sustainable entrepreneurship should simultaneously address financial capabilities, environmental education, and technological accessibility to maximize their impact on sustainable development. Full article

Social media platforms have become essential tools for businesses aiming to engage audiences through innovative communication, particularly in the food and beverage industry. This study explores the impact of three core digital marketing strategies, namely, social media advertisements, electronic word of mouth, and digital influencers, on customer responsiveness in the Lebanese food and beverage sector. Based on a cross-sectional survey of 400 participants, the findings reveal that social media advertisements significantly and positively influence customer responsiveness (β = 0.227, p < 0.001). Likewise, electronic word of mouth strongly predicts customer responsiveness (β = 0.453, p < 0.001), affirming the power of customer-generated content in shaping brand perceptions. Furthermore, the presence of digital influencers emerged as a significant predictor of consumer reaction (β = 0.236, p < 0.001), suggesting that consumers regard influencers as credible sources when making food-related decisions. Among all predictors, electronic word of mouth demonstrated the strongest effect. Control variables such as gender, age, and social media usage intensity showed no significant effect on customer responsiveness. These findings underscore the strategic value of rich media content and peer influence in shaping consumer behavior in the food and beverage industry. The study offers practical insights for marketers seeking to enhance customer engagement and brand responsiveness in digital spaces. Full article

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article

Digital competences (DC) are vital for improving employability, especially in tourism, where adapting to technology and communicating effectively are key. Proficiency in digital tools and a second language (SL) significantly enhances organizational performance and competitiveness, supporting sustainable development and innovation in dynamic business environments. This study explores the causal link between digital competences and employability dimensions, including second-language skills, in SMEs within the tourism sector in Quindío and Valle del Cauca, Colombia. Using a quantitative approach, data from 114 employees were collected through a semi-structured survey and analyzed via partial least squares structural equation modelling (PLS-SEM) to determine significant relationships. The results reveal that digital competences significantly enhance technological management, occupational experience (OE), anticipation and optimization (AO), and personal flexibility (PF). These skills contribute to sustainable tourism by promoting adaptability, innovation, and inclusive employability. Additionally, second-language proficiency demonstrates strong explanatory power in communication-related aspects. The findings highlight the need for tourism enterprises to prioritize digital upskilling, integrate research and innovation into job functions, strengthen adaptability to organizational changes, and view second-language development as a strategic resource. This study offers valuable insights for designing targeted training strategies aligned with the sector’s dynamic demands and advances the broader discourse on digital literacy in workforce development. Full article

Access to clean and sustainable energy technologies is critical for all nations, particularly developing countries in Africa. Ghana has committed to ambitious greenhouse gas emission reduction targets, aiming for 10% and 20% variable renewable energy integration by 2030 and 2070, respectively. This study explores potential pathways for Ghana to achieve its renewable energy production targets amidst a growing energy demand. An open-source energy modelling tool was used to assess four scenarios accounting for current policies and additional alternatives to pursue energy transition goals. The scenarios include Business as Usual (BAU), Government Target (GT), Renewable Energy (REW), and Net Zero (NZ). The results indicate that total power generation and installed capacity would increase across all scenarios, with natural gas accounting for approximately 60% of total generation under the BAU scenario in 2070. Total electricity generation is projected to grow between 10 and 20 times due to different electrification levels. Greenhouse gas emission reduction is achievable with nuclear energy being critical to support renewables. Alternative pathways based on clean energy production may provide cost savings of around USD 11–14 billion compared to a Business as Usual case. The findings underscore the necessity of robust policies and regulatory frameworks to support this transition, providing insights applicable to other developing countries with similar energy profiles. This study proposes a unique contextualized open-source modelling framework for a data-constrained, lower–middle-income country, offering a replicable approach for similar contexts in Sub-Saharan Africa. Its novelty also extended towards contributing to the knowledge of energy system modelling, with nuclear energy playing a crucial role in meeting future demand and achieving the country’s objectives under the Paris Agreement. Full article

In today’s business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers’ time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful preprocessing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives. Full article