Search Results (2,088)

In an era of rapid technological advancement—particularly with the accelerated development of artificial intelligence and digital technologies—entrepreneurship enables firms to dynamically adjust their strategies in response to environmental uncertainty and helps them maintain sustainable competitive advantages over time. As a key concept in entrepreneurship research, entrepreneurial orientation (EO) has long attracted scholarly attention. However, existing studies on EO have primarily focused on its specific outcomes, while insufficient attention has been paid to its antecedents from the perspective of internal threats. Under the threat of performance shortfalls, firms’ strategic choices are influenced not only by resource constraints but also by managerial cognitive biases. Drawing on Behavioral Theory of the Firm, we explore the moderating roles of managerial overconfidence and myopia in the relationship between performance shortfalls and EO. This study aims to uncover the cognitive “black box” behind why some firms are more likely to trigger entrepreneurial behavior in adverse situations. Based on panel data from 2822 A-share listed companies in China spanning the period from 2009 to 2020, and using a fixed-effects regression model, our findings indicate that both historical and social performance shortfalls have significant positive effects on EO. Further analysis reveals that the positive impact of performance shortfalls on EO is attenuated under conditions of heightened managerial overconfidence and myopia. By enriching the boundary conditions of EO from a cognitive perspective, this study provides a theoretical explanation for how firms can engage in entrepreneurial behavior under threat by reducing cognitive biases, thereby offering both theoretical and managerial insights into how firms can maintain sustainable development under crisis conditions. Full article

This study examines the influence of mass media on the career choices of high school students from Brașov County, Romania, with a focus on their underlying motivational factors. Employing a quantitative design, it draws on data from a standardized questionnaire completed by 1314 students from local high schools. Descriptive and inferential statistical methods were used in the analysis. While some students identify mass media as a key source of career guidance, documentaries and career fairs are more frequently cited as trusted sources. Students’ perceptions of mass media are ambivalent: 55.1% see it as manipulative, while 41.7% and 24.7% acknowledge its informative and educational roles. Personal motivation emerges as the most significant influence, with 64.8% guided by individual talents and abilities, compared to a lower influence from family or media role models. Correlational analysis highlights the importance of personal development, creativity, and collaboration in career motivation. This study suggests that mass media indirectly shapes students’ aspirations by reinforcing values like social recognition, mobility, and identity. Finally, it reveals a strong link between career interest and expectations for respectful, stable, and growth-oriented work environments, pointing to a pragmatic orientation toward professional sustainability. Full article

Black gay and bisexual older men face numerous barriers across the life course that can contribute to negative health and well-being as they age. Drawing on strengths-based social determinants discussed in the health literature and literature on intersectionality, justice, and critical consciousness, this study examines qualitative data from seventeen Black gay and bisexual older men about sources and strategies of resilience and thriving amidst intersecting systems of power and oppression that shape health inequities. The findings revealed an evolution of positive support networks across their life courses, including biological family and families of choice such as “houses” and support groups. Early and ongoing negative experiences relating to intersecting positionalities (e.g., race, gender, sexual orientation) also provided sources of strength and resilience. Participants identified three strategies for building resilience and thriving: naming external ignorance, acknowledging common struggles, and reconciling contradictions. These strategies reflected various levels of critical consciousness that helped them navigate complex and intersecting systems of power that they encountered as Black gay men across the life course. Overall, the findings underscore the importance of considering intersecting systems of power and critical consciousness when examining resilience and social determinants of health and contribute new insights on a vastly understudied population. Full article

Background/Objectives: The social importance of health literacy (HL) is widely understood, and its measurement is the subject of various studies. Due to the recent pandemic, several instruments for measuring HL about COVID-19 have been proposed in different countries, including the HLS-COVID-Q22 questionnaire. The diversity of cultures and languages necessitates the cross-cultural adaptation of this instrument. Thus, the present study translates, adapts, and validates the psychometric properties of the HLS-COVID-Q22 questionnaire to provide its cross-cultural adaptation from English to Spanish (Peru). Methods: As part of ensuring that the final questionnaire accommodates the cultural nuances and idiosyncrasies of the target language, the following activities were carried out: (a) a survey of 40 respondents; and (b) a focus group with 10 participants, followed by expert approval. In addition, the validity and reliability of the health instrument have been ascertained through a further pilot test administered to 490 people in the city of Tacna in southern Peru. Results: The resulting questionnaire helps measure HL in Peru, aiding better-informed decision-making for individual health choices. Conclusions: The presence of such a tool is advantageous in case of similar global health emergencies, when the questionnaire can be made readily available to support a promotion of strategies towards better self-care. Moreover, it encourages other Latin American stakeholders to adjust the instrument to their own cultural, language, and socio-economic contexts, thus invigorating the regional and global expansion of the HL study network. Full article

In the attention-driven environment of online video platforms, understanding the factors that influence content selection and channel subscriptions is crucial for creators, marketers, and platform managers. This study investigates how thumbnails, view counts, video length, genre, and the number of advertisements affect user decision-making on YouTube. Grounded in random utility theory and reference-dependent preference theory, this study conducted a choice experiment with 525 respondents and employed a combined model of rank-ordered and binary logit methods to analyze viewing and subscription behaviors. The results indicate a significant preference for thumbnails with subtitles and shorter videos. Notably, we found evidence of reference-dependent effects, whereby a higher-than-expected number of ads decreased viewing probability, while a lower-than-expected number significantly increased subscription probability. This study advances our understanding of the factors that influence user behavior on social media, specifically in terms of viewing and subscribing, and empirically supports prospect theory in the online advertising market. Our findings offer both theoretical and practical insights into optimizing video content and monetization strategies in competitive social media markets. Full article

Having in view the current global disruptive social and economic landscape, sustainability becomes more important than ever. As producers become more concerned about adopting more sustainable practices, customer awareness towards sustainable behavior must be the focus of all stakeholders. Within this context, the SHIFT framework (proposed in 2019) highlights the manner in which consumers’ traits and attitudes influence their propensity towards sustainable consumption. It consists of five factors considered to be relevant to consumer behavior: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Different from previous studies, this research focuses on applying the SHIFT framework to the automotive industry, taking into consideration the contribution of digital technologies to fostering sustainable consumer behavior throughout the entire product lifecycle. Using a qualitative research approach, the most relevant digital technologies in the automotive industry were identified and mapped in relation to the three phases of consumption (choice, usage, and disposal). The research aimed to develop and test an original conceptual framework, starting from the SHIFT. The results of the study highlight the fact that the digital technologies, in their diversity, are integrated in different ways into each of the three phases, facilitating the adoption of sustainable consumption. To achieve sustainability, the two key stakeholders, consumers and producers, should share a common ground on capitalizing the opportunities offered by digital technologies. Full article

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. While most diagnoses occur in outpatient settings, a subset of cases are incidentally identified during emergency department (ED) visits. The clinical characteristics and treatment decisions of these patients, particularly in relation to social background factors such as living situation and access to primary care, remain poorly understood. Methods: We conducted a retrospective study of patients diagnosed with malignancies in the ED of a single institution between April 2018 and December 2021. Patients diagnosed with lung cancer within 60 days of an ED visit were included. Data on demographics, disease status, treatment decisions, and background factors—including whether patients lived alone or had a primary care physician (PCP)—were extracted and analyzed. Results: Among 32,108 patients who visited the ED, 148 were diagnosed with malignancy within 60 days; 23 had lung cancer. Of these, 69.6% had metastatic disease at diagnosis, and 60.9% received active treatment (surgery or chemotherapy). No significant associations were observed between the extent of disease and either living arrangement or PCP status. However, the presence of a PCP was significantly associated with the selection of best supportive care (p = 0.023). No significant difference in treatment decisions was observed based on age (cutoff: 75 years). Conclusions: Although social background factors such as living alone were not significantly associated with cancer stage or treatment choice, the presence of a primary care physician was associated with a higher likelihood of best supportive care being selected. This may indicate that patients with an established PCP have more clearly defined care goals at the end of life. These findings suggest that primary care access may play a role in shaping end-of-life care preferences, highlighting the importance of personalized approaches in acute oncology care. Full article

As veganism becomes more popular, the number of vegan pregnant women and children is steadily increasing. During vegan pregnancy and early childhood, there is a high risk for nutrient deficiencies that may impair child development. External factors, such as healthcare advice, social networks, and social environments, that affect the diet of vegan pregnant women, parents, and their children, as well as their approach towards dietary supplementation, have not yet been investigated. Various sources of information, combined with a lack of expertise, sparse food and nutritional health literacy, and qualitatively heterogeneous information provision by medical experts, unsettle vegan pregnant women and parents and affect their dietary choices and potentially the health of their children. The VedieS study aims to investigate potential connections between external influences and associated impacts on a vegan diet and the intake of dietary supplements (DS) of pregnant women and children. Two surveys are being conducted within the study: one targeting 1000 vegan pregnant women and parents, and another targeting 60 experts in each of five healthcare groups: gynecologists, pediatricians, general practitioners, pharmacists, and dietitians. This study is the first to examine how socio-economic, social, and further informational factors influence dietary practices during vegan pregnancy and childhood. It highlights the need for reliable, expert-led guidance, as current information sources are often inconsistent and may put these vulnerable groups at risk. Full article

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect. Full article

Knowing the superior biochemical defense mechanisms of sessile organisms, it is not hard to believe the cure for any human sickness might be hidden in nature—we “just” have to identify it and make it safely available in the right dose to our organs and cells that are in need. For decades, green tea catechins (GTCs) have been a case in point. Because of their low redox potential and favorable positioning of hydroxyl groups, these flavonoid representatives (namely, catechin—C, epicatechin—EC, epicatechin gallate—ECG, epigallocatechin—EGC, epigallocatechin gallate—EGCG) are among the most potent plant-derived (and not only) antioxidants. The proven anti-inflammatory, neuroprotective, antimicrobial, and anticarcinogenic properties of these phytochemicals further contribute to their favorable pharmacological profile. Doubtlessly, GTCs hold the potential to “cope” with the majority of today‘s socially significant diseases, yet their mass use in clinical practice is still limited. Several factors related to the compounds’ membrane penetrability, chemical stability, and solubility overall determine their low bioavailability. Moreover, the antioxidant-to-pro-oxidant transitioning behavior of GTCs is highly conditional and, to a certain degree, unpredictable. The nanoparticulate delivery systems represent a logical approach to overcoming one or more of these therapeutic challenges. This review particularly focuses on the lipid-based nanotechnologies known to be a leading choice when it comes to drug permeation enhancement and not drug release modification nor drug stabilization solely. It is our goal to present the privileges of encapsulating green tea catechins in either vesicular or particulate lipid carriers with respect to the increasingly popular trends of advanced phytotherapy and functional nutrition. Full article

Online Probability Panels (OPPs) have emerged as essential research infrastructures for social sciences, offering robust tools for longitudinal analysis and evidence-based policy-making. However, the growing role of the Open Science movement demands systematic evaluation of their compliance. This study compares major European OPPs—including LISS, GESIS, the GIP, ELIPSS, and the Swedish and Norwegian Citizen Panels—focusing on their practices of openness, recruitment, sampling, and maintenance. Through a qualitative analysis of public documentation and methodological reports, the study examines how their diverse approaches influence data accessibility, inclusivity, and long-term usability. Our findings highlight substantial variability across panels, reflecting the interplay between national contexts, governance models, technological infrastructures, and methodological choices related to recruitment, sampling, and panel maintenance. Some panels demonstrate stronger alignment with Open Science values—promoting transparency, interoperability, and inclusive engagement—while others operate within more constrained frameworks shaped by institutional or structural limitations. This comparative analysis contributes to the understanding of OPPs as evolving knowledge infrastructures and provides a reference framework for future panel development. In doing so, it offers valuable insights for enhancing the role of OPPs in advancing open and socially engaged research practices. Full article

The influence of social media personalities has grown significantly, especially among youth audiences who spend substantial time on platforms like TikTok. The emergence and popularity of different types of social media influencers accelerated during the COVID-19 pandemic in many countries, including South Africa. In turn, this period also saw a surge in youth audiences following these influencers. This rapid growth of influencer followings among young people is largely driven by specific types of content that resonate with them, thus encouraging continued engagement. However, the benefits that these young followers gain from engaging with various influencers and the factors driving their preferences for specific influencers remain underexplored, particularly within the context of South African students within higher education. Therefore, this study explores the types of social media influencers most followed by university students at a South African University and investigates the key factors that drive their preferences. A structured online questionnaire was distributed, gathering both multiple-choice and open-ended responses from students. The data were analyzed using categorical frequency counts and thematic analysis. The data highlight how students actively turn to influencers as emotional anchors, role models, and sources of practical guidance. Their engagement reflects a deep need for connection, inspiration, and identity formation in a challenging academic and social environment. These patterns show that influencer content is not just entertainment but plays a critical developmental role. Understanding these motivations helps educators, policymakers, and brands to align better with youth values. The significance of these results lies in how influencer content is now coming in to fill the emotional, cultural, and educational gaps left by traditional systems among the young South African university students in this modern era. 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