Search Results (183)

Digital franchising is increasingly recognized as a technological advancement and a specialized subset of e-commerce, yet its unique entrepreneurial dynamics remain insufficiently explored in the existing literature. Previous studies have primarily focused on platform usability or general e-commerce adoption, often overlooking the motivational, contextual, and capability-based factors that influence individuals’ willingness to engage in digital franchising as either entrepreneurs or consumers. To address this research gap, the present study applies the Motivation-Opportunity-Ability (MOA) framework to examine how personal motivations (e.g., self-expression, financial rewards), perceived platform opportunities (e.g., support, attractiveness), and individual capabilities (e.g., digital literacy, self-efficacy) shape entrepreneurial intention and, in turn, influence consumption adoption intention in digital franchising environments. An online survey was conducted using a non-probability purposive sampling method. The final sample consisted of 491 respondents from Taiwan, all of whom were either entrepreneurs operating digital franchises in the fashion industry or consumers who had purchased fashion products through digital franchising platforms, thereby ensuring contextual relevance to the study’s focus. Data were analyzed using structural equation modeling (SEM). The results indicate that expected external rewards (β = 0.456, p < 0.001) and platform support (β = 0.315, p < 0.001) are the most influential factors in shaping entrepreneurial intention. Furthermore, entrepreneurial intention significantly mediates the relationship between MOA antecedents and consumption adoption intention (β = 0.176, p < 0.001), highlighting its role as a key behavioral mechanism. These findings extend the MOA framework to a new empirical setting and offer practical implications for platform developers, franchisors, and policymakers seeking to promote participation in digital franchising. Future research is encouraged to explore cross-industry comparisons, generational differences, and longitudinal approaches to further enrich the understanding of digital franchising adoption dynamics. Full article

Research on the factors influencing sustainable fashion consumption, particularly green apparel buying behavior (GABB), has grown significantly in the last decade. Understanding how to promote GABB while reducing fast-fashion consumption is of critical importance to researchers, marketers, and policymakers. However, deriving actionable insights requires robust methodologies. Therefore, the goal of this systematic narrative review was to analyze existing literature on GABB, to identify key drivers, and to critically examine the methodological approaches, applied theoretical backgrounds, and utilized geographical scope. Following a structured multi-stage review process—including a database search, screening, and synthesis—n = 15 empirical studies focusing on GABB were included. The identified drivers are categorized into five factors: sociodemographic, personal, behavioral, social influences, and product attributes. Additionally, the review identified methodological shortcomings, including a predominant reliance on self-reported data, a lack of experimental designs and longitudinal studies, and a limited sampling scope across studies. Addressing these limitations in future research is essential to develop practical interventions that encourage sustainable fashion consumption and guide effective marketing and policy strategies. Full article

Missing values in time series data present a significant challenge, often degrading the performance of downstream tasks such as classification and forecasting. Traditional approaches address this issue by first imputing the missing values and then independently solving the predictive tasks. Recent methods have leveraged self-attention models to enhance imputation quality and accelerate inference. These models, however, predict values based on all input observations—including the missing values—thereby potentially compromising the fidelity of the imputed data. In this paper, we propose the Uncertainty-Aware Self-Attention (UASA) model to overcome these limitations. Our approach introduces two novel techniques: (i) A self-attention mechanism with a partially observed diagonal that effectively captures complex non-local dependencies in time series data—a characteristic also observed in fractional-order systems. This approach draws inspiration from fractional calculus, where non-integer-order derivatives better characterize complex dynamical systems with long-memory effects, providing a more comprehensive mathematical framework for handling temporal data. And (ii) uncertainty quantification in data imputation to better inform downstream tasks. The UASA model comprises an upstream component for data imputation and a downstream component for time series prediction, trained jointly in an end-to-end fashion to optimize both imputation accuracy and task-specific objectives simultaneously. For classification tasks, the UASA model demonstrates remarkable performance even under high missing data rates, achieving a ROC-AUC of $99.5\%$, a PR-AUC of $58.5\%$, and an F1-SCORE of $49.3\%$. For forecasting tasks on the AUST-Gait dataset, the UASA model achieves a Mean Squared Error (MSE) of 0.72 under $0\%$ missing data conditions (i.e., complete data input). Under the end-to-end training strategy evaluated across all missing data rates, the model achieves an average MSE of 0.74, showcasing its adaptability and robustness across diverse missing data scenarios. Full article

This work reports the design of a 1x2 photonic digital switch controlled by an electrically induced metasurface, configurated by a rectangular array of points where the refractive index is locally changed through the application of an external bias. The device is simulated using the Beam Propagation Method (BPM) and Finite Difference Time Domain (FDTD) algorithms and the structure under evaluation is an amorphous silicon 1x2 multimode interference (MMI), joined to an arrayed Metal Oxide Semiconductor (MOS) structure Al/SiNx/a-Si:H/ITO to be used in active-matrix pixel fashion to control the output of the switch. MMI couplers, based on self-imaging multimode waveguides, are very compact integrated optical components that can perform many different splitting and recombining functions. The input–output model has been defined using a machine learning approach; a high number of images have been generated through simulations, based on the beam propagation algorithm, obtaining a large dataset for an MMI structure under different activation maps of the MOS pixels. This dataset has been used for training and testing of a machine learning algorithm for the classification of the MMI configuration in terms of binary digital output for a 1x2 switch. Also, a statistical analysis has been produced, targeting the definition of the most incident-activated pixel for each switch operation. An optimal configuration is proposed and applied to demonstrate the operation of a digital cascaded switch. This proof of concept paves the way to a more complex device class, supporting the recent advances in programmable photonic integrated circuits. Full article

This study compares the compression force of cortical screws used in lag fashion with partially threaded cannulated screws and fully threaded headless cannulated screws as fixation methods for humeral condylar fractures in dogs. Cadavers of eleven dogs weighing an average of 10.99 ± 2.51 kg (6.1–14.4 kg) were used. The humeri were subjected to simulated fracture by performing an osteotomy at the trochlea of humerus and classified into three groups: Group 1 applied a 3.0 mm cortical screw applied in a lag fashion, Group 2 applied a 3.0 mm partially threaded cannulated screw, and Group 3 applied a 3.5 mm fully threaded headless cannulated screw. The samples were then placed in a material testing machine, and a compression force was applied vertically to the lateral condyle until failure. There were statistically significant differences in failure load between the groups (p = 0.009). The maximum failure load in Group 3 was significantly higher than in Group 2 (p = 0.014), while there were no statistically significant differences between Group 1 and Group 2) or between Group 1 and Group 3. Partially threaded cannulated screws and fully threaded headless cannulated screws can be alternatives to traditional stabilization methods, offering simpler procedures and additional advantages. Full article

This paper addresses the fifth-century comic coinage gnômotupos, which has not otherwise received scholarly attention. Translators of Aristophanes and Aristotle have typically glossed it into English as “maxim-coining” (with equivalents in other languages). This is a sensible inference from a fourth-century use of γνώμη, “maxim”, and the verb τύπτειν, “stamping”. It also tracks the importance of maxims to Sophistic-era adoption of wisdom-culture and the lore of the Seven Sages. Nevertheless, this typical gloss is incorrect. The term instead emphasizes “idea”, as an insight, technique, or view relevant to some matter. “Stamping” (τύπτειν) an idea means coming up with an apt idea and giving it shape and articulacy. In a characteristic use of the adjective, Aristophanes speaks of gnômotupoi andres (Frogs). These are men who are skilled at “fashioning ideas”, coming up with their content and their form. My claim is that Aristophanes has captured something crucial about the period we call the Sophistic movement or Greek enlightenment. The formulation, circulation, and competition of ideas is a matter of increasing self-consciousness in Athens. So too are those who formulate, circulate, and compete in them: intellectuals or, as gnômotupoi andres might be translated, “ideas-men.” I even contend that those referred to as “sophists”, sophistai, may in many ways be understood as gnômotupoi andres. Full article

Background: One repetition maximum (1RM) is a vital metric for exercise professionals, but various testing protocols exist, and their impacts on the resulting 1RM, barbell kinetics, and subsequent muscular performance testing are not well understood. This study aimed to compare two previously established protocols and a novel self-led method for determining bench press 1RM, 1RM barbell kinetics, and subsequent muscular performance measures. Methods: Twenty-four resistance-trained males (n = 12, 24 ± 6.1 years) and females (n = 12, 22.5 ± 5.5 years) completed three laboratory visits in a randomized crossover fashion. During each visit, a 1RM was established using one of the three protocols followed by a single set to volitional fatigue using 80% of their 1RM. A Sex:Protocol repeated measures ANOVA was used to determine the effects of sex and differences between protocols. Results: No significant differences were observed between the protocols for any measure, except for 1RM peak power (p = 0.036). Post hoc pairwise comparisons failed to identify any differences. Males showed significantly higher 1RM, average, and peak power (ps < 0.001), while females demonstrated a greater average concentric velocity (p = 0.031) at 1RM. Conclusions: These data suggest the protocol used to establish 1RM may have minimal impact on the final 1RM, 1RM barbell kinetics, and subsequent muscular endurance in a laboratory setting. Full article

Digital competencies are critical to success in higher education, and yet these skills are often not explicitly taught to students. We have previously designed and evaluated a university-wide digital skills programme using quantitative methods. In the current study, we aim to better understand the student experience of this programme by conducting semi-structured interviews with those completing the programme. Twelve students were interviewed, and data were thematically analysed to reveal five themes. Firstly, students defined digital competencies in line with tridimensional models but also noted that these competencies were deployed in a goal-directed fashion. Secondly, prior learning was explored, with some students noting they had received training as part of specific qualifications at school but many relying on self and peer-teaching. This fed into the third theme, which related to motivations for training in which students noted the appeal of a comprehensive programme with certification on completion but also a need to address their lack of skills or confidence and maximise their university experience. The fourth theme revealed that the student learning journey through the programme varied considerably. Online learning was perceived as having strengths and weaknesses and whilst the diversity of resources was welcomed, pacing was mixed. Finally, the data demonstrated training was impactful, both in terms of teaching and learning and the wider student experience, allowing students to be more digitally aware and proficient in all areas of digital competency. The findings of the current study indicate that there is value in offering university-wide digital skills training. Full article

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 10⁶ MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 10⁴ MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers. Full article

As the fashion industry faces increasing scrutiny over its environmental impact, collaborative consumption models such as online fashion renting offer potential solutions for fostering sustainability. This study examines the role of environmental, social, and governance (ESG) factors alongside behavioral drivers in shaping consumer intentions toward online fashion renting in China, a model of collaborative consumption that contributes to sustainability by reducing new product demand and promoting the reuse of fashion items. The data was gathered from 403 Chinese customers using a standardized questionnaire. Structural equation modeling (SEM) was used to examine the given study hypotheses. The current study empirically demonstrates that customers’ attitudes, past sustainable behavior, and subjective norms are significant indicators of consumers’ intentions toward online fashion renting. The results further indicate that relative advantage, compatibility, perceived ownership, psychological risk, green self-identity, and experience value are the key drivers of consumers’ attitudes toward online fashion renting. Additionally, the ESG factors were found to have a significant positive impact on consumer attitudes toward online fashion renting, underscoring their importance in driving sustainable consumption patterns. By integrating behavioral and ESG perspectives, the study contributes to the growing discourse on how sustainable consumption patterns can be encouraged within the fashion industry, offering theoretical and managerial implications for fostering sustainable behavior. Directions for future research are also suggested. Full article

Knowledge-aware recommendation systems have shown superior performance by connecting user item interaction graph (UIG) with knowledge graph (KG) and enriching semantic connections collected by the corresponding networks. Among the existing methods, self-supervised learning has attracted the most attention for its significant effects in extracting node self-discrimination auxiliary supervision, which can largely improve the recommending rationality. However, existing methods usually employ a single (either node or edge) perspective for representation learning, over-emphasizing the pair-wise topology structure in the graph, thus overlooking the important semantic information among neighborhood-wise connection, limiting the recommendation performance. To solve the problem, we propose Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec). The hierarchical property of the method is shown in two perspectives. First, to better reveal the knowledge graph semantic relations, we design a Triple-Graph Masked Autoencoder (T-GMAE) to force the network to estimate the masked node features, node connections, and node degrees. Second, to better align the user-item recommendation knowledge with the common knowledge, we conduct contrastive learning in a hybrid way, i.e., both neighborhood-level and edge-level dropout are adopted in a parallel way to allow more comprehensive information distillation. We conduct an in-depth experimental evaluation on three real-world datasets, comparing our proposed HKRec with state-of-the-art baseline models to demonstrate its effectiveness and superiority. Respectively, Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset. Full article

This study considers ways to increase the consumption of sustainable fashion given the significant environmental and social damages associated with the industry. A series of experiments were conducted examining the impacts of choice architecture (nudges) under field conditions in collaboration with one of Israel’s largest shopping centers. This study sought to identify which interventions at the retail level successfully motivate sustainable fashion behavioral change regarding purchases and willingness to pay more, along with agreement with several statements regarding the climate crisis and sustainable fashion. Among the types of nudges examined in this field study were providing information, increasing accessibility to sustainable alternatives and appealing to social identity in relation to demographics and green self-image. This study found that offering alternatives to consumers constituted the most effective way to “nudge” consumers toward more sustainable purchasing behavior. Nonetheless, this does not negate the contribution of providing information and strengthening social norms regarding sustainable fashion. Additionally, in all groups, most participants reported that they did not know how to distinguish between sustainable and non-sustainable fashion, nor did they believe that the clothes they purchased were actually sustainable. The findings emphasize the need for policies that will increase the accessibility of sustainable fashion. Full article

Children’s dress is a constituent element of individual and group identity as well as an indicator of social change. Exploring childhood in three Greek rural communities in Laconia, Kythera, and Crete as well as in their respective diaspora in the United States, this study aims at shedding light on the (re)presentation of children in photographic records through clothing, perceived as the material projection on the self and the group (familial, ethnic, transnational). Drawing from theoretical and methodological approaches of distinct fields, such as history, fashion, photography, material and visual studies, and social anthropology, the study explores dynamic changes and shifting meanings in the way children were perceived and projected or asserted themselves through tangible sources, namely photographs, and clothing. The time period examined spans from the 1900s to the late 1930s without rigidly defining, as shifts witnessed in this time period were occurring in the last years of the 19th century, while the aftermath of the 1930s recession years could be felt beyond the period under study. Full article

This study addresses the impact of urban parks on the self-rated health of office workers under 40, a demographic experiencing significant increases in depressive symptoms during the pandemic. This study in Baise City, China, aims to fill this gap by exploring the relationships between landscape quality, leisure time spent in parks, place attachment, and self-rated health among 411 office workers aged 18 to 40. Structural equation modeling was used to assess these relationships, and multigroup analysis (MGA) in SmartPLS evaluated differences between subgroups. The findings reveal a strong link between urban park landscape quality and leisure time spent in parks, place attachment, and self-rated health. Although the old-fashioned park showed lower overall performance in the study variables compared to the modern park, it had a stronger relationship between landscape quality and place attachment. Leisure time spent in parks did not directly impact self-rated health but was mediated by place attachment. MGA results indicated that while leisure time in parks positively affected self-rated health for single participants, it had a negative effect for married participants. These results underscore the importance of tailoring urban park design and management to accommodate the varying needs of different demographics. This research provides new insights into enhancing office workers’ self-rated health through environmental design and supports the objectives of the Healthy China strategy and Sustainable Development Goal 11. Full article

This study explores the impact of social media platforms on enhancing social responsibility, employing a rigorous research framework based on the Uses and Gratifications Theory. We developed and tested a model to investigate how motivations for using social media influence social responsibility. A quantitative methodology was utilized, analyzing data from a sample of 520 participants using SmartPLS 4. The findings reveal various social media motivations—specifically information seeking, information sharing, self-status, social interaction, entertainment, being fashionable, and relaxation—significantly and positively impact social responsibility. The results underscore the constructive role of social media motivations in fostering social responsibility. They also suggest that further investigations into additional dimensions could provide deeper insights into how digital media might be leveraged to benefit society more broadly and enhance the concept of social responsibility. This study contributes to the expanding discourse on digital media’s potential to effect positive societal change. Full article