This study examines the impact of Environmental, Social, and Governance (ESG) scores on business operating performance, using data from 2,137 publicly listed Taiwanese firms between 2016 and 2023. Operating performance is assessed using both traditional labor productivity ratios and Data Envelopment Analysis (DEA). The empirical findings indicate that the ESG dimensions influence operating performance asymmetrically: the Environmental (E) dimension has a negative effect, while the Governance (G) dimension has a positive effect. In contrast, the Social (S) dimension does not show a consistent impact on operating performance. Moreover, the relationship between ESG and performance is moderated by industry and firm size. Mediation analysis using the Sobel test further reveals that operating performance partially mediates the effect of ESG on both financial and market performance, measured by Return on Assets (ROA) and Tobin's Q, respectively. Social and governance strategies are most effective when they emphasize human capital as a mediating channel.
PurposeThis study mainly investigated the construction of a sustainable culinary creativity competence scale from the perspective of sustainable development goals to assess the professional sustainability of chefs' work.Design/methodology/approachThe questionnaire survey method was used in this study, taking chefs as the subjects in Taiwan. A total of 400 questionnaires were sent out and 374 valid ones were collected, with a recovery rate of 93.5%.FindingsThis study obtained a total of 10 competence dimensions, including sustainable products, product design, culture inheritance, sustainable strategic management, enterprise sustainability management, sustainable management services, culinary aesthetic, creativity inheritance, creative diversity, and professional culinary skill, after validations by exploratory and confirmatory factor analyses. The results for the scale indicated good discriminant validity, convergent validity, and reliability.Practical implicationsIn practice, managers can use the sustainable culinary creativity competence scale to evaluate employees' sustainability performance and design suitable job-rotation and training plans.Originality/valueThis study provides valuable insight by developing a sustainable culinary creativity competence scale tailored for culinary professionals. It helps identify sustainability-related skills, training needs, and development opportunities for chefs across various foodservice sectors. By applying this scale, organizations can enhance employee capabilities, strengthen competitive advantages, and support progress toward sustainability goals. The research also offers policy implications for government support systems that nurture sustainable restaurant development.
PurposeAs competition in the app market intensifies, branded apps must continuously maintain their attractiveness to foster positive brand and app outcomes. The aim of this study is to propose a model of branded app attractiveness, clarify its conceptualisation and construct a scale to measure the construct.Design/methodology/approachData were collected from well-known branded apps in Taiwan. Study 1 included 173 and 203 valid samples, respectively, to explore and validate the factor structure of branded app attractiveness. Study 2 collected 291 valid samples to test whether branded app attractiveness influences app and brand outcomes through branded app engagement.FindingsThe results identified branded app attractiveness as a construct comprising six subdimensions: propinquity attractiveness, reciprocity attractiveness, similarity attractiveness, uniqueness attractiveness, task attractiveness and physical attractiveness. The newly developed scale consisted of 24 items and demonstrated satisfactory reliability, convergent validity, discriminant validity and criterion-related validity. Additionally, the findings showed that branded app attractiveness promoted branded app engagement, which subsequently drove app outcomes such as continuance intention, word-of-mouth intention and willingness to pay, as well as brand outcomes such as brand loyalty and feedback intention. Moreover, brand anthropomorphism positively moderated the relationship between branded app attractiveness and branded app engagement.Originality/valueThis research contributes to the literature by identifying six types of branded app attractiveness and developing scales to measure them. Furthermore, we examine the mediating role of branded app engagement and the moderating role of brand anthropomorphism in the proposed model.
Large-scale systems, including transportation networks, supply-chain structures and energy dispatch systems, typically exhibit high-dimensional graph structures with dynamic topology, heterogeneous connectivity and substantial noise. These characteristics create persistent challenges for Graph Convolutional Networks (GCNs) in terms of computational scalability and sensitivity to distributional shifts. To address these limitations, this study introduces Subsampling-based Graph Convolutional Network (SGCN), a unified framework that combines multi-subgraph parallel training with performance-aware weighting for parameter aggregation. By decomposing full-graph learning into a sequence of size-controlled subgraph training tasks, SGCN removes the dependence on global neighborhood expansion and naturally supports large-scale parallel execution across GPUs, which significantly reduces memory consumption and training time. Moreover, the performance-aware weighting adaptively down-weights subgraphs that contain out-of-distribution(OOD) nodes or structural noise, enabling the model to obtain stable global representations without relying on explicit detection procedures. Experiments on the Cora, Pubmed and Amazon Computers datasets show that SGCN achieves predictive performance comparable to or exceeding that of standard GCNs and widely used sampling-based methods, while providing substantial computational efficiency gains. Under a variety of OOD node corruption levels and structural perturbation settings, SGCN demonstrates consistently stronger robustness, particularly in high-noise environments. Overall, SGCN provides a scalable and robust training framework for deep graph models in operations research. It can be applied to problems such as traffic prediction, routing, network flow optimization, supply chain risk analysis, and power grid monitoring. Future work will focus on better subgraph scheduling, adaptive sampling for dynamic graphs, and extensions to heterogeneous, multilayer, and temporal graphs, so as to further improve its use in large-scale industrial systems.
Bronchiectasis has traditionally been characterized as a neutrophil-driven disease, yet emerging evidence suggested inflammatory heterogeneities. The prognostic significance of elevated serum immunoglobulin E (IgE) in patients without peripheral eosinophilia remains unclear. We conducted a multicenter prospective cohort study between 2017 and 2020 across 16 institutions in Taiwan. Individuals with bronchiectasis but without allergic bronchopulmonary aspergillosis were included. Patients were stratified by baseline absolute eosinophil count (cutoff 300 /uL) and serum IgE level (≤ 100, 100–500, > 500 IU/mL). The primary endpoint was severe exacerbations resulting in hospitalization at one year. Secondary endpoints included all-cause mortality, distribution of sputum pathogen, imaging pattern, and lung function. A total of 579 individuals were enrolled. Nontuberculous mycobacteria (10.7
