Search results

In recent years, the demand for garments has significantly increased, requiring manufacturers to speed up their production to attract customers. Cut order planning (COP) is one of the most important processes in the apparel manufacturing industry. The appropriate stencil arrangement can reduce costs and fabric waste. The COP problem focuses on determining the size combination for a pattern, which is determined by the length of the cutting table, width, demand order, and height of the cutting equipment.

This study proposes new heuristics: genetic algorithm (GA), symbiotic organism search, and divide-and-search-based Lite heuristic and a One-by-One (ObO) heuristic to address the COP problem. The objective of the COP problem is to determine the optimal combination of stencils to meet demand requirements and minimize the total fabric length.

A comparison between our proposed heuristics and other simulated annealing and GA-based heuristics, and a hybrid approach (conventional algorithm + GA) was conducted to demonstrate the effectiveness and efficiency of the proposed heuristics. The test results show that the ObO heuristic can significantly improve the solution efficiency and find the near optimal solution for extreme demands.

This paper proposes a new heuristic, the One-by-One (ObO) heuristic, to solve the COP problem. The results show that the proposed approaches overcome the long operation time required to determine the fitting arrangement of stencils. In particular, our proposed ObO heuristic can significantly improve the solution efficiency, i.e. finding the near optimal solution for extreme demands within a very short time.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

This study aims to produce composite pigments, including SHS/ZnAl-LDHs, IDS/ZnAl-LDHs and SNND/ZnAl-LDHs, with improved coloration, enhanced photostability and thermostability and biocompatibility.

The chemical structures of the composite pigments were characterized by X-ray diffraction spectroscopy and Fourier transform infrared spectroscopy. Photostability and thermal stability were assessed using ultraviolet-visible spectroscopy and colorimetry. The coverage of the dyes was determined through black-and-white tile testing, and specific RGB values were used to indicate color expressiveness. Finally, a four-color eyeshadow was formulated, and safety tests were conducted via human patch test and cellular assays to confirm the safety and reliability of the samples.

The experimental results demonstrate an enhancement in the photo and thermal stability of the SHS/ZnAl-LDHs, IDS/ZnAl-LDHs and SNND/ZnAl-LDHs composites, along with their superior performance in terms of covering power and color saturation. These composite pigments also exhibit high safety, making them well-suited for cosmetic applications.

The composite pigments based on hydrotalcite can be used in the cosmetic industry without causing any harm to the environment and human health.

The addition of hydrotalcite enables better application of pigments in cosmetics.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

The study aims to understand how cultural route heritage is conceptualized and managed in China by systematically reviewing the research literature on Chinese cultural route heritage (CRH). The study intends to inspire further discussion on the theoretical and practical development of cultural routes since the development is still at a liminal stage in China.

A total of 253 research articles related to Chinese cultural rote heritage from major Chinese and English research databases China National Knowledge Infrastructure (CNKI), Web of Science (WOS) and Scopus have been comprehensively identified and reviewed for the purpose of the study.

Four major themes of research on Chinese CRH have been identified: conceptual evaluation, list of the routes and characteristics of the routes, conservation and utilization. The results revealed that China has very rich resources in CRH, many of which were formed a long time ago, which exist across vast geographic regions and have assumed multiple functions and undergone dynamic reciprocal exchanges among diverse cultures and ethnicities.

The paper summarizes some major obstacles faced by CRH in China and proposes a strategic model to address the need for a more sustainable development of CRH in the Chinese context.

The paper offers a comprehensive overview of CRH in China and discusses practical issues in management and development of heritage great in size, number and complexity.

Effective post-disaster communication is essential for destination marketing organizations to encourage visitors following natural disasters. This research aimed to analyze the impact of two typical types of post-disaster communication messages – solidarity messages and testimonial messages – on post-disaster visiting intention. This research proposes effective communication strategies for post-disaster destination marketing.

For the case of Jiuzhai Valley, China, which experienced market recovery after two years of rebuilding following a 2017 earthquake, this study designed three contextual experiments based on post-disaster communication scenarios on the Internet and social media to test the causal chain between recovery message types and post-disaster visiting intentions. The data were analyzed using analysis of variance, independent samples t-tests, and the bootstrap method.

The results indicate that both solidarity messages and testimonial messages evoked higher visiting intention than the no-message group. However, solidarity messages (vs testimonial messages) were more effective when shared on the destination management organization's official account than when they were shared on an influencer's account, with their effects being mediated by the symbolic – as opposed to the hedonic – value of tourist behavior.

Destination management organizations should actively carry out marketing communication through the Internet and social media for areas that have experienced disasters. Crisis communicators should consider inviting popular social media influencers to visit the destination and share their experiences to enhance market confidence, while also paying more attention to the operation of official social media accounts.

This study sheds light on the use of the Internet and social media as tools for post-disaster marketing. By expanding on post-disaster communication theory, this study fills a research gap regarding the effectiveness of tourism marketing strategies after a crisis.

The purpose of this paper is to investigate the relationship between tax avoidance and earnings persistence in the light of a developing economy, with the main focus on China.

In the analysis, the author conducts a survey on the tax avoidance situation of Chinese listed companies from 2012 to 2020. Then, a multivariate regression analysis is performed in order to analyse the relationship between corporate tax avoidance and earnings persistence.

The findings of the present study show that tax avoidance has a significant positive effect on earnings persistence. However, when the degree of tax avoidance is high, the “risk effect” of tax avoidance exceeds the “value effect”, and tax avoidance will reduce the persistence of earnings. This conclusion is even more prominent when the company is non-state-owned. Further research shows the increase of institutional investors’ shareholding ratio can improve “value effect” of tax avoidance, lessen “risk effect” of tax avoidance, and positively affect the relationship between tax avoidance and earnings persistence.

This study provides evidence for investors to understand the dual effect of tax avoidance on earnings persistence. The results may have implications for regulatory bodies. They can provide a better understanding of the corporate governance role of institutional investors in curbing opportunistic tax avoidance.

This study enriches the research on tax avoidance effects by analysing the impact of tax avoidance on earnings persistence. This study also compensates for the shortcomings of analysing earnings persistence mainly from the perspective of tax differences in the past, and promotes the study of the corporate governance effects of institutional investors under different levels of tax avoidance.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.