ORCID Profile
0000-0001-8978-0506
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: AOSIS
Date: 30-12-2016
Abstract: This study explores consumers’ decision-making in terms of intention to switch to foreign brands from domestic brands when purchasing cell phones and sports shoes. A survey of 584 undergraduates in Guangdong, China, shows that domestic brands retain their low quality-conscious, low fashion-and-recreational-conscious and low price-conscious customers and attract low brand-conscious and high choice-confused buyers from foreign brands. Foreign brands typically retain their consumers who are highly conscious of fashion and recreation and keep and draw customers with low choice confusion. High-price-conscious consumers and those who are highly brand-confused will assess foreign and domestic brands when searching for bargains. Regarding managerial implications, local brands should offer products of high quality at low pricesand constantly invest in R& D foreign brands may expand their customer bases and build interactive brand channels all companies can retain brand-confused customers with preferential packages and design their marketing strategies based on decision-making styles of their target consumers.
Publisher: Inderscience Publishers
Date: 2015
Publisher: SAGE Publications
Date: 10-2021
DOI: 10.1177/21582440211061565
Abstract: Improving consumer trust is critical for enhancing purchase intentions. This study assessed the effect of organic labeling awareness and food safety attitudes as mediating variables on the relation between green product awareness and organic food purchase intentions among consumers. The research s le comprised 404 respondents from Shantou, Shenzhen, and Guangzhou, China, collected by systematic random s ling. Structural equation modeling was used to analyze research data. First, green product awareness did not influence organic food purchase intentions. Second, organic labeling awareness and food safety attitudes mediated the relationship between green product awareness and organic food purchase intentions. The findings indicate that organic labeling awareness and food safety attitudes directly influenced consumers’ organic food purchase intentions while they were aware of green products.
Publisher: MDPI AG
Date: 20-10-2022
DOI: 10.3390/S22207998
Abstract: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for ex le, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Publisher: Elsevier BV
Date: 11-2023
Publisher: SC ASERS SRL
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 08-2013
DOI: 10.1057/JMA.2013.15
Publisher: Wiley
Date: 04-10-2023
DOI: 10.1111/IJCS.12875
Abstract: This study reviews the literature on the residence after retirement via Systematic Literature Network Analysis, which consists of the systematic literature review and bibliometric network analysis. There are three research questions, including (1) what the most recent studies are on the residence after retirement, (2) Who the most significant authors, documents, and sources are in residence after retirement, and (3) whether the extant research structure could guide the future agenda for the residence after retirement. Based on the systematic literature review, this study extracts 50 publications from the Scopus database by keyword, document type, source type, publication period, language, and subject area relevant to the residence after retirement. This study analyzes data employing Nvivo, VOSviewer, and SciMAX. The clusters of authors, countries, keywords, documents, and sources linked with relevant studies on the residence after retirement are according to the co‐authorship (1) author, (2) country/territory, (3) keyword, and (4) document. Skitmore, M., Xia, B., Buys, I., Hu, X., Hu, Y., and Chen, Q. who the most influential authors are in residence after retirement. The themes of aging, continuing care retirement community, retirement village, and their associated keywords contribute to future research on the residence after retirement.
Publisher: Walter de Gruyter GmbH
Date: 05-07-2023
DOI: 10.2478/AMNS.2023.2.00011
Abstract: The author proposes a model evaluation based on the GA-SVM model to better understand the evaluation of the company’s relationship and core competitiveness. The system index is reduced by relative gray analysis, the support vector machine model is optimized by a genetic algorithm, and the specific algorithm steps are introduced. Select models from the top 100 enterprises in China’s construction industry in 2020 published by China Construction Industry Market, using relative gray to reduce the measure, and then use the genetic algorithm support vector machine (GA-SVM) model for the training model to achieve the evaluation of the core competencies of the target construction technique business. The experimental results show that the relative error of prediction of the GA-SVM(Genetic algorithm-support vector machine) model for the evaluation of the competitive core of the business is not more than 5, which meets the should be made of accurate predictions. Therefore, choosing the Gaussian radial basis kernel function (RBF) as the kernel function of the GA-SVM intelligent evaluation model is good. It proves that the GA-SVM model can analyze the relationship between the role and the important competition.
Publisher: Inderscience Publishers
Date: 2022
Publisher: Frontiers Media SA
Date: 09-10-2023
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Wong Ming Wong.