University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

The impact of corporate social responsibility (CSR) performance and perceived brand quality on customer-based brand preference

Abstract: Purpose – This paper aims to investigate how corporate social responsibility (CSR) performance (i.e. to the environment, society and stakeholders) and perceived brand quality influence brand preference. The mediating effect of perceived brand quality on the relationship between CSR performance and brand preference is also studied. Design/methodology/approach – In 2011, 243 valid responses to questionnaire surveys were collected from a convenience sample in China. Regression analyses were used to test the hypotheses. Findings – Customers’ brand preference can be enhanced by CSR performance. Performance in each of the three CSR domains (i.e. environment, society and stakeholders) positively impacts brand preference, although to different degrees. The impact of CSR on stakeholders has the strongest influence on Chinese customers’ brand preference among the three CSR domains. Perceived brand quality was found to be a mediator of the relationship between CSR performance and brand preference. Research limitatio...

Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data

Abstract: Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.

Cost X-Efficiency in China's Banking Sector

Abstract: Employing the stochastic frontier approach, this paper investigates cost X-efficiency in China's banking sector over the period 1985-2002. The objective is to assess whether different ownership types and banking reforms affect X-efficiency. A two-stage regression model is estimated to identify the significant variables influencing X-efficiency. The results show that on average, banks are operating 50-60% below the X-efficiency frontier. The jointstock banks are found to be more X-efficient than the state-owned commercial banks, and it appears that X-efficiency was higher during the first phase of bank reform.