Search results

This study aims to understand the impact of market power and competition on earnings management, particularly discretionary accruals, in the Chinese and Taiwanese tourism industries. China and Taiwan differ not only in their political and social systems but also in their economic systems. The research aims to provide managers and investors with stock selection strategy in the decision-making process.

Accounting data consisted of 60 publicly traded travel companies in China and Taiwan from 2000 to 2014. Methodology included correlation matrix for the variables, univariate and multivariate regression and competition analysis.

Based on empirical results, the authors found a significant negative correlation between market power and discretionary accruals and market concentration (or lower market competition) and discretionary accruals in both the Chinese or Taiwanese markets. Although the Chinese travel companies enjoyed higher market power and market concentration, they engaged in less earnings manipulation than their Taiwanese counterparts as a result of the Chinese Government regulation.

Based on listed travel companies, generalization of the research results to entire tourism industry is limited. This study compares the travel companies’ practices of smoothing out earnings between China and Taiwan, thus helping managers and investors in making their financing, investment decisions.

This research contributes to the earnings management literature by examining a specific industry of tourism. This paper is original in two ways. The authors linked market power and market competition with earnings management simultaneously and then compared the Chinese and Taiwanese tourism industries in manipulating earnings.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.