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This research uses social identity theory to propose that environmental social responsibility perceptions influence green commitment, and then influence pro-environmental behaviors, which are moderated by institutional pressure. Data were collected from 100 employees of technology firms in Taiwan, and the results support all hypotheses. This research chose technology firms as empirical data because Taiwan's technological level is known to the world, which can reduce sampling errors caused by the lack of environmental knowledge. Finally, this research not only advances the literature on sustainability issues in organizational management but also provides a paradigm to help firms implement pro-environmental behaviors to achieve competitive advantage and sustainable development goals. [ABSTRACT FROM AUTHOR]

This study investigates whether there is a "China-concept factor", a common variation of stock returns, for firms that are listed in Taiwan stock markets and have real investments in China. We employ a methodology similar to that used by Lamont et al. (2001) in examining whether there is a financial-constraints factor. Listed firms in Taiwan stock markets for the period 1990–2004 are used to form portfolios of firms based on observable characteristics related to their real investments in China. We find that firms investing heavily in China have stock returns moving together over time, which suggests that firms investing in China are subject to common shocks. Firms investing heavily in China are found to exhibit higher average stock returns. There exists a China-concept factor for firms listed in Taiwan stock market and have real investments in China. [ABSTRACT FROM AUTHOR]

Correct registration in the hospital can avoid wasting medical resources and shorten the time of diagnoses. The purpose of this research is to build a hospital registration system to help patients register in the hospital. This system can assist patients, who are not familiar with medical topics, to determine which department they should register by the spontaneous descriptions. Patients' spontaneous descriptions of syndromes were transcribed into text formation. These descriptive texts were analyzed by the keywords punctuation process and syndromes's descriptions analysis process to get the important features, including the syndrome words, degree words, affected region of body, frequency, time and place. Then, an information retrieval based department making process was employed to calculate the relation score between each department and these important features. Finally, the system suggested the most possible departments they should register to the patients. In the experiment, 50 descriptions collected from 50 patients are as the input to the system. The preliminary result shows the top one correct rate is 88%. The proposed system also shows the higher performance than the baseline system.