Search results

In drawing on the conservation of resources theory and the broaden-and-build theory, the present research investigates the dynamic of social resources (i.e. servant leadership) and personal resources (i.e. psychological empowerment and positive affect) in the determination of the nurses' optimal performance (i.e. deep acting).

The research involved collecting three waves of data on 481 frontline nurses at a large hospital in Taiwan, each a month apart. The hypotheses were tested using PROCESS mediation and moderated mediation regression models.

The results supported the indirect relationship between servant leadership and deep acting through psychological empowerment as well as the moderating effect of positive affect on the mediation model.

The findings shed new light on the interplay of different resources and also provide practical implications for the development of frontline supervisors and nursing staff to be compatible with a serious orientation toward the quality of their professional functioning.

We model international short-term capital flow by identifying technical trading rules in short-term capital markets using Genetic Programming (GP). The simulation results suggest that the international short-term markets was quite efficient during the period of 1997–2002, with most GP generated trading strategies recommending buy-and-hold on one or two assets. The out-of-sample performance of GP trading strategies varies from year to year. However, many of the strategies are able to forecast Taiwan stock market down time and avoid making futile investment. Investigation of Automatically Defined Functions shows that they do not give advantages or disadvantages to the GP results.

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled “Applications of Artificial Intelligence in Economics and Finance” at the “2003 International Conference on Artificial Intelligence” (IC-AI ’03) held at the Monte Carlo Resort, Las Vegas, NV, USA, June 23–26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations.

This study aims to investigate the antecedents and outcomes of excessive use of personal social media at work. The prevalence of personal social media in the work environment can easily lead to excessive use and negative consequences. Understanding the predictive factors and negative consequences of employees' excessive use of personal social media at work is important to develop their appropriate use of social media and improve their job performance.

Based on dual-system theory and the person-environment fit model, this study develops a research model to examine the effect of habit and self-regulation on excessive use of personal social media at work and that of the outcomes of excessive use on employee job performance through strain. This study conducts a questionnaire survey on 408 employees to test the research model and hypotheses empirically.

Results suggest that the imbalance between habit and self-regulation drives excessive personal social media use of employees at work. Furthermore, excessive use of personal social media has a strong impact on employee strain, which can significantly decrease job performance.

First, this study considers excessive use of personal social media at work as a result of two different cognitive systems, that is, an automatic system and a controlled system, thereby extending the dual-system theory to explain excessive use of personal social media in the work context. Second, unlike previous studies that focused on the outcomes or explored the antecedents of excessive social media use at work respectively, the study employs the person-environment fit model and examines the systematic influence of excessive social media use at work from a broad perspective by linking its antecedents and outcomes.

This study examines how professional technicians' teaching styles and students' learning readiness affect cooking skills performance in culinary inheritance.

This study constructed a learning performance model from the situated cognition perspective using a sample of students at universities and vocational colleges on a professional technician course. A total of 4,000 questionnaires were mailed to students, of which 2,018 were returned.

Students regard technical professors as teaching experts and expect them to care for their learning, while professional technicians' knowledge sharing significantly increases students' learning performance. The findings provide insight into professional technicians' teaching styles for academics.

This study focuses on the situated cognition perspective and its correlation with students' learning performance and discusses professional technicians' knowledge sharing as an important influencing factor.

Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

This study aims to compare similarities and differences in library and information science (LIS) and non-LIS undergraduates’ conceptions and perceptions of smart libraries via drawing analysis.

In this study, a total of 156 undergraduate students described their perceptions of smart libraries as drawings and textual descriptions. A modified coding scheme with 8 categories and 51 subcategories was used to analyse the undergraduate students’ drawings.

Most of the undergraduate students’ conceptions of smart libraries still involve self-checkout and learning/reading, focusing on information appliances, technical services, activities and objects. The differences are that the LIS undergraduates’ drawings showed smart libraries with robots, interactive book borrowing with technology tools, intelligent services, location-aware services or mobile applications, whereas non-LIS undergraduates presented smart libraries as readers (learners), other activities and no smart technology services. LIS undergraduates focused on providing patron services with technologies. Non-LIS undergraduates were more likely to draw a complex space with immediate access to books or digital resources, quiet reading and the freedom to engage in library activities.

The results provide a baseline for future research on the topic and provide preliminary evidence of using the methods to discern LIS and non-LIS undergraduates’ conceptions of smart libraries.

The purpose of this paper is to investigate the relationship between leader-member exchange (LMX), job satisfaction (JS), life satisfaction (LS), and psychological capital (PC) for employee relation management.

A total of 319 valid questionnaires were received from employees of financial and electronics manufacturing industries in Taiwan, and they were then analyzed using a structuring equation model with SPSS 12.0 and LISREL 8.8.

Good LMX increases psychological capital. Psychological capital enhances both JS and LS, and psychological capital serves as a full mediator for both JS and LS. A moderating effect of industrial characteristics is found between LMX and LS and between psychological capital and LS.

The findings suggest that managers need to focus on leadership style in order to develop employees’ life and JS through building psychological capital.

This study not only obtained further support for predictions derived from the LMX theory but was also a more extensive analysis of the meaningful relationships between job and LS with a mediating effect of psychological capital on employee relation management.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.