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This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

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.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from medical professionals at six university-/medical university-affiliated hospitals in Taiwan. A total of 600 questionnaires were distributed, and 309 (51.5%) usable questionnaires were analyzed using structural equation modeling in this study.

This study certified that medical professionals’ perceived MR, HSI and HHI in MOOCs positively affected their emotional LE, cognitive LE and social LE elicited by MOOCs, which together explained their LP in MOOCs. The results support all proposed hypotheses and the research model accounts for 84.1% of the variance in medical professionals’ LP in MOOCs.

This study uses the S-O-R model as a theoretical base to construct medical professionals’ LP in MOOCs as a series of the psychological process, which is affected by MR and interaction (i.e. HSI and HHI). Noteworthily, three psychological constructs, emotional LE, cognitive LE and social LE, are adopted to represent medical professionals’ organisms of MOOCs adoption. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies using the S-O-R model and psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context, and this study’s contribution on the application of capturing psychological constructs for completely explaining three types of technological features as external stimuli to medical professionals’ LP in MOOCs is well-documented.

This paper is a study of the current trends and conditions of electronic resources for Chinese studies, based on a recent survey on the Internet of 29 Chinese libraries in North America and eight Chinese libraries in China, Taiwan and Hong Kong. The survey discussed current electronic resources for Chinese studies, with a union list of major Chinese language databases currently used in libraries in Asia and the US. Current views on the use and development of electronic resources for Chinese studies were summarised.

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

As an alternative to hiring financial specialists or investment consultants, robo-advisors offer financially automated investment services. This study aims to investigate how robo-advisors' service attributes, risk attitude and financial self-efficacy influence customers' choice preferences of adopting robo-advisors.

Two hundred fifty-one online surveys were used to collect data, and choice-based conjoint analysis was conducted.

Results show that increasing annual fees negatively impact customers' choice preferences. Promotion, general investment education and additional human assistance have a positive impact. Furthermore, risk-seeking and risk-averse customers require more human assistance than risk-neutral customer and customers with high levels of financial self-efficacy prefer more general investment education and additional human assistance than those with lower levels. In addition, customers in the older age group prefer promotion, general investment education and additional human assistance, while wealthy customers prefer lower annual fees, higher general investment education and more additional human assistance compared to middle-class and low-income groups.

This study contributes to robo-advisor providers to provide appropriate service attributes for each customer group.

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

This study applies D3.js and social network analysis (SNA) to examine the impact of collaboration patterns, research productivity patterns and publication patterns on the Ministry of Education (MOE) evaluation policies across all Management Information Systems (MIS) departments in Taiwan.

This study first retrieved data from the Ministry of Science and Technology of Taiwan (MOST) website from 1982 to 2015, the Journal Citation Reports (JCR) website, the Web of Science (WOS) website and Google Scholar. Then it applied power-law degree distribution, cumulative distribution function, weighted contribution score, exponential weighted moving average and network centrality score to visualize the MIS collaborations and research patterns.

The analysis concluded that most MIS professors focused primarily on SCIE-/SSCI-/TSSCI-/core indexed journals after 2005. Professors from public universities were drawn to collaboration and publishing in high-quality-based journals, while professors from private universities focused more on quantity-based publications. Female professors, by contrast, have a slightly higher single-authorship publication rate in SCIE-/SSCI-/TSSCI-indexed journals than do male professors. Meanwhile, professors in northern Taiwan emphasized quantity-based journal publications, while a focus on quality was more typical in the south. Furthermore, National Cheng Kung University has the most single-authorship or intrauniversity publications in SCIE-/SSCI-/TSSCI-/core journals, and National Sun Yat-Sen University published more SSCI-indexed articles than SCIE-indexed articles. All of these findings show that there is an explicit relation between MOE evaluation policies and MIS faculty members' collaboration/publication strategies.

The above findings explain how MOE evaluation policies affected MIS faculty members' collaboration and publication strategies in Taiwan, and the authors hope that such findings can constitute a resource for understanding and characterizing networking with MIS departments in Taiwan.