Wai-Man Wong

Bio: Wai-Man Wong is an academic researcher from Open University of Hong Kong. The author has contributed to research in topics: Medicine & Technology acceptance model. The author has an hindex of 2, co-authored 2 publications receiving 1418 citations.

Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics

Abstract: The explosion in Internet usage and huge government funding initiatives in digital libraries have drawn attention to research on digital libraries. Whereas the traditional focus of digital library research has been on the technological development, there is now a call for user-focused research. Although millions of dollars have been spent on building 'usable' systems, research on digital libraries has shown that potential users may not use the systems in spite of their availability. There is a need for research to identify the factors that determine users’ adoption of digital libraries. Using the technology acceptance model (TAM) as a theoretical framework, this study investigates the effect of a set of individual differences (computer self-efficacy and knowledge of search domain) and system characteristics (relevance, terminology, and screen design) on intention to use digital libraries. Based on a sample of 585 users of a university’s award-winning digital library, the results strongly support the utilization of TAM in predicting users’ intention to adopt digital libraries, and demonstrate the effects of critical external variables on behavior intention through perceived ease of use and perceived usefulness. All of the individual differences and system characteristics have significant effects on perceived ease of use of digital libraries. In addition, relevance has the strongest effect on perceived usefulness of digital libraries.

Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics

Abstract: The explosion in Internet usage and huge government funding initiatives in digital libraries have drawn attention to research on digital libraries. Whereas the traditional focus of digital library research has been on the technological development, there is now a call for user-focused research. Although millions of dollars have been spent on building “usable” systems, research on digital libraries has shown that potential users may not use the systems in spite of their availability. There is a need for research to identify the factors that determine users' adoption of digital libraries. Using the technology acceptance model (TAM) as a theoretical framework, this study investigates the effect of a set of individual differences (computer self-efficacy and knowledge of search domain) and system characteristics (relevance, terminology, and screen design) on intention to use digital libraries. Based on a sample of 585 users of a university's award-winning digital library, the results strongly support the utilization of TAM in predicting users' intention to adopt digital libraries, and demonstrate the effects of critical external variables on behavior intention through perceived ease of use and perceived usefulness. All of the individual differences and system characteristics have significant effects on perceived ease of use of digital libraries. In addition, relevance has the strongest effect on perceived usefulness of digital libraries.

Effectiveness of Pharmacological and Nonpharmacological Interventions for Managing the Fatigue–Sleep Disturbance–Depression Symptom Cluster in Breast Cancer Patients Undergoing Chemotherapy

Abstract: Background The symptom cluster of cancer-related fatigue–sleep disturbance–depression (F-S-D) is common among breast cancer (BC) patients undergoing chemotherapy. Given the coexisting nature and synergistic effect of this symptom cluster, interventions for managing it are expected to benefit patient outcomes. Objectives The aims of this study were to examine the effectiveness and identify the essential components of interventions used to manage the F-S-D and quality of life (QOL) in BC patients undergoing chemotherapy. Methods A systematic review was performed in March 2020 through 7 electronic databases. Relevant studies were assessed using the inclusion criteria. The level of evidence was assessed using the Cochrane risk-of-bias tool. The results were summarized and synthesized in narrative forms. Results Sixteen randomized controlled trials were included. Results showed that bright light therapy, acupressure, and psychological nursing interventions were useful in managing F-S-D in BC patients. Exercise and diet counseling alleviated F-D, whereas stress management and a health promotion program alleviated S-D. Bright light therapy, exercise, diet counseling, and psychological nursing interventions enhanced the QOL of these patients. Conclusion Interventions that could alleviate F-S, F-D, S-D, and F-S-D in BC patients and enhance their QOL were identified. Future studies should investigate the effects of evidence-based multimodal interventions that integrate psychological support, education on the management of chemotherapy side effects, and diet counseling and exercise on F-S-D in and reduced QOL of BC patients undergoing chemotherapy. Implications for Practice Nurses act as patient advocates, and the development of evidence-based interventions for managing F-S-D and QOL is significant to nursing practice.

Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach

Abstract: Introduction Prenatal maternal stress (PNMS), including exposure to natural disasters, has been shown to serve as a risk factor for future child psychopathology and suboptimal brain development, particularly among brain regions shown to be sensitive to stress and trauma exposure. However, statistical approaches deployed in most studies are usually constrained by a limited number of variables for the sake of statistical power. Explainable machine learning, on the other hand, enables the study of high data dimension and offers novel insights into the prominent subset of behavioral phenotypes and brain regions most susceptible to PNMS. In the present study, we aimed to identify the most important child neurobehavioral and brain features associated with in utero exposure to Superstorm Sandy (SS). Methods By leveraging an explainable machine learning technique, the Shapley additive explanations method, we tested the marginal feature effect on SS exposures and examined the individual variable effects on disaster exposure. Results Results show that certain brain regions are especially sensitive to in utero exposure to SS. Specifically, in utero SS exposure was associated with larger gray matter volume (GMV) in the right caudate, right hippocampus, and left amygdala and smaller GMV in the right parahippocampal gyrus. Additionally, higher aggression scores at age 5 distinctly correlated with SS exposure. Discussion These findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions.

A Theoretical Integration of User Satisfaction and Technology Acceptance

Abstract: In general, perceptions of information systems (IS) success have been investigated within two primary research streams--the user satisfaction literature and the technology acceptance literature. These two approaches have been developed in parallel and have not been reconciled or integrated. This paper develops an integrated research model that distinguishes beliefs and attitudes about the system (i.e., object-based beliefs and attitudes) from beliefs and attitudes about using the system (i.e., behavioral beliefs and attitudes) to build the theoretical logic that links the user satisfaction and technology acceptance literature. The model is then tested using a sample of 465 users from seven different organizations who completed a survey regarding their use of data warehousing software. The proposed model was supported, providing preliminary evidence that the two perspectives can and should be integrated. The integrated model helps build the bridge from design and implementation decisions to system characteristics (a core strength of the user satisfaction literature) to the prediction of usage (a core strength of the technology acceptance literature).

The Technology Acceptance Model: Past, Present, and Future

Abstract: While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.

Measuring information systems success: models, dimensions, measures, and interrelationships

Abstract: Since DeLone and McLean (D&M) developed their model of IS success, there has been much research on the topic of success as well as extensions and tests of their model. Using the technique of a qualitative literature review, this research reviews 180 papers found in the academic literature for the period 1992–2007 dealing with some aspect of IS success. Using the six dimensions of the D&M model – system quality, information quality, service quality, use, user satisfaction, and net benefits – 90 empirical studies were examined and the results summarized. Measures for the six success constructs are described and 15 pairwise associations between the success constructs are analyzed. This work builds on the prior research related to IS success by summarizing the measures applied to the evaluation of IS success and by examining the relationships that comprise the D&M IS success model in both individual and organizational contexts.