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The digital divide is a concern, as the inequality of information access might have significant influences on social development and quality of life. The purpose of this paper is to examine the perceived benefit of Digital Opportunity Centers (DOCs) programs on remote area participants from the perspective of computer anxiety and personal information ability.

The Taiwanese Government has built DOCs in remote areas to provide information technology (IT) training and learning programs to citizens residing in these areas. DOC program participants in Taiwan voluntarily completed a self-report questionnaire; the authors received 2,105 completed questionnaires, with a response rate of 84.2 percent. This research used partial least-squares (PLS) to empirical the research model.

Using PLS, the results show that information and communication technology ability influences the perceived benefit of DOC programs; computer anxiety has significantly negative effects on package software use, internet use, and IT usefulness; and internet use and IT usefulness have positive effects on perceived benefits.

IT is continuously advancing, but digital resources are still lacking within remote areas. DOCs provide citizens different types of learning experiences related to economic, social, and educational development. DOC programs provide participants with opportunities to obtain and improve basic IT knowledge and abilities and decreasing the digital divide.

The purpose of this study is to develop and empirically test a model that can predict factors affecting student recycling behavior. The theoretical model was based on motivation, place attachment, environmental concern and interpersonal altruism.

A cross-sectional study was conducted with college students in Taiwan using self-report questionnaires. Of the 800 distributed questionnaires, 523 were completed (response rate of 65.4%) and were analyzed using structural equation modeling. Partial least squares (PLS) were used to test the models and hypotheses.

The results showed that environmental concern, motivation, interpersonal altruism and place attachment have significant positive effects on recycling behavior and motivation and place attachment have significant positive effects on interpersonal altruism. This research contributes to the existing literature by discriminating between two sorts of motivation: intrinsic and extrinsic. Based on these findings, suggestions for future research and practical implications are presented.

Few studies have linked motivation, interpersonal altruism, environmental concern and place attachment to recycling behavior. Therefore, this study aimed to explore these relationships, specifically as they affect college students’ behavior. This paper anticipates that increased knowledge about recycling behavior could be used to support the wider adoption of recycling practices.

The purpose of this paper is to combine task-technology fit, theory of planned behaviour and individual technology optimism, and propose a better hybrid technology behavioural intention model to explain Taiwanese students’ usage behaviour for weblog learning.

A 31-item questionnaire with eight constructs was administered to undergraduate and graduate students enrolled in three universities in Taiwan. A total of 380 voluntary, usable responses were received, and a research model estimated using Smart PLS was used to interpret the structural relation results.

The results of the research model were analysed using a structural equation modelling approach to test six hypotheses and three moderating hypotheses; significant support were found for seven of them. Accordingly, this study considered the level of technology optimism as a moderator to explore whether it impacts perceived behavioural control, attitudes and social influences on behavioural intention related to weblog learning.

This research provides a better understanding of individual and system characteristics, as well as social factors regarding weblog learning system acceptance and intention.

The incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.

Data of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.

The ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.

The study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.