Users Acceptance of E-Government System in Sintok, Malaysia: Applying the UTAUT Model

E-government services have become a vital tool to provide citizens with more accessible, accurate and high-quality services and information. E-government system provides an efficient dissemination of information to people and eases people to communicate directly with government services. The utilization of ICT through e-government enhancing efficiency and effectiveness of service delivery in the public sector. The system is regarded as one of the vital elements to be a developed country. The application of e-government indicates the readiness and ability of the nation utilizing technology within public administration periscope. Although the Malaysian government has introduced e-government for many years, its acceptance still not very high. Therefore, this paper studies the key factors of Malaysian citizens’ in Sintok, Kedah, a semi- rural area on approval on e-government services based on the Unified Theory of Acceptance and the Use of Technology (UTAUT Model). The survey data was collected from 83% respondents to measure people understanding and awareness toward e-government system. The results show that there is an excellent understanding among Malaysian towards e-government system

Factors influencing students' acceptance of m-learning: An investigation in higher education

M-learning will play an increasingly significant role in the development of teaching and learning methods for higher education. However, the successful implementation of m-learning in higher education will be based on users' acceptance of this technology. Thus, the purpose of this paper is to study the factors that affect university students' intentions to accept m-learning. Based on the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors that influence the acceptance of m-learning in higher education and to investigate if prior experience of mobile devices affects the acceptance of m-learning. A structural equation model was used to analyse the data collected from 174 participants. The results indicate that performance expectancy, effort expectancy, influence of lecturers, quality of service, and personal innovativeness were all significant factors that affect behavioural intention to use m-learning. Prior experience of mobile devices was also found to moderate the effect of these constructs on behavioural intention. The results of this research extend the UTAUT in the context of m-learning acceptance by adding quality of service and personal innovativeness to the structure of UTAUT and provide practitioners and educators with useful guidelines for designing a successful m-learning system

Migrating agile methods to standardized development practice

Situated process and quality frame-works offer a way to resolve the tensions that arise when introducing agile methods into standardized software development engineering. For these to be successful, however, organizations must grasp the opportunity to reintegrate software development management, theory, and practice

Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations

Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim is to forecast future anomalies by assimilating observed anomalies on an estimate of the model climate. The large variety of experimental setups, models and observational networks adopted worldwide make it difficult to draw firm conclusions on the respective advantages and drawbacks of FFI and AI, or to identify distinctive lines for improvement. The lack of a unified mathematical framework adds an additional difficulty toward the design of adequate initialization strategies that fit the desired forecast horizon, observational network and model at hand. Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and use the notation and concepts of data assimilation theory to highlight their error scaling properties. This analysis suggests better performances using FFI when a good observational network is available and reveals the direct relation of its skill with the observational accuracy. The skill of AI appears, however, mostly related to the model quality and clear increases of skill can only be expected in coincidence with model upgrades. We have compared FFI and AI in experiments in which either the full system or the atmosphere and ocean were independently initialized. In the former case FFI shows better and longer-lasting improvements, with skillful predictions until month 30. In the initialization of single compartments, the best performance is obtained when the stabler component of the model (the ocean) is initialized, but with FFI it is possible to have some predictive skill even when the most unstable compartment (the extratropical atmosphere) is observed. Two advanced formulations, least-square initialization (LSI) and exploring parameter uncertainty (EPU), are introduced. Using LSI the initialization makes use of model statistics to propagate information from observation locations to the entire model domain. Numerical results show that LSI improves the performance of FFI in all the situations when only a portion of the system's state is observed. EPU is an online drift correction method in which the drift caused by the parametric error is estimated using a short-time evolution law and is then removed during the forecast run. Its implementation in conjunction with FFI allows us to improve the prediction skill within the first forecast year. Finally, the application of these results in the context of realistic climate models is discussed

Unified Task Force Report to the Rebuild Iowa Advisory Commission

A unified report from the nine disaster recovery task forces outlining the way to a long-term recovery for Iowa surpassing just a return to normal

Evidence-based implementation practices applied to the intensive treatment of eating disorders: Summary of research and illustration of principles using a case example

Implementation of evidence‐based practices (EBPs) in intensive treatment settings poses a major challenge in the field of psychology. This is particularly true for eating disorder (ED) treatment, where multidisciplinary care is provided to a severe and complex patient population; almost no data exist concerning best practices in these settings. We summarize the research on EBP implementation science organized by existing frameworks and illustrate how these practices may be applied using a case example. We describe the recent successful implementation of EBPs in a community‐based intensive ED treatment network, which recently adapted and implemented transdiagnostic, empirically supported treatment for emotional disorders across its system of residential and day‐hospital programs. The research summary, implementation frameworks, and case example may inform future efforts to implement evidence‐based practice in intensive treatment settings.Published versio

Sentara Healthcare: A Case Study Series on Disruptive Innovation Within Integrated Health Systems

Examines how integration and ties with health plans, physicians, and hospitals helped protect against revenue volatility and enabled experimentation; factors that facilitate integration; innovative practices; lessons learned; and policy implications