Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

User acceptance of information technology: toward a unified view

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

计量经济分析 = Econometric analysis

Attachment--and loss.

Fast growing interest in telemedicine and increased investment in its enabling technology have made physician technology acceptance a growing concern for development and management of telemedicine. We used Theory of Planned Behavior to investigate technology acceptance by physicians who practiced in public tertiary hospitals in Hong Kong. Our data supported the investigated theory, whose explanatory power of physicians' technology acceptance was moderate, however. Overall, physicians showed positive attitudes towards use of telemedicine technology and exhibited moderate intention to use it, particularly for clinical tasks. Furthermore, several implications for development and management of telemedicine can also be drawn from the findings.

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Investigating physician acceptance of telemedicine technology: a survey study in Hong Kong

Telemedicine development in Taiwan has proliferated considerably since the 1990s, as manifested by its inclusion as a core application area in the National Infrastructure Initiative and other national initiatives. Judged by service volume and sustainability, all existing programs are largely in an experimental stage. The desired transition or advancement to real-world clinical settings requires a host organization addressing important program management challenges. The presented research investigated telemedicine development in Taiwan and analyzed the services provided by representative programs. Based on our analysis results, we discuss important implications for program management that are critical to the ultimate success of telemedicine in a healthcare organization.

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Investigating telemedicine developments in Taiwan: implications for telemedicine program management

Drug-related problems, particularly those that result from sub- or overtherapeutic doses of high-alert medications, have become a growing concern in clinical medicine. In this paper, we use a model-tree-based regression technique (namely, M5) and support vector machine (SVM) for regression to develop learning-based systems for predicting the adequacy of a vancomycin regimen. We empirically evaluate each system's accuracy in predicting patients' peak and trough concentrations in different clinical scenarios characterized by renal functions and regimen types. Our data consist of 1099 clinical cases that were collected from a major tertiary medical center in southern Taiwan. We also examine the use of bagging for enhancing the prediction power of the respective systems and include in our evaluation a salient one-compartment model for performance benchmark purposes. Overall, our evaluation results suggest that both M5 and SVM are significantly more accurate than the benchmark one-compartment model in predicting patients' peak and trough concentrations across all investigated clinical scenarios. M5 appears to benefit considerably from bagging, which has a positive but seemingly smaller effect on SVM. Taken together, our findings indicate supervised learning techniques that are capable of effectively supporting clinicians' use of vancomycin or similar high-alert drugs in their patient care and management.

Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

Infectious disease informatics is a subfield of security informatics that focuses on information analysis and management issues critical to the prevention, detection, and management of naturally occurring or terrorist-engineered infectious disease outbreaks. We have developed a research prototype called BioPortal which provides an integrated environment to support cross-jurisdictional and cross-species infectious disease information sharing, integration, analysis, and reporting. This paper reports a pilot study evaluating BioPortal's usability, user satisfaction, and potential impact on practice.

Evaluating an infectious disease information sharing and analysis system

Effective website design is critical to the success of electronic commerce and digital government. Most prior website design research has taken a computational or cognitive/behavioral approach which may not yield optimal designs demanded by specific requirements. We consider website design as a structural problem which can be examined using analytical approach, such as mathematical optimization. Specifically, we propose a framework which classifies real-world design problems into generic website design categories and maps each resulting category into a graph model which can be analyzable or solved using appropriate analytical techniques. Our framework consists of generic designs and graph models, together with the necessary mapping. We classify the Web site applications and review their features proposed by previous research. We describe a generic website design category using its objective and key constraints that correspond to important design requirements. By modeling website design problems using well-defined structures and rigorous analysis methods, this framework is able to measure website accessibility in a systematic and quantifiable manner, arguably more desirable than existing qualitative ad-hoc practices. Overall, our framework can facilitate the website design process, enhance design quality, and increase ease of analysis, implementation and continuous improvement.

Paul Jen-Hwa Hu

Papers

Investigating physician acceptance of telemedicine technology: a survey study in Hong Kong

Investigating telemedicine developments in Taiwan: implications for telemedicine program management

Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

Evaluating an infectious disease information sharing and analysis system

Towards Analytical Approach to Effective Website Designs: A Framework for Modeling, Evaluation and Enhancement.