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Paul Jen-Hwa Hu

Other affiliations: University of South Florida
Bio: Paul Jen-Hwa Hu is an academic researcher from University of Utah. The author has contributed to research in topics: Information technology & Information system. The author has an hindex of 37, co-authored 135 publications receiving 9589 citations. Previous affiliations of Paul Jen-Hwa Hu include University of South Florida.


Papers
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Journal ArticleDOI
TL;DR: The results suggested that TAM was able to provide a reasonable depiction of physicians' intention to use telemedicine technology, and suggested both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context.
Abstract: The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation and management issue While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (TAM) in explaining physicians' decisions to accept telemedicine technology in the health-care context The technology, the user group, and the organizational context are all new to IT acceptance/adoption research The study also addressed a pragmatic technology management need resulting from millions of dollars invested by health-care organizations in developing and implementing telemedicine programs in recent years The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong Our results suggested that TAM was able to provide a reasonable depiction of physicians' intention to use telemedicine technology Perceived usefulness was found to be a significant determinant of attitude and intention but perceived ease of use was not The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed

1,924 citations

Journal ArticleDOI
TL;DR: Results of the study highlight several plausible limitations of TAM and TPB in explaining or predicting technology acceptance by individual professionals and suggest that instruments that have been developed and repeatedly tested in previous studies involving end users and business managers in ordinary business settings may not be equally valid in a professional setting.
Abstract: The proliferation of innovative and exciting information technology applications that target individual “professionals” has made the examination or re-examination of existing technology acceptance theories and models in a “professional” setting increasingly important. The current research represents a conceptual replication of several previous model comparison studies. The particular models under investigation are the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and a decomposed TPB model, potentially adequate in the targeted healthcare professional setting. These models are empirically examined and compared, using the responses to a survey on telemedicine technology acceptance collected from more than 400 physicians practicing in public tertiary hospitals in Hong Kong. Results of the study highlight several plausible limitations of TAM and TPB in explaining or predicting technology acceptance by individual professionals. In addition, findings from the study also suggest that instruments that have been developed and repeatedly tested in previous studies involving end users and business managers in ordinary business settings may not be equally valid in a professional setting. Several implications for technology acceptance/adoption research and technology management practices are discussed.

1,386 citations

Journal ArticleDOI
TL;DR: Findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power.

1,063 citations

Journal ArticleDOI
TL;DR: Examination of physicians' decisions to accept telemedicine technology suggests several areas where individual "professionals" might subtly differ in their technology acceptance decision-making, as compared with end users and business managers in ordinary business settings.
Abstract: The recent proliferation of information technology designed to support or enhance an individual professional's task performance has made the investigation of technology acceptance increasingly challenging and significant. This study investigates technology acceptance by individual professionals by examining physicians' decisions to accept telemedicine technology. Synthesized from relevant prior research, a generic research framework was built to provide a necessary foundation upon which a research model for telemedicine technology acceptance by physicians could be developed. The research model was then empirically examined, using data collected from more than 400 physicians practicing in public tertiary hospitals in Hong Kong. Results of the study suggest several areas where individual "professionals" might subtly differ in their technology acceptance decision-making, as compared with end users and business managers in ordinary business settings. Specifically, physicians appeared to be fairly pragmatic, largely anchoring their acceptance decisions in the usefulness of the technology rather than in its ease of use. When making decisions to accept a technology, physicians expressed considerable concerns about the compatibility of the technology with their practices, placed less importance on controlling technology operations, and attached limited weight to peers' opinions about using the technology. Based on results obtained from this study, the initially proposed framework for technology acceptance by individual professionals was revised to a "hierarchical, three-layer" structure with the individual context at the inner core, the implementation context on the outermost layer, and the technological context residing in the middle. Implications for information systems research and telemedicine management practice that have emerged from the study's findings are also discussed.

666 citations

Journal ArticleDOI
TL;DR: The results support the expanded model that provides a rich understanding of the changes in the pre‐usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention.
Abstract: This study presents two extensions to the two-stage expectation-confirmation theory of information systems (IS) continuance. First, we expand the belief set from perceived usefulness in the original IS continuance model to include three additional predictors identified in the unified theory of acceptance and use of technology, namely effort expectancy, social influence and facilitating conditions. Second, we ground the IS continuance model in the context of transactional systems that involve transmission of personal and sensitive information and include trust as a key contextual belief in the model. To test the expanded IS continuance model, we conducted a longitudinal field study of 3159 Hong Kong citizens across two electronic government (e-government) technologies that enable citizens' access to government services. In general, the results support the expanded model that provides a rich understanding of the changes in the pre-usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention. Finally, we discuss the theoretical and practical implications of the expanded model.

632 citations


Cited by
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Journal ArticleDOI
TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
Abstract: 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.

27,798 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).

13,246 citations

01 Jan 2002

9,314 citations

Book
01 Jan 2009

8,216 citations

Journal Article

5,680 citations