Paul Jen-Hwa Hu

Information Technology Acceptance by Individual Professionals: A Model Comparison Approach

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.

Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories

Abstract The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, 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. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed.

Adoption of telemedicine technology by health care organizations: An exploratory study

Recent advances in information and biomedicine technology have significantly increased the technical feasibility, clinical viability, and economic affordability of telemedicine-enabled service collaboration and delivery. Health care organizations around the world have become increasingly interested in acquiring and implementing telemedicine technology to improve or extend existing patient care and services. The ultimate success of telemedicine in an adopting organization requires adequate attention to both technological and managerial issues. This study examined organizational technology adoption, an essential management issue facing many health care organizations interested in or currently evaluating telemedicine. On the basis of a framework proposed by Tornatzky and Fleischer [1], we developed a research model for targeted technology adoption and empirically evaluated it in a survey study that involved most of the public health care organizations in Hong Kong. Results from our exploratory study suggest that the model exhibits reasonable significance and explanatory utility to differentiate between adopting and nonadopting organizations. Specifically, the collective attitude of medical staff and perceived service risks were found to be significant determinants of targeted technology adoption. Several research and management implications that emerged from our study findings are also discussed.

The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services

Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with this fast-growing service, this study investigates the role of flow experience, a sensation that occurs as a result of significant cognitive involvement. The study examines how service system characteristics affect the cognitive states of the flow experience, which determines customer satisfaction. The flow construct and total experience design suggest a structural model that is empirically tested using responses from a large sample of online investors. In support of the model and most of the hypotheses it suggests, the empirical results clarify the important antecedents and consequence of flow experience in online financial services and suggest the viability of using a dual-layer experience construct to investigate customer satisfaction.These findings can help researchers and service providers understand when, where, and how flow experience is formulated in online financial services.

A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations

Lower back pain (LBP) is a common medical problem that deprives many individuals of their normal lifestyles and keeps them from routine activities. Diagnosing LBP is challenging because it requires highly specialized knowledge involving a complex anatomical and physiological structure as well as diverse clinical considerations. Although a handful of studies have proposed or developed systems to support LBP diagnosis and improve knowledge sharing, these systems have limited scope, lack systematic evaluations, and/or ignore diagnoses that consist of multiple parts (i.e., decision outcomes), each of which corresponds to a particular medical condition, disease, or abnormality. In this study, we design, implement, and evaluate a Web-based decision support system that employs an intuitive and easy-to-use framework to assess the patients information and recommend a diagnosis consisting of one or multiple parts. Our system design addresses the challenging characteristics of a LBP diagnosis and uses verbal probability estimation to represent and reason about the associated uncertainty. Our evaluations are systematic, including knowledge base verification, system validation using a modified Turing test, and clinical efficacy assessment involving 5 clinicians and 180 real-world cases collected from geographically dispersed clinics. Our evaluation design is more thorough than those used by most previous studies, and the proposed system is relatively ready for clinical deployment. Therefore, this study both contributes to decision support systems research and has advanced clinical support for LBP diagnosis. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.

Predicting Adoption Probabilities in Social Networks

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns, yet predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally weighted expectation-maximization method for Naive Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power and that confounding factors are critical to adoption probability predictions.

Evaluating telemedicine systems success: a revised model

Broadly, telemedicine refers to the use of information and telecommunication technologies to distribute information and/or expertise necessary for healthcare service provision, collaboration and/or delivery among geographically separated participants, including physicians and patients. Evaluation of telemedicine systems success or effectiveness is a fundamental challenge to healthcare organizations. A review of previous telemedicine research suggests a focused evaluation approach, thus offering limited discussion of comprehensive views of systems success or systematic and practical guidance to its evaluations. Based on a prevalent information systems success model, this paper proposes and describes a revised model for evaluating telemedicine systems success in clinical and organizational settings. Plausible applications of the proposed model are illustrated using teleradiology as an example. In addition, several future research directions that extend the reported study are also highlighted.

Toward an analytical approach for effective Web site design: A framework for modeling, evaluation and enhancement

Effective website design is critical to the success of electronic commerce and digital government. We consider website designs as structural problems and propose a conceptual framework for classifying different design scenarios into generic categories. We describe each generic design category using objective and constraints, and map it to a specific graph model analyzable or solvable analytically. By modeling website designs using well-defined structures and established analytical methods, our framework allows systematic and quantitative design evaluations and improvements. The proposed framework can complement existing practices by enhancing the design process and quality with increasing ease of analysis, implementation and continuous improvement.

Technology implementation for telemedicine programs

Lessons learned in Hong Kong highlight important issues and common pitfalls in telemedicine technology implementation.

Knowledge mapping for rapidly evolving domains: A design science approach

Knowledge mapping can provide comprehensive depictions of rapidly evolving scientific domains. Taking the design science approach, we developed a Web-based knowledge mapping system (i.e., Nano Mapper) that provides interactive search and analysis on various scientific document sources in nanotechnology. We conducted multiple studies to evaluate Nano Mappers search and analysis functionality respectively. The search functionality appears more effective than that of the benchmark systems. Subjects exhibit favorable satisfaction with the analysis functionality. Our study addresses several gaps in knowledge mapping for nanotechnology and illustrates desirability of using the design science approach to design, implement, and evaluate an advanced information system.