What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Management
- Marketing
Paul Jen-Hwa Hu mainly focuses on Knowledge management, Marketing, Telemedicine, Technology acceptance model and Technology management.
Many of his research projects under Knowledge management are closely connected to E-Government with E-Government, tying the diverse disciplines of science together.
His biological study deals with issues like Usability, which deal with fields such as Continuance.
His work in Telemedicine tackles topics such as Exploratory research which are related to areas like Health care, Technical feasibility and Service.
Paul Jen-Hwa Hu combines subjects such as Theory of planned behavior, Expectancy theory, Transactional leadership and Expectation confirmation theory with his study of Technology acceptance model.
His study looks at the relationship between Technology management and fields such as End user, as well as how they intersect with chemical problems.
His most cited work include:
- Examining the technology acceptance model using physician acceptance of telemedicine technology (1433 citations)
- INFORMATION TECHNOLOGY ACCEPTANCE BY INDIVIDUAL PROFESSIONALS: A MODEL COMPARISON APPROACH (1021 citations)
- Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories (808 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Knowledge management, Marketing, Telemedicine, Health care and Usability.
His work carried out in the field of Knowledge management brings together such families of science as The Internet and Technology acceptance model.
Paul Jen-Hwa Hu integrates Technology acceptance model with Explanatory power in his research.
His work on Service quality as part of general Marketing research is often related to Consumer decision process, thus linking different fields of science.
His Health care study incorporates themes from Data mining and Management information systems.
His research integrates issues of Infectious disease and Social media, World Wide Web, Information sharing in his study of Usability.
He most often published in these fields:
- Knowledge management (26.45%)
- Marketing (14.19%)
- Telemedicine (12.26%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (10.97%)
- Deep learning (2.58%)
- Data science (7.10%)
In recent papers he was focusing on the following fields of study:
Paul Jen-Hwa Hu mainly investigates Artificial intelligence, Deep learning, Data science, Machine learning and Multimedia.
In general Artificial intelligence, his work in Artificial neural network, Feature selection, Feature and Naive Bayes classifier is often linked to Weighting linking many areas of study.
The study incorporates disciplines such as Social influence, Customer retention and Viral marketing, Social network in addition to Data science.
His Social influence study combines topics in areas such as Similarity, Data mining and Opinion leadership.
His work in the fields of Machine learning, such as Multi-label classification and Logistic regression, overlaps with other areas such as Patient need and Weighted network.
Paul Jen-Hwa Hu has included themes like Computer user satisfaction and Engineering management in his Multimedia study.
Between 2015 and 2021, his most popular works were:
- Managing Citizens’ Uncertainty in E-Government Services: The Mediating and Moderating Roles of Transparency and Trust (106 citations)
- Children’s Internet Addiction, Family-to-Work Conflict, and Job Outcomes: A Study of Parent–Child Dyads (20 citations)
- Examining the Mediating Roles of Cognitive Load and Performance Outcomes in User Satisfaction with a Website: A Field Quasi-Experiment (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Management
- The Internet
Paul Jen-Hwa Hu mostly deals with Cognitive load, Quasi-experiment, Multimedia, User satisfaction and Computer user satisfaction.
Cognitive load is often connected to Field in his work.
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