Bibliographic record
Abstract
Dr. Jian Ming Luo is an Assistant Professor in the Faculty of International Tourism and Management at City University of Macau, China. His research focuses on economic, social, environmental, and cultural impacts of tourism, corporate social responsibility, entertainment, and consumer behavior. Dr. Luo received a Doctor of Hotel and Tourism Management degree from the Hong Kong Polytechnic University in 2014. His doctoral dissertation “Hotel development under urbanization in China” employed four different estimation methods: pooled OLS, Feasible Generalized Least Squares, Random Effect model, and Fixed Effect model to study urbanization effects at provincial, regional, and city levels. A conceptual model was developed to describe the relationship between urbanization and hotel development. This research provided implications to the hospitality and tourism industry. Dr. Luo received his undergraduate degree with a major in Computer Science from Shantou University, China, his Master of Science degree with a major in Hotel and Tourism Management from the Hong Kong Polytechnic University, China, and a Master of Business Administration degree from Murdoch, Australia. He had extensive international and multinational tourism working experience in China, Hong Kong, Macau, and Canada. Apart from his involvement in the tourism industry, he also has wide-ranging research and teaching experience. At the City University of Macau, Dr. Luo has taught undergraduate modules in Gaming Business Management, Travel Agent Operation and Management, Service Marketing, International Hospitality Management, and e-Commerce. Dr. Luo has taught post-graduate modules in Service Management (Master Level), Environmental Management (Master Level), and Case Study in Tourism Business Administration (Doctoral Level). Dr. Luo supervises both Masters and PhD students conducting research related to hotel and tourism sustainability. Dr. Luo has also been the Doctoral Program Coordinator at the City University of Macau. He has been responsible for the daily operation of the Ph.D. program activities and the research seminars. Dr. Luo has published many journal articles, book chapters and conference papers since starting his academic career. His recent publications have appeared in prestigious international academic journals, such as the International Journal of Hospitality Management, Current Issues in Tourism, Journal of Destination Marketing & Management, Journal of Travel & Tourism Marketing, Sustainability, Journal of Hospitality and Tourism Management, Journal of Quality Assurance in Hospitality and Tourism, Cogent Business & Management, etc. He has also published books, including “Entertainment Tourism” by Routledge, “The Challenges and Issues of Tourism Development in China”, “Corporate Social Responsibility and Responsible Gambling in Gaming Destination”, and “Urbanization and Tourism Development in China” by Nova Science Publishers. Dr. Luo has also engaged in community research activities. He has conducted several research projects in Hong Kong and Macau. He was the principle investigator for the project “Development of Scale to Measure Entertainment Tourism Experience in Macau” and “Developing the Macau Corporate Social Responsibility Index for Gaming Industry”. Both projects were supported by the Macau Foundation. Dr. Luo is extremely proud to be the Associated Editor of the International Journal of Marketing Studies and an editorial board member of the International Journal of Business and Management and SAGE Open. He has served as an invited reviewer for Tourism Management, Current Issues in Tourism, International Journal of Contemporary Hospitality Management, Journal of Quality Assurance in Hospitality and Tourism, and Asia Pacific Journal of Tourism Research. He has also been a reviewer for many tourism conferences, such as the Grad Conference 2014, ICHRIE2017, and Asia Pacific CHRIE 2018. Dr. Luo believes in the value of community involvement and contribution. In his next phase of research, Dr. Luo will continue to develop entertainment tourism models. In particular, he is interested in understanding the needs and experiences of customers in relation to entertainment products. Future research will also investigate the competition between entertainment destinations. Dr. Luo also collaborates with colleagues using big data from social media, such as Flickr, to examine tourist behaviors. In his times away from research, Dr. Luo enjoys traveling with his family, poker, movies, and cooking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".