Exploring Managers' Skills Affecting Dynamic-Innovative Capabilities and Performance in New Normal Era

The human resources department, as a dynamic mechanism in the hotel business, is a supporter and a manager who manages the corporation to grow by planning, supervising, and assuring the expected performance leads to desirable outcomes. The situation of spread of the COVID-19 virus has resulted in businesses and labor departments having to adapt to survive by upgrading existing knowledge and adding new skills. Therefore, this research aims to describe components and models of necessary skills development for performance affecting dynamic capabilities and performance in a new normal era for human resources managers of five-star hotels in Phuket Province, which are crucial components in an increased corporation’s sustainability and performance in terms of personnel efficiency, assets, funds, and information. This research is quantitative, and research data was collected from a total of 384 human resource managers of five-star hotels. There was a mutual discussion of factor analysis and structural equation results with three human resources managers who have been successful for not less than seven years in their work. The components consisted of systematic consideration through the following causes: necessary skills; professional skills, work skills, and emotional skills, mediator variables; dynamic capabilities, and organizational performance. This research also discussed five guidelines for developing the necessary skills for performance. As various factors have affected the performance in the new normal era, the human resources executives of five-star hotels in Phuket province should apply them and consider them together with their business plans for setting the strategic plan of organizational management, management, administrative, and human resources development. Doi: 10.28991/HIJ-2023-04-01-03 Full Text: PD

Properties of Bayesian student model for INQPRO

Employing a probabilistic student model in a scientific inquiry learning environment often presents two challenges. First, what constitute the appropriate variables for modeling scientific inquiry skills in such a learning environment, considering the fact that it practices exploratory learning approach? Following exploratory learning approach, students are granted the freedom to navigate from one GUI to another. Second, do causal dependencies exist between the identified variables, and if they do, how should they be defined? To tackle the challenges, this research work attempted the Bayesian Networks framework. Leveraging on the framework, two student models were constructed to predict the acquisition of scientific inquiry skills for INQPRO, a scientific inquiry learning environment developed in this research work. The student models can be differentiated by the variables they modeled and the causal dependencies they encoded. An on-field evaluation involving 101 students was performed to assess the most appropriate structure of the INQPRO's student model. To ensure fairness in model comparison, the same Dynamic Bayesian Network (DBN) construction approach was employed. Lastly, this paper highlights the properties of the student model that provide optimal results for modeling scientific inquiry skill acquisition in INQPRO