Subongkod, Mallika

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Subongkod, Mallika
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mallika.su@kmitl.ac.th

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    Item type:Publication,
    Antecedents of Brand Loyalty of the Airline Business in Thailand
    (2023-07-01)
    Hongsakul, Bharis
    ;
    Subongkod, Mallika
    This research aimed to analyze the structure and relationship and to study the path of effects between customer behavior, customer relationship management, airline service quality, digital marketing, and brand loyalty. The study population comprised individuals aged 20 years and above who utilized airline services. The sample was selected using a simple random sampling method. A questionnaire was used for data collection. The sample consisted of 400 participants. Then analysis the measurement model and structural equation modeling before hypotheses testing by Partial Least Square Structural Equation Modeling (PLS-SEM) with SmartPLS 4. 0 software. The results found that the causal relationship structure was consistent with the empirical data. Customer behavior, customer relationship management, and airline service quality had a positive influence on digital marketing, and digital marketing had a positive influence on brand loyalty, but airline service quality did not have an influence on brand loyalty. The study results revealed that digital marketing was a mechanism driving customer behavior, customer relationship management, and airline service quality, leading to how to build brand loyalty in the airline business in Thailand.
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    STRUCTURAL EQUATION MODELING FOR THE BUSINESS PERFORMANCE OF PRIVATE HOSPITALS IN THAILAND: MANAGEMENT PERSPECTIVE
    (2024-01-01)
    Subongkod, Mallika
    ;
    Hongsakul, Bharis
    This research aimed to study the causal factors affecting the business performance of private hospitals in Thailand from a management perspective. The sample consisted of 411 executives from private hospitals in Thailand, selected through purposive sampling. Data were collected via questionnaire, with SEM being used for analysis. The results indicated that the development of an enterprise resource system, including the competency and capability of entrepreneurs, positively influenced the focus on competitive differentiation. In turn, this focus had a positive effect on customer relationship management. Customer relationship manage-ment positively impacted brand loyalty, which subsequently enhanced business performance. In contrast, the competency and capability of entrepreneurs did not have an effect on business performance. The findings suggest that the growth and sustainability of business performance in private hospitals depends on various supportive factors. These range from policy formulation and the development of technological systems in services to strategies for building customer relationships, all contributing to competitive advantages, service loyalty, and success in achieving set goals.
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    3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring
    (2022-08-01)
    Duangsuwan, Sarun
    ;
    Prapruetdee, Phoowadon
    ;
    Subongkod, Mallika
    ;
    Klubsuwan, Katanyoo
    Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO<inf>2</inf>) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), particulate matter (PM<inf>2.5,10</inf>), and sulfur dioxide (SO<inf>2</inf>), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM<inf>2.5,10</inf> and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.