Browsing by Author "Parleda Sampaothong"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A Study of government owned airport’s service quality: The case of Hua Hin Airport
    (โรงเรียนการท่องเที่ยวและการบริการ มหาวิทยาลัยสวนดุสิต, 2019-06-30) Parleda Sampaothong; Neeyakarn Limaroon; Kwanrat Jansirinara
    The purposes of this study was to study the satisfaction level of passengers towards the service quality of Hua Hin Airport by using SERVQUAL instrument to analyse the GAP of passenger expectation and perception. The researchers aim to use the outcome of this study for further preparation for the readiness of Hua Hin Airport in the direction of service quality. This study was quantitative research with survey by distributing “Satisfaction Survey” Questionnaire to 180-sample population. The questionnaire focuses on the analysis of SERVQUAL with 5 dimensions of Tangibility, Reliability, Responsiveness, Assurance and Empathy. The results of study were as follows; (1) There were no significant differences between passenger expectation and perception toward Hua Hun Airport service quality. (2) Tangibility is the most sensitive factor toward passenger expectation. (3) Empathy is the number one satisfaction that passengers considered on Hua Hin Airport service quality. (4) Shopping & dinning area, Ground transportation options and Flight Information screens around terminal areas were indicated as the most urgent issues for improvement from the Gap Analysis. (5) There were some parts of SERVQUAL and Gap Analysis demonstrated that Hua Hin Airport performance obtained perception over expectation. Those areas were on some parts of Tangibility, Responsiveness, Assurance and all part of Empathy
  • Thumbnail Image
    Item
    Estimating a Regional Airport Air Passenger Demand Using an Artificial Neural Network Approach: The Case of Huahin Airport, Thailand
    (LAMBERT Academic Pubishing, 2022-03-20) Panarat Srisaeng; Glenn Baxter; Parleda Sampaothong
    Abstract: Artificial neural networks (ANNs) are a promising modelling approach for predicting an airport’s air passenger demand. The study proposed and empirically tested an artificial neural network model to predict the annual passenger demand for Huahin Airport, a regional and tourist focused airport located in Thailand. The ANN input variables included Thailand’s population size, Thailand’s real GDP, world jet fuel prices, Thailand total passengers carried, Thailand’s tourist numbers and Thailand’s unemployment rates. The data were trained using the Levenberg-Marquandt back-propagation algorithm. The ANN comprises eight neurons in the hidden layer and one neuron in the output layer. 80 per cent of the data was used in the training phase with the remaining data divided into validation (10 per cent) and testing (10 per cent) phases. The proposed ANN provided very accurate prediction values. The coefficient of determination R value of model was around 0.995, and the mean absolute percentage error (MAPE) of the final ANN model was 13.27%. The study found that the four key determinants of Huahin Airport annual air passenger demand were Thailand population size, the commencement of AirAsia services at Huahin Airport, Thailand’s tourist numbers, and Thailand’s real GDP.
  • Thumbnail Image
    Item
    Self-Drive Tourism: Unlocking the Potential of the Thailand Riviera
    (2024-11-25) Parleda Sampaothong; Narin Sungraksa; Kwanrat Jansirinara; Thapana Tangjui; Panarat Srisaeng; Neeyakarn Limaroon
    The Thailand Riviera is a key element in the government's strategic plan to boost the nation's economy through targeted tourism development initiatives. Despite its natural beauty and numerous attractions, awareness of the region remains limited, suggesting the need for enhanced promotional efforts. This study aims to develop a proactive marketing and public relations model to promote tourism in the Thailand Riviera, with a focus on self-drive tourism as a case study. A mixedmethods approach was employed, including a survey of 400 tourists to examine their behaviors and perceptions of the Thailand Riviera, as well as in-depth interviews with 24 key informants, including tourists,government officials, and tourism professionals. The EDFR research method was applied to establish a proactive marketing and public relations model, incorporating the insights of 17 experts. A qualitative approach was used to test and verify the proposed self-drive tourism model. The findings suggest that the marketing and tourism promotion strategy for the Thailand Riviera should emphasize self-drive tourism and highlight a distinctive regional identity in alignment with provincial tourism policies under the Thailand Riviera brand. To build brand awareness, mascot marketing should be implemented, positioning the mascot as a tourism ambassador. The proposed self-drive tourism model includes the Thailand Riviera Self-Drive Salt Route in Phetchaburi, connected to the Thailand Riviera Self-Drive Coastal Route in Prachuap Khiri Khan, with community-based tourism activities along the scenic routes. This form of tourism has the potential to reach remote areas, promoting income distribution within local communities and contributing to national economic growth. Overall, this study provides valuable insights into the potential of self-drive tourism as a key driver for promoting tourism in the Thailand Riviera.