ค้นหาผลงานวิชาการ

This study examined the influence of Shariah-oriented attributes on Indonesian Muslims’ intention to visit Thailand, which is a non-Muslim country. This stimulus–organism–response (SOR) model was used to examine the relationships between Shariah-oriented tangible and intangible attributes (stimulus), perceived halal safety and Muslim trust (organism), and visit intention (response). The data from 387 Indonesian Muslim respondents were analyzed using partial least squares structural equation modelling combined with Importance–Performance Map Analysis (IPMA). The results supported six of seven hypotheses establishing that Shariah-oriented attributes significantly influenced perceived halal safety, Muslim trust, and visit intention. Notably, perceived halal safety showed a significant direct negative effect on visit intention (β = −0.108, p < 0.05); it did not significantly mediate the relationship between Shariah-oriented attributes and visit intention (β = −0.049, p = 0.059). Muslim trust demonstrates a strong positive mediating effect (β = 0.236, p < 0.001). The IPMA results revealed that Shariah-oriented tangible attributes demonstrated both high importance and excellent performance, while intangible attributes showed high importance but moderate performance, indicating a priority area for improvement. These findings highlight that Muslim trust and tangible Shariah-compliant attributes are crucial for attracting Muslim tourists to non-Muslim destinations, providing valuable insights for tourism stakeholders.

Fall detection for elderly care has gained substantial research attention, with Wi-Fi Channel State Information (CSI) emerging as a promising non-intrusive sensing modality that addresses privacy concerns associated with camera-based systems. However, the adoption of deep learning in this domain requires models that balance high accuracy with computational efficiency, enabling deployment on resource-constrained devices. This study presents a comparative analysis of four lightweight Convolutional Neural Network (CNN) architectures-MobileNetV2, MobileNetV3, EfficientNet-B0, and ShuffleNetV2-that has been presented for Wi-Fi CSI-based fall detection. The models were evaluated using classification metrics, including accuracy, loss, precision, recall, and F1-score, as well as efficiency metrics such as model size, parameter count, FLOPs, and inference time. Experimental results showed that the highest classification accuracy of 98.61% was achieved by EfficientNet-B0, along with the lowest loss and best F1-score (0.986), while a near-fastest inference time of 8.53 ms was maintained. This combination of predictive performance and computational efficiency has been demonstrated to highlight strong potential for practical, real-time fall detection applications. Meanwhile, ShuffleNetV2 achieved a comparable accuracy of 98.47% and an $F 1$-score of 0.985, with the fastest inference time of $\mathbf{7. 1 9}$ ms and a small model size of only 5.21 MB. These results indicate that ShuffleNetV2 provides an excellent trade-off between accuracy and computational cost, making it a highly suitable candidate for deployment on real-time edge devices.

Understanding destination image perceptions is critical for tourism destinations seeking to maintain competitive advantage and foster visitor loyalty. While the traditional literature suggests that first-time and repeat visitors differ significantly in their cognitive and affective destination image perceptions due to experiential differences, emerging evidence from destinations with established branding challenges these conventional assumptions. Thailand, as a globally prominent destination with sustained branding initiatives since 1998, provides an ideal context for examining whether visitor experience moderates destination image formation and loyalty outcomes. This study investigates differences in cognitive and affective destination image perceptions and destination loyalty between first-time and repeat international travellers to Thailand, applying the cognitive–affective–behavioural (CAB) model to examine how these constructs influence revisit and recommendation intentions across visitor segments. Data were collected from 392 international tourists visiting three major southern coastal destinations in Thailand (Phuket, Krabi, and Phang-Nga) through face-to-face surveys using purposive sampling. The sample comprised 185 first-time travellers and 207 repeat visitors. Partial least squares structural equation modeling (PLS-SEM) with multigroup analysis was employed to examine structural relationships and test for significant differences between visitor cohorts using parametric, Welch–Satterthwaite, and permutations tests. Contrary to theoretical expectations, multigroup analysis revealed no statistically significant differences between first-time and repeat travellers across all examined pathways (all permutation p-values > 0.05). Both groups demonstrated equivalent perceptions regarding how cognitive image influences affective image, and how these dimensions affect revisit and recommendation intentions. Affective image emerged as the dominant predictor of destination loyalty for both segments, while cognitive image primarily served as an enabler of emotional responses. These findings challenge traditional assumptions about experiential differences between visitor types suggesting that mature destinations with consistent long-term branding may achieve perceptual uniformity that transcends direct experience. Destination marketing organizations should implement unified rather than segmented strategies, prioritizing emotional engagement mechanisms over rational attribute promotion to cultivate destination loyalty across all visitor segments. However, these findings are specific to coastal leisure destination and may not fully generalize to other destination types.

Increasing awareness of sustainability issues, together with ever‐changing technological disruption, has led enterprises, especially in emerging markets, to invest more in digital technologies to enhance their sustainable competitiveness in the market. Relying on 163 firm‐year observations covering the years 2019–2023 from Indonesia, Malaysia, and Thailand, we explore the effects of digital transformation (DT) on the corporate sustainability performance. Our findings support resource‐based view theory , suggesting the adoption of digital technology by firms primarily drives their sustainability performance, particularly in environmental and social domains. To ensure the robustness of our main findings, we perform several robust tests, including the 2SLS IV method, panel fixed‐effect regression, and quantile regression. The practical implications of our findings highlight the importance for both listed companies and regulators in developing corporate and national digital strategies aimed at improving the corporate sustainability performance of the country.

The structural complexity of Chinese characters presents significant challenges in handwriting evaluation, particularly in educational contexts where accuracy and efficiency are paramount. To address the limitations of traditional assessment methods and explore the potential of technological solutions, this study compared teacher-led and Optical Character Recognition (OCR)-based assessments of Chinese handwriting, focusing on time efficiency, scoring accuracy, and teachers' perspectives on the evaluation process. Utilizing a sequential mixed-methods explanatory design, the research involved 50 Thai 12th-grade students majoring in Chinese at a public high school in Thailand, as well as three experienced Chinese language teachers. Quantitative data were collected through a paired sample t-test, which revealed that OCR assessments were significantly faster than teacher-led evaluations (Mean = .36 vs. 5.39 min; t (49) = 16.56, p < 0.001) but consistently scored lower (Mean = 24.36 vs. 31.36; t (49) = 7.3, p < 0.001). Qualitative data from thematic analysis of teacher narratives emphasized the efficiency and physical ease provided by OCR technology, though concerns were raised about its reliance on internet connectivity and inability to interpret nuanced handwriting features. Teachers expressed contrasting views, with some advocating for manual evaluation's accuracy and others favoring the hybrid use of OCR as a preliminary tool to optimize efficiency without compromising precision. The findings accentuate the potential of integrating OCR technology into handwriting assessment while addressing its limitations, offering practical implications for enhancing Chinese language instruction and evaluation in diverse educational contexts.

Nowadays, every business focuses on customer relationship management (CRM) to deliver their customers better services and to establish a competitive advantage over their competitors. Significantly, customer insights with solid customer relationships improve customer retention and satisfaction, thereby contributing to profit. Thus, customer segmentation based on cluster analysis is critical to customer identification in CRM. In addition, it can identify the potential customers and their needs to be matched with marketing strategies. However, unfortunately, this approach has led to a gap between the marketing persons who care about the business implications and clustering output with the data science complexity barrier. Moreover, most clustering methodologies give only groups or segments, such that customers of each group have similar features without customer data relevance. Thus, this work sought to address these concerns by using a hierarchical approach. This research proposes a new effective clustering algorithm by combining Recency, Frequency, and Monetary (RFM) model with formal concept analysis (FCA). This new methodology uses the advantages of FCA in building the knowledge representation; therefore, the obtained construction contains both implicit and explicit knowledge. Explicit knowledge shows information represented in the hierarchical structure model, while implicit knowledge is embedded in the structure with its implication properties. Thus, the knowledge structure from FCA reveals relationships among data points in an easily understood manner. The proposed model was evaluated and compared with K-means clustering and hierarchical clustering using the online retail II dataset from the UCI Machine Learning Repository. The proposed method provides enough and appropriate information for marketers to perceive the value of the clustering results for creating practical marketing strategies in real-world business by offering the marketers both customer segmentation and the relationships in customer data at the same time.

Purpose: This study explores ways to make Thai durian production more competitive, concentrating on sustainable practices and difficulties experienced by durian growers. Method: This is mixed-method research. Multiple techniques were used. A thorough grasp of the market dynamics was gathered through in-depth interviews with 30 industry stakeholders, including durian producers, exporters, distributors, government officials, and industry professionals. 200 responders from 14 southern regions of Thailand, where durian production is crucial because of traditional farming practices and a favorable climate. Examining quality control practices, distribution effectiveness and sustainable farming methods revealed areas that might be improved to increase industry competitiveness. Results and Conclusions: The study also revealed difficulties, including losses from post-harvest handling, barriers to entry into the market, regulatory limits, and inconsistent quality. The study also examined consumer preferences for sustainable products and their willingness to pay more. Originality/Value: The findings reveal a sustainably produced durian goods market since they show an increasing consumer trend towards eco-friendly alternatives. Future recommendations call for incorporating technology into the supply chain, doing in-depth customer preference research, conducting a comparative analysis, and conducting long-term evaluations of sustainable practices. Policymakers, businesspeople, and researchers working to improve the competitiveness and sustainability of the Thai durian industry will find this study helpful.

Nowadays, leading manufacturers of enterprise-grade networking devices offer the dynamic ARP inspection (DAI) feature in their Ethernet Switches to detect and prevent ARP cache poisoning attacks from malicious hosts. However, MikroTik Ethernet switches do not yet support this feature. Within MikroTik-based networks, three potential approaches exist to prevent ARP cache poisoning attacks, each with drawbacks. This paper proposes an innovative approach called Gateway-controlled ARP (GCA) to prevent ARP cache poisoning attacks on a router-on-a-stick (RoaS) network using MikroTik networking devices, where a single router performs inter-VLAN routing through one physical interface. With this approach, all Ethernet switches are configured to forward ARP messages from hosts directly to the router for inspection and handling. A RouterOS script based on the GCA approach was implemented and executed on the router to handle all incoming ARP requests from any host in all VLANs, ensuring all hosts receive legitimate ARP responses from the router. This approach can effectively prevent spoofed ARP packets sent by malicious attackers. This approach was tested and evaluated on an actual RoaS network, focusing on processing time, CPU Load, and response time. The evaluation results show that the approach effectively prevents ARP cache poisoning attacks.

การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อ 1) ศึกษาพฤติกรรมการซื้อสินค้าออนไลน์ 2) ศึกษา ความสัมพันธ์ระหว่างปัจจัยการรับรู้ความเสี่ยงกับพฤติกรรมการซื้อสินค้าออนไลน์ และ 3) เปรียบเทียบ การรับรู้ความเสี่ยงที่ส่งผลต่อกลุ่มพฤติกรรมการซื้อสินค้าออนไลน์ระหว่าง Social Commerce และ E - Marketplace กลุ่มตัวอย่างคือกลุ่มผู้บริโภคทั้งเพศชายและหญิงที่มีการซื้อสินค้าออนไลน์ผ่านทาง Social Commerce และ E - Marketplace จํานวน 420 คน ได้มาจากการสุ่มตัวอย่างแบบหลาย ขั้นตอน เครื่องมือที่ใช้ในการวิจัยคือแบบสอบถาม สถิติที่ใช้ในการวิเคราะห์ข้อมูล ได้แก่ ความถี่ ร้อยละ ค่าเฉลี่ย ส่วนเบี่ยงเบนมาตรฐาน การทดสอบค่าที (T - test) การทดสอบค่าเอฟ (F - test) และ การทดสอบไคสแควร์ (Chi - square test) ผลการวิจัยพบว่า ส่วนใหญ่เป็นเพศหญิง คิดเป็นร้อยละ 9.00 อายุ 20 - 30 ปี คิดเป็นร้อยละ 50.71 มีสถานภาพโสด คิดเป็นร้อยละ 78.10 มีอาชีพธุรกิจส่วนตัว คิด เป็นร้อยละ 28.36 มีการศึกษาระดับปริญญาตรี คิดเป็นร้อยละ 66.19 และมีรายได้เฉลี่ย ต่อเดือน 10,001 - 20,000 บาท คิดเป็นร้อยละ 39.05 การรับรู้ความเสี่ยงจากผู้ซื้อสินค้าออนไลน์ใน ภาพรวมอยู่ในระดับมาก พฤติกรรมการซื้อสินค้าออนไลน์ส่วนใหญ่สั่งซื้อสินค้าประเภทแฟชั่นมาเป็น ลําดับที่ 1 ความสวยความงามมาเป็นลําดับที่ 2 และวัสดุอุปกรณ์/เครื่องมือ/เครื่องใช้ไฟฟ้ามาเป็นลําดับ ที่ 3 มีค่าใช้จ่ายในเฉลี่ยต่อครั้ง 500 - 1,000 บาท ความถี่เฉลี่ยต่อเดือน 1 - 3 ครั้ง และเหตุผลที่เลือกซื้อ สินค้าออนไลน์คือสะดวกรวดเร็ว ปัจจัยการรับรู้ความเสี่ยงทั้ง 6 ด้านมีความสัมพันธ์กับพฤติกรรมการซื้อ สินค้าออนไลน์อย่างมีนัยสําคัญทางสถิติที่ระดับ 0.05 การเปรียบเทียบการรับรู้ความเสี่ยงที่ส่งผลต่อ พฤติกรรมการซื้อสินค้าออนไลน์ระหว่าง Social Commerce และ E - Marketplace พบว่า ไม่มีความ แตกต่างกัน