Search Results (158)

This study examined the extent to which corporate social responsibility (CSR) dimensions shaped hotel reputation (REP) through customer trust (TRU), and whether customer engagement (ENG) moderated this relationship in the Mekong Delta context. Although prior studies had acknowledged the importance of CSR in shaping customer perceptions, they had paid limited attention to the distinct effects of multidimensional CSR and the conditions under which TRU was transformed into REP. A structured questionnaire survey was administered to 874 hotel guests in the Mekong Delta, Vietnam. The measurement scales were adapted from previous studies and refined through a pilot test. Partial least squares structural equation modeling (PLS-SEM), together with bootstrapping, was used to test the direct, mediating, and moderating effects. CSR dimensions exhibited heterogeneous effects. Customer-oriented responsibility had the strongest impact on both TRU and REP, followed by environmental and economic responsibilities. Legal responsibility directly enhanced REP but did not significantly affect TRU, whereas community responsibility strengthened TRU without directly influencing REP. TRU significantly improved REP and mediated most CSR–REP relationships. ENG positively moderated the TRU–REP link, amplifying reputational outcomes. The study contributed to CSR and hospitality research by modeling CSR as a multidimensional construct and by identifying ENG as a boundary condition that strengthened the TRU–REP mechanism. Managers should prioritize customer-focused CSR and enhance engagement to maximize reputational gains. Full article

Microplastic pollution in tropical urban rivers has become an increasing environmental concern due to rapid urbanization, inadequate waste management, and hydrological transport processes. This study investigated the occurrence, characteristics, and spatiotemporal distribution of microplastics in the Can Tho River, Vietnam, along an urban–peri-urban–rural gradient during dry and wet seasons. Surface-water samples were collected at 15 sites and analyzed for microplastic abundance, density, shape, color, and size composition using stereomicroscopic identification and statistical analyses. Microplastics were detected at all sampling sites in both seasons, indicating widespread contamination throughout the river system. Although seasonal differences in overall abundance and density were not statistically significant at the basin scale, clear spatial variability was observed, particularly in urban and peri-urban regions. Fibers and fragments were the dominant shapes, while blue, purple, and green particles were the most common color categories. Particles larger than 1000 µm accounted for the largest proportion of detected microplastics, and continuous size-distribution analysis revealed broadly similar overall distributions, although a greater proportion of smaller particles was observed during the dry season. The results suggest that hydrological conditions, urbanization, and land-use characteristics may contribute to the observed spatial and seasonal patterns of microplastic distribution in the Can Tho River. Peri-urban zones exhibited the greatest seasonal variability, highlighting their role as transitional areas that may influence microplastic redistribution in tropical river systems. This study provides baseline information for understanding microplastic pollution in the Mekong Delta and supports future river management strategies. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

This study aims to explore perceptions of English as a Second Language (ESL) students on integrations of local culture into English language teaching using Artificial Intelligence approaches. Research samples included 511 ESL students of the English Faculty of four public universities in Vietnam’s Mekong Delta region. The input factor dimensions comprise demographics, level of local culture integration, facilities, and curriculum-related factors. The output factor was necessities for local culture–English-teaching integration. Two supervised machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were applied with oversampling to address data imbalance issues. Results indicated that the oversampling case achieved the highest performance. The research shows that the DT model was slightly better than the SVM with an accuracy of 97% and AUC of 98%. Feature importance analysis identified curriculum, facilities, and students’ hometown as key predictors. The findings provided empirical evidence to support data-informed curriculum reform in culturally responsive English language teaching. This study also develops a novel method to explore students’ perceptions and offers practical suggestions for improving academic quality. This study is expected to enhance institutional student recruitment while contributing to the region’s sustainable development and the broader goal of preserving intangible cultural heritage in Vietnam’s Mekong Delta. Full article

In the published publication (Nguyen, 2026), there was an error regarding the affiliation; the author requested to only display his primary affiliation and remove the second affiliation (School of Marketing and International Business, Victoria University of Wellington, Wellington 6140, New Zealand) in the published article [...] Full article

Culinary tourism is increasingly conceptualized as a strategic domain of destination competitiveness, in which gastronomic experiences serve as mechanisms for cultural representation and localized value creation. However, existing research remains fragmented in explaining how multidimensional culinary motivations translate into specific consumption behaviors, particularly in emerging destinations. Addressing this gap, this study develops and tests a motivation–behavior linkage framework grounded in push–pull motivation theory, conceptualizing culinary motivations as heterogeneous drivers with differential effects on intention to purchase local food. A sequential mixed-methods design was employed, beginning with an initial qualitative phase to refine measurement constructs, followed by a quantitative survey of 396 domestic tourists with prior culinary experience in Vietnam’s Mekong Delta. Data were analyzed using reliability assessment, exploratory factor analysis, and multiple regression modeling. The findings reveal a differentiated structure of influence: cultural experience, sensory appeal, and health concerns significantly enhance purchase intention, with cultural experience and sensory appeal emerging as the most influential predictors. In contrast, interpersonal and prestige motivations are non-significant, indicating that experiential and functional values outweigh social-symbolic drivers in this context. The study contributes by advancing push–pull theory through a behavior-specific, mechanism-based linkage; identifying context-dependent boundary conditions in an emerging destination; and refining culinary motivation by distinguishing experiential–functional from social-symbolic drivers. These insights inform more targeted strategies for promoting local food consumption and sustainable culinary tourism development. Full article

Rice straw mulching is a soil management practice that influences soil microbial communities. However, its effects on nematode communities under upland rice systems in salt-affected soils remain unclear. This study examined nematode community responses to rice straw mulching at rates of 0, 3.5, 7.0, and 10.5 t ha⁻¹ in paddy fields at two sites, Lieu Tu and Long Phu, in Soc Trang Province, Mekong Delta, Vietnam. A total of 37 and 35 nematode genera were identified in Lieu Tu and Long Phu, respectively. Bacterivores were the dominant group, followed by herbivores. Acrobeloides, Hirschmanniella, Chronogaster, Aporcelaimellus, and Prismatolaimus were prevalent in Long Phu, while Acrobeloides, Prismatolaimus, Hirschmanniella, and Alaimus dominated in Lieu Tu. The highest mulching rate (10.5 t ha⁻¹) increased total nematode abundance, particularly cp1 and cp2 groups in Long Phu, while the application of 7.0 t ha⁻¹ increased the proportion of omnivorous feeders in Lieu Tu. Mulching increased total nematode biomass and metabolic footprints, indicating improved soil fertility. At Long Phu, mulching also increased biodiversity, as reflected by the higher species richness and Shannon–Wiener indices. The highest mulching application rate (10.5 t ha⁻¹) increased the relative abundance of cp2 functional guilds at both sites. Mulching reduced the relative abundance of plant-parasitic nematodes at both sites, and increased cowpea yield from 5.1 to 13.9 t ha⁻¹ and 5.67 to 9.70 t ha⁻¹ at Lieu Tu and Long Phu, respectively. These findings suggest that the rice straw mulching at 10.5 t ha⁻¹ improves soil structure and nematode diversity, thereby supporting agricultural sustainability in salt-affected soils under climate change conditions. Full article

Precipitation variability in the VMD is a critical determinant of agricultural productivity, freshwater availability, and flood and drought dynamics in one of Southeast Asia’s most climate-vulnerable regions. Teleconnections between PPTA and three dominant climate modes (Niño 3.4, DMI and PDO) were quantified at ten meteorological stations from 1981 to 2025 using Pearson lag-correlation and WTC. ENSO is identified as the primary interannual driver, exhibiting a peak negative correlation at a lag of two months (r = −0.304, p < 0.001; 9.2% variance explained). The IOD exerts a secondary, delayed influence, peaking at lags of 11 to 12 months (r = 0.186, p < 0.001; 3.5% variance). The PDO functions as a persistent decadal modulator: positive phases suppress annual precipitation by 4.6%, while negative phases enhance it by 14.5% relative to the long-term mean (6.4% variance). WTC analysis reveals non-stationary coherence at 2–5 year (ENSO) and 8–16 year (PDO) periodicities. Compound El Niño and positive PDO events result in the most severe precipitation deficits, with non-linear responses during strong ENSO phases. These results establish a multi-index teleconnection framework that supports seasonal drought early warning and climate-adaptive water resource management in the VMD. Full article

Transitioning to a circular economy (CE) is now a strategic priority for countries to decouple economic growth from environmental degradation. However, in developing contexts, the readiness of environmental resource sectors to adopt CE principles is unknown due to a lack of data and uneven institutional capacity. This study presents the first regional baseline assessment of circularity readiness in Vietnam’s environmental resource sectors, focusing on land, mining, water and waste. A five-dimensional readiness framework (policy, resource management, innovation, business, awareness) was developed and applied across Vietnam’s six ecological–economic regions. A Delphi process with 12 experts was conducted in three rounds to capture and refine expert judgments, supplemented by triangulated proxy indicators (e.g., plastic recycling rates, wastewater treatment coverage). Readiness scores were aggregated at dimension and regional levels and analyzed using radar charts, heatmaps and hierarchical clustering. Results showed significant regional disparities. The Southeast (SE) and Red River Delta (RRD) have high readiness due to clearer policy frameworks, stronger institutions and more dynamic business ecosystems. The Northern Midlands and Mountains (NMM) and Central Highlands (CH) have low readiness due to infrastructural gaps, weak innovation and limited public engagement. The Mekong Delta (MD) and North Central Coast (NCC) have medium readiness, reflecting partial progress but uneven implementation. The study made three contributions: (1) a new context-specific framework for CE readiness in environmental resource sectors; (2) the value of expert-based, proxy-informed methods in data-scarce contexts; and (3) a policy roadmap for different regional readiness levels. Findings suggest that the CE should be integrated into resource planning, regional observatories should be established and CE-related research and development (R&D) should receive investment. Future research should move towards standardized quantitative indicators and predictive models to track how readiness changes under policy interventions. Full article

The excessive use of inorganic nitrogen (N) fertilizers in rice production poses significant environmental and economic challenges, particularly in intensive farming systems such as those in the Mekong Delta, Vietnam. This study aimed to evaluate the potential of Herbaspirillum seropedicae (H. seropedicae), an endophytic N-fixing bacterium, to enhance soil fertility, improve rice growth, and maintain yield while reducing N fertilizer inputs in Dai Thom 8 rice under field conditions. A randomized complete block design with five treatments, including different nitrogen reduction levels combined with bacterial inoculation, was employed. The results showed that treatments integrating H. seropedicae significantly improved soil properties, including soil organic matter, total nitrogen, and available nutrients, compared to the control. Growth parameters such as plant height, tiller density, and chlorophyll content were also enhanced, particularly in treatments with bacterial inoculation. Yield components, including grain number and filled grains per panicle, were significantly increased, leading to higher grain yield. The highest yield was observed in T5 (5.72 t ha⁻¹), while T3 and T4 achieved comparable yields with reduced N inputs. Additionally, grain quality analysis revealed increased protein content without negatively affecting starch composition. These findings highlight the potential of H. seropedicae as a biofertilizer to improve N use efficiency and reduce dependency on chemical fertilizers. The study provides strong evidence for integrating microbial inoculants into sustainable rice production systems. Among the treatments, T3 (50% N reduction combined with bacterial inoculation) is recommended as the optimal strategy due to its balance between high yield and reduced input costs, contributing to environmentally friendly and economically viable agriculture. Full article

Drought is a recurrent hazard in the Vietnamese Mekong Delta (VMD), with major implications for agriculture, water resources, and rural livelihoods. This study assesses drought variability in Kien Giang Province, Vietnam, from 1992 to 2024 using two multiscale indicators: the Multivariate Standardized Precipitation Index (MSPI) and the Multivariate Standardized Precipitation Evapotranspiration Index (MSPEI). Principal Component Analysis (PCA) was applied to Standardized Precipitation Index (SPI)- and Precipitation Evapotranspiration Index (SPEI)-based time series spanning multiple accumulation periods (3–48 months) to derive integrated drought signals and to reduce redundancy across timescales. The results show that the first principal component (PC1) captured a high proportion of total variance across stations, indicating strong coherence in drought dynamics across the province. Both MSPI and MSPEI successfully identified major historical drought episodes, particularly the severe events of 2004–2005 and 2015–2016. However, the two indices differed in their temporal behaviour: MSPI responded more directly to precipitation deficits, whereas MSPEI showed slower post-drought recovery in recent years, suggesting greater sensitivity to evaporative demand and climatic water-balance stress. These differences indicate that evapotranspiration-sensitive indices may provide added analytical value in warming coastal environments. Overall, the combined multiscale framework offers a robust basis for drought monitoring, comparative assessment, and water-resource planning in Kien Giang and other drought-prone coastal delta settings. Full article

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article

This study examines the socio-demographic characteristics, rice production practices, and breeding preferences of farmers across three major rice-growing regions of Vietnam: the Mekong Delta, Central Vietnam, and North Vietnam. A survey of 109 rice farmers captured information on cultivation status, livelihood activities, and preferred breeding traits for rice improvement. The results reveal clear regional differentiation in farm structure, production objectives, and varietal preferences. Rice farming in the Mekong Delta is predominantly commercially oriented, characterized by larger landholdings and greater male participation, whereas rice production in Central and Northern Vietnam is more subsistence-oriented, with higher female involvement. Farmers across regions consistently valued locally adapted rice varieties, but articulated region-specific trait priorities shaped by agro-ecological conditions. In the Mekong Delta, preferences emphasized soft grain quality and salinity tolerance, reflecting coastal production constraints. In Central Vietnam, farmers prioritized heat tolerance and resistance to pests and diseases, while in Northern Vietnam, cold tolerance and grain quality attributes, including aroma and harder texture, were most important. Major biotic stresses, particularly blast and bacterial blight, also showed significant regional variation in reported incidence. By linking these region-specific preferences to clearly defined Target Populations of Environments (TPEs), this study provides a practical framework for aligning breeding targets with real-world production conditions. The findings offer actionable guidance for participatory breeding and decentralized varietal evaluation under the Biodiversity for Opportunities, Livelihoods, and Development (BOLD) initiative, as well as other rice improvement programs. To our knowledge, this represents the first multi-region evidence from Vietnam that systematically integrates agro-ecological variation with a TPE-based breeding approach, supporting the development of climate-resilient, farmer-preferred rice varieties and more sustainable rice production systems. Full article

This study focuses on the calibration and verification of a large-scale coupled numerical model to simulate the complex hydrodynamic–wave–sediment transport processes in the coastal and estuarine regions of the Mekong River Delta (MRD), Vietnam. Using the MIKE 21/3 modeling system, the research integrates Hydrodynamics (HD), Spectral Wave (SW), and Mud Transport (MT) modules across a computational domain of 270 × 300 km. The models were rigorously tested using field measurement data from three distinct periods: May 2004 (dry season calibration), September 2017 (first verification), and June 2024 (second verification). The results from the hydrodynamic model demonstrated high accuracy in predicting water levels, with the average Root Mean Square Error (RMSE) values ranging between 4.4% and 5.8%. The wave spectral model showed reliable performance, with the average RMSE values for wave height ranging from 15.1% to 18.0%. Furthermore, the Mud Transport module successfully captured suspended sediment concentrations (SSC), yielding average RMSE values between 26.0% and 32.1% after the fine-tuning of site-specific parameters such as critical shear stress for erosion and deposition. The study highlights the critical importance of utilizing site-specific sedimentological parameters to accurately predict morphological changes in highly dynamic estuarine environments. This validated model provides a robust tool for assessing coastal erosion and developing protection measures in regions that are increasingly vulnerable to climate change and human activities. Full article