The epidemic of the COVID-19 has spread rapidly worldwide. This phenomenon has changed people's lifestyles as well as business activities. Many businesses are unable to operate normally, which is caused /or affected by supply chain disruption. Therefore, supply chain resilience after the COVID-19 pandemic is essential to maintaining the liquidity of businesses and increasing supply chain efficiency. This research aimed to examine factors influencing supply chain resilience and construct a structural equation model for supply chain resilience after the COVID-19 pandemic in Thailand's industries. A research framework was developed according to previous literature in the context of supply chain resilience. Five constructs, namely Technology, Flexibility, Collaboration, Agility, and Supply chain resilience, with seven hypotheses were established. A questionnaire survey was developed from the research framework and previous literature. Then, the validity and reliability test of the questionnaire were performed with the Index of Item Objective Congruence (IOC) technique and Cronbach’s alpha, respectively. The data was obtained from 426 business organizations in both the industrial and service industry in Thailand. The structural equation model (SEM) technique was conducted to examine the relationship between constructs. The results revealed that agility was only a factor that directly influenced supply chain resilience, while technology had an indirect effect on it via agility. However, technology has had direct effects on flexibility, agility, and collaboration.

This study examines the sustainability of Agricultural Trade Supply Chains (ATSC) from the perspective of agricultural sustainable development, with a focus on five key aspects: production, processing and storage, transportation and logistics, trade, and consumer and market. Based on an analysis of 756 academic papers published between 2013 and 2022, scientific metric techniques were used to identify changes in the field's sustainability. The study highlights opportunities for the development of a sustainable agricultural product trade supply chain, including the use of blockchain technology to improve transparency and traceability, increasing consumer demand for sustainable and eco-friendly products, corporate zero-deforestation commitments, bioenergy market expansion, and the role of farm advisors in promoting sustainable production methods. This paper contributes to a structured understanding of the status and future directions of ATSC sustainability, emphasizing the importance of joint efforts across all links of the supply chain to meet the sustainable needs of consumers and promote environmental protection and social-friendly markets.

This study aimed to explore the approaches for transforming Small and Medium Enterprises (SMEs) into high-value markets, focusing on constructing a structural equation model of guidelines for the development of small and medium enterprises into high-value markets. The conceptual research framework consisted of one exogenous latent variable, the Value Discovery element, combined with the four endogenous latent variables, Value Development, Value Communication, Value Distribution, and Value Experiences. The study utilized mixed-methodology research, including qualitative research through in-depth interviews with nine experts, a focus group discussion with 11 successful entrepreneurs, and quantitative research through a survey of 500 SMEs in Thailand. The results revealed that SMEs operating in high-value markets are divided into tangible and intangible products, representing 250 respondents from each group. The five elements of the guidelines for the development of small and medium enterprises into high-value markets were found to be of high importance both overall and in each element. The developed SEM model was consistent with the empirical data of five elements with 27 observed variable factors. Furthermore, the hypothesis test results for analyzing the causal influence between latent variables in the SEM model revealed that all six hypotheses were supported at the 0.001 level of statistical significance. The value of the study is beneficial for SMEs who target to do business in high-value markets and can improve their competitiveness, develop unique value propositions, and provide better customer experiences.

The research objective is to investigate the effect of brand equity on Thai cosmetic purchasing decisions. The study involved 500 individuals aged 20-59 who had purchased and used Thai cosmetic products within the past year. A multistage sampling method was employed to select the participants. Data collection was carried out using questionnaires, and both descriptive and multivariate statistical analyses were conducted, explicitly utilizing Second-order Confirmatory Factor Analysis and Structural Equation Modeling. The research results showed four factors influencing the purchasing decisions of Thai brand cosmetic products: Brand Equity, Buying Behavior, Consumer Satisfaction, and Perceived Value. The Second-order Confirmatory Factor Analysis and Structural Equation Model demonstrated that these factors met the assessment criteria when applied to the empirical data. Consequently, if a brand can establish brand equity that surpasses its competitors in the eyes of consumers, it will contribute to long-term profitability and sustained growth.

The aim of this study is to identify the influence of food attributes, perceived value, consumer trust and nutritional health behavior that contribute to consumer repurchase intention of foods with health claims. Descriptive Statistics was used to analyze the demographic profile of the 313 sample of respondents. Path analysis was conducted for analyzing the causal and effect relationship between variables expressed by means of a path coefficient. The results showed that there were only indirect effects between food attributes and Intention to repurchase. However, perceived value was shown to have both positive direct and indirect effects on intention to repurchase, which were significantly mediated by nutritional health behavior and consumer trust. Research results suggest food managers develop the value of food products and monitor the customer trust and the changes of market on the food with health claims for the competition in a current environment.

This article aims to conceptualize and examine the influence of top management green commitment and green intellectual capital on sustainable business performance of Thailand's thrift and credit cooperatives. Data were collected from a sample of 319 top and middle managers and full-time employees of Thailand's thrift and credit cooperatives and analyzed using Structural Equation Model (SEM). The study's findings demonstrated that top management green commitment and green intellectual capital have a positive influence on sustainable business performance. Top management green commitment and green intellectual capital also have a positive influence on green human resource management. Furthermore, green human resource management has a positive influence on sustainable business performance. Additionally, the novelty of this study is the contribution of green intellectual capital as an intangible resource for organizations integrated with top management green commitment through green human resource management practice to drive an operation in achieving sustainable business performance and a competitive advantage for future researchers. All business sectors can implement this strategy in operation, which may improve their cleaner production and service capacities.

This research aimed to study the factors affecting the supply chain efficiency of water tourism entrepreneurs in the Northeast provinces of Thailand around the Mekong River Basin. A questionnaire was distributed to and subsequently collected from 246 samples in 7 provinces including Loei, Nong Khai, Bueng Kan, Nakhon Phanom, Mukdahan, Ubon Ratchathani, and Amnat Charoen by proportional allocation and convenience sampling using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) by ADANGO. According to the results, it was found that customer orientation directly affected the supply chain efficiency of water tourism. Knowledge management directly affected the supply chain efficiency of water tourism and supply chain marketing implementation capability as a transmission factor for the supply chain efficiency of water tourism. For these reasons, water tourism entrepreneurs should focus mainly on customer orientation with full-service efficiency. Internal management should be planned as well, e.g., communication training, service training and cultural revitalization of communities around tourist attractions through cooperation with involved agencies, which will enhance the supply chain efficiency of water tourism.

This study aims to explore the application of financial supply chain management on agricultural product trade, and an example simulation case that Thai durian exports to China. After collection of variables and relationships in previous literature, a model of durian trade supply chain with material flow and cash flow has been created. As a result of the simulation, we found that cash flows of downstream participants are strongly higher than the upstream, which is partly proving that the downstream participants have more opportunities to invest upstream for intervention. Besides, we suggest government agencies, business sector, and financial institutions should strengthen communication and cooperation to maintain stability of current Thai durian trade supply chain, meanwhile, remain on high alertness to postharvest, delivery delays and durian demand to prevent the impact of supply chain disruption on cash flow.

Micro, small, and medium-sized enterprises (MSMEs) are important in driving a country’s economy and cover the entire manufacturing sector. Therefore, entrepreneurs with specific characteristics are needed for MSMEs to be successful. This study analyzed the exploratory components of entrepreneur characteristics in sustainably successful MSMEs. Data from interviewing 500 entrepreneurs who successfully operated MSMEs were analyzed using descriptive, referential, and multivariate statistics. Analysis of the entrepreneur characteristics’ exploratory components revealed that their Kaiser-Meyer-Olkin index was 0.969, and eigenvalues after axis rotation were 6.545–8.899. Four components were extracted: leadership, personality, management skills, and morality/ethics. Entrepreneurs’ characteristics in sustainably successful MSMEs include the ability to prioritize tasks appropriately, punctuality, people management skills, consideration of non-violation of rights, and non-imitation of others.

Gas Metal Arc Welding (GMAW) is an extensively implemented arc welding process through the control of input process parameters and the metal from the filler wire. Despite its popular use in various industries, the complex interrelationship between the actual bead and the varying welding parameters makes it challenging to predict appropriate bead geometries via mathematical modeling in a continually changing welding process. In this study, the Regression Learner App was used to compare the performance of supervised Machine Learning (ML) predictive models comprising the Linear Regression (LR), Regression Tree (RT), Support Vector Machine (SVM), Ensembles of Tree (ET), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) using GMAW dataset. The dataset was scaled and normalized at a range of -1 to +1 to facilitate the visualization of the variation effect. The wire feed speed, voltage, weld velocity, unmelted wire length, and melted wire volume were considered as the input parameters to predict the bead geometry. In addition, the five-fold cross-validation was employed to avoid overfitting and poor generalization. Finally, statistical indicators, namely the Coefficient of Determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were performed on all developed models to evaluate their performance. Thus, the fine tree and ANN models achieved the highest prediction accuracies of 88–91%, signifying their potential use in future research. In short, the present study demonstrated the performance of various supervised ML algorithms for bead geometry prediction, which would assist the selection of appropriately supervised ML algorithms in future studies.