This study aims to study the macroeconomic factors that affect the rate of return of securities in the transport and service category of the Stock Exchange of Thailand. The category comprises three groups: media services, tourism, hotel and recreation and transport and logistics. The study employs quarterly data from 2001 to 2019 estimated with the ARDL bounds testing approach. The results reveal that media services, tourism, hotel and recreation and transport and logistics groups in the tourism and service category were cointegrated with stock exchange volume (in thousands of shares), exchange rate, gold bullion price, interest rate and diesel oil price. This study also found the long-term equilibriums of the macroeconomic factors deviated from the short-run to long run for three groups. The gold bullion price factor is a major factor in determining the long run. This study suggests that the gold bullion price is an alternative indicator for investment. Investors in the transport and logistics group should focus on the exchange rate, the gold bullion price, the interest rate and the diesel oil price before investment. Investors should consider the level of the exchange rate before investing in the media service group in the Stock Exchange of Thailand while investors in the tourism, hotel and recreation group of the Stock Exchange of Thailand should be recognize the stock exchange volume.

Web based shopping has been the quickest developing channel of looking for over 10 years with deals developing at a yearly pace of 25%. One review connected properties of a site to drive purchasing conduct. Given the quick development of web-based shopping and the qualities of web-based shopping that energizes drive purchasing (for example open every minute of every day), drive purchasing on the web is probably going to be pervasive. Accordingly, this study plans to investigate the effects of site credits and persuasive variables on internet-based motivation purchasing. A self-directed poll was utilized to gather information for this review. The 400 respondents, in line with the required sample size, were people who have online buying experience in Thailand. The data analysis method is Structural Equation Modelling (SEM). The results of the study revealed that influence of web site attributes has a positive relationship with motivation factors. Moreover, motivation factors have a positive relationship with online impulse buying. Additionally, two components of motivation factors which are utilitarian and hedonic motivations, mediate the relationship between web site attributes and online impulse buying.

Machine learning (ML) is implementing artificial intelligence (AI) research within medicine that has made dramatic progress in recent years. In addition to standard treatments, the role of complementary and alternative medicine should be mentioned. Traditional Thai medicine has received growing acceptance as a complementary approach to modern medicine by using local herbs. A vast amount of Thai herbal knowledge and information is freely available on the Internet. The reader must evaluate each website and decide to use trustworthy and appropriate information. This study aimed to acquire Thai herbal knowledge recorded in the Thai language system on the Internet by scraping websites using programming techniques. The knowledge was extracted with programming, and the types of Thai herbs were classified corresponding to target symptoms by the machine learning algorithm. The ML method organized the process when sufficient achievement was reached in order to give reliable and high accuracy results from the training data set. The validation of extracted knowledge was achieved by using the part-of-speech tag patterns analysis. This study showed that the programming and machine learning system was appropriate for obtaining and classifying Thai herbal medicines knowledge.

Fake news has become a major problem affecting people, society, the economy and national security. This work proposes a combined deep learning model based on the ideal distance weighting method for fake news detection. The proposed model was validated on the ISOT and COVID-19 fake news datasets. Firstly, the ISOT and COVID-19 fake news datasets were collected. Secondly, the training-based models were used to provide accuracy values. After that, these values were transformed into criteria weights using the new ideal distance weighting method. Finally, the prediction value of the proposed model is calculated by the criteria weights. The results show that the proposed method is effective to distinguish the fake news datasets.

This study investigated the continuous intention to use digital payment solutions in online transactions to mitigate the spread of the COVID-19 virus. Primary data was collected from individuals using digital payment systems in Bangkok, Thailand using a structured questionnaire from a total of 400 respondents. The study adopted the Technology Acceptance Model (TAM). Data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) in AMOS 26. The results revealed that factors that significantly affected continuous intention to use digital payments were perceived ease of use, satisfaction, attitude, and social distancing. Satisfaction mediated the effects of perceived usefulness, perceived ease of use, and social distancing on continuous intention to use. Attitude mediated the effect of perceived usefulness on continuous intention to use. The study recommends that concerned policymakers and institutions should consider users’ satisfaction, social distancing, and perceived ease of use when developing digital payment systems.

The purpose of this study is to provide criteria for selecting operations managers at the ‘service encounter’ for mobile telecommunication companies, and develop a system for this multi-criteria decision-making scheme based on the Analytical Hierarchy Process (AHP). There are three main criteria for evaluating the capability of service-encounter operation managers: (1) the ability to design service process; (2) the ability to operate service process; (3) the ability to conduct improvement. The AHP operation manager selection tool was developed based on the complex problems at the service encounter. It was created as a decision support system which was used to recruit and evaluate operations managers’ capability for the purpose of career advancement.

The new method to chart the Hodges-Lehmann estimator control chart is proposed in this study. The evaluation of the three nonparametric control charts - the Sign test (ST), Mann-Whitney (MW), and the Hodges-Lehmann estimator (HL), for the known process distribution using normal and Weibull data represent the symmetric and asymmetric shapes of the process based on the original method through the 10000 run lengths simulation. The result illustrates that the average run length performance of the ST and MW correspond to their respective test statistics but for HL’s performance, the result indicates that the average run length is much greater than that derived from Wilcoxon signed rank statistics. The Hodges-Lehmann estimator control chart by the new approach for the known process distribution will be the alternative method for the process that needs to robust outliers’ properties from this statistics. In addition, the simulation demonstrates that the performances of the Sign test (ST) from mean and median processes are varied in the skewed distribution, and moreover, the Sign test (ST) from the median process represents more accurate performance. Meanwhile, for the control groups, MW generated within control limits or without restriction shows slightly different performance. The performance of dual scheme for the above-mentioned variable parameters control charts also produce the weighted average values that effect from the tight control scheme to the regular control scheme.

In this paper, a novel combination method is offered to integrate the results of two new relative closeness models, called relative closeness benevolent (RCB) and relative closeness aggressive (RCA) models, for ranking all DMUs. To prove the applicability of the proposed method, it is examined in three numerical examples, performance assessment problem, six nursing homes and fourteen international passenger airlines. Firstly, RCB and RCA models were formulated in order to generate the cross-efficiency intervals matrix (CEIM). After obtaining CEIM, the RC index was utilized to generate a combined cross-efficiency matrix (combined CEM). In combined CEM, target DMUs were viewed as criteria and DMUs were viewed as alternatives. After that, the weights of each criterion were generated using a new weighting method based on standard deviation technique (MSDT). Finally, all DMUs were evaluated and ranked. Comparison with existing cross-efficiency models indicates the more reliable results through the use of the proposed method.

This paper presents a methodology to improve the strength or the Modulus of Rupture (MOR) of fibre cement. The Six Sigma approach with the DMAIC steps was applied to a case study company. This research started from defining problem, setting the project objective and the project scope. Next, the measurement system was analyzed and the process map was set up. The potential factors of the problem was then determined. Due to there were many factors that affect the MOR, the Cause and Effect Matrix and the Failure Mode and Effect Analysis technique were then used to reduce the number of factors to be studied further. Next, three process factors, which were the pulp slurry freeness, the film-layer thickness, and the pressure step, were optimized using the results from the Box-Behnken experimental design. Other 13 remaining factors were improved by creating or revising the standard work instructions and training the operators. After that, the statistical process control and the control plan were set up to control the production processes. After improvement, the process capability index (Ppk) significantly increased from 0.26 to 1.35.

There is a high risk of R & D based innovation being commercialized, especially in the innovation transfer process which is a concern to many entrepreneurs and researchers. The purpose of this research is to develop the criteria of R & D commercialization capability and to propose a combined technique of Structural Equation Modelling (SEM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for R & D project evaluation. The research utilized a mixed-method approach. The first phase comprised a qualitative study on commercialization criteria development though the survey research of 272 successful entrepreneurs and researchers in all industrial sectors in Thailand. The data was collected with a structured questionnaire and analyzed by SEM. The second phase was involved with SEM-TOPSIS technique development and a case study of 45 R & D projects in research institutes and incubators for technique validation. The research results reveal that there were six criteria for R & D project commercialization capability, these are arranged according to the significance; marketing, technology, finance, non-financial impact, intellectual property, and human resource. The holistic criteria is presented in decreasing order on the ambiguous subjectivity of the fuzzy-expert system, to help with effectively funding R & D and to prevent a resource meltdown. This study applies SEM to the relative weighting of hierarchical criteria. The TOPSIS approach is employed to rank the alternative performance. An integrated SEM-TOPSIS is proposed for the first time and applied to present R & D projects shown to be effective and feasible in evaluating R & D commercialization capacity.