Knowledge management tools and technology are the main factors bringing competitive advantage to organisations. This study extends the depth of study on small business entrepreneurs by examining the effect of knowledge acquisition, in particular financial knowledge that leads to the need for knowledge acquisition tools and technology. There is evidence to support that a lower level of existing financial knowledge in small business entrepreneurs encourages them to acquire more financial knowledge since they tend to be responsible for all tasks related to business survival. This finding is not consistent with several other previous studies. In addition, relevant knowledge on how to raise adequate capital to establish their business and the accurate calculation of cost are the two most important aspects of financial knowledge for encouraging small business entrepreneurs to seek effective knowledge acquisition tools and technology. The evidence provides insight for the government authorities supporting small businesses to provide financial knowledge to small business entrepreneurs during the pre-start-up phase and thereafter by providing the relevant knowledge on financing and cost calculation to strengthen and sustain these businesses. This will ultimately lead to an improvement in small business failure and increase the country’s economic growth.

The objectives of this study are to develop e-Commerce self-assessment application based on research and to evaluate the adoption of the system by using the Technology Acceptance Model (TAM) which are designed for SMEs self-assessment and the government agencies are able to retrieve the report. The system development deployed Microsoft .NET CORE Technology with C# language, HTML/CSS/JS for Mobile Web Development. All data will store on Aurora RDS. The web deployed on Amazon public cloud that will automatic scale when a lot of traffic arrived. Since the indicators from the research have been developed as an application, users will be able to use it conveniently anytime and anywhere. The system will help entrepreneurs realize the level of ecommerce knowledge of their own in various areas and help them to know what needs to be improved and more importantly, government officials can allocate budget for training in order to improve entrepreneurs’ performance. The evaluation result of system adoption in 30 cosmetic and supplementary food entrepreneurs showed that for Perceive Usefulness, the use of application systematically revealed the strength, weakness and potential of themselves and was beneficial for self-assessment in online selling skills, <em>xÌ„</em> = 4.50 equally. Perceived Ease of Use, it is very easy to do self-assessment through application, <em>xÌ„</em> = 4.63. Attitude Toward Using, in general, the attitude toward using the application was positive, <em>xÌ„</em> = 4.73. Behavioral Intention to Use, the intention to use this application in the future, <em>xÌ„</em> = 4.57 and 100% of 30 respondents were interested to use the system.

A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.

Digital innovations have changed the way many industries operate, but the construction industry has been slow to adopt these technologies. However, challenges such as low productivity, project overruns, labor shortages, and inefficient performance management have motivated Thailand’s Department of Highways to adopt digital innovations to build a competitive advantage. Because this industry requires a large work force, obstacles to collaboration can result in ineffective project management. We aimed to improve collaboration on bridge inspections that typically requires the involvement of many people, personal judgement, and extensive travel to survey bridges across the country. One major challenge is to standardize human judgement. To address these challenges, we developed a user-centric bridge visual defect quality control mobile application to improve collaboration and assist field technicians to conduct visual defect inspection. Our results can be used as a case study for other construction firms to embrace digital transformation technologies. This research also demonstrates the new-product development process using the new technology in known markets innovation development and technology acceptance model. We offer several recommendations for future research, including other infrastructure applications.

Word boundary ambiguity in word segmentation has long been a fundamental challenge within Thai language processing. The Conditional Random Fields (CRF) model is among the best-known methods to have achieved remarkably accurate segmentation. Nevertheless, current advancements appear to have left the problem of compound words unaccounted for. Compound words lose their meaning or context once segmented. Hence, we introduce a dictionary-based word-merging algorithm, which merges all kinds of compound words. Our evaluation shows that the algorithm can accomplish a high-accuracy of word segmentation, with compound words being preserved. Moreover, it can also restore some incorrectly segmented words. Another problem involving a different wordchunking approach is sentence boundary ambiguity. In tackling the problem, utilizing the part of speech (POS) of a segmented word has been found previously to help boost the accuracy of CRF-based sentence segmentation. However, not all segmented words can be tagged. Thus, we propose a POS-based word-splitting algorithm, which splits words in order to increase POS tags. We found that with more identifiable POS tags, the CRF model performs better in segmenting sentences. To demonstrate the contributions of both methods, we experimented with three of their applications. With the word merging algorithm, we found that intact compound words in the product of topic extraction can help to preserve their intended meanings, offering more precise information for human interpretation. The algorithm, together with the POS-based word-splitting algorithm, can also be used to amend word-level Thai-English translations. In addition, the word-splitting algorithm improves sentence segmentation, thus enhancing text summarization.

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The objective of this research was to compare the seven main functions of ATM banking services from five banks in Thailand. The selection of the five ATM banks was based on the fact that they contained different hierarchical menu structures. In the research, four groups each with 200 participants were separated into two parts. The first group of participants was required to complete a questionnaire in order to identify the seven main tasks of ATM banking, whilst the second group was required to perform the experiment on the ATM simulator. The second group was subdivided into four groups; students, employees, government and state enterprises officers and agriculturists. To compare seven major functions, a simulator of each of the five banks' ATM machines was developed and then tested in the laboratory environment. Usability was evaluated in terms of effectiveness, efficiency, satisfaction and the percentage difference. The results suggest that different menu structures will affect the usability of ATM banking. Moreover, the different types of user provide a different score based on usability measurement. Only one bank received the highest score on most of the usability criteria for all the different user groups.