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    Item type:Publication,
    Adaptive big data maturity model using latent class analysis for small and medium businesses in Thailand
    (2022-11-15)
    Limpeeticharoenchot, Santisook
    ;
    Cooharojananone, Nagul
    ;
    Chavarnakul, Thira
    ;
    Charoenruk, Nuttirudee
    ;
    Atchariyachanvanich, Kanokwan
    Big data analytics (BDA) is widely adopted in large enterprises. However, very few small- and medium-sized enterprises (SMEs) have adopted BDA because they lack the relevant knowledge, which makes BDA development expensive and unsuitable. A big data maturity model (BDMM) is a tool for assessing the stage for using big data in a company, and it acts as a guide for improvement. However, most BDMMs are designed for large enterprises using rule-based scoring, which is static over time. Developing a suitable BDMM for SMEs is a challenging task for professionals in terms of acquiring small-scale expertise owing to the lack of case studies for verifying the maturity level. This study proposes a new BDMM for Thai SMEs and a new methodology for developing a dynamic model using latent class analysis (LCA), which explains the behaviour of each latent class and provides non-rule-based scoring. We define four types of capabilities in SMEs: organizational and attitude factors, information technology, technology, and people readiness. Data are collected from 135 SMEs in Thailand. We introduce a methodology for developing multiple building stages of the BDMM. Further, we experiment with several clusters suitable for SMEs using statistic-based and data visualization approaches. The proposed BDMM is validated via a secondary evaluation of 11 firms, nine months after the initial evaluation. Further, we introduce a web-based application for respondents to obtain their firm's assessment results. The visualization-based result helps the respondents compare their business with other companies at the same or higher maturity level. In summary, SMEs can use the proposed BDMM to plan for continuous self-improvement and thus optimize their business using BDA to maximize its value to the organization.
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    Self-directed learning with knowledge management model to enhance digital literacy abilities
    (2020-11-01)
    Silamut, Aime acha
    ;
    Petsangsri, Sirirat
    Self-Directed Learning (SDL) is a process in which adult learners initiate their inner motivation to learn by themselves to increase their knowledge, skills and experiences from resources and evaluating learning outcomes. Knowledge Management (KM) is the process of creating, sharing, using and managing the knowledge and resources of an organization. Learners use KM to gain their knowledge. To access to these resources, the learners must know Digital Literacy Abilities (DLA). We developed a model for SDL with KM to enhance DLA: The model was synthesized from related works and verified by five experts in various fields. Data instrument was derived from focus group discussion including a five-level Likert scale of the appropriateness of the model, and experts’ opinions and recommendations. Then, collected data were analyzed with content analysis; mean (x̄), standard deviation (S.D.). After discussion, the model was adjusted according to the experts’ recommendations. It contained three circles from inner to outer: SDL, KM, and DLA, respectively. SDL had four steps: readiness triggers, setting goal and planning, learning activities, and learning’s evaluation. KM contained five steps: knowledge discovery, knowledge capture and storage, knowledge application and distribution, knowledge evaluation, and knowledge creation. DLA had four aspects: operation literacy, analysis and evaluation literacy, value and creation literacy, and social-culture-organization cooperation literacy. Overall, the results of analyzed data from the experts towards on appropriateness of the model were at high level (x̄ = 4.4, S.D. = 0.6). This model will be continued to implement for adult learners’ activities.