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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.
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Stored only when both papers are in the corpus — 19 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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