Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

Abstract

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.

Znanstveno-raziskovalni izzivi na poti digitalne preobrazbe

“…S tem pa se ustvarja kultura, ki temelji na podatkih. (Windt et al, 2019); (Pillay & van der Merwe, 2021); (Coleman et al, 2016); (Peña et al, 2018); (Limpeeticharoenchot et al, 2020); (Mouhib et al, 2020) (Pillay & van der Merwe, 2021); (Comuzzi & Patel, 2016); (Coleman et al, 2016); (Peña et al, 2018); (Limpeeticharoenchot et al, 2020); (Mouhib et al, 2020);…”

Četrt stoletja digitalnih poslovnih modelov

“…(Pillay & van der Merwe, 2021); (Comuzzi & Patel, 2016); (Coleman et al, 2016); (Peña et al, 2018); (Limpeeticharoenchot et al, 2020); (Mouhib et al, 2020).…”

Challenges of big data adoption in Malaysia SMEs based on Lessig’s modalities: A systematic review

“…Architecture Coleman et al (2016), Shah et al (2017), Del Vecchio et al (2018, Soroka et al (2017), Ardagna et al (2016), Vajjhala and Ramollari (2016), Kızıltan (2018), Moyne and Iskandar (2017), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Seseni and Mbohwa (2018), Sivarajah et al (2017), Gupta et al (2018), Limpeeticharoenchot et al (2020), Makhele (2018), andRajabion (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Ardagna et al (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Sivarajah et al (2017, Baharuden et al (2019), Limpeeticharoenchot et al (2020), andMakhele (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Soroka et al (2017), Kızıltan (2018), Kalan and Unalir (2016), Limpeeticharoenchot et al (2020), Makhele (2018), andNemati andKhajeheian (2018).…”

“…Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Ardagna et al (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Sivarajah et al (2017, Baharuden et al (2019), Limpeeticharoenchot et al (2020), andMakhele (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Soroka et al (2017), Kızıltan (2018), Kalan and Unalir (2016), Limpeeticharoenchot et al (2020), Makhele (2018), andNemati andKhajeheian (2018). academic, and medical sectors is much lower (Mardis, 2016).…”