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

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 ado...
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Autor*in:	Santisook Limpeeticharoenchot [verfasserIn] Nagul Cooharojananone [verfasserIn] Thira Chanvanakul [verfasserIn] Nuengwong Tuaycharoen [verfasserIn] Kanokwan Atchariyachanvanich [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	big data maturity model big data smes bdmm mobile application bdmm

Übergeordnetes Werk:	In: International Journal of Interactive Mobile Technologies - International Association of Online Engineering (IAOE), 2018, 14(2020), 18, Seite 87-106
Übergeordnetes Werk:	volume:14 ; year:2020 ; number:18 ; pages:87-106

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3991/ijim.v14i18.16295

Katalog-ID:	DOAJ004814002

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spelling	10.3991/ijim.v14i18.16295 doi (DE-627)DOAJ004814002 (DE-599)DOAJde25f44d6d6b4bc8b535c4f91bb259d6 DE-627 ger DE-627 rakwb eng TK5101-6720 Santisook Limpeeticharoenchot verfasserin aut Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. big data maturity model big data smes bdmm mobile application bdmm Telecommunication Nagul Cooharojananone verfasserin aut Thira Chanvanakul verfasserin aut Nuengwong Tuaycharoen verfasserin aut Kanokwan Atchariyachanvanich verfasserin aut In International Journal of Interactive Mobile Technologies International Association of Online Engineering (IAOE), 2018 14(2020), 18, Seite 87-106 (DE-627)558042449 (DE-600)2406982-6 18657923 nnns volume:14 year:2020 number:18 pages:87-106 https://doi.org/10.3991/ijim.v14i18.16295 kostenfrei https://doaj.org/article/de25f44d6d6b4bc8b535c4f91bb259d6 kostenfrei https://online-journals.org/index.php/i-jim/article/view/16295 kostenfrei https://doaj.org/toc/1865-7923 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2086 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2020 18 87-106
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allfieldsGer	10.3991/ijim.v14i18.16295 doi (DE-627)DOAJ004814002 (DE-599)DOAJde25f44d6d6b4bc8b535c4f91bb259d6 DE-627 ger DE-627 rakwb eng TK5101-6720 Santisook Limpeeticharoenchot verfasserin aut Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. big data maturity model big data smes bdmm mobile application bdmm Telecommunication Nagul Cooharojananone verfasserin aut Thira Chanvanakul verfasserin aut Nuengwong Tuaycharoen verfasserin aut Kanokwan Atchariyachanvanich verfasserin aut In International Journal of Interactive Mobile Technologies International Association of Online Engineering (IAOE), 2018 14(2020), 18, Seite 87-106 (DE-627)558042449 (DE-600)2406982-6 18657923 nnns volume:14 year:2020 number:18 pages:87-106 https://doi.org/10.3991/ijim.v14i18.16295 kostenfrei https://doaj.org/article/de25f44d6d6b4bc8b535c4f91bb259d6 kostenfrei https://online-journals.org/index.php/i-jim/article/view/16295 kostenfrei https://doaj.org/toc/1865-7923 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2086 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2020 18 87-106
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author	Santisook Limpeeticharoenchot
spellingShingle	Santisook Limpeeticharoenchot misc TK5101-6720 misc big data maturity model misc big data smes misc bdmm mobile application misc bdmm misc Telecommunication Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand
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topic_title	TK5101-6720 Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand big data maturity model big data smes bdmm mobile application bdmm
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title_sort	innovative mobile application for measuring big data maturity: case of smes in thailand
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title_auth	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand
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