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

Authors

  • Santisook Limpeeticharoenchot Technopreneurship and Innovation Management Program, Chulalongkorn University
  • Nagul Cooharojananone Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University
  • Thira Chanvanakul Department of Commerce, Chulalongkorn Business School, Chulalongkorn University
  • Nuengwong Tuaycharoen Center of Learning and Teaching Innovation at Dhurakij Pundit University
  • Kanokwan Atchariyachanvanich Information Technology, King Mongkut's Institute of Technology Ladkrabang

DOI:

https://doi.org/10.3991/ijim.v14i18.16295

Keywords:

Big Data Maturity Model, Big Data SMEs, BDMM mobile application, BDMM

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.

Author Biographies

Santisook Limpeeticharoenchot, Technopreneurship and Innovation Management Program, Chulalongkorn University

Santisook Limpeeticharoenchot is currently a PhD candidate from Technopreneurship and Innovation Management Program, Chulalongkorn University. He received his B.Eng degree with honours in Electrical Engineering, master degree in business and economics from Chulalongkorn University. At present, he is doing research in the field of Big Data Analytic, Business Analytic and Maturity Model.

Nagul Cooharojananone, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University

Nagul Cooharojananone is an associate professor at Chulalongkorn University, Thailand. He received his B.S. degree In Computer Science from Mahidol University. He received his M.Eng and Ph.D. in Information and Communication Engineering from the University of Tokyo. His research interests include Multimedia Technology and Mobile Application.

Thira Chanvanakul, Department of Commerce, Chulalongkorn Business School, Chulalongkorn University

Thira Chanvanakul received his B.E. in civil engineering and M.B.A. from Chulalongkorn University, Thailand, and his M.Sc. and Ph.D. in Engineering Management from University of Missouri-Rolla (which is now the Missouri University of Science and Technology), USA. He is currently Assistant Professor and Head of Department of Commerce at Chulalongkorn Business School, Chulalongkorn University, Thailand. His research interests are in the areas of ̄financial engineering, operations management, quantitative analysis, applications of artificial intelligence, particularly neural networks, fuzzy logic and expert systems for business, ̄financial forecasting and investment. His research has been published in journals such as Expert Systems with Applications, Neurocomputing and Journal of Energy Engineering.

Nuengwong Tuaycharoen, Center of Learning and Teaching Innovation at Dhurakij Pundit University

Nuengwong Tuaycharoen is an assistant professor in Computer Engineering and a director of the Center of Learning and Teaching Innovation at Dhurakij Pundit University(DPU),Thailand. She received her B.Eng degree with honours in Computer Engineering from Chulalongkorn University, Thailand. She received her M.S and Ph.D. in Electrical and Computer Engineering from University of Maryland, College Park, USA.Her interests are in Software Development, Web and Mobile Application Development, Agile Methods, and Online and Active Learning.

Kanokwan Atchariyachanvanich, Information Technology, King Mongkut's Institute of Technology Ladkrabang

Kanokwan Atchariyachanvanich received the B.Sc. degree in information technology from Assumption University, the M.S. degree in information management from the Asian Institute of Technolo-gy, the M.P.A. degree in international development from Tsinghua University, and the Ph.D. degree in informatics from the Graduate University for Advanced Stud-ies, Japan. She is currently an Assistant Professor with the Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Thailand. Her prior research has been published in international journals and international con-ferences, such as ACM SIGecom Exchanges, the International Journal of Electron-ic Customer Relationship Management, E-business and Telecommunications, the International Conference on Electronic Commerce, and the International Confer-ence on Computer and Information Science. Her research interests include e-learning, information technology adoption, e-business management, and consumer behavior in the digital market.

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Published

2020-11-10

How to Cite

Limpeeticharoenchot, S., Cooharojananone, N., Chanvanakul, T., Tuaycharoen, N., & Atchariyachanvanich, K. (2020). Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand. International Journal of Interactive Mobile Technologies (iJIM), 14(18), pp. 87–106. https://doi.org/10.3991/ijim.v14i18.16295

Issue

Vol. 14 No. 18 (2020)

Section

Papers