Big Data Analytics Adoption Model for Malaysian SMEs

Big Data Analytics (BDA) was utilized to analyze and examine big data sets. This system is able to analyze enormous data sets which exist in different formats and to extract useful information within the data which may be used to improve business decision-making, predict sales, enhance customer relationships, and ultimately lead to generating increased revenues and profits. Multinational and large companies are starting to adopt BDA to acquire the benefits and advantages from this technology. However, the rate of adoption of BDA by Small and Medium Enterprises (SMEs) is low. There is a need and desire that SMEs should start to adopt BDA in order to stay one step ahead of their rivals, and at the same time, to remain competitive in the market. Hence, this study aims to identify the factors influencing the adoption of BDA in Malaysian SMEs and propose a BDA adoption model for Malaysian SMEs.

[1]  Tiago Oliveira,et al.  Understanding the Adoption of Business Analytics and Intelligence , 2018, WorldCIST.

[2]  Zulfiqar Hussain Pathan,et al.  Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique , 2018, Sustainability.

[3]  Ozalp Vayvay,et al.  An Overview of Big Data for Growth in SMEs , 2016 .

[4]  M. Chuah,et al.  Are Malaysian companies ready for the big data economy? A business intelligence model approach , 2015 .

[5]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[6]  Kalyan Agrawal,et al.  Investigating the determinants of Big Data Analytics (BDA) adoption in Asian emerging economies , 2015, AMCIS.

[7]  Billy Mathias Kalema,et al.  Developing countries organizations’ readiness for Big Data analytics , 2017 .

[8]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[9]  Elisabetta Raguseo,et al.  Big data technologies: An empirical investigation on their adoption, benefits and risks for companies , 2018, Int. J. Inf. Manag..

[10]  B. M. Kalema,et al.  Big Data Analytics readiness: A South African public sector perspective , 2016, 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech).

[11]  Marta Indulska,et al.  Factors influencing effective use of big data: A research framework , 2020, Inf. Manag..

[12]  Kin Meng Sam,et al.  Understanding Adoption of Big Data Analytics in China: From Organizational Users Perspective , 2018, 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[13]  Surabhi Verma,et al.  An extension of the technology acceptance model in the big data analytics system implementation environment , 2018, Inf. Process. Manag..

[14]  Chun-Hsiung Liao,et al.  User acceptance of computer-mediated communication: The SkypeOut case , 2009, Expert Syst. Appl..

[15]  Patrick Mikalef,et al.  Big data analytics and firm performance: Findings from a mixed-method approach , 2019, Journal of Business Research.

[16]  K. Agrawal Investigating the determinants of Big Data Analytics (BDA) adoption in emerging economies , 2015 .

[17]  Raja Haslinda Raja Mohd Ali,et al.  A proposed framework of big data readiness in public sectors , 2016 .

[18]  Jong-Hyun Park,et al.  The Factors of Technology, Organization and Environment Influencing the Adoption and Usage of Big Data in Korean Firms , 2015 .

[19]  Surabhi Verma,et al.  Perceived strategic value-based adoption of Big Data Analytics in emerging economy: A qualitative approach for Indian firms , 2017, J. Enterp. Inf. Manag..

[20]  Amir Manzoor,et al.  A study of big data for business growth in SMEs: Opportunities & challenges , 2018, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).

[21]  Shirley Coleman,et al.  How Can SMEs Benefit from Big Data? Challenges and a Path Forward , 2016, Qual. Reliab. Eng. Int..

[22]  Anke Schüll,et al.  On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors , 2018, ICEIS.

[23]  Syed Mithun Ali,et al.  Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh , 2019, Comput. Ind. Eng..

[24]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[25]  Irwin Brown,et al.  Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry , 2015, SAICSIT '15.