Proceedings of the 8th International Conference on Entrepreneurship and Business Management (ICEBM 2019) UNTAR

The Impact of Big Data Analytics Adoption on the Performance of Malaysian Small and Medium Enterprises

Authors
Loh Chun Hong, Teoh Ai Ping
Corresponding Author
Loh Chun Hong
Available Online 29 June 2020.
DOI
https://doi.org/10.2991/aebmr.k.200626.021How to use a DOI?
Keywords
Small and Medium Enterprises, Big Data Analytics, Adoption, Performance, Strategic Agility, Malaysia
Abstract
In today’s digitalized era, the adoption of Industry 4.0 technology pillars can boost businesses by enhancing organizational performance. As one of the crucial pillars, big data potentially has been receiving heightened interests from academics and practitioners. Big Data Analytics (BDA) is viewed as a new strategic weapon in business that allows organizations to gain valuable insights for decision-making towards better performance. Most multinationals and larger companies may already be implementing BDA aiming at enhancing organizational performance, but many Small and Medium Enterprises (SMEs) are still at the exploratory stage. The present study proposes the outcome of BDA adoption on performance among Malaysian SMEs and the role of strategic agility as moderator.
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Proceedings
8th International Conference of Entrepreneurship and Business Management Untar (ICEBM 2019)
Part of series
Advances in Economics, Business and Management Research
Publication Date
29 June 2020
ISBN
978-94-6252-980-9
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.200626.021How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Loh Chun Hong
AU  - Teoh Ai Ping
PY  - 2020
DA  - 2020/06/29
TI  - The Impact of Big Data Analytics Adoption on the Performance of Malaysian Small and Medium Enterprises
BT  - 8th International Conference of Entrepreneurship and Business Management Untar (ICEBM 2019)
PB  - Atlantis Press
SP  - 112
EP  - 116
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200626.021
DO  - https://doi.org/10.2991/aebmr.k.200626.021
ID  - Hong2020
ER  -