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Developing Big Data Analytics (BDA) Utilization Model in Indonesia

13 pagesPublished: September 20, 2022

Abstract

This paper intends to develop a model of Big Data Analytics (BDA) utilization in Indonesian context. This is important due to the lack of related research on what factors influencing company to adopt BDA as their strategic and somehow secret weapon to win in nowadays intensified competition among companies in industries. By the model, this paper aims to contribute additional knowledge on what factors influence company to adopt this emerging technology as part of their strategic action in winning the market. Thus, intended questionnaires are distributed to about 206 companies. However, only 124 responses can be gathered and proceed using Part-Least Square (PLS) Structural Equation Modeling (SEM) and TOE Framework by adopting SmartPLS 3.0. By processing those data, two significant insights can be generated. First, BDA adoption in Indonesia is mainly encouraged degree of technology savviness, organizational readiness, and better anticipating environmental changes. Second, Organization readiness is also influenced by technology savviness and environmental changes anticipation. the company needs to master its technology which in Indonesia, compatibility and relative advantage could be significant issue. Thus, for those who want to adopt this emerging technology need to develop technology savviness and organization readiness while anticipate environmental changes.

Keyphrases: Big Data Analytics, Partial-Least Square (PLS) Structural Equation Modeling (SEM), TOE framework

In: Tokuro Matsuo (editor). Proceedings of 11th International Congress on Advanced Applied Informatics, vol 81, pages 177--189

Links:
BibTeX entry
@inproceedings{IIAIAAI2021-Winter:Developing_Big_Data_Analytics,
  author    = {Jonny},
  title     = {Developing Big Data Analytics (BDA) Utilization Model in Indonesia},
  booktitle = {Proceedings of 11th International Congress on Advanced Applied Informatics},
  editor    = {Tokuro Matsuo},
  series    = {EPiC Series in Computing},
  volume    = {81},
  pages     = {177--189},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/lHKQ},
  doi       = {10.29007/g75c}}
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