Factors influencing intent to adopt big data analytics in Malaysian government agencies

Mad Khir Johari Abdullah Sani, Muhamad Khairulnizam Zaini, Noor Zaidi Sahid, Norshila Shaifuddin, Tamara Adriani Salim, Noorazah Md Noor

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In Big Data Analytics (BDA), many government agencies directly raised their ICT expenditure in their effort to understand the attitude of the users towards new technologies. This research is intended to analyze factors affecting IT practitioners’ behavioral intentions in adopting (BDA) using a combination of multiple technology acceptance models. The synergistic three IS theory strengths: (1) Task Technology Fit (TTF), (2) Unified Technology Acceptance and Utilization Theory (UTAUT), and the (3) Initial Trust Model (ITM). The concept was validated in Malaysian government agencies, one of the highly dependent BDA promoters and initiators. 186 respondents in the Information Management departments of public agencies were recruited as part of the rigorous methodology to gather rich data. Partial least squares were analyzed by the structural models (PLS). The two key factors determine behavioral intention to adopt BDA in government agencies. Firstly, the assumption that the technology is going to produce great results raises the expectation of performance. Technological fit was the second determinant factor. Initial trust, on the other hand, was found to be adversely related to the BDA intention. Implicitly, the proposed model would be useful to IT officers in public agencies in making investment choices and designing non-adopter-friendly outreach strategies because they have more barriers to acceptance than adopters and lead adopters in the reward ladder. All public agencies will benefit from the findings of this study in gaining awareness of BDA application and fostering psychological empowerment of employees to adopt this revolutionary approach. The article outlines how dynamic TTF, UTAUT and ITM are for researchers to integrate in their emerging decision support framework for the study of new technology adoption.

Original languageEnglish
Pages (from-to)1315-1345
Number of pages31
JournalInternational Journal of Business and Society
Issue number3
Publication statusPublished - 2021


  • Adoption model
  • Big data analytics
  • Library and information management
  • Public agencies


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