Technological, organizational and environmental factors influencing on user intention towards big data technology adoption in Malaysian educational organization
Bibliographic record
Abstract
Studying the factors in influencing the users’ intentions to adopt Big Data technology in Malaysia is crucial. This study adopted three grand theories which consisted of Dissemination of Innovation (DOI) theory, Technology Acceptance Model (TAM), and Technology-Organization Environment (TOE) framework. The model specifies technological, organizational, and environmental factors as determinants of the users’ intentions to adopt big data technology. This study aims to review the technology, organizational and environmental factors that can determine the intentions towards big data technology adoption. A total of 224 questionnaires were obtained and screened. Data was analyzed using the Partial Least Square Modelling of Structural Equations due to one of the best software for verifying structured data on structural equations modelling (SEM) Smart PLS 3.0 as analytical tools. This study finds that the predictor variables of compatibility, security are significant and critically direct to the users’ intentions to adopt big data technology. The results indicate that the model is suited for studying users’ intentions to adopt Big Data technology in educational organizations. This study can help the Malaysian ministry of education to emphasize the important factors in further developing the use of Big Data technology in organizations. The findings provide important recommendations and implications for BDA technology practitioners and application developers, which could coincide with successful BDA technology deployment. This study provides practitioners with practical recommendations for guidance in incorporating and endorsing BDA activities in their organizations in order to maximize the benefits of revolutionary technology, particularly in government agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".