Investigation of the Antecedents of Digital Transformation and Their Effects on Operational Performance in the Jordanian Manufacturing Sector
Bibliographic record
Abstract
Digitalization is viewed as an important promoter of competitiveness, offering future avenues to new value and revenue opportunities. Nevertheless, the factors that determine the drivers of digital transformation (DT) adoption still need to be explored and understood further. Based on the RBV and institutional theory, this study examines the roles played by organizational culture, IT readiness, and customer demands of firms in the implementation of DT and the consequent improvement of operational performance. The results of a survey carried out among 226 manufacturing companies in Jordan indicate that these antecedent factors have a significant and positive effect on the adoption of DT and operational performance. The results also demonstrate that the implementation of DT enhances the operational performance of firms by increasing their efficiency and effectiveness. This research adds to the existing literature on digital transformation through an examination of the antecedents of its adoption. These findings are useful, as they assist firms in viewing digital transformation as an overarching opportunity that needs to be leveraged to improve their operational performance and competitiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".