Digital transformation and the challenges associated with applying digital technologies in achieving strategic flexibility in public administration: a case study in Jordan
Bibliographic record
Abstract
The study aimed to identify the impact of digital transformation and analyze the obstacles and complexities facing the integration of digital technologies into Jordanian Customs and their subsequent impact on achieving strategic flexibility. The study aims to uncover challenges, evaluate their effects, and suggest recommendations to enhance adaptive strategies within Jordanian Customs considering digital transformations. The study included employees in Jordanian General Customs, including customs officials, IT staff, and administrative staff working on digital initiatives. A population of 670 individuals was identified, from whom a purposive sample of (465) directly involved in digital transformation efforts was selected to participate in the study. The study used the descriptive analytical approach to comprehensively investigate the challenges of digital transformation within Jordanian Customs. Surveys, interviews, analysis of regulatory documents formed data collection methods. Quantitative data were subjected to descriptive analysis and regression modeling, while qualitative insights were thematically analyzed to provide a comprehensive understanding of the challenges faced. The analysis revealed the results, the most important of which is that digital transformation has a positive, statistically significant impact in its four dimensions (strategy, organizational culture, transformational leadership, and human resources) in achieving strategic agility in Jordanian Customs, and reveals multi-faceted challenges prevailing within Jordanian Customs, including structural constraints. Infrastructure, resistance to change, cyber security vulnerabilities, and skills gaps among the workforces. The regression analysis highlighted the significant impact of these challenges in hindering the achievement of strategic flexibility within the Customs Department. Based on the results of the study, Jordanian Customs is recommended to take proactive measures to confront the challenges identified. This includes investing in a robust technology infrastructure, implementing targeted training programs to improve employee skills, promoting a culture of innovation, and establishing cross-departmental collaboration to enhance adaptability and strategic flexibility. The study recommended increasing attention to training workers and raising their capabilities to deal with digital transformation positively and improve services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".