Managing Financial and Operational Risks Through Digital Transformation: The Mediating Influence of Information and Communication Technologies’ Adoption and Resistance to Change
Bibliographic record
Abstract
This study examines the relationships among ICT adoption, resistance to change, and digital transformation, focusing on their influence on financial and operational risk management. The research utilizes a quantitative design, drawing on data from 768 Moroccan professionals across multiple industries. Structural Equation Modeling (SEM) was employed to analyze both direct and indirect effects within the proposed theoretical framework. The findings indicate that ICT adoption has a positive effect on digital transformation, whereas resistance to change exerts a negative influence. Additionally, digital transformation mediates the impact of ICT adoption and resistance to change on financial and operational risks, thereby reducing these risks. These results underscore the potential role of change management in facilitating digital transformation and mitigating risk. From a managerial and policy standpoint, the study highlights the importance of fostering an organizational environment supportive of ICT adoption and addressing resistance to change. Integrating digital transformation into risk management strategies may also contribute to organizational resilience. This research extends existing knowledge by clarifying how digital transformation mediates the relationship between technology adoption, organizational behavior, and risk management.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".