Digital Transformation in Business: Strategies and Implications for Organizational Change
Bibliographic record
Abstract
Advanced algorithms, robotics, and analytics, among other digital technologies, are revolutionizing the dynamics of the workforce in organizations. Hence, the writers of this study have examined the consequences of emerging technology on Organizational Behavior. A significant proportion of the existing research on this topic has primarily examined the technology aspects, while neglecting the comprehensive perspective and its impact on organizational behavior. The uniqueness of this study resides in its ability to offer a comprehensive overview of the key digital technologies and assess their impact on employees and leadership. In order to achieve this objective, and considering the current relevance of the subject, the authors chose to examine the effects of digital technologies on organizational behavior. They accomplished this by conducting a thorough analysis of existing literature and organizing it according to the specific technologies and their implications. The article is divided into three sections. Firstly, the definitions of Organizational Behavior and digitalization were examined to establish a theoretical framework. This was followed by an analysis of the impacts and effects of digitalization on leadership and employees. Finally, the findings were summarized in a structured scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".