What Aspects Explain the Relationship between Digital Transformation and Financial Performance of Firms?
Bibliographic record
Abstract
The emergence of digital transformation and digitization has significantly influenced business growth, particularly in response to the COVID-19 pandemic. This study conducts a systematic bibliometric analysis to investigate the relationship between digital transformation and firms’ financial performance. The primary objectives are identifying research gaps and proposing future research directions and policy implications. Specifically, we examine the evolution of digital transformation in companies and its impact on their financial performance, while highlighting the major trends in digital transformation research. Employing text mining techniques, network analysis, and a systematic literature review (SLR), we evaluated 153 articles published between 2014 and 2023. Our analysis delves into academic publication journals, geographical locations, authors’, and academic institutions’ contributions, assessing their influence on the existing literature’s development. Our findings indicate a current absence of a consistent theoretical framework in the scientific literature pertaining to the study of digital transformation and its effects on firms’ financial performance. Furthermore, we have pinpointed specific areas that warrant further investigation, including SMEs, non-listed companies, and intermediary or mediating variables. Finally, this systematic bibliometric analysis contributes to the ongoing discourse on digital transformation and its influence on firms’ financial performance, summarizing the current scientific research and proposing new research directions for future studies, while also offering valuable insights for researchers, policymakers, and practitioners.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".