Influence of Digital Transformation on Relational Capital and Digital Entrepreneurial Resilience
Bibliographic record
Abstract
Research on digital transformation, conducted so far, has revealed its explanatory power on the performance of companies. However, its effectiveness for relational capital and the resilience of digital entrepreneurship remains little explored. Even studies on the model, involving digital transformation and digital entrepreneurial performance, are few. To shed light on this grey area, interviews, and surveys of players in the digital economy were conducted. The mixed methodology was applied. Data generation was conducted through 15 semi-structured interviews and 160 surveys per survey. The digital transformation project decomposition approach was used. The results showed that the appropriation of new digital technologies, the creation of web media and the use of digital platforms improve the dynamics of relational capital, which increases the resilience of digital entrepreneurship. But, above all, it is the relational capital, made up of business networks, customer relations, database management mechanisms that promote the growth of new companies. The results of this article are useful for researchers who will find a renewed definition of digital transformation with proven new elements that prove relevant. While entrepreneurs and consultants will find new ways to effectively improve and enhance relationship capital and digital entrepreneurship. The study is part of the theory of dynamic capabilities and suggests that there is a differentiated relational contingency at each of the phases of the construction of digital transformation projects. It proposes a relevant action plan for researchers and entrepreneurs.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".