Digital transformation in an emerging economy: exploring organizational drivers
Bibliographic record
Abstract
While there is sufficient evidence from empirical studies that digital technologies are strategic resources for value creation, existing literature on the theme lacks general concepts that explore an organization’s strategic resources concerning digital orientations and transformation initiatives. This study hence builds on a resource-based view with digital orientation literature to conceptualize a novel strategic orientation concept to understand the attitude toward digital innovation integration among firms in an emerging and developing country. This study tests a new conceptual framework using survey data from 472 employees of small-to-medium-sized service-based firms and employing structural equation model analysis with a variance-based SEM approach. Our empirical results showed that IT infrastructural availability and digital innovation investment are significant organizational drivers that have a direct relationship with attitude toward digital innovation integration among firms whereas IT competencies, digital innovation management, and knowledge of digital innovation are not significant with attitude toward digital innovation integration. More importantly, our findings advance the literature on a firm’s strategic resources and bring the realms of strategy and digital orientation closer together. The study provides valuable insights for management and shareholders regarding the specific drivers that affect the adoption of digital technologies among service-based SMEs in emerging economies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".