Mapping the Impact of Business Model Innovation on Firm Productivity: A Bibliometric Analysis and Global Perspective
Bibliographic record
Abstract
The study explores the impact of business model innovation on firm productivity with the help of a systematic bibliometric analysis. The purpose is to distill key themes, critical research needs, and possible future directions. A systematic search was performed with the Web of Science database (2011 to 2024) using PRISMA 2020 guidelines. Of these studies, after applying defined inclusion and exclusion criteria, the study retained 273 studies; of those, 217 explicitly considered productivity at the firm level. This results in the following three central research themes: digitalization, business model innovation, and sustainability, which reflect how firms adjust to technological and environmental as well as strategic demands. The paper discusses three examples: theoretical fragmentation and regional biases within research on health worker migration and less integration of institutional and contextual factors. One of the gaps here is that there is a paucity of empirical evidence from emerging economies where firms face their own unique set of barriers to innovation and productivity. This work adds a level of clarity to what has been studied and what is unexplored, both enhancing academic knowledge and setting clear directions for managers and policymakers. It is time for more geographic ranges and collaboration across fields, such as with health care or business models that are likely to unfold over time.
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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 | 0.030 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.201 | 0.272 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".