Organizational innovation for sustainable globalization: balancing technological transition with climate and geopolitical challenges
Bibliographic record
Abstract
Purpose This study explores how organizational innovation supports environmental sustainability through digital transformation, green technologies and operational efficiency amid technological, environmental and geopolitical challenges. Design/methodology/approach A qualitative approach and secondary data analysis are used to examine innovation across technological, managerial and business model dimensions, with a focus on integrating sustainability into corporate strategy. Findings The study finds that organizations can align profitability with sustainability by adopting green technologies and digital solutions that enhance efficiency and resilience. However, aligning innovation with sustainability goals remains challenging, particularly in complex regulatory and geopolitical environments. Embedding ecological civilization principles into strategy is vital for long-term competitiveness. Research limitations/implications Reliance on secondary data and qualitative methods may limit generalizability. Future research should incorporate empirical studies and quantitative analyses to validate these findings. Practical implications The study offers strategic insights for integrating sustainability into innovation processes. It highlights the need for adaptive corporate strategies to enhance resilience in volatile markets. Social implications Promoting sustainability-driven innovation contributes to environmental protection, renewable energy adoption and responsible corporate behavior. Originality/value This research provides theoretical and practical contributions by examining the role of digital transformation, AI analytics and green technologies in achieving sustainable innovation and competitive advantage.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".