Life Cycle-Based Generic Business Strategies for Sustainable Business Models
Bibliographic record
Abstract
The need to transition to a more sustainable economy is one of the most significant challenges society has ever faced. Despite the evidence that adopting a more sustainable business model is linked to more stable profits, many conventional manufacturers do not know where to begin. This study aims to identify generic business strategies that conventional manufacturers can use to improve their business models and thus be more sustainable and/or develop new sustainable business models. In order to identify such strategies, data were gathered from 105 Korean business cases involving a wide range of products and services via online searches and interviews. Business cases were chosen based on whether they relied on a new business paradigm and directly or indirectly generated economic, social, and environmental benefits. Through analyses of the data, generic business strategies were extracted for each life cycle stage. The results showed that the success of a sustainable business model depends on a mixture of pertinent generic business strategies from the life cycle perspective. A conventional business model that focused on a particular life cycle stage and strategy was not very successful. However, a new business model using generic business strategies (such as eco-design as well as product-service system (PSS)-oriented strategies geared at the relevant life cycle stage) produced significant environmental, economic, and social performance improvements. Not only an appropriate mixture of generic business strategies but also systematic support such as infrastructural support is required if manufacturers are to achieve the potential sustainability of a new company-specific business model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".