B2B Networking, Renewable Energy, and Sustainability
Bibliographic record
Abstract
While the benefits and advantages of using renewable energies are remarkable, and their prices have been decreasing dramatically and are expected to fall further, the diffusion and adoption of renewable energies still lag fossil energies. This paper improves our understanding regarding the role of the interrelationship among businesses (as an example of B2B networking amongst parent and subsidiary firms). Furthermore, it demonstrates the way/s that such interrelationships can contribute to the diffusion and adoption of sustainable and energy-efficient technologies. This study describes four diffusion channels in the interrelated firms which can help with promoting and using renewable and sustainable energies. The paper also reports the actual share of each diffusion channel contributing to implementing sustainable energy-efficient technologies in practice. The findings suggest that parent organisations enforce the majority (over 50%) of sustainable and energy-efficient technologies implemented in a B2B environment. In comparison, inter-subsidiary relationships are responsible for less than 30% of the implemented sustainable and energy-efficient technologies in organisations. The findings are in line with the forced perspective theory. They could, to some degree, explain the differences in the levels of implementation of sustainable and energy-efficient technologies in practice. These findings can help practitioners prioritise the diffusion channels when they want to facilitate the implementation of new technologies in their organisations. While some organisations may expect a more successful implementation of innovations initiated by subsidiaries than those enforced by parent organisations, the levels of success of the adoption of sustainable and energy-efficient technologies are not examined in this study. Further research is recommended to investigate the extent of association between different diffusion channels and the levels of success in terms of the adoption of innovation. We did not find similar studies to compare the results, which could be one of the limitations of this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".