Sustainability Innovation: Drivers, Capabilities, Strategies, and Performance
Bibliographic record
Abstract
Abstract Though concern for environmental issues dates back to the 1960s, research and practice in the field of sustainability innovation gained significant attention from academia, practitioners, and NGOs in the early 1990s, and has evolved rapidly to become mainstream. Organizations are changing their business practices so as to become more sustainable, in response to pressure from internal and external stakeholders. Sustainability innovation broadly relates to the creation of products, processes, technologies, capabilities, or even whole business models that require fewer resources to produce and consume, and also support the environment and communities, while simultaneously providing value to consumers and being financially rewarding for businesses. Sustainability innovation is a way of thinking about how to sustain a firm’s growth while sustainably managing depleting natural resources like raw materials, water, and energy, as well as preventing pollution and unethical business practices wherever the firm operates. Sustainability innovation represents a very diverse and dynamic area of scholarship contributing to a wide range of disciplines, including but not limited to general management, strategy, marketing, supply chain and operations management, accounting, and financial disciplines. As addressing sustainability is a complex undertaking, sustainability innovation strategies can be varied in nature and scope depending upon the firm’s capabilities. They may range from incremental green product introductions to radical innovations leading to changes in the way business is conducted while balancing all three pillars of sustainability—economic, environmental, and social outcomes. Sustainability innovation strategies often require deep structural transformations in organizations, supply chains, industry networks, and communities. Such transformations can be hard to implement and are sometimes resisted by those affected. Importantly, as sustainability concerns continue to increase globally, innovation provides a significant approach to managing the human, social, and economic dimensions of this profound society-wide transformation. Therefore, a thorough assessment of the current state of thinking in sustainability innovation research is a necessary starting point from which to improve society’s ability to achieve triple bottom line for current and future generations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".