Exploring sustainability-oriented innovation capabilities in the Indonesian manufacturing firms, 80th Annual Meeting of the Academy of Management (AOM), Vancouver, Canada (2020).
Bibliographic record
Abstract
Although the studies on sustainability-oriented innovation (SOI) have grown significantly in the last decades, to date research on specific SOI capabilities required by the firm to be a more sustainable innovator is still under-explored. Capability-based perspective is revisited to become a foundation for this empirical study. Specifically, capability theories linked to innovation and sustainability fields involved, including innovation management capabilities (IMC), natural resource-based view (NRBV), and social RBV (SRBV) with dynamic capabilities as overarching theory. As the nature of this research is exploratory, a qualitative approach is employed uses semi-structured interviews to 33 owner and manager of manufacturing firms in Indonesia, supplemented by site visit and archival documentation for triangulation. The findings suggested that around half of the firms studied adopting SOI with an operational optimisation approach. It is found from the data that transition is exists between SOI approaches. Firms operating at a higher level of SOI approach have specific dynamics capabilities above baseline ordinary SOI capabilities (production, marketing, environmental and social) that help them become a more sustainable innovator. These SOI dynamics capabilities include capture SOI idea, proactivity to SOI opportunity, mechanism to implement SOI, stakeholder management for SOI, SOI governance, and SOI continual learning.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".