The Role of Sustainability Orientation in Outsourcing: Antecedents, Practices, and Outcomes
Bibliographic record
Abstract
With growing awareness of environmental issues and firm social responsibilities, outsourcing firms increasinglyrecognize the need to be sustainability-oriented as they pursue competitiveness in the global market. However,firms are not doing this purely for altruist reasons as they still have to make a profit and account to shareholders.Therefore thispaper investigates the antecedents that push firms that adopt outsourcing initiatives to besustainability-oriented. The authors develop a model that identifies the antecedents, goals, practices, andoutcomes associated with the sustainability-outsourcing linkage. The model starts by identifying fourantecedents (regulations, supply chain demand, firm reputation, and marketplace requirements) that cause ofsustainability orientation in a firm. Then, internal (i.e., product design) and external (i.e., supplier management)practices thatfirms may adopt in order to cope with their sustainability goals are introduced. Finally, the modelevaluates the impact of these practices on their outsourcing performance from the triple bottom line perspective.So this is a conceptual paper that sheds light on the role of sustainability orientation in outsourcing firms’performance, and calls for more research attention on sustainable outsourcing.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".