How green is my outsourcer? Measuring sustainability in global IT outsourcing
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the intersection of sustainability and global IT outsourcing (GITO). GITO is well established as a business practice towards reducing costs and improving performance. Sustainability issues related to carbon footprint and greenhouse gases are increasingly important for all organizations. Responsible and economic energy management is a critical business capability and environmental responsibility in global outsourcing. Design/methodology/approach Drawing on empirical work undertaken in the UK and North America together with content analysis of public data from leading GITO providers, this paper presents a model for measuring sustainability in outsourcing. Findings The research findings demonstrate a growing environmental maturity in GITO firms, as measured against external recognized standards such as the Global Reporting Initiative, the Carbon Disclosure Project, the UN Global Compact and the ISO environmental and social responsibility standards. Practical implications In the context of social, economic and political discussions regarding sustainability, this paper contributes to our practical and theoretical understanding of GITO providers and the impact of environmental issues in outsourcing. Social implications Consumers, governments and society at large demonstrate increasing expectations for sustainability from all organizations. Outsourcers can provide improved sustainability capability to their buyers in this important area. Originality/value Environmental and social responsibility in global outsourcing has received little attention in academic research. This paper provides a starting point for further investigation of the role of sustainability in 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".