Antecedents and outcomes of green information technology Adoption: Insights from an oil industry
Bibliographic record
Abstract
Growing environmental concerns have led to increased demand for ‘green’ or environmentally friendly business. This has resulted in growing interest in the research of Green Information Technology (GIT). However, to date, such research has had a disproportionate emphasis on organisational antecedents while often overlooking outcomes. The current study aims at giving a better insight into the state of GIT adoption among oil companies in Sudan. If these companies were to adopt a green business model, it would significantly impact the environment given that they typically contribute significantly to environmental degradation. To this end, this study a) determines the level of awareness of GIT adoption among employees of oil companies in Sudan, b) identifies the key factors affecting the GIT adoption, c) examines the effect of training, top management support, perceived ease of use, perceived usefulness, relative advantages, and GIT behaviour on GIT adoption, and (d) examines the effect of GIT adoption on outcomes, namely business performance, competitive advantage, and process innovation. From a sample of 292 respondents, the result revealed that top management and GIT behaviour were two of the four antecedents not supported by data, thereby rendering them insignificant. Surprisingly, the survey data supports all three hypotheses that recognise a positive relationship between GIT adoption and the outcomes. This study provides important empirical evidence from oil companies that lack a green adoption policy that encourages them to consider joining the green bandwagon. The study concludes that most respondents are aware of GIT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".