The effects of internet of things, strategic green purchasing and green operation on green employee behavior: Evidence from hotel industry
Bibliographic record
Abstract
Many people are aware of taking care of the global environment, so they demand environmentally friendly business activities. The government also has responded to the commotion by requiring companies to produce friendly and safe products or services to their customers. Hotel industries react to it by implementing the concept of green hotels. The purpose of this study is to examine whether the Internet of Things, strategic green purchasing and green operation have impacted green employee behavior in star-hotels in East Java. Eighty-two (82) questionnaires were distributed, but only 62 questionnaires were valid, with a response rate of 75.60 %. SEM-PLS (Structural Equation Modelling Partial Least Square) was used to analyze the data. The results show that the Internet of Things (IoT) has a significant impact on green hotel operation, with the T-statistic value of 0.378. green purchasing has a significant impact on green hotel operation, with the T-statistic value of 0.545, and green employee behavior, with the T-statistic value of 0.346. The Internet of things (IoT) has no significant impact on green employee behavior directly but through green hotel operation. The use of energy efficiency and the existence of good waste management as indicators of green hotel operation has an impact on green employee behavior of 0.346.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".