The impact of artificial intelligence capabilities on the sustainability with the mediating role of green innovation in the Jordanian hotels sector
Bibliographic record
Abstract
The hotel industry in Jordan plays a crucial role in stimulating economic expansion by attracting tourists and creating job prospects. The industry can benefit from the use of Artificial Intelligence (AI) to improve sustainability through the promotion of green innovation, efficient resource utilization, and reduction of environmental harm. Hence, this study designs a model to enhance the environmental, economic, and social sustainability in the Jordanian hotels Sector. The study aimed to examine the impact of the AI Capabilities (tangible, intangible and human) on social, economic, and environmental sustainability with the mediating effect of the green innovation. The population of this study is all employees in 19 eco-friendly hotels in Jordan, they were 18,850 distributed over four Jordanian regions (Amman, Aqaba, Dead Sea and Petra). A total of 377 questionnaires distributed to respondents using stratified sampling. The study used SEM with SMART-PLS 4 to analyze the data collected. The measurement model applied to analyze the reliability and reliability of the model, the path coefficient in the structural equation model used to test the study hypotheses. The results of this study supported most of the study’s hypotheses, as it supported the impact of tangible and human capabilities on the sustainability, while the study did not find any direct impact of the intangible capabilities on the sustainability in the hotel sector in Jordan. The results show significant direct impact of the three AI capabilities; tangible, intangible and human on the green innovation, also the study found significant impact of the green innovation on the sustainability. The study confirms the three mediation hypotheses of the green innovation on the impact of the AI capabilities on the sustainability in the Jordanian hotel sector. The study provides important implications to the managers in the Jordanian hotel sector to enhance their environmental, economic and social sustainability by improving AI capabilities and innovation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".