Sustainable digital transformation in the energy sector: The role of artificial intelligence training in achieving Jordan’s green growth strategy
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DOIhttp://dx.doi.org/10.21511/ee.17(1).2026.01
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Article InfoVolume 17 2026, Issue #1, pp. 1-11
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Type of the article: Research Article
Abstract
This paper aims to examine the role of artificial intelligence (AI) training effectiveness in achieving a green growth strategy in Jordan, particularly at the Jordanian Electric Power Company, which represents the Jordanian energy sector. The analysis is supported by the multifaceted program evaluation framework by Daniel Stufflebeam (CIPP). AI training is considered a strategic intangible asset that promotes the growth of rare and invaluable intangible human capital. Quantitative cross-sectional research design was applied, targeting employees of the Jordanian Electric Power Company. Using a simple random sampling, 178 valid responses were directly engaged. The assessment of the theoretical and structural models was done using SPSS and SmartPLS. The results indicated that AI training effectiveness is a significant predictor of the green growth strategy’s outcomes (β = 0.562, t = 8.990, p < 0.001), explaining 31.6% of the variance. The strongest predictor among the CIPP dimensions was the dimension of input (β = 0.556, R2 = 0.310), then the dimensions of context (β = 0.532, R2 = 0.283), process (β = 0.516, R2 = 0.266), and product (β = 0.487, R2 = 0.237). These findings indicate that highly developed training programs, when designed according to the organizational context, resource-rich, and executed effectively, yield quantifiable skills development and play an influential role in meeting the goals of the national green growth strategy.
Acknowledgment
Gratitude is expressed to the Middle East University, Amman, Jordan, for the financial support to cover this article’s publishing fee.
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JEL Classification (Paper profile tab)O33, M15, I25, Q55, Q42
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References35
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Tables3
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Figures1
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- Figure 1. Structural model
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- Table 1. Demographics
- Table 2. Mean, standard deviation, loading, Cronbach’s alpha, CR, and AVE
- Table 3. Regression analysis results
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