The role of data analytics and business intelligence in enhancing the relationship between e-HRM practices and job satisfaction
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DOIhttp://dx.doi.org/10.21511/ppm.23(2).2025.14
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Article InfoVolume 23 2025, Issue #2, pp. 208-219
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This study investigates the mediating and moderating roles of data analytics and business intelligence in the relationship between e-HRM practices and job satisfaction in Jordanian SMEs. The data were obtained from a survey of 368 employees through questionnaires; the data were analyzed via SPSS and PROCESS Macro v3.5. The results show that e-HRM practices, such as e-recruiting, e-training, e-compensation, and e-performance, have a high and significant positive impact on job satisfaction (R² = 0.93, F = 1221.14, β = 0.613, p = 0.01). More precisely, concerning the dimensions of e-HRM, both e-training (β = 0.356, p < 0.001) and e-performance (β = 0.072, p = 0.02) were positively related to job satisfaction, while e-compensation had no statistical effect. Data analytics mediated this relationship positively by increasing its impact by 0.33 and contributing to an R² of 0.875 (p < 0.01). Furthermore, data analytics had a direct influence on job satisfaction (R² = 0.48, F = 333.93, β = 0.691, p < 0.001). Business intelligence moderated e-HRM-job satisfaction relationships through the amplification of its effect by 0.40 and had an indirect R² of 0.875 (p < 0.01). These results have presented the critical roles that data analytics and business intelligence play in the optimization of HR practices and employee satisfaction. The study has shown the need for Jordanian SMEs to adopt these technologies to enhance their HR capabilities and workforce engagement.
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JEL Classification (Paper profile tab)L25, M12, M15
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References33
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Tables7
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Figures2
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- Figure 1. Conceptual research model
- Figure 2. Structural model
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- Table 1. Detailed sample distribution by company size
- Table 2. Respondent characteristics
- Table 3. Validity and reliability of the study items
- Table 4. Internal validity of Pearson correlation
- Table 5. Direct effects using multiple leaner regression
- Table 6. Indirect effects using PROCESS Macro v3.5
- Table A1. Questionnaire
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