The impact of artificial intelligence on organizational performance: The mediating role of employee productivity
Bibliographic record
Abstract
The adoption of Artificial intelligence (AI) technology in the workplace is becoming more common, with several research highlighting both its positive and adverse influence on employee productivity (EP) and organizational performance (OP). Furthermore, there is a scarcity of research focused on the mediator role of EP in the nexus between AI and OP. This ambiguity underscores the need for a comprehensive understanding of how AI interacts with EP and OP within specific organizational contexts, such as Ethio Telecom. Therefore, this study aimed to investigate the impact of AI on OP with a mediating role of EP. Quantitative data was collected through an online survey using Kobo Toolbox from 172 purposively selected employees. AI was modeled as third third-order formative construct, while EP and OP were first-order reflective constructs. The variables were measured using validated multi-item questionnaires with a 7-point Likert scale. The association between these variables was investigated with PLS-SEM in SMART PLS 4.1.03. The results showed that there were positive and significant relationships between AI and EP, AI and OP, EP and OP, and AI on OP through EP. Furthermore, EP served as a partial mediator between OP and AI. These findings are consistent with previous studies and theories, such as the resource-based view and human capital theories. The results suggest that organizations can dramatically improve performance in the digital age by implementing AI and creating a work environment that encourages productivity.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".