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Assessing The Relationship Between AI-Assisted Report Generation and Employee Productivity, Decision-Making, And Well-Being Among NIA-UPRIIS Employees
Subject area: Management and Commerce · Area of research: Engineering Management
DOI: 10.64388/IREV10I1-1719996
Abstract
This study examined the relationship between AI-assisted report generation and employee productivity, decision-making, and well-being among employees of the National Irrigation Administration–Upper Pampanga River Integrated Irrigation System (NIA-UPRIIS). A quantitative descriptive-correlational research design was employed involving 300 employees who had experience using AI-assisted tools in their work. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Cronbach's Alpha, and Pearson Product-Moment Correlation through the Statistical Package for the Social Sciences (SPSS). The findings revealed that respondents generally agreed on the utilization of AI-assisted report generation, with an overall mean of 3.887, indicating that AI tools are commonly used in report preparation and work-related tasks. Employee productivity, decision-making, and well-being were likewise rated positively. Reliability analysis showed Cronbach's Alpha coefficients ranging from 0.823 to 0.895, indicating high internal consistency of the research instrument. Pearson correlation analysis revealed significant positive relationships between AI-assisted report generation and employee productivity (r = 0.373), employee decision-making (r = 0.307), and employee well-being (r = 0.351), all significant at the 0.01 level. The study concludes that AI-assisted report generation is associated with improved workplace performance and employee well-being. The findings support the continued adoption of AI technologies in government organizations, accompanied by appropriate training, ethical guidelines, and organizational support to maximize their benefits.
Keywords
Artificial Intelligence, Employee Decision-Making, Employee Productivity, Employee Well-Being, Report Generation.
References
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How to cite this paper
@article{1719996,
author = {Ma. Andrea I. Balagtas, Christopher Ladignon, Noel Florencondia},
title = {Assessing The Relationship Between AI-Assisted Report Generation and Employee Productivity, Decision-Making, And Well-Being Among NIA-UPRIIS Employees},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {2304-2316},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719996.pdf},
abstract = {This study examined the relationship between AI-assisted report generation and employee productivity, decision-making, and well-being among employees of the National Irrigation Administration–Upper Pampanga River Integrated Irrigation System (NIA-UPRIIS). A quantitative descriptive-correlational research design was employed involving 300 employees who had experience using AI-assisted tools in their work. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Cronbach's Alpha, and Pearson Product-Moment Correlation through the Statistical Package for the Social Sciences (SPSS). The findings revealed that respondents generally agreed on the utilization of AI-assisted report generation, with an overall mean of 3.887, indicating that AI tools are commonly used in report preparation and work-related tasks. Employee productivity, decision-making, and well-being were likewise rated positively. Reliability analysis showed Cronbach's Alpha coefficients ranging from 0.823 to 0.895, indicating high internal consistency of the research instrument. Pearson correlation analysis revealed significant positive relationships between AI-assisted report generation and employee productivity (r = 0.373), employee decision-making (r = 0.307), and employee well-being (r = 0.351), all significant at the 0.01 level. The study concludes that AI-assisted report generation is associated with improved workplace performance and employee well-being. The findings support the continued adoption of AI technologies in government organizations, accompanied by appropriate training, ethical guidelines, and organizational support to maximize their benefits.},
keywords = {Artificial Intelligence, Employee Decision-Making, Employee Productivity, Employee Well-Being, Report Generation.},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1719996}
}