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The Influence of Key Performance Indicators (KPIs) on Employee Performance and Company Productivity at PT Nogopatmolo Banjarmasin Noor Syifa Hayati; Teguh Wicaksono; Abdurrahim Abdurrahim
Harmony Management: International Journal of Management Science and Business Vol. 2 No. 3 (2025): International Journal of Management Science and Business
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/harmonymanagement.v2i3.303

Abstract

This study aims to examine: (1) the influence of Key Performance Indicators (KPIs) on employee performance at PT Nogopatmolo Banjarmasin, (2) the influence of employee performance on company productivity at PT Nogopatmolo Banjarmasin, (3) the influence of KPIs on company productivity at PT Nogopatmolo Banjarmasin, and (4) the influence of KPIs on company productivity through employee performance as a mediating variable. This research employed a quantitative approach. The population consisted of all employees at PT Nogopatmolo Banjarmasin, totaling 120 individuals. A sample of 54 respondents was selected using the Slovin formula. Primary data were collected through interviews and questionnaires, while secondary data were obtained through documentation studies. The data were analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method with the SmartPLS 3.0 application. The results of the study indicate that: (1) KPIs have a positive and significant effect on employee performance at PT Nogopatmolo Banjarmasin, (2) employee performance has a positive and significant effect on company productivity, (3) KPIs have a positive and significant effect on company productivity, and (4) KPIs have a positive and significant effect on company productivity through employee performance as a mediating variable. These findings suggest that the proper implementation of KPIs can enhance employee work quality and sustainably boost company productivity. A single paragraph, maximum 250 words. Abstract content must contain (1) an overview of the object of research, (2) problems, and research objectives, (3) proposed methods, (4) main findings and results and synthesis of main ideas, and (5) conclusions.
Learning With Integrity: The Future Of Ethical Artificial Intelligence In Academia Abdurrahim; Zulfikar, Rizka; Purboyo; Widyanti, Rahmi; Basuki
Applied Business and Administration Journal Vol. 4 No. 2 (2025): Financial Accountability, Technological Innovation, and Sustainable Development
Publisher : Ebiz Prima Nusa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62201/abaj.v4i02.216

Abstract

This study explored the evolution of scholarly research addressing ethical concerns related to artificial intelligence (AI) within academic settings. Despite the growing use of AI technologies in higher education—ranging from instructional tools to administrative applications—limited empirical work has systematically examined how ethical issues have been conceptualized and discussed. A bibliometric analysis was conducted to address this gap, using data extracted from the Scopus database and 107 documents covering 2015 and 2025. The study employed the PRISMA method for data screening. Bibliometric mapping was performed using Biblioshiny-R, which enabled comprehensive visualization through co-occurrence networks, thematic maps, and trend analyses. The findings revealed a significant increase in scholarly output and interdisciplinary collaboration on AI ethics in academia. Key themes included algorithmic bias, transparency, accountability, fairness, and responsible innovation. Notably, the research highlighted a progressive shift from technical concerns toward more socially grounded issues such as inclusivity, data governance, and digital justice.  The study identified core publications shaping the field and suggested that ethical AI in education remains an emerging but critical area for future inquiry. These findings provide a robust foundation for developing evidence-based, globally relevant policy frameworks that promote fair, transparent, and accountable AI integration in higher education.