Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Aptisi Transactions on Management
Maria, Lily
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Structural Framework for Effective Time Management in Dynamic Work Environments Sana, Eirene; Jacqueline, Greisy; Nathalie, Julia; Maria, Lily; Callula, Brigitta
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2256

Abstract

This paper presents a structural framework to enhance time management profi- ciency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The evaluation demonstrates significant improvements in time management efficiency and client satisfaction across various professional contexts.  For instance, by employing the eisenhower Matrix and Pareto Principle, project managers achieved a 20% im- provement in project completion times. The framework’s adaptability is further highlighted by a 25% reduction in project turnaround time in a marketing agency and a 30% increase in project visibility in a startup. These findings underscore the framework’s practical implementation as a holistic approach to managing time effectively and achieving long-term success. Continuous refinement, real- time feedback integration, and exploring the impact of emerging technologies are recommended for further enhancing the framework’s effectiveness. This research contributes valuable insights for organizations aiming to navigate the complexities of modern work environments.
Leveraging Artificial Intelligence for Competitive Advantage in Indonesian SMEs Jaya, Aswadi; Cahyono, Dwi; Maria, Lily
APTISI Transactions on Management (ATM) Vol 9 No 3 (2025): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v9i3.2524

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern business, offering significant opportunities to enhance operational efficiency, improve customer engagement, and foster innovation. In Indonesia, Small and Medium Enterprises (SMEs) play a crucial role in driving economic growth and employment, yet their adoption of AI technologies remains limited due to resource constraints, lack of expertise, and uncertainty about return on investment. This study aims to investigate how AI adoption can be leveraged to achieve competitive advantage in Indonesian SMEs. Using a mixed-methods approach, data were collected through surveys and in-depth interviews with SME owners across various sectors. The findings indicate that AI applications such as predictive analytics, chatbot-based customer service, and demand forecasting have contributed to increased productivity, cost efficiency, and market competitiveness. The study provides a practical framework for AI implementation tailored to the needs and capacities of Indonesian SMEs, offering insights for business practitioners, policymakers, and researchers seeking to advance digital transformation in the SME sector.
Co-Authors Aswadi Jaya, Aswadi Callula, Brigitta DWI CAHYONO Jacqueline, Greisy Nathalie, Julia Sana, Eirene