Frontier Management Science

Ranking Influential Factors on Job Satisfaction Based on University Students' Perspective

Tue, 30 Apr 2024 00:00:00 +0000

This study utilizes Herzberg's Two-Factor Theory to quantitatively analyze university students' perspectives on future work motivation. Addressing the challenges in securing and sustaining meaningful employment, the research focuses on the impact of motivators on job attraction. A structured survey collects data from a representative sample of university students to quantify relationships between specific motivators and overall job attraction. Anticipated findings contribute empirical insights into the unique dynamics of youth employment, refining Herzberg's theory in this context. Implications extend to informing organizational practices, policies, and interventions for enhancing the job satisfaction and well-being of young workers.

Comparative Analysis of Forecasting Techniques for Enhancing Coconut Oil Export Predictions in the Philippines

Dustin Loreño, Aimee Olpenda — Tue, 30 Apr 2024 00:00:00 +0000

In the agricultural domain, the accurate forecasting of crop yields is crucial for economic stability and planning. The Philippines, being one of the world’s largest producers of coconut oil, has a significant portion of its agricultural sector influenced by the predictability of this commodity’s yield. While traditional forecasting methods have been employed, their accuracy fluctuates, necessitating the exploration of more reliable techniques. This study evaluates Grey Forecasting, Moving Average, Forecast by Forecasting Sheet, and Regression Analysis methods for predicting coconut oil production, comparing them over two decades. Through rigorous statistical analysis using measures like MAD, MSE, and MAPE. Grey Forecasting emerges as more consistent and accurate. In 2023, there was an increase of approximately 13.86% compared to 2022. In 2024, this figure rose to about 25.08% compared to 2023. Similarly, in 2025, there was an increase of roughly 18.17% compared to 2024. The study's contribution lies in its comprehensive long-term data analysis, offering new insights into Grey Forecasting's application. These findings could significantly impact Philippine agricultural planning and policies, prompting further research to refine forecasting methods. Emphasizing the value of advanced predictive models in agriculture, the study advocates for informed decision-making and resource allocation. Future research should focus on refining these models by incorporating broader datasets and advanced algorithms to improve accuracy and reliability.

Prioritizing Success Factors for Start-ups in Indonesia Using the Best Worst Method (BWM)

Yulita Dwi Safitri, Rina Pebriana, Eni Suasri — Wed, 25 Sep 2024 00:00:00 +0000

The rapid growth of Indonesia’s start-up ecosystem presents both opportunities and challenges for new ventures striving for sustainability and competitiveness. This study applies the Best Worst Method (BWM) to prioritize key success factors for start-ups in Indonesia, providing a structured framework for decision-making. Six critical factors were evaluated: access to funding, innovation capability, market competition, regulatory environment, talent acquisition, and scalability potential. Through pairwise comparisons, this research identifies access to funding as the most critical factor, while scalability potential is considered the least influential in determining start-up success. The findings offer valuable insights for entrepreneurs, investors, and policy-makers, highlighting areas where targeted support can enhance the growth and sustainability of start-ups. This study contributes to the ongoing discourse on start-up development in emerging economies by providing a decision-making tool to guide strategic priorities within Indonesia’s dynamic entrepreneurial landscape.