Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
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This Author published in this journals
All Journal Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Novatio : Journal of Management Technology and Innovation Logistica : Journal of Logistic and Transportation
Toja, Andi Batari
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Transportation

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Analysis of Air Cargo Cost Efficiency at Sentani Airport Papua: The Role of Logistics Infrastructure, Tariff Policies, and Technological Innovation Toja, Andi Batari; Bunahri, Rifqi Raza
Dinasti International Journal of Economics, Finance & Accounting Vol. 5 No. 5 (2024): Dinasti International Journal of Economics, Finance & Accounting (November - De
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijefa.v5i5.3498

Abstract

This study examines the factors influencing air cargo cost efficiency in Papua, where shipping costs are disproportionately high. The research focuses on three variables: logistics infrastructure, tariff policy, and technological innovation. A quantitative approach was applied, with data collected from 50 logistics employees at Sentani Papua Airport using a Likert scale questionnaire. Multiple linear regression analysis was used to assess the partial and simultaneous effects of these factors. The results show that logistics infrastructure and tariff policy have significant positive effects on air cargo cost efficiency, with t-test values of 6.774 and 9.972, respectively. However, technological innovation has no significant effect, with a significance value of 0.076. The F-test results indicate that the independent variables jointly influence the dependent variable, with an Fcount of 40.635, far exceeding the Ftable value of 2.81. The three factors account for 72.1% of the variation in air cargo cost efficiency, while the remaining 27.9% is influenced by other factors. This study suggests that improving logistics infrastructure and tariff policies can enhance cost efficiency, benefiting companies and consumers.
Strategic Enablers of ROI in Data Driven Marketing: The Role of Leadership, Culture, and BI Maturity Widayat, Tri Agung; Toja, Andi Batari
Novatio : Journal of Management Technology and Innovation Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/novatio.v3i1.858

Abstract

This study investigates how data-driven marketing practices influence return on investment (ROI), focusing on the organizational enablers that shape their effectiveness. While the rapid growth of big data and analytics offers firms new opportunities, many struggle to translate these resources into measurable financial outcomes. This research addresses this gap by examining how leadership orientation, cultural readiness, and business intelligence (BI) maturity enhance the effectiveness of data-driven strategies in improving ROI.  Using a mixed-methods approach, the study combines survey data with illustrative case studies to uncover how firms align data strategies with performance outcomes. Case evidence, such as Hugo Boss’s €15 million investment in data infrastructure, is used to complement the quantitative results and illustrate practical relevance.  Findings show that predictive analytics and self-service BI can substantially increase ROI compared to traditional marketing methods. Their effectiveness is strengthened when supported by transformational leadership, a strong data culture, and organizational learning. Moreover, firms with mature BI systems demonstrate greater agility in responding to market changes, while competitive industry conditions further amplify the benefits of data-driven strategies. This study contributes a comprehensive model linking data strategies, organizational enablers, and financial performance. It offers practical insights for managers seeking to maximize the value of analytics investments through strategic alignment, cultural transformation, and committed leadershi.
Intrinsic Versus Extrinsic Motivation in Logistics: A Quantitative Analysis of Performance Outcomes Budiyanto, Albert; Masito, Fitri; Toja, Andi Batari
Logistica : Journal of Logistic and Transportation Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i1.733

Abstract

Employee motivation is a critical determinant of organizational performance, particularly in logistics warehousing where efficiency and resilience are paramount. This study investigates the influence of intrinsic and extrinsic motivation on employee performance at PT Aerojasa Cargo using Herzberg’s two-factor theory as a framework. A quantitative survey was conducted with 55 employees selected from 120 using Slovin’s formula. Motivation (knowledge, skills, rewards, behavioral direction, persistence) and performance (accuracy, timeliness, quality, quantity, neatness) were measured through a 5-point Likert-scale questionnaire, and data were analyzed using SPSS 26. Regression analysis revealed a strong correlation (R² = 0.833, p < 0.05), with intrinsic factors such as persistence and recognition emerging as the strongest predictors of performance, surpassing extrinsic motivators like salary. These findings provide robust empirical evidence of the relevance of Herzberg’s theory in Indonesia’s logistics sector and underscore the need for HR strategies that prioritize intrinsic motivators. Practical contributions include designing recognition systems, training, and career development programs to enhance employee persistence and behavioral alignment.
Bridging Gaps in Transport Demand Forecasting through Artificial Intelligence and Machine Learning Masito, Fitri; Toja, Andi Batari; Judijanto, Loso
Logistica : Journal of Logistic and Transportation Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i4.1065

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool for transportation demand forecasting, addressing the limitations of traditional statistical approaches. This study systematically reviews recent literature to evaluate AI methodologies, their applications, and the systemic factors that shape adoption. Peer-reviewed studies published between 2018 and 2025 were identified from Scopus, Web of Science, and Google Scholar. Findings reveal that AI techniques, particularly deep learning and ensemble models, consistently outperform conventional forecasting methods in predictive accuracy and adaptability. Integration of spatio-temporal and geospatial data further enhances model robustness, supporting more responsive strategies for sustainable urban mobility. Applications span passenger transport, freight logistics, public transit optimization, and electric vehicle charging demand. Nonetheless, challenges persist, including data scarcity, computational demands, interpretability concerns, and uneven adoption between developed and developing regions. The review underscores the need for supportive policies, collaborative data management, and fairness-aware models. Overall, leveraging AI in transport forecasting is essential to build efficient, adaptive, and inclusive mobility systems while aligning future research with long-term planning and sustainability goals.
Co-Authors Budiyanto, Albert Bunahri, Rifqi Raza Judijanto, Loso Masito, Fitri Widayat, Tri Agung