DESCRIPTION
The Journal of Global Business is an Journal for those who present their research at the Global Business Conference held twice a year in Manila, Philippines. This conference is organized by the Association of Training Institutions for Foreign Trade in Asia and the Pacific. The Journal contains researches of professors in business and other fields.
ISSN: 2350-7179 (Online Journal)
Journal issues
Volume 1, issue 1 (2012)
volume 2, issue 1 (2013)
volume 3, issue 1 (2014)
volume 4, issue 1 (2015)
volume 5, issue 1 (2016)
volume 7, issue 1 (2018)
volume 8, issue 1 (2019)
volume 9, issue 1 (2020)
volume 10, issue 1 (2021)
volume 10, issue 2 (2021)
volume 11, issue 1 (2022)
volume 11, issue 2 (2022)
volume 12, issue 1 (2023)
volume 12, issue 2 (2023)
volume 13, issue 1 (2024)
VOLUME 13, ISSUE 2 (2024)
Volume 14, Issue 1 (2025)
Volume 14, issue 2 (2025)
VOLUME 14, ISSUE 3 (2025)
VOLUME 14 ISSUE 2 (2025)
JGB 19301
“Too Good to Be True?: The Role of Cognitive Biases in Falling for Online Financial Scams”
Eric S. Parilla / Read Full Paper
Keywords
Cognitive Biases, Scam Susceptibility, Dual-Process Theory, System I and System II, Online Scams, Behavioral Fraud Prevention, Filipino Digital Behavior, Cognitive Vulnerability, Cybercrime Psychology, Bias-Processing Vulnerability Model (BPVM)
Abstract
The study raises questions about how cognitive biases influence the likelihood of Filipino respondents falling victim to online financial scams, with cognitive processing style bridging and mediating. Using as a basis the Dual-Process Theory of Cognition, the question is posed: are responses to a scam stimulus based on rapid, intuitive thinking (System I), or on slow and reflective reasoning (System II)? Adopting a quantitative-correlational design, data were gathered from 530 individuals through a structured survey that targeted the five biases under analysis: authority bias, optimism bias, scarcity heuristic, confirmation bias, and availability heuristic. Results indicated that optimism and authority biases were more prevalent; however, the majority of participants reported that they rarely engage in risky online behavior —that is, they tend to resort to a reflective type of thinking (System II). According to the mediation analysis, the cognitive processing style mitigates the effect that biases have on scam susceptibility. In the latest Bias-Processing Vulnerability Model (BPVM), it is proposed that vulnerability to scams arises from both bias and the type of processing. Therefore, these findings support the further development of cognitive interventions for building digital resilience.
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JGB 19315
“Crop Suitability Recommendation Based on Soil Parameters and Environmental Factors with Gradient Boosting Trees and Random Forest Algorithm”
Jhenica Trisha Laguit, Britanny Baldovino, Angel Nica Elegado, Aldwyn Reaño, Raevenn Bangsal, Carlos Opeña, Aira Reyes & Hitler Kumar Wanget / Read Full Paper
Keywords
Machine Learning, Random Forest, Gradient Boosting, Crop Suitability
Abstract
This study develops a crop suitability recommendation model using soil parameters and environmental factors with Random Forest and Gradient Boosting Trees algorithms. The goal is to develop a tool that assists in selecting the most suitable crops to plant based on soil nutrients, pH, moisture, humidity, temperature, and rainfall. The dataset was cleaned and analyzed using exploratory data analysis (EDA) to understand distributions and relationships. EDA involved univariate and bivariate analyses. Both machine learning models were evaluated using accuracy, precision, recall, and F1 score metrics. Results show that humidity, rainfall, and temperature are the most relevant factors affecting crop suitability prediction. Feature impact analysis further revealed the relative influence of soil and environmental variables on model outcomes. After comparing the performance of the Gradient Boosting and Random Forest algorithms, Gradient Boosting was selected as the best-performing model integrated into a demonstrative system. This system aims to support farmers and relevant stakeholders in choosing crops more effectively, reducing crop failure risks, and improving resource efficiency. The study demonstrates how machine learning can be applied to improve crop planning and promote sustainable agriculture.
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Manila Bay plays a crucial role in the Philippines’ fisheries sector, yet faces such as overfishing, pollution, and habitat degradation threaten its sustainability. Traditional fish stock assessment methods often fail to capture complex population dynamics, highlighting the need for data-driven approaches. This study utilized machine learning to enhance fisheries production prediction and support informed decision-making. Utilizing commercial fisheries production data from 2019 to 2023, an ensemble model combining K-Nearest Neighbors, Multi-Layer Perceptron, and Logistic Regression (KNMLPR) was implemented to predict fish species abundance. Results indicate that neural network models outperform standalone K-Nearest Neighbors models in predictive accuracy. The study identifies ten dominant species in Manila Bay, including Largehead Hairtail (Espada), Common Ponyfish (Malaway), Devi’s Anchovy (Dilis), Slipmouth (Sapsap), Squid (Pusit), Cavalla (Talakitok), Lizardfish (Kalaso), Collectively Fry and Fish (Dulong), Herring (Law-law), and Barracuda (Torcillo). These species serve as indicators of trophic shifts and overfishing pressures therefore, the integration of machine learning improves predictive analytics, equipping policymakers with precise data to guide sustainable fisheries management, including adaptive open and closed fishing season policies.
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JGB 19324
“Tempest on the Trading Floor: Assessing the Impact of Tropical Cyclones on the Property Sector of the Philippine Stock Market from 2017 to 2024”
Ocampo Tan, Michelle Brendy C., Dao, Sealtiel Fran G., Dorotheo, Jeffrey Paul C., Tampipeg, Michel Van Andrea A. & Guevarra, Jose Kesian F. / Read Full Paper
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This study examined how functional, social, emotional, epistemic, and conditional consumption values affect students’ commitment to pursue college education, focusing on Generation Z in Ligao City and other municipalities in the 3rd District of Albay. Anchored on the Theory of Consumption Values (Sheth et al., 1991), this study explores how this shapes decision-making among prospective students at IDS Colleges Inc. (IDSC) and is supported by a basic User, Attitude and Image (UAI) survey. Using a descriptive-correlational method with surveys from 218 students and 70 parents, the study found that all five consumption values significantly influence students’ decision to enroll in college, except for conditional values. Findings highlight the importance of cost, academic quality, and social influence in shaping educational choices and provide marketing implications for private higher education institutions.
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