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)

VOLUME 14 ISSUE 3 (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.

JGB 19302

“The Impact of Heritage Sites in Iloilo City”

Seanna Mae D. Detoyato, MHM / Read Full Paper

Keywords

Heritage sites, economic development, cultural preservation, heritage tourism

Abstract

This study examines the multifaceted impact of heritage sites on the economic development and cultural preservation of Iloilo City, Philippines. Recognized for its rich Spanish colonial legacy and vibrant local traditions, Iloilo City’s heritage sites—including churches, mansions, plazas, and commercial districts—serve as both historical landmarks and active contributors to urban identity and tourism. Employing a qualitative research design, the study draws on structured interviews with key informants from local government and cultural agencies, analyzed through thematic analysis. Findings reveal that heritage sites drive tourism, generate employment, and support small enterprises through adaptive reuse and the promotion of local products. Culturally, these sites foster community pride, serve as educational resources, and ensure the transmission of traditions to future generations. The local government’s interventions—ranging from policy frameworks and restoration projects to community engagement and public-private partnerships—have been instrumental in sustaining the physical and cultural integrity of these sites. However, challenges remain, including limited stakeholder representation, lack of quantitative data, and the need for long-term sustainability. The study recommends broader data collection, enhanced community involvement, and stronger policy enforcement to ensure inclusive and sustainable heritage conservation. Iloilo City’s holistic approach offers valuable insights for other urban centers seeking to balance modernization with the preservation of unique historical and cultural identities.

JGB 19304

“Artificial Intelligence: Friend or Foe”

Jenny Lyn D. Masgong & Mavreen L. Dureza, MMIP, RGC / Read Full Paper

Keywords

Artificial Intelligence (AI), Narrative Analysis, Student Perceptions, Technology Acceptance Model (TAM), Qualitative Research

Abstract

This qualitative study, employing narrative analysis, explores the perceptions of students at St. Therese – MTC Colleges, La Fiesta Site, on the role of Artificial Intelligence (AI) in their academic lives. As AI increasingly reshapes educational landscapes, it becomes essential to examine how students construct meanings around its use, balancing its perceived benefits and challenges. Anchored in Davis’s Technology Acceptance Model (TAM), the study investigates how students narrate their experiences in relation to AI’s usefulness, ease of use, ethical implications, and influence on academic practices.

Narrative data were gathered through open-ended written responses from first- to fourth-year students, reflecting their lived experiences and interpretations of AI in learning. The analysis focused on six areas: the convenience of AI, the factors influencing its use, the types of information accessed, ethical considerations, its effects on study habits, and its impact on student behavior.

The findings highlight diverse student narratives. Many accounts portrayed AI as a valuable support system for enhancing efficiency, simplifying complex topics, and managing academic workloads. Motivations such as time constraints, accessibility, and the drive for improved academic outputs were frequently emphasized. Students described AI as useful in generating ideas, checking grammar, summarizing information, and organizing written outputs. However, narratives also surfaced ethical concerns, particularly around plagiarism, academic dishonesty, and issues of fairness and data privacy. Some students recounted experiences of increased confidence and independence, while others expressed apprehensions about dependency, reduced initiative, and a decline in critical engagement.

The study concludes that students’ perceptions of AI are shaped by both enabling and constraining experiences. AI is framed as both a friend and a foe, depending on its application and the user’s sense of responsibility. The findings further underscore the need for educational institutions to establish comprehensive AI-use guidelines aligned with global digital ethics standards to ensure responsible and equitable student engagement.

JGB 19306

“System Usability Scale Evaluation of A Web-Based Hotel Management Educational Tool for Saint Louis University’s Hospitality & Tourism Management Students”

Lee Majors M. Fajilan, Kasima Rose M. Mendoza, Eliza Joyce E. Palaroan & Ma. Araceli D. Tambol / Read Full Paper

Keywords

Hotel Management System, System Usability Scale, Hospitality & Tourism Management Education, Web-based Application, Usability Evaluation

Abstract

This study presents a usability evaluation of a web-based Hotel Management System (HMS) designed as a supplemental learning resource for Hospitality & Tourism Management students (HTM) at Saint Louis University. The HMS was developed using open-source web technologies such as HTML, CSS, JavaScript, and PHP. The web-based HMS simulates hotel operations such as room management, pricing, billing, and booking. It is currently deployed in the Front Office Laboratory of the School of Accountancy, Management, Computing and Information Studies, providing students with hands-on experience on technologies similar to the systems used in the hospitality industry.

The research employed a Cause-and-Effect framework to systematically investigate the factors influencing the usability of the HMS. The causes considered included user-related factors such as participant demographics and prior experience, system-related factors encompassing interface design and navigation flow, task and context factors involving learning activities and task complexity, and interaction and feedback factors derived from user engagement and qualitative input. Participants, who were current HTM students during the academic year 2025 - 2026, were introduced to the HMS features and guided on system interaction. Usability feedback was then collected using the System Usability Scale (SUS) questionnaire alongside qualitative observations of user navigation and experiences.

The analysis of the SUS score of 73.68 and qualitative feedback reveals a clear link to TAM’s constructs. According to TAM (Davis, 1989), a system’s ease of use and perceived usefulness are strong determinants of its acceptance and usability. In this study, students who found the HMS easy to navigate and helpful for their learning tasks reported higher usability scores. Similarly, feedback on the system’s perceived usefulness - particularly the ability to simulate real-world hotel operations - correlated with positive SUS responses. This alignment between TAM and SUS results underscores the validity of TAM’s relevance in explaining the observed usability outcomes.

JGB 19307

“myReach: Development of A Web-Based Assessment Platform for Accountancy Students of Saint Louis University”

Conrado Chan, Kasima Rose Mendoza, Josephine Dela Cruz & Rizza Montevirgen / Read Full Paper

Keywords

Web-Based Assessment, CPA Licensure Examination, Student Performance Monitoring, Educational Technology, Iterative Software Development Lifecycle

Abstract

The CPA licensure examination is widely recognized as one of the most rigorous professional examinations in the country, serving as a critical benchmark for entry into the accounting profession. However, the consistently low success rates among examinees indicate potential gaps in academic instruction, curriculum alignment with professional standards, and the adequacy of review practices.  In response, some universities integrated innovative tools to support student readiness. This study introduces myReach, a web-based assessment platform developed for BS Accountancy students of Saint Louis University, aimed at improving exam preparedness through simulated assessments and performance tracking.

Using open-source web technologies such as HTML, CSS, JavaScript, and PHP, the development followed an iterative software development lifecycle. The developers collaborated with the Department Head of the Accountancy Department, who maintained oversight throughout the project to gather functional requirements, validate features, and align the system with institutional goals. The platform consists of three system modules: student, faculty, and department head (administrator). Each module is designed with distinct functionalities to support content delivery and performance analysis.

Students can take simulated board examinations that generate real-time results and identify specific subject areas where they struggle. Faculty members can review individual and class-wide performance trends, while the department head manages the assessment content and system settings. The platform provided students with awareness of their academic standing and more focused learning strategies.

The platform aided the students in identifying weak subject areas more effectively, allowing for more focused study. Similarly, the platform provided a monitoring tool to determine student performance, informing the faculty and administrators of a strategy to address the weaknesses of student learning.

JGB 19308

“A Web-Based Hotel Management System for Experiential Learning Among Hospitality & Tourism Management Students at Saint Louis University”

Ria Andrea N. Fernandez, Kasima Rose M. Mendoza, Justin Jarret Montemayor & Carl Kendrick Pascua / Read Full Paper

Keywords

Hotel Management System, Experiential Learning, Hospitality & Tourism Management Education, Descriptive Analytics, Iterative Software Development

Abstract

The hotel and tourism industry increasingly integrates digital technologies and data analytics to enhance guest experiences, improve operational workflows, and drive business performance. Consequently, graduates in hospitality programs should possess practical knowledge of Hotel Management Systems (HMS) used across the industry. The Commission on Higher Education (CHED) promotes outcomes-based and experiential learning in Hospitality and Tourism Management (HTM) curricula in the Philippines. However, several higher education institutions—including Saint Louis University (SLU)—face limitations in providing hands-on training due to the high cost, technical complexity, and restrictive licensing of commercial HMS platforms.

To address this challenge, the researchers developed a web-based HMS specifically designed for HTM students at SLU. The system simulates essential hotel operations such as room reservations, room management, billing, and pricing within a realistic, secure, and cost-effective academic environment.

The system was built following a Modified Waterfall Software Development Life Cycle (SDLC) model, which introduced iterative feedback loops within its structured phases. Development was carried out using open-source web technologies, including HTML, CSS, JavaScript, PHP, and MySQL, which enabled a responsive and interactive user interface backed by a dynamic server-side processing layer.

During the development process, regular consultations with faculty and student users were conducted to ensure that the system met the pedagogical needs of the HTM curriculum. Based on feedback gathered in the requirements and testing phases, system features were continuously refined to enhance usability and educational relevance.

Once fully developed and tested, the HMS was deployed on a local Apache web server and made accessible exclusively within SLU’s Front Office Laboratory at the Maryheights Campus. This localized deployment ensures a controlled training environment, allowing students to gain hands-on experience without the financial or technical constraints associated with commercial HMS platforms.

JGB 19310

“Analyzing Coffee Production Efficiency in the Philippines Using a DEA Model: Evidence from Input Cost Trend (1997 – 2021)”

Zhaina Karylle Carbonell, Charlene Leigh L. Alvero, Harold Makiling & Vicente Salvador E. Montaño / Read Full Paper

Keywords

Coffee Production, Agricultural Inputs, Cost Trends, Farm Sustainability, Data Envelopment Analysis (DEA)

Abstract

The objective of this study is to measure the technical and scale efficiency of coffee production in the Philippines during the year 1997-2021 based on the Data Envelopment Analysis (DEA), namely the CCR and BCC Models, using each year as a decision-making unit (DMU), the research seeks to measure how efficiently inputs like fertilizer, pesticides, labor, land, fuel, rent, and harvesting expenses are converted into coffee output. The results show that while technical efficiency remained consistent throughout the period, scale efficiency declined sharply from above 0.8 in the late 1990s to as low as 0.162 in 2021. This decline indicates that inefficiencies are mainly scale-related rather than poor technical farming practices. Input expenses exhibit wide variation, with fertilizer use averaging 5,336.68 pesos and labor costs averaging 10,943.88 pesos annually. Coffee yield averaged 730.44 kg/ha, with fluctuations reflecting stagnation in productivity. This research will contribute to the existing studies on coffee farming in the Philippines and provide policymakers with valuable insights that can guide the formulation of new policies. To revitalize the Philippine coffee industry, strategic actions should prioritize the development of infrastructures, strengthen government policies, and implement comprehensive agricultural reforms.

JGB 19311

“Hierarchical Risk Parity Portfolio Design of Traditional Risky Assets and Cryptocurrencies in the Philippines during COVID-19”

Jan Marie Claire Edra, Edwin Valeroso & Junette Perez / Read Full Paper

Abstract

While cryptocurrencies created an exciting option for the Philippines, the instrument remains an investment with considerable risks. Public interest in cryptocurrencies in the Philippines surged as digital assets gained popularity during the COVID-19 pandemic. This study introduces a portfolio of Philippine bonds, stocks, and foreign currency combined with cryptocurrencies using the hierarchical risk parity approach, which leverages graph theory and machine learning. Out-of-sample comparisons with traditional portfolio strategies indicate that the HRP portfolio delivers superior risk-adjusted returns. The optimal portfolio allocates only a small portion to cryptocurrencies, with the majority invested in conventional assets. This is interesting as it debunks the idea that cryptocurrencies are the next best thing. It further establishes the fundamental view on investing: tangible hard-core assets are still considered safe havens. Interestingly, although cryptocurrencies have gained much interest, the asset's real value is questionable; it remains an instrument founded on no underlying asset and is more often fueled by speculation.

JGB 19313

“Bayesian ARDL Analysis of the Relationship Between the Philippines’ GDP Growth and Agricultural Public Expenditure”

Ylaika Lyneth R. Concepcion, Ashley V. Gamoso, Will Francko P. Quisel & Vicente Salvador E. Montaño / Read Full Paper

Keywords

GDP growth, Agriculture Expenditure, Bayesian ARDL, Time series analysis

Abstract

This study investigates the relationship between the Philippines' economic growth (GDP), agricultural public expenditure, using the Autoregressive Distributed Lag (1,1) approach to analyze the short and long-term interaction among these variables. Using time series data from 1975 to 2024 from the World Bank Development Indicator, the ARDL model provides insights into the complex interplay between economic development and environmental sustainability. The objective of this study is to determine if there is a need to increase the agricultural public expenditure in relation to the GDP of the Philippines. This study contributes to the economic growth of the Philippines by giving a statistical perspective on how it can grow. In the case of the Philippines, although agricultural expenditure often drives stability and productivity, the findings reveal that there is no significant relationship between GDP and agricultural public expenditure in the short run. The findings indicate that while agriculture remains essential for employment, food security, and rural development, the way public funds are allocated has not shown measurable short-term contributions to national growth. This study emphasizes the need for more targeted and effective agricultural investments with research and development as the emphasis.

JGB 19314

“Revisiting the Phillips Curve: A VECM Approach to Unemployment and Inflation Dynamics in the Philippines”

Vicente Salvador E. Montaño, Mecailah L. Auxillo, Kimberly Sheen P. Badilles & Egelyn Bangcayon / Read Full Paper

Keywords

Unemployment, Inflation, Phillips Curve, Vector Error Correction Model (VECM), Macroeconomic Dynamics

Abstract

This study examines the Phillips Curve in the Philippines by using a Vector Error Correction Model (VECM) with monthly data from 1971 to 2021. It looks how inflation and unemployment interact in both the short and long term, as these are key factors in macroeconomic policy. Initial tests showed that both variables are integrated of order one and cointegrated, which supports the use of the VECM. The findings show that the short-term relationship between inflation and unemployment is weak and not statistically significant, which questions the traditional view of the Phillips Curve. However, in the long run, there is a strong positive relationship, suggesting that inflation and unemployment can rise together over time because of structural issues, supply shocks, and inefficiencies. Inflation tends to adjust to restore balance, while unemployment changes more slowly. Also, shocks to unemployment affect inflation with a delay, but unemployment itself remains mostly steady. These results question whether the traditional Philips Curve applies to developing countries like the Philippines. Instead, they show that expectations, productivity limits, and supply-side problems play a bigger role in driving inflation. For policymakers, this means that relying only on the Philipps curve is not enough for making decisions about monetary and labor policies. Effective strategies should combine inflation targeting with reforms that make the labor market more flexible, address productivity issues, and reduce the impact of external shocks.

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.

JGB 19316

“Exploring Herzberg’s Two-Factor Theory and Its Influence on the Operational Performance of Employees at McDonald’s Marilao Tres Picos”

Carlo M. Miranda, Virlie J. Arispe, Joliana May Chua, Virlie J. Arispe & Joliana May Chua / Read Full Paper

Keywords

Two-Factor Theory, motivation factors, hygiene factors, operational performance, Fast Food

Abstract

This study examined the relationship between Herzberg’s Two-Factor Theory, encompassing motivation and hygiene factors, and the operational performance of employees at a fast-food restaurant in Marilao, Bulacan. Using a descriptive-correlational quantitative research design, survey data were collected from 56 employees representing both service crew and management staff. The results showed that motivational factors, such as achievement and advancement, significantly influence employee performance. Employees reported higher motivation when their accomplishments were recognized and when opportunities for career growth were available.

Meanwhile, hygiene factors, including supervision, workplace relationships, and working environment, were found to affect employee satisfaction but had a lower influence on their performance. Statistical analysis revealed a very weak negative correlation between motivation and hygiene factors, consistent with Herzberg’s theory that job satisfaction and dissatisfaction operate independently.

The study concludes that employees are more productive when both their intrinsic motivations and work environment needs are addressed. It is recommended that the fast food company continue to enhance leadership training, promote open communication, and provide career development opportunities to sustain motivation, satisfaction, and operational excellence among employees.

JGB 19317

“Benefits and Barriers in Digital Marketing Strategies In The New Retail Landscape in China”

Qian Xiao / Read Full Paper

Keywords

benefits and challenges, digital marketing strategies, China, retail sector

Abstract

This study uses a quantitative descriptive-correlational technique with 377 respondents to examine the benefits and challenges of digital marketing tactics in China's retail industry. Data were gathered using a self-created, expert-validated, reliability-tested questionnaire. A 4-point Likert scale, weighted mean, Chi-Square, and Pearson r were utilized for analysis. The results indicate that respondents strongly agreed on the benefits of digital marketing, especially its contributions to customer interaction, brand awareness, sales growth, and customer acquisition and retention. Despite these benefits, effective adoption is hampered by a number of issues. A high mean score of 3.02 indicates that resource constraints—which include financial, human, and technological limitations—emerge as a significant barriers. Similar high scores (mean = 3.12) are given to technological difficulties, such as data protection and regulatory compliance, highlighting the necessity of a strong digital infrastructure and compliance with changing rules. It is also commonly known that market saturation and fierce rivalry pose serious challenges to digital marketing initiatives. Retail companies should give top priority to strategies that make use of well-known platforms like Douyin and WeChat in order to optimize outreach and engagement. In addition, focused efforts are needed to solve the barriers that have been identified, especially the technological complexity and resource limitations. To reduce these, it is necessary to invest in IT infrastructure, hire qualified staff, and make sure data privacy laws are followed. Policymakers and industry stakeholders should also support training initiatives and offer tools that might assist SMEs in overcoming resource constraints.

JGB 19318

“Opportunities and Challenges of E-commerce Usage Among MSMEs in Guangdong, China Towards Economic Development”

Dingqiao Yin, Dr. Miguela M. Mena & Dr. Guillermina C. Vizcarra / Read Full Paper

Keywords

opportunities, challenges, e-commerce, MSMEs, Guandong, China

Abstract

This descriptive correlational research design study was conducted to determine the assessment of the respondents on the opportunities and challenges of e-commerce usage among  the MSMEs in Guandong, China. The respondents are the 100 (one hundred) managers and staff of different enterprises aged 18 to 59 years in Guangdong, China.   They were chosen using quota  sampling method.  The   assessment of the respondents were computed and quantified using the mean.  Further, the tests of significant difference in the given answers by the    respondents were done through the use of the Kruskal-Wallis H Test and Mann-Whitney U Test. Spearman’s rho was used to determine the relationship between the opportunities and challenges on E-commerce usage. The   opportunities available to MSMEs in terms of E-commerce usage were assessed as very good. Respondents strongly agreed on the challenges encountered on the E-commerce usage relative to funding, government support, human resources and economic condition.  The study found no significant difference in the assessment of the opportunities and challenges on the  e-commerce usage when demographic profile of the respondents was considered. Analysis revealed the significant relationship between the opportunities and challenges on the usage of e-commerce among MSMEs in Guandong China.  The study underscores the necessity for coordinated action from government agencies, industry stakeholders, and technology providers to support MSMEs in their digital journey. Strengthening support systems in funding, training, and infrastructure could significantly empower MSMEs to capitalize on the opportunities offered by e-commerce and ensure sustainable digital growth across Guangdong’s diverse economic landscape. 

JGB 19319

“Assessing Service Quality of Augmented Reality on Mobile Application”

Joven H. Gubot, Aubrey Joyce S. Labe, Mary Ruth G. Mellarpis & Dr. Guballo, Jayvie O. / Read Full Paper

Keywords

Assessing Service Quality, Augmented Reality, Mobile Application, Tangibility, Reliability

Abstract

In today’s generation, technology is rapidly evolving, and one significant advancement is Augmented Reality (AR). AR enhances digital experiences by allowing users to visualize products in real-life settings through mobile applications, making marketing more interactive and engaging. This study aims to examine the significant differences in respondents’ assessment of the service quality of AR on mobile applications when grouped according to their profile. The researchers employed a quantitative approach using the Snowball Sampling Technique, which resulted in non-probability data. Respondents assessed their level of agreement with the service quality of AR in terms of Tangibility, Reliability, Responsiveness, Assurance, and Empathy, using a Likert scale. Results showed significant differences in perceptions of service quality based on age, sex, and AR usage. By age, there were significant differences in Reliability, Responsiveness, and Empathy, while Tangibility and Assurance remained consistent. When grouped by sex, only Reliability showed a significant difference, while the other dimensions did not. In terms of AR usage, respondents showed significant differences in Tangibility, Reliability, and Assurance, whereas Responsiveness and Empathy showed no significant variation. The study concludes that user profiles influence how AR service quality is perceived, especially in areas related to dependability and interaction. It recommends improving AR mobile applications by optimizing platforms, creating interactive 3D ads, building user trust, collaborating with influencers, ensuring accessibility, and maintaining high-quality graphics and smooth performance.

JGB 19320

“Medical Hospital Servicescapes and Intention to Recommend: Evidence from Private Hospital Clients”

Emma Rose A. Amar, Julia Pauline E. Mayor, Marianne G. Rabanillo & Dr. Guballo, Jayvie O. / Read Full Paper

Keywords

servicescapes, patient satisfaction, intention to recommend, private hospitals, healthcare environment

Abstract

This research expands the knowledge of servicescapes and healthscapes by focusing on the Philippine healthcare sector, particularly hospitals in the National Capital Region (NCR). The findings show how regional cultural expectations, patient behavior, and environmental factors influence the way physical surroundings, spatial layout, and sign, symbols, and artifacts affect patient loyalty and their likelihood to recommend these services. A descriptive correlational analysis was conducted using data from 337 patients through structured interviews and surveys, assessing perceptions of the servicescape and their influence on future return intentions. Pearson correlation analysis revealed a strong positive relationship, with key factors such as cleanliness, accessibility, signage clarity, and overall facilities significantly impacting patient loyalty. The quality of patient-provider interactions also contributed to a positive recommendation. Outliers in the data underscored areas needing improvement, particularly in environment and communication. Findings, suggest that improving physical hospital environments can enhance patient satisfaction and retention, offering valuable insights for hospital administrators and policymakers. Future research aims to incorporate additional variables, including patient demographics, healthcare quality, emotional responses, and affordability, to develop a comprehensive understanding of the factors influencing patient loyalty. 

JGB 19321

“Enhancing Sustainable Fisheries Management in Manila Bay Through Ensemble Learning-Based Prediction”

Dr. Sherrlyn M. Rasdas / Read Full Paper

Keywords

Fisheries, Sustainability, Machine Learning, Prediction, Ensemble Model

Abstract

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.

JGB 19322

“Hebei Province Digital Economy Potential towards Development of Smart Transportation: Inputs for Sustainability”

Feng Baoguo & Guillermina C. Vizcarra / Read Full Paper

Keywords

digital economy, smart transportation, Hebei, China

Abstract

This descriptive quantitative correlational research design study was conducted to determine the assessment on the starting factors in the establishment of digital economy and smart transportation in terms of national development planning and policy guidance, local development planning and policy guidance, and market demand, the evaluation factors in terms of infrastructure construction, technological evaluation and establishment of industry norms. The 156 respondents include middle and senior management personnel, as well as employees from three categories of organizations involved in the establishment, operation, and management of smart transportation: smart transportation operational management companies, traffic survey and planning design institutes, and manufacturers of supporting equipment and systems. Quota sampling was employed in the selection of the respondents. The   assessment of the respondents   was computed and quantified using the mean.  Further, the tests of significant difference in the given answers by the    respondents were done through the use of the T-test and ANOVA.  Significant relationship between the starting factors and evaluating factors was done through Spearman rho. The   assessment   on   the variables of starting and evaluating factors was very good. Notably, there were no   significant differences in their assessment when considering demographic profiles   as   test factor.   This study validates the key roles of starting and evaluation factors in driving the development of digital economy and smart transportation, and further reveals the correlations at different levels. The research findings provide valuable references for government departments, enterprises, and researchers, laying the foundation for further promoting the high-quality development of smart transportation and the digital economy in Hebei Province.

JGB 19323

“Impact of Internal and External Factors on Banking profitability: A study on the determinants of bank profitability in the Philippines”

Jewel Abbey Ramilo / Read Full Paper

Keywords

Bank profitability, Universal Banks, Philippines, Corruption, Government Effectiveness

Abstract

The study investigates what internal and external factors influence the profitability in the Philippine banking sector, focusing on the top 10 private domestic universal banks from 2011 to 2023. Based on regression methodology, specifically Fixed Effects model the researcher identified key determinants. Bank Size and Diversification were found to have significant positive effects on profitability, while negative relationship was identified for operating expenses. Additionally, inflation was the only economic indicator found to have a negative impact on profitability. The findings underscore the importance of strategic policy and operational adjustments for banks to improve profitability.

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

Keywords

Philippine Stock Market, Tropical Cyclones, Property Sector, Market Behavior

Abstract

The study examines the impact of tropical cyclones on the stock performance and liquidity of the Philippine property sector using an event study methodology. It explores how extreme weather events influence market dynamics, investor sentiment, and liquidity conditions. Stock price movements, trading volumes, and related market indicators are analyzed to assess the extent of abnormal market behavior triggered by tropical cyclones. The research aims to highlight the sensitivity of the property sector to environmental shocks such as tropical cyclones. Understanding the financial vulnerabilities and market reactions given the increasing frequency and intensity of weather events in the Philippines will improve risk management practices and enhance disaster resilience in the sector; thereby, mitigating potential losses and improve the overall stability of the sector. The findings of this study show that tropical cyclone events consistently lead to negative abnormal returns, liquidity disruptions, and extreme tail risks in the Philippine property sector, regardless of market equity, book-to-market equity ratio, momentum, return on equity, and investment-to-assets ratio. Consequently, the researchers urge investors, policymakers, businesses, and researchers to rethink how climate risk is assessed and managed as its financial impact is already unfolding and will likely intensify with continued climate change.

JGB 19325

“Predicting Business Failure in the ASEAN-6: Integrating ESG Factors with Machine Learning Techniques Across Publicly Listed Firms”

Ocampo Tan, Michelle Brendy C. / Read Full Paper

Keywords

ESG, Financial Distress, Machine Learning, Predictive Analytics

Abstract

This study examines and investigates the integration of Environmental, Social, and Governance (ESG) factors with traditional financial indicators in predicting business failure among publicly listed firms in the ASEAN-6: Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam. It aims to identify early signs of financial distress using a combination of financial ratios alongside ESG score. Building upon the methodology of Kaleem et al. (2024), the study investigates whether ESG score can enhance prediction when paired with machine learning (ML) techniques. The researchers applied eleven ML algorithms on a sample of 784 publicly listed firms from 2019 to 2023. This study adopts a quantitative design using a five-year rolling period of data collection and analysis, allowing for the observation of long-term financial deterioration across ASEAN-6 firms. Financial and ESG data were sourced from Refinitiv Eikon, and pre-modeling techniques such as multicollinearity diagnostics and LASSO regression were employed to enhance the model. The study found that ESG factors do not enhance business failure prediction in the ASEAN-6 region, as financial metrics alone suffice for high accuracy. ASEAN-6 firms are facing challenges unique to emerging markets, such as environmental disruptions, regulatory fragmentation, and limited ESG standardization. The study contributes to a growing body of literature on sustainable finance, predictive analytics, and financial resilience. The research holds practical significance for stakeholders seeking a deeper understanding of business failure forecasting in dynamic economic environments.

JGB 19326

“Understanding the Influence of Consumption Values in Students’ Commitment to Pursue College: Marketing Implications for Infotech Development Systems (IDS) Colleges, Inc. in Ligao City, Albay”

Shaira Mae Cabredo-Mediavillo, MMC, & Dr. Miguel Paolo Paredes / Read Full Paper

Keywords

Education, Marketing, Consumer Behavior, Universities & Colleges

Abstract

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.

JGB 19327

“Shaping the Learning Journey: The Effects of AI Tools on the Study Habits of First-Year BSBA Students”

Jovilyn D. Mendoza & Maria Catherine I. Arboleda / Read Full Paper

Keywords

Artificial Intelligence, AI tools, study habits, self-regulated learning, business education, higher education

Abstract

Artificial Intelligence (AI) has become a transformative force in education, reshaping how students learn, organize, and complete academic tasks. This study investigated the effects of AI tools—specifically ChatGPT, Grammarly, QuillBot, and Quizlet—on the study habits of first-year Bachelor of Science in Business Administration (BSBA) students at the National Teachers College during the first semester of Academic Year 2025–2026. Anchored on Bandura’s Social Cognitive Theory and Zimmerman’s Self-Regulated Learning Theory, the study employed a quantitative descriptive-correlational design. Using stratified random sampling, 235 student respondents completed a structured questionnaire assessing AI usage, purpose, and perceived effects on study behavior. Results revealed that most students regularly utilized AI tools for writing assistance, summarizing lessons, and exam preparation. Findings indicated that AI tools positively influenced students’ time management and learning efficiency, while their effect on critical thinking and independence remained moderate. Statistical analysis demonstrated a significant positive correlation between AI usage frequency and study habits (r = .34, p < .05), with variations observed across BSBA majors. Additionally, ANOVA results showed significant differences among majors (F(3, 231) = 4.12, p < .05). Despite these advantages, challenges such as overreliance on AI-generated outputs, issues of academic integrity, and limited AI literacy emerged. The study concludes that while AI tools contribute to greater efficiency and academic organization, responsible use and guided institutional support are vital to ensuring that AI integration enhances meaningful, self-regulated learning among business students.

JGB 19328

“Real Estate Industry in Davao City: An Application of Artificial Intelligence (AI)”

Janine Sato-Betonio, Teodita C. Iranon, Resty C. Pedroso, Ferlyn Grace Ronquillo & John Mark Antonio / Read Full Paper

Keywords

Artificial Intelligence (AI), Real Estate, Davao City, Convenience, Data Handling, Property Matching, Virtual Property Tours, Financial Management, Multitasking, Security, Personalization, Efficiency, Trust, Accessibility

Abstract

This study explored the impact of Artificial Intelligence (AI) on the real estate industry in Davao City, focusing on how AI influences productivity, convenience, data handling, property matching, and virtual property tours. As technology continues to transform how properties are bought, sold, and managed, the study sought to provide valuable insights that can guide future developments and promote a more accessible, efficient, and modern real estate market. It aimed to determine the following: a) the demographic profile of the respondents in terms of age, gender, civil status, educational attainment, years in the real estate business, and position; b) the profile of real estate practitioners in terms of real estate broker, sales person, consultant, assessor, and appraiser; and c) the structural factors that could be developed in the real estate business. Data were obtained from 200 real estate practitioners in Davao City using purposive sampling. Through surveys and Exploratory Factor Analysis (EFA), the study identified how AI enhances industry practices and uncovered thirteen significant factors, including productivity, financial management, multitasking, security, personalization, efficiency, trust, and accessibility. The findings revealed that all these variables contribute to improving the real estate process, confirming that AI plays a vital role in reshaping the industry. The hypothesis stating that no structural factors could be developed in the real estate business was rejected, proving that AI-driven factors exist and can strengthen operations. Overall, the study highlights that AI is not only improving efficiency and convenience but also transforming how real estate professionals and clients interact, leading to a more advanced and customer-centered industry. 

JGB 19329

“Forecasting the Digital Payment Revolution in the Philippines: A Bayesian Logistic Growth Model Analysis”

Trexia Marie Borong, Alexa Impuerto, Andrei Jezryl Sabanal, & Joanna Lynn Mercado / Read Full Paper

Keywords

Digital Payment Adoption, Technology Adoption S-Curve, Bayesian Logistic Growth Model, Philippines

Abstract

This study shows the reformation of  financial transactions through the digital payment revolution. It presents how the study is being reshaped globally, in areas where the developing market and rising economy, specifically the Philippines. The study utilizes a Bayesian Logistic Growth Model where it explains the trajectory forecasts of digital payment adoption here in the Philippines in years 2025-2030. By analyzing national level data from 2013 to 2024 published by the Bangko Sentral ng Pilipinas (BSP), this research explore the diffusion pattern of digital payments through an S-curve that aligns with Roger's  Diffusion of Innovation Theory. The result shows an increase in adoption trend in digital cash payment transactions in 2023 and reaching 57.4% of all retail transactions by 2024. The Bayesian model calculates a growth rate (r) of 0.45 and identifies an inflection point (t₀) at 2022.5, signifying the peak period of acceleration, with an estimated saturation level (K) of 90%. Statistical diagnostics, including an Ř value below  1.01 and a Bayesian R² of 0.982, which indicates the model's credibility. The forecast predicts that approximately 82.7% of digital payments will be made by 2030. This data-driven forecast provides quantitative insight for policymakers, fintech firms, and investors to strengthen digital finance infrastructure and support economic growth.

JGB 19330

“The Industry Context Effect: A Multivariate Analysis of AI-Driven Performance Differentials Across Economic Sectors”

Ryza Bangalisan, Eric Kennedy Pelpinosas, Lora May R. Zulueta & Vicente Salvador E. Montaño, DBA / Read Full Paper

Keywords

Artificial Intelligence (AI), Industry Sector, Performance Outcomes, Cost, Revenue, MANOVA

Abstract

The objective of this study is to determine whether the financial benefits of artificial intelligence (AI) adoption in the areas of cost reduction and revenue enhancement significantly differ across major industry sectors in 2024. This study used the non-experimental quantitative research design to determine any pre-existing pattern and relationship between variables gathered from secondary sources. Moreover, the study seeks to observe and compare the effects of this characteristic based on several dependent variables simultaneously. A statistically significant MANOVA result (e.g., p<0.05 for Wilk’s Lambda) leads to the rejection of the null hypothesis, conveying that industry context does not have a significant overall effect on the multivariate profile of AI-driven performance outcomes (Warne, 2014). The findings depict significant performance divergence by industry sector, which is basically considered due to different rates of technological adoption and diffusion as conveyed in evolutionary economics approaches and Schumpeterian theory. To implement measures for improving uneven rates of technological adoption, key strategies include supporting training programs to upskill workers in emerging technologies, monitoring sectoral innovation gaps, and addressing organizational resistance through pilot programs. These approaches not only accelerate adoption but also ensure long-term resilience and competitiveness. 

JGB 19333

“Yolov11-based Mobile Application for Damage Classification and Severity Level Identification of Roads in The Philippines”

Jazper Louie Ysmael A. Bustria, Neil Angelo Q. Briones, Rhotre Matthieu A. Riboroso, Josephine Dela Cruz, Beverly Estephany Ferrer, Victoriano Ferrer Jr. / Read Full Paper

Keywords

Road Damage Detection, Deep Learning, YOLOv11, Image Classification, Android Application

Abstract

Road transport infrastructure in the Philippines suffers from accelerated deterioration due to environmental factors and increasing traffic, yet road inspections still rely on inefficient and subjective manual assessments. While deep learning models count as promising alternatives, existing studies are often trained on international datasets, which fail to generalize to the unique conditions and damage characteristics found in the Philippines. This study addresses the gap by developing a classification system using a YOLOv11 model with data augmentation. The model, trained on a dataset with images mainly from DPWH Baguio City, supplemented with smartphone captures, Google Street View, and the RDD2020 dataset, was evaluated using a 5-fold cross-validation strategy. The system also incorporates a patch-based sliding window technique to assess damage severity, which was integrated into a native Android application to demonstrate its viability as a practical agent for on-site use. The model demonstrated significant performance improvements, achieving an accuracy of 86% on unseen data. This study establishes the effectiveness of a localized deep learning approach and provides a practical framework for an automated solution to detect road damage nationwide.

      

JGB 19334

“A Deep Learning-Based System for Strawberry Plant Disease Identification”

Kurshan Craig Sandler Casilen, Julianne Mikaela Guiao, Christine Angela Lapus, Adrian James Ordonio, Abigail Palacay, Joanalyn Mae Palangdan, Kailey Ti, Josephine Dela Cruz & Beverly Ferrer / Read Full Paper

Keywords

Deep Learning, Plant Disease Detection, Strawberry  Disease Detection, Agricultural AI, Leaf Disease Classification

Abstract

Strawberry crops are highly susceptible to various plant diseases that can significantly reduce yield and quality, posing economic challenges to farmers. This study presents a deep learning-based approach for detecting strawberry diseases using MobileNetV2. The model was trained and evaluated using a dataset of 7,398 annotated images across seven disease categories, including angular leaf spot, anthracnose fruit rot, blossom blight, gray mold, leaf spot, powdery mildew on fruit, and powdery mildew on leaves. Key evaluation metrics such as accuracy, precision, recall, and F1-score were employed to assess model performance. The MobileNetV2 model achieved an overall accuracy of 99% in detecting strawberry diseases. The model demonstrated strong generalization capabilities and potential for real-time application on mobile and edge devices. For preliminary testing, the trained model was integrated into a web-based application that allows users to upload strawberry images and receive classification results. This served as a testing ground for the model’s usability and response in a web environment. This study underscores the efficacy of deep learning in agricultural disease detection and suggests further refinements, such as dataset expansion and model ensemble techniques, to enhance robustness and deployment readiness.

JGB 19337

“Addressing the Challenges of Implementing Battery Swapping Tech in the Philippines.”

Mike Gerald David, Adame Derick Alvarez, Johan Kimi Go & Francis Hubert Billones / Read Full Paper

Keywords

Battery swapping technology, Filipino EV market, Sustainable Energy, Electric Vehicle

Abstract

This study explores the challenges of implementing battery swapping technology for electric vehicles (EVs) in the Philippines. It begins by examining the current Filipino EV market and existing charging methods, focusing on how range anxiety affects consumer confidence and adoption. The paper then compares the Philippine landscape by analyzing regional experiences, charging preferences, and policy frameworks in Southeast Asia (SEA), North America (NA), Europe (EU), and advanced EV markets such as China, Japan, and South Korea, to understand why some regions have embraced or rejected battery swapping. Using a comparative case study methodology, the research identifies major barriers to adoption, including infrastructure costs, dissonance in charging structure, and limited government spending. A SWOT analysis is used to weigh the potential benefits and drawbacks of introducing battery swapping locally. The initial findings suggest that the battery swapping method significantly reduces energy replenishment time, but requires major infrastructure requirements like a battery swapping station, while the inherent plug-and-play capability of EV charging enhances its versatility and reduces infrastructure requirements, as it can operate using only a conventional electrical outlet. According to Chaniago, Sutopo, and Ma’aram (2025), the cost modeling in Malaysia demonstrates that government subsidies favoring battery swapping infrastructure over direct charging can lower user costs and accelerate EV adoption.

JGB 19338

“Fine-Tuning Lightweight Transformer-Based Models for Event Management SMEs: A Comparative Study & RAG-Enhanced Chatbot Solution”

Beverly Estephany Ferrer, Josephine Dela Cruz, Rafael M. Lachica, Xymond Louisse M. Alcazar, Anthony R. Llena, Ian Bennedick L. Retuta, & Ka Hang Christian T. Yuen / Read Full Paper

Keywords

Lightweight Transformer Models, Event Management SMEs, RAG, Chatbot, Fine-Tuning

Abstract

This study explores the use of lightweight Transformer-Based Models in the field of Small and Medium Enterprises (SMEs), with a comparative analysis of T5 Flan, BART, GPT-2, and Qwen 2.5-0.5B. Commercial Artificial Intelligence (AI) models require a moderate amount of resources and skill to operate, motivating this study to consider plug-and-play lightweight alternatives and the adaptability of updated information at the SMEs’ end using the Retrieval Augmented Generation (RAG) Pipeline. The researchers collected a set of corpora from 44 publicly available SME websites and forums, which was converted into a curated dataset for AI model fine-tuning, model development, and evaluation. The collection yielded 57,879 examples of augmented Q&A, enumerations, list examples, comparison tables, and multi-turn examples. The researchers found that T5 scored highest in the model evaluation; however, the researchers chose Qwen 2.5 for the EaseAI model based on its unique features for a customer service persona, which generate responses in a personified tone. Quantization is advocated in this study to reduce resource requirements while retaining or improving LLM efficiency in low-end systems.

JGB 19339

“Participation of Micro Enterprises in the Economy of Davao City”

Glyza K. Avergonzado, Impress Ira Hernando & Prof. Rufa Lozuraga / Read Full Paper

Keywords

Entrepreneurship, Job Creation, Economic Contribution, Micro-enterprises, Industry Distribution, Quantitative Survey. Davao City

Abstract

This study explored the role of micro-enterprises in the economy of Davao City by examining their longevity, investment levels, employee numbers, and industry contributions. The findings revealed that micro-enterprises were diverse and resilient, offering numerous job opportunities and supporting local economic stability. Challenges such as access to financing, regulatory issues, and competition from larger firms were also examined. Entrepreneurs believed that micro-enterprises provided sufficient job options and quality employment. Although no single factor significantly affected job creation, collectively, all factors had a substantial impact on employment in Davao City. Utilizing a quantitative survey methodology, the research found that most micro-enterprises operated with business capital ranging from 10,000 to 50,000 PHP, with 52.0% within this range. Business distribution was relatively even across trade (66.0%), manufacturing (22.0%), and services (30.0%), with the majority employing between one to four employees. Additionally, 32.0% of these enterprises had been operational for over 5 years and above, underscoring their stability and sustained economic contribution. The study aimed to evaluate the economic role of micro-enterprises by analyzing their distribution across trade, services, and manufacturing sectors, their contributions to employment and local revenue, identifying success factors and challenges, and providing policy recommendations.