International Conference on Technological Intelligence and Business Strategies

Authors - Angeline B. Elegio, Darrel A. Cardana, Kathlyn L. Quion
Abstract - This study assesses the implementation of the Intellectual Property and Technology Business Management (IPTBM) framework at Bohol Island State University (BISU), a geographically peripheral state university in the Philippines. Using a longitudinal descriptive case study design, the study reviewed institutional documents, policies, IP records, training reports, curricular materials, and technology utilization records from 2019 to 2025, with emphasis on the IPTBM implementation period from 2023 to 2025. Findings show that IP applications increased from 89 in 2023 to 156 in 2025, representing a 75.3% overall increase, while IP registrations rose from 45 to 112, equivalent to a 148.9% increase. Sectoral filings were concentrated in Food with 74 filings or 24.7%, ICT with 64 filings or 21.3%, and Creative Works with 54 filings or 18.0%, reflecting the university’s regional innovation priorities. Capacity-building also expanded, with trained participants increasing from 723 in 2023 to 1,839 in 2025, or a 154.4% increase. However, IP utilization remained largely extension-mediated, accounting for 26 of 29 recorded utilization activities or 89.7%, while commercialization through licensing and revenue generation remained emerging. Viewed through the Triple Helix framework, the findings suggest that IPTBM functioned as an enabling mechanism for strengthening IP governance, human capital development, and university-level innovation management. The study contributes longitudinal, institution-level evidence on how resource-constrained HEIs can move toward integrated IP protection, utilization, and innovation governance.

Authors - Ovia Rizvi, Sadman Kabir, Md. Tafshir Jaman Takib, Abir Sen Gupta, Sayra Islam Saki, S.M. Sayem
Abstract - The global shift toward cashless payment systems has transformed financial transactions, yet adoption in developing countries such as Bangladesh remains limited. This study investigates the determinants of cashless payment adoption in Bangladesh by examining user perceptions and behavioral drivers. Drawing on survey-based evidence from 369 respondents, the PLS-SEM analysis identifies facilitating conditions, perceived security, initial trust and intrinsic motivation as the most influential factors shaping adoption. In contrast, digital financial literacy, social influence and IT innovation acceptance were found to have little impact, suggesting that peer effects and novelty alone do not encourage sustained use. Moreover, initial trust and intrinsic motivation showed significant mediating impact between the drivers and the adoption of cashless payment systems. The findings highlight the importance of robust infrastructure, strong security protocols and user-centric design in promoting digital financial inclusion. Policy implications emphasize collaborative efforts by regulators and service providers to expand infrastructure, enforce cybersecurity standards and foster user trust. These measures are critical for accelerating Bangladesh’s transition toward a secure and inclusive cashless society.

Authors - Aditya Nova Putra, Tri Wiyana, Setiani Putri Hendratno, Nora Fitriawati, Ida Bagus Putu Aditya
Abstract - The world of social media marketing is shifting from traditional con-tent delivery to personalized solutions, algorithmic recommendation systems, AI generated content and Automated Customer Service Chat. While these technologies can increase relevance and responsiveness, the consumer impact of AI-powered brand communications is conditional upon perceptions of brand messaging as being trustworthy and human-centered, socially meaningful. The study constructs a consumer-behavior framework of the impact of human-centric AI-driven social media intelligence on trust in AI-based brand content, e-WOM, pur-chase intention and perceived sustainability. Situated in the fields of digital marketing, social media intelligence and behavioral consumer analytics, this study aims to investigate a quantitative survey conducted among Indonesian social media users who have been exposed to AI-assisted or AI-generated brand communication. Data was analyzed with PLS-SEM with trust being treated as the inner psychological mechanism and e-WOM as the outer social amplification mechanism transferring AI-enabled marketing to purchase intention and perception of Sustainability. Moving beyond technological adoption, this research on AI marketing highlights customer intelligence, consumer trust construction, online recommendations and responsible digital value creation. In practice, the framework guides firms in designing AI-enabled social media strategies that are persuasive, credible, customer- and sustainability-oriented.

Authors - Chaithra G, Ambika P R, Manjunath R, Shivashankar, Niranjan R, Sowmya Naik P
Abstract - Gross Domestic Product (GDP) forecasting using traditional Econometric models is indispensable for evidence-based decision-making. However, these models are often limited in their ability to handle linear relationships and adapt to high-dimensional data. This paper introduces EcoVision, an open-source web-based forecasting platform that incorporates AI and machine learning to accurately predict GDP and other associated socio-economic variables using the Gap minder dataset (175 countries, 1998-2018). Four machine learning models were used: Support Vector Machine Regression, Polynomial Regression, Decision Tree Regression, and Random Forest Regression. These were built using Python and the Flask/Scikit-learn stacks. Models were evaluated using Average Absolute Error, Squared Error, and R² values. Results show that the Decision Tree Regression model has a perfect fit (R² = 1.0, AAE = 0, SE = 0), making it the best model compared to the other models. The web interface is built using pure HTML5/CSS3/Chart.js. The integrated "Gemini API Module" enables the automatic generation of easily understandable policy summaries, thus allowing for faster extraction of insights. Results from testing the system on 3532 clean data records proved that the system is accurate in forecasting ≥ 85%, Artificial Intelligence summary relevance ≥ 80%, and export success 100%, making it a potential decision-support system for economists, researchers, and governments.

Authors - R Suganya, S Priya, Sheeja Pon Chakravarthy,Pragadheesh Thirumal M
Abstract - This paper presents a real-time intelligent surveillance system designed to detect weapons and violent activities using deep learning techniques [2]. The system integrates the YOLOv7 object detection model [7] for weapon recognition and a CNN-based violence detection module for behavioral analysis. Real-time video streams from CCTV cameras are processed to identify potential threats, and alerts are transmitted via MQTT for immediate notification. Experimental evaluation demonstrates that the YOLOv7 model achieves a mean Average Precision ([email protected]) of 55.3% for weapon detection, while the CNN model [11] attains 96% accuracy in classifying violent actions. The system operates at an average speed of 25–30 frames per second with low latency, confirming its feasibility for live surveillance applications. The proposed architecture enhances public safety by providing automated, accurate, and real-time monitoring capabilities.

Authors - Melati Budi Srikandi, Jonathan Jacob Paul Latupeirissa, Yolanda Masnita, Rizki Dewantara, Ni Made Prasiwi Bestari, Ayu Made Bianca Juarez
Abstract - The shift from human gatekeepers to AI-driven algorithmic curation has fundamentally changed the concept of "Agenda-Setting" theory in the digital age. This change is significant because AI now influences public issue salience; however, there is a notable gap in public awareness. This study examines AI's role in social media information curation and its effects on public discourse and agenda setting. To do this, the research employs a systematic literature review guided by PRISMA principles, analyzing data collected from the Scopus database to identify current research trends. Moving from “handheld” to “automatic” curation results in more personalized interfaces that foster “filter bubbles” and “echo chambers,” according to the analysis. It demonstrates that understandings of “algorithmic news bias” are more influenced by user partisanship and ideological cues than purely technical causes. In conclusion, this suggests that media theories need to be refined to include automated gatekeeping as a core component. Algorithmic literacy serves as a filter against distortions, aiming to reduce disinformation and digital conflict within society. To address the bottlenecks in communication processes, future research and policy should focus on improving algorithmic literacy, given its undeniable influence over human decision-making.

Authors - Helmy Wijaya, Vallencia Ricca Widjaja, Fernand Jetshen Clevanno, Ichwan Masnadi
Abstract - With rapid digitalization happening in the hospitality industry today, hotels are now able to interact more digitally with their guests using innovative customer service solutions. Aspects of technology adoption can be studied from the perspective of customer intelligence to gain behavioral insights about customers, which in turn can help hoteliers improve their user experience with such technologies and create a higher rate of adoption amongst customers. This research explores what factors affect hotel customers' intention to adopt online check-in technology by implementing the Technology Acceptance Model (TAM) with an exploratory factor analysis aimed at customer behavioral insights. Using quantitative explanatory research methods, data from 150 respondents in Jakarta was gathered through online questionnaires. Structural equation modeling was analyzed through Partial Least Squares SEM (PLS-SEM). Empirical results showed that PU and PEOU affected users' attitude toward using online check-in technology. The users' disposition exerted a considerable influence on their aspiration to utilize the digital check-in technology. The effect of PU and PEOU on intention was fully mediated by attitude, implying how affective evaluation by customers has an impact on customers' behavioral intention.