This study focuses on the development of a technology roadmap for the creation of New Psychoactive Substances – Monitoring System (NPS-MS) as an Early Warning System (EWS) for the Dangerous Drugs Board (DDB) and its partner agencies following the technology roadmap process by Bray and Garcia (1997), using the European model for the creation of guidelines and procedures, and anchored on technology management principles including Knowledge Management, Technology Foresight, and Project Management.

Focus group discussions were conducted along with other interviews, and benchmarking activities. An analysis of internal and external factors was conducted using SWOT and STEEPL. These tools were employed to inform this study and realize its objectives.

Based on the analysis of responses from the experts from different government agencies, the development of an EWS is vital to combat the threats of NPS. With this, a technology roadmap will ensure the system’s adoption and efficient implementation.

The developed technology roadmap includes technology inventory and assessment for the defining and planning stages; partnership and development for the executing stage; and capacity-building activities and institutionalization of the system for the delivering stage. Strategies for monitoring and evaluation were also plotted in the technology roadmap.

The technology roadmap for NPS-MS will help the DDB to effectively and efficiently establish the NPS-MS, expedite exchange of information, facilitate close monitoring of emerging drug trends causing public health risks, allow early detection of changes in the drug market, and provide evidence-based information in the creation of policies and strategies to combat the proliferation of NPS.

The real estate sector is one of the important industries in the Philippines. However, it does not take advantage of technological approaches to transform and grow. Thus, this research paper aims to explore characteristics of disruptive technologies and its adoption in this industry.

The research identifies factors that directly influence the adoption of disruptive technologies and the different challenges that are faced by industry players with its adoption. In achieving these objectives, a multi-approach methodology is used to complete this research. Further on, this study uses a comprehensive review of existing research studies. It also utilizes surveys to collect additional data from industry stakeholders.

The study starts with identifying the disruptive technology trends that have the potential to revolutionize and transform the real estate industry. These digital technologies include artificial intelligence, blockchain computing, virtual reality, and big data analytics, drones, and others. The research dwells into the factors that drive or impede the incorporation of these technologies having regulatory frameworks, market readiness, and organizational culture in play. The study makes use of analyzing case studies and real-world examples to provide a better understanding the benefits and challenges that goes with integrating disruptive technologies in the Philippine context.

The study further concludes with recommendations for industry stakeholders, policymakers, and technologists that aim to foster a culture of innovation which ensures sustainable growth of the real industry in the Philippines, amid the era of digital disruption. Hence, this research aims to contribute significant and valuable insights into the current state of disruptive technology adoption in the Philippines, particularly, in the real estate sector.

This paper entitled “The Future of ICT Strand of La Salle College Antipolo: Scenarios and Strategies for the Creation of Implementation Plan in the Post-Pandemic Era”, a technology foresight is to be conducted to determine whether the Strand’s direction for the upcoming years will still be relevant and further represents the requirements of the future especially in the emerging world of Artificial Intelligence (AI).

This study specifically aims to provide plausible scenarios and situations in the coming years for the Senior High School ICT Strand, a newly established program for Grades 11 and 12 and an addition to the existing STEM, ABM, HUMSS and Arts and Design program offering of the school when Senior High School or the K-12 was formally launched by the Department of Education in 2016. This study’s purpose is to create technological catch-up that is also vital in gearing the ICT direction of the school both in terms of assets, resources, infrastructure and possible systems and academic improvements and implementation.

The theoretical framework used in this paper are scenario planning for the Strand’s current and future program and ICT direction that will result to technology transfer through acquisition where assimilation and diffusion will involve improvement of management strategies, knowledge management and technological capability building of the Strand especially after the pandemic era where the digital divide was evidently compressed and the world’s reliability in the use of modern technology was accelerated which along with it are the young students of today that are very much vibrant in considering Information Technology as their college and future employment consideration.

Within the framework of technology foresight, this study has explored the insights and perspectives of Baguio City's big four universities toward the adoption of Artificial Intelligence (AI) in higher education.

Through scenario building analysis, a technology foresight technique that captures future plausible scenarios through intuitive and logical linking of causal relationships of past and current key predictable variables (KPVs) and critical uncertainties (CUs), six plausible future scenarios have been developed. These scenarios provide a structured approach to analyzing the crucial role of higher education leaders vis-a-vis the impact of adopting AI in private higher education universities, specifically in Baguio City.

A qualitative technology acceptance survey allowed the author to explore Baguio City university leaders’ perceptions toward AI’s ease of use, usefulness, and their behavioral intention to adopt AI.

The internal environmental scan or Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, in conjunction with the external environmental scan or Political, Economic, Social, Technological, Environmental, Legal (PESTEL) analysis, allowed the author to explore Baguio City universities’ internal strengths, weaknesses, opportunities, and threats vis-a-vis external environmental factors, namely: political, economic, social, technological, environmental, and legal aspects that have a profound effect in the adoption and implementation of AI; thus, the study has revealed that internal institutional strengths can be used to mitigate the impact of institutional weaknesses, as well as mitigate external threats, thereby, will lead to opportunities.

Furthermore, this technology foresight study provides a comprehensive analysis that will guide the leadership of Baguio City universities prepare for a range of possible outcomes.

This capstone project aimed to develop a machine learning model using data from 2022 to 2023 to help in predicting the errors in the application submitted by an aspiring financial advisor. The existing controls employed by the company were also assessed to recommend feasible solutions to improve the current recruitment process. A high error rate in the applications leads to a longer average turnaround time to complete the process which prevents a company from growing its manpower. This study used R software’s MLR package to create a multilabel classification task given that there can be multiple error classifications that can apply to a single data point. The model was trained until its performance metrics reached a level acceptable to the business. The results showed that 40% of the errors in the two-year period are encountered in a single requirement, while the remaining 60% is comprised by 18 other error classifications. While there are existing controls and guidelines to inform the applicants about the recruitment process and requirements, these are not enough given the consistently high error rate. The study concludes with a set of recommended action items involving user education and platform enhancements that the company may implement both in the short- and long-run. The data-based results and recommendations offer valuable insights to the company in order to improve the online recruitment process. Lastly, the machine learning model used in this study can be re-applied to other multilabel classification tasks, both inside and outside the company.