The Future of Broadcasting: Technology, Policy and AI Integration in the Philippine Setting
The TV Broadcast has achieved a potential new level of evolution that could either help achieve improvement on the current condition of possible extinction due to new technologies such as OTT, VOD, and different ICT applications. The migration of Digital TV aims to provide a new landscape and opportunities to maximize technology, thus helping achieve the next level of benefits through entertainment, livelihood, news, connectivity, and new business models. However, the technology adoption of each stakeholder is crucial in ensuring implementation success. This paper is a survey of the processes, action plans, and governance that each country that implemented ISDB-T standards went through as they achieved the different milestones in their goal to turn off analog TV and roll out digital terrestrial television broadcasting. The integration of AI through the Agent-Based Model aims to bridge possible technology gaps, policy alignments, changes in business model, and support needed for a successful migration which results in the theoretical framework. The proposed Technology Adoption Model can be implemented for the ongoing Philippine digitization activities.
Agle, B. R., Mitchell, R. K., & Sonnenfeld, J. A. (1999). Who matters to Ceos? An investigation of stakeholder attributes and salience, corporate performance, and CEO values. Academy of Management Journal, 42(5), 507–525.
Anvari-Moghaddam, A., Rahimi-Kian, A., Mirian, M. S., & Guerrero, J. M. (2017). A multi-agent based energy management solution for integrated buildings and microgrid system. Applied Energy, 203, 41–56.
Argyris, C., & Schoen, D. (1978). Organization Learning: A Theory of Action Research, Addision-Wesley. Reading, MA.
Baucus, M. (1994). The case for an environmental technology act. EPA J., 20, 37.
Beck, U. (1997). The Reinvention of Politics: Rethinking modernity in the global social order, trans. Ritter, M., Cambridge: Polity.
Christensen, K., Ma, Z., Demazeau, Y., & Jørgensen, B. N. (2020). Agent-based modeling of climate and electricity market impact on commercial greenhouse growers’ demand response adoption. 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 1–7.
Costanza, R., Daly, H., Folke, C., Hawken, P., Holling, C., McMICHAEL, A. J., Pimentel, D., & Rapport, D. (2000). Managing our environmental portfolio. BioScience, 50(2), 149–155.
Developing countries less equipped to use ICTs to minimize disruption caused by coronavirus. (2023). https://www.itu.int/en/ITU-D/Statistics/Pages/publications/covid19.aspx
El-Hajjar, M., & Hanzo, L. (2013). A survey of digital television broadcast transmission techniques. IEEE Communications Surveys & Tutorials, 15(4), 1924–1949.
Ghoshal, S. (2005). Bad management theories are destroying good management practices. Academy of Management Learning & Education, 4(1), 75–91.
Harbo, S. F., Zaferanlouei, S., & Korpås, M. (2018). Agent based modelling and simulation of plug-in electric vehicles adoption in Norway. 2018 Power Systems Computation Conference (PSCC), 1–7.
Hart, R. A. (2008). Stepping back from ‘The ladder’: Reflections on a model of participatory work with children. In Participation and learning (pp. 19–31). Springer.
Henle, C. A. (2006). Bad Apples or Bad Barrels’ A Former CEO Discusses the Interplay of Person and Situation with Implications for Business Education. Academy of Management Learning & Education, 5(3), 346–355.
Illeris, K. (2009). A comprehensive understanding of human learning. Contemporary Theories of Learning: Learning Theorists... in Their Own Words, 7–20.
Kowalska-Pyzalska, A. (2016). An analysis of factors enhancing adoption of smart metering platforms: An agent-based modeling approach. 2016 13th International Conference on the European Energy Market (EEM), 1–6.
Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21–38.
Laszlo, C. (2003). The sustainable company: How to create lasting value through social and environmental performance. Island Press,
Li, W., Bai, Q., Zhang, M., & Nguyen, T. D. (2018). Automated influence maintenance in social networks: An agent-based approach. IEEE Transactions on Knowledge and Data Engineering, 31(10), 1884–1897.
LICUP, R. J. M., & MATERUM, L. (n.d.). A FRAMEWORK UNDERSTANDING ORGANIZATIONAL CULTURE AND SUSTAINABILITY TOWARDS TECHNOLOGY ADOPTION WITH THE AID OF MANAGEMENT INFORMATION SYSTEMS IN THE ℡ECOMMUNICATIONS SECTOR.
Ma, Z., Schultz, M. J., Christensen, K., Værbak, M., Demazeau, Y., & Jørgensen, B. N. (2019). The application of ontologies in multi-agent systems in the energy sector: A scoping review. Energies, 12(16), 3200.
Mintzberg, H. (2005). The magic number seven—Plus or minus a couple of managers. Academy of Management Learning & Education, 4(2), 244–247.
Mvungi, N. H., Anatory, J., & Simba, F. (2013). Digital Terrestrial Broadcasting Technologies and Implementation Status. Proceedings of World Academy of Science, Engineering and Technology, 75, 646.
Nakahara, S., Okano, M., Takada, M., & Kuroda, T. (1999). Digital transmission scheme for ISDB-T and reception characteristics of digital terrestrial television broadcasting system in Japan. IEEE Transactions on Consumer Electronics, 45(3), 563–570.
Nasir, S., Asif, M., & Ullah, S. (2018). An Agent Based Model for Diffusion of Smart Grid Technology in the Society. 2018 14th International Conference on Emerging Technologies (ICET), 1–6.
Rai, V., & Robinson, S. A. (2015). Agent-based modeling of energy technology adoption: Empirical integration of social, behavioral, economic, and environmental factors. Environmental Modelling & Software, 70, 163–177.
Reinhardt, R., Hietschold, N., & Gurtner, S. (2019). Overcoming consumer resistance to innovations–an analysis of adoption triggers. R&D Management, 49(2), 139–154.
Schiera, D. S., Minuto, F. D., Bottaccioli, L., Borchiellini, R., & Lanzini, A. (2019). Analysis of rooftop photovoltaics diffusion in energy community buildings by a novel GIS-and agent-based modeling co-simulation platform. IEEE Access, 7, 93404–93432.
Schumacher, A., Merz, R., & Burg, A. (2020). A mmWave Bridge Concept to Solve the Cellular Outdoor-to-Indoor Challenge. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 1–6.
Simovska, V. (2008). Learning in and as participation: A case study from health-promoting schools. In Participation and Learning (pp. 61–80). Springer.
Takada, M., & Saito, M. (2006). Transmission system for ISDB-T. Proceedings of the IEEE, 94(1), 251–256.
Værbak, M., Ma, Z., Christensen, K., Demazeau, Y., & Jørgensen, B. N. (2019). Agent-based modelling of demand-side flexibility adoption in reservoir pumping. 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON), 1–4.
Waddock, S., & McIntosh, M. (2009). Beyond corporate responsibility: Implications for management development. Business and Society Review, 114(3), 295–325.
Wilber, K. (2001). A brief history of everything. Shambhala Publications.
Wilber, K. (2017). A brief history of everything. Shambhala Publications.
Yamada, A., Matsuoka, H., Ohya, T., Kitahara, R., Hagiwara, J., & Morizumi, T. (2011). Overview of ISDB-Tmm services and technologies. 2011 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 1–5.
Ye, D., Zhang, M., & Vasilakos, A. V. (2016). A survey of self-organization mechanisms in multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(3), 441–461.