SMART GOVERNANCE IN THE TELECOMMUNICATIONS INDUSTRY UTILIZING SYSTEM DYNAMICS

• Main Author: Shalahuddin, Muhammad
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/86860
• Institution: Institut Teknologi Bandung

Governance encompasses the interaction between government and citizens in the formulation, implementation, and evaluation of public policies. This process involves assessing both the immediate and long-term impacts of policies across various aspects to maximize benefits and minimize negative consequences. The rapid pace of change demands a dynamic form of governance, in which governments must respond to continuously evolving external conditions. One solution to achieving this is through the implementation of smart governance, which leverages Information and Communication Technology (ICT) to support decision- making based on accurate data. This study aims to develop a model and simulation to assist in the formulation of appropriate 5G regulations based on the conditions at hand. System dynamics enables regulators to comprehend the structure and dynamics of complex systems, thus aiding in the formulation of more effective regulations amid the rapid technological advancements, such as 5G. System dynamics provides simulations that help not only in understanding the direct impact of a policy but also the long- term implications and potential side effects that may not be apparent in static analyses. System dynamics is grounded in the cause-and-effect relationships between variables. Causal testing can help ensure that changes in one variable logically and empirically affect other variables. This study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to validate the causal relationships within the Causal Loop Diagram (CLD) with a small data sample (fewer than 50 data points). Unlike traditional analysis, which tends to require large datasets, fsQCA allows testing with smaller datasets while still producing accurate results. The strength of fsQCA lies in its flexibility in handling partial relationships between variables, using set values ranging from 0 to 1. This makes it an ideal tool for validating complex relationships in CLD, where data variation and uncertainty often arise. In this study, fsQCA has proven capable of delivering accurate results for cases with limited data. By using fsQCA, this iv research can empirically evaluate whether the variables in the system dynamics model have valid causal relationships. This validation is crucial to ensure that the outcomes of system dynamics simulations are not only based on model assumptions but also have a strong empirical foundation. The 5G modeling and simulation using system dynamics begins with the issue of 5G frequency allocation. The allocation of 5G frequencies is conducted in stages to support the migration of services that use the same frequency. Three simulation scenarios reveal the same pattern, where the number of customers increases annually until it reaches stability due to the finite nature of the frequency spectrum. This pattern is referred to as goal-seeking, where growth slows as it approaches a stable point. Additionally, the study proposes the use of system dynamics in modeling 5G implementation to address the shortcomings of previous research in mobile broadband policy recommendations. The simulations show that investment in 5G reduces service prices, increases the number of consumers, improves societal welfare, and boosts Gross Domestic Product (GDP). These positive impacts highlight the potential of 5G to drive national economic growth. This study also uses Fuzzy Cognitive Maps (FCM) to compare system dynamics, as both are based on the cause-and-effect relationships between variables. The comparison shows that system dynamics and FCM adopt different approaches. System dynamics focuses on the relationships that form dynamic loops between variables, while FCM allows for gradual transitions and qualitative reasoning. System dynamics tends to be more mathematical, whereas FCM is more intuitive and qualitative, making it easier for stakeholders to understand. The differences in results between the two methodologies depend on the assumptions of the model and the level of detail in the data used. This study concludes that fsQCA is an effective method for validating causal relationships in CLD in studies with small sample sizes. The 5G frequency allocation simulations demonstrate goal-seeking behavior, while the implementation of 5G has proven to have a positive contribution to the economy. The comparison between system dynamics and FCM suggests that the choice of methodology can influence simulation outcomes. The application of smart governance using system dynamics can assist regulators in making policy decisions related to 5G implementation. By understanding and modeling the dynamics of complex systems, regulators can craft policies that accommodate rapidly changing external conditions. Therefore, the adoption of smart governance is expected to provide an innovative solution to address governance challenges in the digital era, ultimately enhancing the quality of life and ensuring sustainable national development.