Public Policymaking Framework Based on System Dynamics and Big Data

Public Policymaking Framework Based on System Dynamics and Big Data

Feldiansyah Bakri Nasution, Nor Erne Bazin, Rika Rosalyn, Hasanuddin Hasanuddin
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJSDA.2018100103
(Individual Articles)
No Current Special Offers


In the process of public policymaking, it is recommended to use system dynamics (SD) and Big Data in e-government. SD could assist in viewing problems in complex system holistically, while Big Data in e-government is for collecting data and information which are used for developing model of system dynamics and its equations. On this occasion, partial least square – structural equation modeling (PLS-SEM) is used to validate the relationship between public policymaking (PP), system dynamics (SD) and Big Data in the e-government. Brief explanation about survey and PLS-SEM are discussed. Some of the statistic parameters, such as path coefficients or beta coefficients, P values, R Square coefficients and effect size are shared and analyzed. These terms are parameters for us to see how strong the relationship between PP, SD and Big Data.
Article Preview


Public policy is a way for policy makers to manage the development of a country. It is ideally to attain prosperity for its citizens and government. But unfortunately, many public policies that are created do not achieve their goals. This is because (1) the problem mapping is not done well, so the public policy is made incorrectly (2) the process is disturbed by other factors, such as political issues (3) the implementation of public policy is misinterpreted by the authorized institution (4) the monitoring to make sure that the public policy has run properly is not good, so that the deviation of public policy implementation is not detected from the beginning. In the simple way, all those reasons are because of the complexity of the system surrounding the problem which is not considered at the beginning. Azar (2012) suggests System Dynamics for solving problem in the complex system. System Dynamics can help to simulate some scenarios of problems in the complex system and its possible solution. This technique can save cost and time for finding optimized solution. Some papers show the contribution of System Dynamics for policymaking (Guma et al., 2018; Gholizad et al., 2017; Bhushan, 2017; Oyo and Kalema, 2016; Bajracharya, 2016). The key element of System Dynamics is to identify the model of the system. Big Data is able to help in creating the model. Some other papers show the contribution of Big Data in System Dynamics (Pruyt, 2016; Nasution et al., 2017a; Hassanien et al., 2015). For certain cases, the manipulation or reduction of Big Data is needed before processing it (Azar and Hassanien, 2014).

Nasution et al. (2017b) suggests not only System Dynamics but also Big Data for public policymaking (PP). In generic, it can be seen in Figure 1. Basically, the Big Data is used for creating the model and defining the equation for each variable in System Dynamics (SD). The outcome of simulation on the System Dynamics is used in planning public policymaking and its implementation strategies. The feedback line (dot line) is used as the learning process to make a correction on the previous process.

Figure 1.

Big Data, System Dynamics and Public Policymaking


Nasution et al. (2017a) gives a good sample how Big Data is used in modeling of System Dynamics as well as creating equations among variables. In the same paper, System Breakdown Structure is also introduced (Nasution and Bazin, 2017). The simulation is then executed to obtain the best possible conditions, so a policy can be applied. Nasution et al. (2017b) introduced the conceptual framework based on these 3 components: Big Data, System Dynamics and Public Policymaking.

The success of Big Data is related to the level of E-Government Maturity (Romijn, 2014). The higher the E-Government Maturity level, the more intensive the utilization of Big Data is in the government. At the highest level of E-Government Maturity, the data and information are utilized optimally to feed into the SD or public policymaking (Bri, 2009). It is also explained by Nasution et al. (2018). The next step, the simulation is conducted and the output of SD feeds into the process of public policymaking.

Complete Article List

Search this Journal:
Volume 12: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 11: 5 Issues (2022)
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2014)
Volume 2: 4 Issues (2013)
Volume 1: 4 Issues (2012)
View Complete Journal Contents Listing