Dynamic systems model of innovation capacity: Applications and game developers in DKI-Jakarta

The creative economy is a field enduring extremely rapid growth. One of the areas of concern is the subsector of application and game developers due to its contribution to increasing the gross regional domestic product and creating employment opportunities, particularly in the province of DKI Jakarta, Indonesia. This paper aims to identify development models for the information and communication sector, particularly for application and game developers in DKI Jakarta. We employed a mixed-methods approach, data and information were collected from key informants selected using a technique of purposive sampling. Main findings of the study demonstrate that the dynamic system method can be used as a development model for the application sector and game developers in DKI Jakarta, as it can potentially increase the benefits of program strategies that the DKI Tourism and Creative Economy Agency will implement. © 2023 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


Introduction
The creative economy is a sector that is experiencing highly rapid development at the global level (United-Nation, 2020).This economic concept emphasizes human creativity in applying ideas, knowledge, information, and technological innovation (Brouillette, 2020;Fazlagic & Skikiewicz, 2019;Pratomo et al., 2021;Setiawan, 2018).According to (Chollisni et al., 2022), creative economybased industries play a crucial role in the economic development of a region.For this reason, governments around the world, including the government of Indonesia, are attempting to make the creative economy the primary driver of economic development after Covid-19 (Deloitte, 2021).
According to the Standard Classification of Indonesian Business Field (KBLI), at least sixteen creative industries are emerging in Indonesia, including those that application and games developers, architecture, interior design, visual communication design, product design, animated film and video, photography, crafts, and craftsmanship, the performing arts, visual arts, publishing, advertising, television and radio, music, and fashion are all creative endeavors.The information and communication industry's subsector of application and game developers is one of the topics of concern.Regional leaders are concerned about the growth of creative economic activities since it is crucial for enhancing the city's image (Ayu et al., 2020) and providing employment.According to Central Bureau of Statistics 2020 data, the number of creative industry employees increased from 18,497,322 in 2018 to 19,240,184 in 2019 (Figure 1).Of these, 46,806 people are employed in the application and game developer's sub-sectors.Jakarta's Special Capital City Region (DKI Jakarta) is also experiencing this creative economic expansion.According to 2019 Central Bureau of Statistics data, the creative economy in DKI Jakarta Province experienced an average growth rate of 8.15 percent in 2017 -2018.The average value of this growth is higher than DKI Jakarta's overall average economic growth of 6.18 percent and noncreative economic growth of 5.94 percent.Furthermore, in 2023 the information and communication business sector contributes to the DKI Jakarta Province's Gross Regional Domestic Product (PGRDP) of 294,865 billion.It indicates that the information and communication sector, particularly the application and game developer subsector, has substantial growth potential.
DKI Jakarta Province is focusing on a strategy for the sustainable development of a digital economy.The development of application and game developers will drive the growth of digital goods and services, followed by other activities in the information and communication industry, such as electronic goods, optics and software.It is expected that the growth of economic activity will be able to accommodate labor to increase the community's welfare.
Dynamic systems are models commonly used in decision-making when developing strategies for various sectors (Currie et al., 2018).Despite this, the contribution of dynamic systems to application and game developers in the government sector has yet to be investigated.For this reason, the authors are interested in Determining models for developing the information and communication sector, particularly in the application and game developers' field in DKI Jakarta, using the dynamic system method.

Creative Economy
The creative economy refers to the activities of production, distribution, and consumption of goods and services based on text, symbols, and images, as well as various series of activities based on creativity, talent, or individual skills, which result in the production of products that contain intellectual property (Vieira de Jesus et al., 2020).Many view this creative economy as an expansion of the cultural economy because, in addition to the traditional cultural sector (music, dance, circus, fine arts, etcetera), it also encompasses sectors related to information and communication technology (digital games, animation, and software development) in addition to culture-based sectors associated with conventional industries (such as fashion, design, architecture, and advertising) (Guilherme, 2017).
The creative economy sector is one of the new economic sources that the Indonesian government encourages as an instrument for job creation in every province (Husin et al., 2021).According to (Utomo & Dew, 2021), the creative economy creates added value economically, socially, culturally, and environmentally, thereby enhancing competitiveness and quality of life.The large population of Indonesia, of which 70 percent are of productive age, gives the country a high potential for developing a creative economy (Syahbudi et al., 2023).

Information and Communication Technology for Applications and Game Developers
Today, Information and Communication Technology (ICT) has permeated every aspect of human life.Information and communication technology is significant in work, business, government, education and entertainment (Ratheeswari, 2018).According to UNESCO, as cited by Shokeen et al., "Information and Communication Technology is a discipline of science, technology, and engineering, as well as management techniques used in handling information, its application, and its relationship to social, economic, and cultural issues." The economic sectors comprising the Information and Communication Technology industry are computer production and assembly, communication network development, and software and application development (Shanin et al., 2021).In the government sector, Information and Communication Technology is urgently needed to increase efficiency in administrative tasks and community services (Farmansyah & Isnalita, 2020).(Nor et al., 2019) state that the application of information and communication technology in e-government includes using intranets and the Internet to connect the requirements of residents, businesses, and other activities.In addition, public administration has developed numerous information and communication applications, such as e-procurement, efinance, e-election, etcetera (Ojoh & Uwadia, 2019).The Indonesian government compiled the Business Classification (KBLI) as a guide for identifying the category of business activity.

Innovation Capacity
Creative economy and innovation are two terms that cannot be separated from one another (Hidayat & Asmara, 2017).According to Law No. 11 of 2009 Article 1 Paragraph 13, "Innovation is the result of thought, research, development, study, and/or application that contains novel elements, has been implemented, and provides economic or social benefits."In the following article, article 34, it is stated that "Inventions and Innovations referred to in Article 1 are intended to be solutions to national problems, combine technical, functional, business, social cultivation, and aesthetic perspectives and/or contexts, and generate added value of products and/or production processes for the benefit of society." Regional innovation capacity is a region's ability to produce commercial innovation flows.According to Nelson, as cited in (Barrichello et al., 2020), innovation capacity is comprised of research and development activities and a collection of institutions that influence a region's technological capabilities.Increasing regional innovation capacity requires instruments, frameworks, and mechanisms for managing innovation to support innovation activities.Effective regional policy formulation requires reference data (Santoso et al., 2022).

Analytic Hierarchy Process
According to Saaty (1980), cited in (Sahin & Yurdugul, 2018), "Analytic Hierarchy Process (AHP) is one of several decision-making methods that mathematically model the decision-making process and are used to solve complex problems."One of the advantages of AHP is the ability to evaluate qualitative and quantitative factors in one assessment (Alsamaray, 2017).(Puspitasari et al., 2021) added that another advantage of AHP is that it permits detailed, structured, and systematic problem-solving to become interdependent, fundamental components with a high degree of adaptability.(Taherdoost, 2020) describes the steps required to execute the AHP.In the initial steps, issues and decision-making objectives are hierarchically organized into related decision elements: decision criteria and alternatives.The hierarchical structure in Figure 2 reflects the problem organization.(Taherdoost, 2020) The next step involves pair comparisons, in which questionnaires are distributed to respondents (such as managers, experts, users, etcetera) to collect their opinions.Each decision maker determines the desired quantity for each member, and the individual judgments are transformed into group judgments using the geometric mean.The 1 to 9 rating scale indicates the relative significance of the compared elements, where 1 means "Equally Important Preferred" and 9 "Extremely Important Preferred" (Taherdoost, 2020).

Dynamic System Models
System dynamics is a simulation-based modeling technique used to identify the main factors of a problem with implications for decision analysis (Pedram et al., 2019).According to (Uriona & Grobbelaar, 2017), the modeling process in a dynamic system consists of five repeated steps: problem formulation, dynamic hypothesis, model formulation, model testing (validation), and policy formulation/evaluation.Dynamic systems are frequently appealing to policymakers due to their applicability across many policy fields and the ability to conceptualize interrelated issues across policy fields (Eker et al., 2018).
The five symbols shown in Figure 3 express the dynamic system model in graphical language.Each symbol's explanation is as follows: i.
The stock (box image) represents the accumulation of a system flux (e.g., population).ii.
Rate specifies the rate of addition (inflow) (e.g., the number of births) or reduction (outflow) (e.g., the number of fatalities) of the stock each period, indicating the system's activity.iii.
The Converters state input that can be expressed in numbers, formulas, or graphics is determined by the model builder.iv.
Connectors (arrows indicate the flow of information (relationships) in the system (source arrows indicate variables that affect, and variables that are affected are located at the end of the arrows).v.
Cloud states system limits.

Research and Methodology
This research employed a mixed method, incorporating both qualitative and quantitative approaches.The quantitative approach helps researchers collect data from multiple participants and increases the generalizability of findings, whereas the qualitative approach provides an in-depth understanding and reverence for participants' perspectives (Dawadi et al., 2021).The qualitative approach to this research was conducted by conducting in-depth interviews with key informants and observing direct locations.A quantitative approach is used to confirm the regional innovation capacity variable.Referring to (Santoso et al., 2022) three variables were used as a reference in observations: general innovation infrastructure, specific industry clusters, and linkage between general innovation infrastructure and specific industry clusters.These variables are adapted to the research environment's conditions.
The research respondents were key informants, Mrs. Restuning Dyah Widyanti, the DKI Province's Sub-Coordinator for Creative Economy Development and Expert Staff for Maritime Economics and Creative Economy.All of these respondents are considered experts who are involved in and knowledgeable about the creative economy, particularly application and game developers in DKI Jakarta.
This research paradigm includes an interpretive paradigm.According to (Alharahsheh & Pius, 2020) the interpretive paradigm enables researchers to gain informational profundity by eliciting experiences and perceptions from specific social contexts.This paradigm is consistent with the research objectives, as the researchers aim to understand the phenomena that occur in DKI Jakarta Province based on the creative economy sector in the application and game developer fields from the perspective of key informants.

Definition of Operational Variables
This study employs interdependence techniques.The interdependence technique determines the relationship between a set of variables without distinguishing between dependent and independent variables (Taherdoost, 2020).This study applies three variables: general innovation infrastructure, specific industry clusters, and the linkage between general innovation infrastructure and specific industry clusters.This research indicator results from an elaboration of prior research (Santoso et al., 2021) and (Santoso et al., 2022).
The detailed definitions of each research variable are provided in Table 1.Ease of financing Source: (Santoso et al., 2021(Santoso et al., , 2022) )

Data Analysis
Data analysis techniques used qualitative and quantitative in this mixed method research.Applying Miles and Huberman's theory, the interactive model is a qualitative analysis technique.This technique consists of three main elements: data reduction, data presentation, and conclusion drawing and testing.Furthermore, quantitative data analysis used analytic hierarchy process techniques and dynamic systems using Vensim PLE (Personal Learning Edition) software.

Research Object Description
The average elevation of the DKI Jakarta Province is seven meters above sea level.Its land area is 662.33 km2, and its water area is 6,977.5 km2.There are 110 islands in the Thousand Islands and 27 rivers and canals in DKI Jakarta.It is bordered to the north by the Java Sea, to the south and east by West Java Province, and the west by Banten Province.
Based on Law No. 29 of 2007, DKI Jakarta is the national capital with special status and autonomy.In 2001, 1 administrative district, five administrative cities, 44 sub-districts, and 267 urban villages were established.The population of DKI Jakarta in 2022 is 10,679,951, with a density of 16,084 inhabitants per square kilometer.Central Jakarta has the highest density, with 20,618 people per square kilometer.
The economic potential of DKI Jakarta is comprised of the primary sector (agriculture, forestry, fisheries, and mining), the secondary sector (manufacturing industry, electricity, gas, clean water, and construction), and the tertiary sector (trade, transportation, communication, financial services, non-financial services, and other services).The tertiary sector contributes the most, 75.92 percent, from 2017 to 2021.The information and communication sector contributes significantly to DKI Jakarta's economy.In 2021, its contribution reached 9.33 percent, and the pandemic had no effect.This sector encompasses information production, distribution, technology, and other services.

Process Analytical Hierarchy
The first step in the Analytical Hierarchy Process (AHP) method is to compile a hierarchy of main objectives, criteria, sub-criteria, and alternatives.The hierarchical structure is divided into four levels; the objectives are the levels that will be the measurable criteria in this study.The three criteria are innovation infrastructure, industrial clusters, and linkage between general innovation infrastructure and industrial clusters.These three criteria contain nine sub-criteria, including 18 specific criteria derived from the interview results.Figure 4 depicts the hierarchical structure of the Regional Innovation Capacity evaluation in DKI Jakarta Province.The results questionnaires to two respondents, namely the DKI Province Creative Economy Development Sub-Coordinator and Maritime Economic and Creative Economy Expert Staff, were used to calculate weighting at the criterion level.The results of the respondent's evaluation were calculated using the geometric mean to arrive at a single answer.The priority vector and Eigen factor are then calculated for every criterion.Calculating the Consistency Index (CI) and Consistency Ratio (CR) to determines the level of consistency.
General innovation infrastructure and specific industrial clusters have a higher priority value of 44.28 percent in the innovation capacity criterion.In comparison, the linkage between the two has the lowest priority value of 11.43 percent.Regarding general innovation infrastructure criteria, venture capital comprises nearly fifty percent of the priority value, while research place account for just 2.89 percent.The application of information on specific industry cluster criteria reveals the highest priority value, 54%, while innovation and labor costs have the lowest priority value, 7%.On the linkage criteria between the two, the alternatives with the highest priority values are ease of financing and guaranteed preservation and commercialization of intellectual property (37.73 percent).In contrast, product uniqueness has the lowest value (4.07 percent).In the meantime, the results of the respondent's evaluation can be accepted if the consistency ratio tolerance limit is 10% or 0.1.All criteria's consistency test results are less than 10%.Thus, it is stated that all criteria are consistent.Table 2 describes the results of the weighting and consistency test criteria.

Dynamic System Modeling
The first step to creating a model of a dynamic system is to construct a Causal Loop Diagram (CLD), which depicts the causal relationship between system elements.This relationship may be positive (+) or negative (-).This study's CLD was determined based on the researchers' understanding and information from interviews with Mrs Restuning Dyah Widyanti, the Sub-Coordinator for Creative Economy Development in DKI Province.Innovation capacity is affected by the innovation infrastructure as a whole, the specific industry cluster, and the linkage between the two.Each component is influenced by education and research factors, financing, and intellectual property.These variables will affect the component weights.Figure 5 depicts the outcomes of CLD.In order to complete the modelling phase, the primary model is divided into four sub-models: the sub-model for innovation capacity, the sub-model for general innovation infrastructure, the sub-model for specialized industrial clusters, and the sub-model for linkage.Table 3 describes how this study model was created.

Model Verification
Model verification involves examining the mathematical formulation and variable units with the Vensim PLE software's Check Model and Unit Check features.If an error message appears, it means the model has not been verified and the data collection process must be repeated.The model is verified if the messages "Model is OK" and "Units are OK" are displayed.Figure 7 illustrates the outcomes of model verification.4 shows the accumulated annual running results.i.
The Model Structure Test ensures that the compiled model's structure matches the real system's structure.This study employs the literature on dynamic systems as a basis for developing a model of innovation capacity and includes respondents with an understanding of real concepts and systems.The model was evaluated through discussions with experts.If the evaluator accepts the formulation and unit model of the innovation capacity system, the model is considered structurally valid. ii.
The Model Parameter Test verifies the consistency of the model's input variable values.Validating the logic of the causal relationship between variables is the objective of this test.In a positive cause-and-effect relationship, if one variable increases, it is expected that the other variables will also increase, and vice versa in a negative cause-and-effect relationship.
Figure 9 illustrates that innovation capacity positively correlates with the innovation general infrastructure, specific industry clusters, and the linkage between the two.Increasing innovation capacity will improve the general innovation infrastructure, specific industry clusters, and the linkage between the two.It indicates that the simulation parameters have a positive causal relationship with the logic.Therefore, the model is declared valid under these conditions.Where,  ̅ means the average value of the simulation results, and  ̅ refers to the average value of the actual data (Table 5)  Guaranteed protection and commercialization run optimally, increasing by 3% annually.

6
Ease of Financing Ease of financing by creative entrepreneurs, especially application and game developers, runs optimally, and the difficulty level is reduced by 10% annually.7 Government Regulation Ease of regulation by creative entrepreneurs, especially application and game developers, runs optimally, and the difficulty caused by regulations is reduced by 10% every year.

Source: Authors
From the results of the executed scenarios (Figure 10), the following can be deduced: i.
Innovation general infrastructure scenario: Increasing venture capital variable by 10% per year to $9,294 thousand (10th year) and human resources variable by 1% per year to 89.7% (10th year).
ii. Specific industry clusters scenario: Increase the innovation implementation variable by 5% per year to 85% in the tenth year while decreasing the difficulty level of experts by 10% per year to 10.5% in the tenth year.
iii.Linkage scenario: Increasing the guarantee variable for the protection and commercialization of intellectual property by 3% per year to 91% (10th year), reducing the difficulty level of government regulations by 10% per year to 4.5% (10th year), and reducing the difficulty level of ease of financing by 10% per year to 8% (10th year). iv.
Innovation capacity increases along with improvements in the general innovation infrastructure, specific industry clusters, and linkages.
In developing a dynamic system, regional innovation capacity is influenced by three factors: general innovation infrastructure, specific industry clusters, and linkage between general innovation infrastructure and specific industry clusters.Each component creates its sub-system and has a structure comprised of indicators/variables that are interrelated.The negative polarity is associated with the number of information and communication business industries specific to the application and game developers that require experts.The linkage sub-system structure between general innovation infrastructure and specific industry clusters in information and communication, particularly game applications and game developers, is influenced by intellectual property protection, commercialization guarantees, ease of financing, and government regulations.The ease of government financing and regulation has a negative polarity because it relates to the number of information and communication business sectors specific to application and game developers that require easy financing and government regulations.

Conclusions
The DKI Jakarta Government, particularly the DKI Jakarta Tourism and Creative Economy Agency, is increasing innovation capacity in the application and game developer sub-sectors by optimizing the general innovation infrastructure, specific industry clusters, and the linkage between the two.AHP is used to identify criteria that increase the added value of the creative economy ecosystem, such as general innovation infrastructure, specific industry clusters, venture capital, human resources, innovation application, expertise, protection & commercialization guarantees, financing ease, and government regulations.The author suggests the dynamic system method as a guide for increasing the potential and benefits of strategic programs implemented by the DKI Tourism and Creative Economy Agency to develop the application and game developer sector in DKI Jakarta.
This study can be used as a guide when formulating strategies for developing creative economy ecosystems for sub-application and game developers in DKI Jakarta and Indonesia.By fostering favorable conditions in this ecosystem, it is anticipated that the contribution of the creative economy in the field of application and game developers will enhance the Indonesian economy as a whole and provide value to creative economy stakeholders.In addition, this research can be used as a resource when formulating policies relating to access to financing of creative economy stakeholders, especially applications and game developers.

Figure 1 :
Figure 1: Number of Indonesian Creative Economy Workers (People); Source: Central Bureau of Statistics 2020 (processed)

Figure 3 :
Figure 3: Stock and Flow Diagrams; Source: Authors

Figure 4 :
Figure 4: Hierarchical Structure of Regional Innovation Capacity Evaluation of DKI Jakarta; Source: Authors

Figure 5 :
Figure 5: Causal Loop diagram; Source: Authors Furthermore, CLD is converted into a relationship between stock (level) and flow (inflow-outflow).Stock will accumulate the results of activities in the system, while flow describes the flow of material input and information output.The general infrastructure of innovation is measured through activities such as intellectual property, capital financing, and education & training.Specific industry clusters are measured through activities such as supplier industry, core industry, and competition strategy.The linkage between general innovation infrastructure and specific industrial clusters is measured through innovation intermediation, intellectual property intermediation, and financing intermediation.Figure 6 illustrates the Stock Flow Diagram.
Figure 6 illustrates the Stock Flow Diagram.

Figure 8 :
Figure 8: Graph Simulation Results of Innovation Capacity; Source: AuthorsThe innovation capacity simulation aims to evaluate the growth of the information and communication industry, specifically application and game developers.Figure8depicts the rise in innovation capacity, innovation general infrastructure, specific industry clusters, and the linkage between innovation general infrastructure and specific industry clusters.It is because innovation capacity is a combination of other variables.Table4shows the accumulated annual running results.

Figure 9 :
Figure 9: Graph Simulation Results of Innovation general infrastructure, Specific industry clusters, Linkage between innovation general infrastructure and specific industry clusters; Source: Authors The average test (mean comparison) compares the mean of two data samples: simulated and actual.In this case, we used a sample of variable venture capital.If E1 equals 5%, the model was considered valid.Using the following formula, E1 denotes the average comparison test.1 = ⌊ ̅ −  ̅ ⌋  ̅

Figure 10 :
Figure 10: Simulation and Graph scenario of Innovation general infrastructure, Specific industry clusters, Linkage between innovation general infrastructure and specific industry clusters; Source: Authors Human resources and venture capital influence the structure of the general infrastructure sub-system for innovation in information and communication, particularly among application and game developers.Implementing innovation and expertise influences the structure of specific industrial cluster sub-systems in information and communication, particularly application and game developers.The negative polarity is associated with the number of information and communication business industries specific to the application and game developers that require experts.The linkage sub-system structure between general innovation infrastructure and specific industry clusters in information and communication, particularly game applications and game developers, is influenced by intellectual property protection, commercialization guarantees, ease of financing, and government regulations.The ease of government financing and regulation has a negative polarity because it relates to the number of information and communication business sectors specific to application and game developers that require easy financing and government regulations.

Table 1 :
Definition of Research Operational Variables

Table 2 :
Weighting and Consistency Test Results

Table 4 :
Table Simulation Results of Innovation Capacity for 10 Years Model validation involves testing to ensure the model accurately represents the real system.Model validation in this study comprises two tests:

Table 5 :
Comparison Test of Average Venture Capital Variables Scenario-making was fixing or solving problems that occurred in the simulation as expected.As shown in Table6, scenario creation involves a combination of parameter and structure scenarios.