Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 3081-3099 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: handoko1272200032@surel.untag-sby.ac.id Building a sustainable creative economy: The influence of capital access and government programs Handoko Sosro Hadi Wijoyo1*, Siti Mujanah2, Nanis Susanti3 1,2,3Doctoral Program in Economics, Faculty of Economics and Business, University of 17 August 1945 Surabaya; handoko1272200032@email.untag-sby.ac.id1 (H.S.H.W.) sitimujanah@untag-sby.ac.id (S.M.) nanis@untag-sby.ac.id3 (N.S.) Abstract: The future of Indonesia's economy lies in the creative economy, contributing to GDP and creating jobs. This research is important to understand the policies, entrepreneurial skills, and impact of the creative industry on cultural heritage. The creative industry in Bojonegoro faces the challenge of limited research in several areas. This study analyzes the relationship between access to capital, cultural heritage, and government programs on the performance and growth of the creative industry. This study uses a quantitative method with SEM-PLS to analyze the complex relationships between constructs in the creative industry in Bojonegoro. Samples were taken purposively from creative industry players, with 127 respondents. Data were collected through questionnaires, interviews, and observations to understand the factors that influence the growth of the creative industry. The variables studied include Government Programs, Cultural Heritage, Access to Capital, Creative Industry Performance, and Creative Industry Growth. Testing is done by ensuring the validity and reliability of the data, and analyzing R-Square and hypotheses using T-statistics to confirm the relationship between variables. In this analysis, the accepted hypothesis shows that access to capital has a significant effect on access to capital with an O value of 0.850, T-statistics 23.307, and P-values 0.000. In addition, access to capital also has a positive effect on the growth of the creative industry with O 0.494, T-statistics 5.213, and P- values 0.000. The performance of the creative industry also has a significant effect on the growth of the creative industry, with O 0.446, T-statistics 4.129, and P-values 0.000. Finally, access to capital through the performance of the creative industry has a positive effect on the growth of the creative industry, with O 0.379, T-statistics 4.117, and P-values 0.000. This study confirms the importance of access to capital in supporting the performance and growth of the creative industry. This study found a significant relationship between access to capital, creative industry performance, and the growth of the creative industry in Indonesia. The hypothesis stating that access to capital has a positive effect is accepted, while the hypothesis related to cultural heritage and government programs is rejected. Policy recommendations are focused on strengthening access to capital and preserving cultural heritage to support sustainable creative industries. Keywords: Creative industry, Entrepreneurial skills, GDP, Performance, Policy. 1. Introduction The future of Indonesia's economy lies in the creative economy, considering that the global economy is undergoing significant changes. The terms "Economy" and "Creative" are not new; both have been around for a long time. However, when connected, they create economic value and open up new jobs through the exploration of IPR, contributing significantly to the country's GDP.(Ministry of Tourism and Creative Economy, 2014). A country's GDP is closely related to the creative economy, initiated by Schumpeter (1911), through "creative destruction," stating that new companies with creativity replace those that are less innovative, offering solutions to the scarcity of limited resources.(Pourzakarya, 2023). 3082 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate A limited-resource study to understand how public policies and institutions can support or hinder Local Market Institutions (LMIs) in positioning designers in the job market. In addition, the impact of the creative industry on the preservation of cultural heritage and sustainable tourism is a major focus, given the importance of ensuring that economic development does not sacrifice cultural heritage which is the identity and value of the community. The characteristics and competencies of entrepreneurs also have a significant influence on business performance in the creative industry. By examining these factors, this study can contribute to the development of more effective training programs to improve entrepreneurial skills and business performance. Furthermore, the importance of innovative knowledge in creating long-term relationships with customers is highlighted, where this study aims to identify strategies that can be implemented by creative industry players in increasing interactions with consumers, thereby creating sustainable customer loyalty. In addition, this study highlights the positive influence of cultural capital and community participation on the performance of local banks, indicating the need to formulate more inclusive policies in developing a creative economy that involves local communities. With the increasing academic interest in the impact of climate change on cultural heritage, there is an urgent need to explore the relationship between the creative industry and climate change, and how to manage it to protect cultural heritage. Relationships and collaboration between MSME actors and supporting institutions are also important to increase competitiveness, while evaluation of government efforts in supporting the creative industry can help formulate policies that are more effective and responsive to market dynamics.(Wijoyo, 2021). By aiming at mainstreaming the creative industry in every sector that is the focus of development, this research contributes to creating a national creative industry ecosystem. This will encourage research and development (R&D) activities related to the creative industry, which in turn can produce innovation as a solution to community challenges.(Alisjahbana & Murniningtyas, 2018). The identification of 17 leading creative economy sub- sectors by the Ministry of Tourism and Creative Economy in 2014 became a guideline in government policies and programs that should be decentralized to the regions, including Bojonegoro Regency, to achieve the goal of developing the creative industry as a whole.(Wijoyo & Anitasari, 2021). Findings(Vinodra, 2015), institutions, whether at regional, national, or global levels, have a strong influence on LMIs’ ability to secure designers’ positions within the creative economy. These institutions shape and constrain how LMIs position designers in the labor market, public policy, and the global marketplace.(Jelinčić, 2021), a conceptual framework for assessing the impact of cultural/creative industries (CCI) on cultural heritage preservation and sustainable tourism. The framework includes indicators for evaluating policies and projects related to cultural heritage management and tourism.(Gunawan, 2024), The Influence of Characteristics and Competencies on Business Performance: The results of the study show that entrepreneurial characteristics and competencies have a significant and positive influence on business performance in the creative industry in Indonesia.(Aghazadeh, 2015), The Role of Innovative Knowledge: Innovative knowledge is a key factor in strengthening long-term relationships with customers and creating intelligent marketing strategies. IMS focuses on customer orientation and incorporates new knowledge as an important input to improve company performance.(Mulya et al., 2021), The Influence of Cultural Capital, the Role of Traditional Villages, and Community Participation: The results of the study show that cultural capital, the role of traditional villages, and community participation have a positive and significant influence on the performance of local banks.(Fatorić & Seekamp, 2017), Increasing Academic Interest: The results show that scientific interest in the impacts of climate change on cultural heritage continues to increase. The studies involved a variety of research methods and disciplines spanning natural and social sciences.(Borshalina, 2015), The results of the study show that innovation in the use of natural dyes, such as mango leaves, mahogany bark, tobacco, and so on, can improve the performance of MSME Batik Trusmi. This study also confirms that the innovation is in line with the consumer trend of switching to environmentally friendly products.(Tanasiichuk et al., 2020), This study is expected to find that new product marketing must differ from traditional approaches, because new products face challenges such as lack of consumer awareness and an unformed market. Businesses must develop creative and innovative marketing strategies, and tailor the approach to specific products.(Listiyanto & Manzilati, 2007), Transaction Cost Differences: The study found that there were significant differences in transaction costs between the 3083 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate pre- and post-crisis periods. This means that the 1997 economic crisis may have had an impact on increasing or decreasing transaction costs of loans in commercial banks.(GURESHIDZE, 2016), Creative Clusters Efficiency: One of the main findings is that there is a discussion about whether creative clusters are effective in encouraging creativity and innovation in the cultural industries. This may include an analysis of whether these creative clusters generate new ideas, encourage collaboration between talents, and facilitate the development of new technologies or production methods.(Rehman et al., 2021), Innovation Capability Moderation: Innovation capability plays a significant moderating role in the relationship between intellectual capital and innovative performance, indicating that innovation capability can strengthen the impact of intellectual assets on innovation.(Ramírez et al., 2021), Moderating Role of Family Management: The results show that family management can act as a double-edged sword in the relationship between intellectual capital efficiency and performance, depending on the type of intangible resources used. This indicates that family dynamics in management can either enhance or diminish the effectiveness of resource use.(Santoso, 2020), Investor Engagement: Of the startups that presented to investors, 11% attracted investor interest and received funding from a consortium of venture capital firms, both local and international. This shows that there is a significant opportunity for funding in the creative sector. Increasing Added Value: Matchmaking activities between capital owners and creative economy actors contribute to increasing added value. The policies implemented also play a role in increasing this added value.(Fazlagić & Szczepankiewicz, 2020), Conceptual Model: The study produced a model that allows for the measurement and comparison of local government efforts in supporting the creative industries. This model can be used to evaluate the effectiveness of policies and programs implemented by local governments. Local governments need to adapt to changes in the economic structure, especially with the increasing contribution of the creative industries to GDP. This highlights the importance of government involvement in creating conditions conducive to the development of the creative industries.(Chan & Jaja Raharja, 2018), Marketing Strategy Implementation: The results of the study indicate that the implementation of marketing strategies in creative companies generally gives good results. On average, companies have brand and trademark elements, which shows an awareness of the importance of brand identity. Although many companies do not have patents, they determine prices by adding production costs. This shows a lack of legal protection that can affect pricing strategies.(Purwaningsih & Kusuma, 2015), Influence of Internal and External Factors: The results of the analysis show that both internal factors (human resources, finance, production techniques, and marketing) and external factors (government policies, socio- economic conditions, culture, and the role of related institutions) have a significant influence on SME performance. The influence of external factors on SME performance is greater than internal factors. This shows that support from the government and other institutions is very important for the development of SMEs.(Parkman et al., 2016), The Influence of Innovation Capacity: The results show that innovation capacity (IC) acts as a mediator in the relationship between entrepreneurial orientation (EO) and firm performance, both in the context of individual project success (PS) and overall competitive advantage (CA). The Importance of Combining Entrepreneurship and Innovation: The findings suggest that firms in the creative industry need to develop the right combination of creative capabilities to recognize and exploit market opportunities. This emphasizes the importance of not only adopting an entrepreneurial approach, but also creating an environment that supports innovation.(Anggara & Purnamawati, 2023), Positive Influence: The results of the study show that financial literacy and access to capital have a positive influence on the sustainability of MSMEs in Karangasem Regency. This shows that improvements in financial literacy and access to capital can help MSMEs survive and grow.(Sukarno et al., 2019), Relational Capital Dominance: The results of the study show that Relational Capital is more dominant compared to Human Capital and Structural Capital. This shows the importance of networks, relationships, and collaboration in the success of the creative industry in East Java. Although all components of intellectual capital have a role, more attention needs to be given to the management of Relational Capital to improve performance and competitiveness in this sector.(Raldianingrat & ., 2014), Positive Influence of Human Resource Equity: The results show that human resource equity has a significant positive influence on innovation strategy and creative craft industry performance. Innovation strategy also has a significant positive influence on 3084 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate craft industry performance. This confirms the importance of a combination of good human resource management and innovation implementation to improve performance.(Lita & Meuthia, 2018), Effect of Market Orientation: The results of the study indicate that market orientation has a significant positive effect on organizational innovation, but does not have a significant impact on learning orientation. Both market orientation and learning orientation have a positive relationship with organizational performance, while organizational innovation is not proven to be a driver of performance.(Ahman et al., 2020), The Influence of Innovation Capacity: The results of the study show that innovation capacity has a significant influence on company performance. This emphasizes the importance of innovation in improving the competitiveness and success of the creative industry. Industry players need to optimize innovation capacity through product and process innovation to improve performance, such as creating superior products and leading process innovation with the latest technology.(Huang et al., 2020), Characteristics and Status: This study provides a comprehensive overview of the characteristics, status and challenges faced by China's digital creative industry. The four innovation directions proposed for the sustainable development of China's digital creative industry include digital creative technology and equipment innovation, digital cultural content innovation, innovative design and integrated innovation.(Liu, 2021), Identification of Key Elements: This study identifies key elements of various innovation systems operating in the creative sector, including the role of government policies. Government policies in countries with established creative sectors are compared to countries that are just starting to develop knowledge resources.(Rofaida et al., 2020), Innovation Strategy Recommendations: Determination of innovation strategies with recommendations for local governments in formulating policies that support the growth of the digital creative industry.(Liang & Wang, 2020), Characteristics of CCIs in China: The study found that CCIs in China have many similarities with those in Western cities, but also show unique characteristics: The construction of science and technology parks contributes to the development of CCIs and urbanization.(Marilena Alivizatou-Barakou, 2019), The research identified a variety of new technologies that can be used for ICH access, retrieval, and analysis, including digitization techniques, multimedia software, and web- based platforms.(Rahmadi & Mutasowifin, 2021), Positive Influence: Research shows that there is a positive and significant influence between intellectual capital on the company's financial performance and company value. This means that the higher the intellectual capital, the better the financial performance and company value. The creative industry in Bojonegoro has a major challenge, because a study that only covers two cities cannot be applied widely throughout the world, considering the unique local dynamics in each region, includingBojonegoro. In addition, in assessing how the indicators function in the field. Geographical limitations involving only three main islands in Indonesia, namely Java, Sumatra, and Kalimantan, indicate that the data obtained may not fully represent the conditions of entrepreneurs in other regions, including Bojonegoro. Limitations in data collection through questionnaires can also cause subjective bias, given that respondents may assess their conditions in an overly optimistic manner.(Burhanudin et al., 2020). This study of creative economy development, the proposed hypothetical path indicates a relationship between various factors, such as access to capital, cultural heritage, and government programs, on the growth of the creative industry. In this model, access to capital (X3) plays an important role in supporting the performance of the creative industry (Z) and the growth of the creative industry itself (Y). Adequate access to capital allows creative industry players to innovate and develop more competitive products and services, thus encouraging growth that issustainable in this sector. Furthermore, cultural heritage (X2) on access to capital (Z) and creative industry growth (Y). Rich cultural heritage provides significant added value to the creative industry, creating opportunities for the development of unique products and services, and attracting consumer interest. By utilizing cultural heritage, creative industry players can access better financial resources, thereby improving overall industry performance. Government programs (X1) will play a key role in supporting the performance of the creative industry (Z) and the growth of the creative industry (Y). Appropriate policies and supportive programs can create a conducive environment for the development of the creative industry. A proactive government in providing support, such as fiscal incentives, training, and infrastructure, will 3085 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate strengthen the performance of the creative industry and encourage its growth. Through this hypothetical path, it is seen that access to capital, cultural heritage, and government programs on the growth of the creative industry, but also through the performance of the creative industry itself. This means that good performance in the creative industry will have positive implications for overall industry growth. Thus, stakeholders are expected to focus on strengthening access to capital, preserving cultural heritage, and implementing effective government programs as strategies to encourage sustainable and inclusive creative economic growth. This study analyzes the influence of access to capital on the performance and growth of the creative industry in Indonesia. This study also aims to explore the role of cultural heritage in improving access to capital and supporting the growth of the creative industry. In addition, this study will evaluate the extent to which government programs contribute to the performance of the creative industry and its growth. Through the development and testing of a hypothesis model, this study is expected to show the relationship between access to capital, cultural heritage, government programs, creative industry performance, and overall creative industry growth. Finally, this study will provide policy recommendations that can be used by stakeholders to strengthen access to capital, preserve cultural heritage, and improve the effectiveness of government programs in supporting the creative industry, thereby creating sustainable and inclusive creative economic growth. 2. Research Methodology 2.1. Design This study uses a quantitative method with SEM-PLS due to the analysis of complex relationships between constructs.(Liyi Xia, Yuchao Xu, Yahua Zhang, 2021; Nurlaely et al., 2019; Satriyono et al., 2019). This method excels in handling models with latent variables and multivariate interactions.(Nguyen et al., 2023; Sánchez Jiménez et al., 2024). This study will examine the construct of Access to Capital (X3) on Creative Industry Performance (Z), Cultural Heritage (X2), Government Programs (X1), and Creative Industry Growth (Y) in Bojonegoro. Where, this study is to understand the complex relationship between constructs. This is in line with previous studies that highlight the effectiveness of SEM-PLS in analyzing models with latent variables and multivariate interactions. 2.2. Population and Sampling This study involved creative industry players in Bojonegoro, covering 17 creative economy sub- sectors, with a focus on the homogeneous culinary sub-sector. Sampling was carried out purposively, selecting respondents based on available information. The sample was determined according to the criteria of creative industry players who had been fostered by the Bojonegoro Regency Government, ensuring accurate representation of the population. Sample selection was carried out using the Slovin method to determine the right number of respondents from the population of creative industry players in Bojonegoro. The Slovin method is used to calculate a representative sample size when the population is known, but not all of its members can be accessed. Using the Slovin formula, the population of 186 creative industries in 17 sub-sectors, then the Slovin method was calculated with a margin of error of 0.05 and the value of respondents who participated was 126.96 rounded up to 127 respondents. 2.3. Procedures and Data Collection The data collection procedure in this study was carried out through several stages, namely distributing questionnaires, interviews, and observations. The first step taken was to compile a questionnaire designed to collect information on various constructs that were the focus of the study, such as Government Programs (X1), Cultural Heritage (X2), Access to Capital (X3), Creative Industry Performance (Z), and Creative Industry Growth (Y). Respondents consisted of creative industry players in Bojonegoro, who had been identified through previous mapping. This questionnaire was distributed both online through digital platforms and in person, to ensure a high level of participation. In addition to the questionnaire, interviews were also conducted to obtain more in-depth data and better quality. Interviews were conducted in a semi-structured manner, where the questions asked were related to the 3086 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate items in the questionnaire, but also provided an opportunity for respondents to provide additional information that may not be covered in the questionnaire.(Rehman et al., 2021). This approach allows researchers to dig deeper into respondents' views and experiences regarding the factors that influence the creative industry in the area. The observation process was also carried out to support the data obtained from the questionnaire and interviews. Observations were carried out in the field to see firsthand the conditions of creative industry players, how they operate, and their interactions with government programs and local culture. Through observation, researchers can record the dynamics that occur within the industry and obtain rich contextual information, which in turn strengthens the data analysis.(Ramírez et al., 2021). By combining questionnaire, interview, and observation methods, it is expected that this study can obtain comprehensive and valid data on the factors that influence the growth of the creative industry in Bojonegoro. The data collected will be analyzed using appropriate analysis techniques to produce significant findings and can be used for further development in this field. This triangulation approach is important to ensure that the research results are reliable and provide a complete picture of the condition of the creative industry in Bojonegoro. 2.4. Operational Variables In this study, the variables to be explored have an important role in understanding the dynamics of the creative industry, especially in Bojonegoro. The variables studied include Government Programs, Cultural Heritage, Access to Capital, Creative Industry Performance, and Creative Industry Growth. The determination of these variables is based on a theoretical framework built from various literature sources on operational variables (Table 1). Table 1. Operational variables. No Construct Construct items Question items Item code Reference 1 Government programs (X1) a. Research development; b. Development of coaching education; c. Funding and financing facilitation. a. 2 grains b. 2 grains c. 2 grains a. X1.1 – X1.2 b. X1.3 – X1.4 c. X1.5 - X1.6 (Jones, 1984) 2 Cultural heritage (X2) a. Tangible cultural heritage b. Intangible cultural heritage a. 4 grains b. 4 grains a. X2.1 – X2.4 b. X2.5 – X2.8 (UNESCO, 2022) 3 Access to capital (X3) a. Availability of Capital b. Participation of Actors seeking capital a. 2 grains b. 2 grains a. X3.1 – X3.4 b. X3.5 – X3.8 (Anggara & Purnamawati, 2023) 4 Creative industry performance (Z) a. Market Growth ; b. Profit Growth ; c. Increase in Manpower. a. 2 grains b. 2 grains c. 3 grains a. Z1 –Z2 b. Z3 – Z4 c. Z5 – Z7 (Basir et al., 2017) 5 Creative industry growth (Y) a. Business Environment (Business Environment); a. 3 grains b. 2 grains c. 2 grains a. Y1 –Y3 b. Y4 – Y5 c. Y6 – Y7 (Cho & Moon, 2003), 3087 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate b. Related And Supporting Industries (Related Industries); c. Domestic Demand Includes (Domestic Demand); 2.5. Research Procedures In the SEM-PLS model analysis using the intervening approach, the first step is to test the data quality through two important aspects, namely validity and reliability.(Maulana & Tiarawati, 2020; Xi & Hamari, 2020). Validity is measured using the Pearson correlation coefficient, where the value considered valid is one that exceeds 0.360. This indicates a significant relationship between the variables being tested. Meanwhile, to evaluate reliability, the Cronbach Alpha measure is used, which is expected to have a value of more than 0.400. This evaluation was carried out based on data collected from 127 respondents. After ensuring that the data met the validity and reliability criteria, the analysis was continued with the outer model test. This test includes convergent validity, which sets the threshold value must be greater than 0.500, in accordance with current research. In addition, the average variance extracted (AVE) must also be more than 0.400(Aditya Syahputra & Kurniawati, 2023; Deske Wenske Mandagi et al., 2024). Both of these measures aim to ensure that the variables studied can accurately describe the desired construct. After that, it is necessary to examine discriminant validity and composite reliability. To achieve adequate discriminant validity, the minimum value that must be achieved is more than 0.700. This is important to ensure that the measuring instrument used is truly consistent and accurate in measuring the intended variables 400(Chatterjee et al., 2023; Dzakiyanadira et al., 2024; Muis et al., 2020).. In other words, the measurements taken are not only consistent but also reliable. Furthermore, at the inner model stage, R-Square analysis becomes important to evaluate the extent to which the independent variables can explain the variability in the dependent variable. A good R-Square value should be more than 0.450, indicating that the model can explain the variation in the data well. This is a significant indicator in showing the strength of the relationship between variables. To test the proposed hypothesis, the T-table value is used which must be more than 1.96. This aims to confirm the significance of the relationship between variables in the analyzed model. If the T-statistic value exceeds this threshold, then the hypothesis can be accepted, indicating that there is a significant relationship between the variables being tested(Sibarani et al., 2020).. In addition, to assess the strength of the relationship between variables, the effect size is measured using a construct matrix. This provides an overview of how much impact or influence the independent variable has on the dependent variable. Through this measurement, researchers can determine the level of influence that each variable has on the expected results in the study. 3. Results and Discussion 3.1. Results 3.1.1. Outer Model In this section, the Outer Model in the structural model analysis is explained as the relationship between the latent construct and its indicators. Evaluation is carried out through the validity and reliability of the indicators, with the outer loading standard > 0.700, using a weighting scheme factor and 300 iterations (Figure 1). 3088 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Figure 1. Outer loading. Each item has an outer loading value that measures variables such as capital access (X3), cultural heritage (X2), government programs (X1), and creative industry growth (Y). For example, for the Government Programs construct (X1), the outer loading values of indicators X1.1 to X1.6 range from 0.886 to 0.911, indicating that all of these indicators have a high correlation with the construct. Likewise, for the Cultural Heritage construct (X2), the indicators have strong values, with X2.6 reaching 0.902. In general, outer loadings above 0.700 are considered to meet the criteria for convergent validity, so the values in this table indicate a strong relationship between the construct and its indicators (Table 2). Table 2. Outer loading. Item Access to capital (X3) Creative industry performance (Z) Cultural heritage (X2) Government programs (X1) Creative industry growth (Y) X1.1 0.903 X1.2 0.904 X1.3 0.911 X1.4 0.886 3089 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate X1.5 0.900 X1.6 0.891 X2.1 0.843 X2.2 0.860 X2.3 0.886 X2.4 0.877 X2.5 0.838 X2.6 0.902 X2.7 0.875 X2.8 0.877 X3.1 0.875 X3.2 0.932 X3.3 0.938 X3.4 0.930 Y1 0.873 Y2 0.943 Y3 0.917 Y4 0.923 Y5 0.918 Y6 0.872 Y7 0.937 Y8 0.924 Z1 0.888 Z2 0.916 Z3 0.931 Z4 0.915 Z5 0.938 Z6 0.924 Z7 0.935 The constructs Access to Capital, Cultural Heritage, Government Programs, and Creative Industry Growth show high outer loading values. This is indicated by the values of Cronbach's Alpha, rho_A, Composite Reliability, and Average Variance Extracted (AVE), all of which are above the recommended threshold. Cronbach's Alpha and rho_A are each above 0.9, indicating very good internal consistency. Composite Reliability, with a value above 0.95, shows that each construct has high reliability. AVE, all of which are above 0.7, indicates that most of the indicator variability is explained by the latent construct (Table 3). 3090 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Table 3. Average variance extracted (AVE). Cronbach's alpha rho_ A Composite reliability Average variance extracted (AVE) Access to capital (X3) 0.938 0.941 0.956 0.844 Creative industry performance (Z) 0.970 0.971 0.975 0.848 Cultural heritage (X2) 0.955 0.965 0.961 0.757 Government programs (X1) 0.953 0.963 0.962 0.809 Creative industry growth (Y) 0.972 0.972 0.976 0.835 Access to Capital (X3) has a correlation value with its indicator of 0.919, indicating good validity. Access to Capital (Z) also shows high validity with a value of 0.921, although there is a moderate correlation with Access to Capital (X3) of 0.848, indicating a relationship between the constructs. Cultural Heritage (X2) and Government Programs (X1) each have validity values of 0.870 and 0.899, while Creative Industry Growth (Y) shows a correlation value with its indicator of 0.914. These correlation values indicate that the constructs have adequate discriminant, although there are some correlations between constructs that need to be considered to ensure that there is no multicollinearity that interferes with the model (Table 4). Table 4. Discriminant validation. Access to capital (X3) Creative industry performance (Z) Cultural heritage (X2) Government programs (X1) Creative industry growth (Y) Access to capital (X3) 0.919 Creative industry performance (Z) 0.848 0.921 Cultural heritage (X2) 0.287 0.266 0.870 Government Programs (X1) 0.306 0.244 0.872 0.899 Creative industry growth (Y) 0.869 0.863 0.252 0.252 0.914 3.1.2. Inner Model In this sub-chapter, the Inner model tests the relationship between latent constructs in the structural model. The evaluation of the inner model includes path coefficients, R-square values, and relevance predictions, to assess the strength and direction of the relationship between variables, ensuring the model has good predictability and accuracy in the study (Figure 2). 3091 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Figure 2. Inner model. Access to Capital (Z) has an R Square value of 0.725 and an Adjusted R Square of 0.718. This means that 72.5% of the variation in Access to Capital (Z) can be explained by the independent variables in the model, indicating a high contribution. Furthermore, Creative Industry Growth (Y) has an R Square value of 0.812 and an Adjusted R Square of 0.806, indicating that 81.2% of the variation in this variable is explained by the independent variables. Thus, both constructs have a high contribution to the model, indicating that this model is strong and can explain significant variations in both dependent variables.(Table 5). Table 5. R square. R square R square adjusted Decision Creative industry performance (Z) 0.725 0.718 High contribution Creative industry growth (Y) 0.812 0.806 High contribution Access to Capital (X3) has a very large influence on Creative Industry Growth (Y) with an f² value of 2.372, indicating that this variable makes a significant contribution to the model. In addition, Access to Capital (X3) also affects Access to Capital (Z) with an f² value of 0.348. Cultural Heritage (X2) and Government Programs (X1) show a very small influence on Access to Capital (Z), with f² values of 0.020 and 0.019, respectively. Meanwhile, for Cultural Heritage (X2) and Government Programs (X1), no influence was found on Creative Industry Growth (Y), with an f² value of 0 (Table 6). 3092 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Table 6. F square. Access to capital (X3) Creative industry performance (Z) Cultural heritage (X2) Government programs (X1) Creative industry growth (Y) Access to capital (X3) 2,372 0.348 Creative industry performance (Z) 0.292 Cultural heritage (X2) 0.020 0.000 Government programs (X1) 0.019 0.000 Creative industry growth (Y) The saturated model on the Standardized Root Mean Square Residual (SRMR) value was recorded at 0.047, indicating a good model fit. Similarly, the d_ULS and d_G values were 1.253 and 2.050, respectively, indicating a relatively low level of deviation between the estimated model and the data. (Table 7). Table 7. Estimated model. Saturated model Estimated model SRMR 0.047 0.047 d_ULS 1.253 1.253 d_G 2,050 2,050 Chi-Square 1,256,922 1,256,922 NFI 0.798 0.798 Chi-Square which also shows a value of 1,256,922 reflects how well the model fits the data, although this value may seem high, but must be seen in the context of the sample size. In addition, the Fit Index (NFI) Value of 0.798 supports the validity of the model, although this value is still below the ideal threshold (Table 7). 3.2. Hypothesis Testing Hypothesis testing is one of the important methods in data analysis that aims to determine the validity of assumptions or claims put forward in the study. In the context of this study, we evaluate the relationship between variables, including Access to Capital (X3), Cultural Heritage (X2), Government Programs (X1), and Creative Industry Growth (Y). Each hypothesis is tested using several statistics, such as Original Sample (O), Sample Mean (M), Standard Deviation (STDEV), T Statistics, and P Values, to determine whether the alternative hypothesis can be accepted or rejected (Table 8). 3093 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Table 8. Hypothesis testing. Hypothesis code Hypothesis path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Decision Ha1 Access to capital (X3) -> Access to capital (Z) 0.850 0.846 0.036 23.307 0.000 Alternative hypothesis is accepted Ha2 Access to capital (X3) -> Creative industry growth (Y) 0.494 0.503 0.095 5.213 0.000 Alternative hypothesis is accepted Ha3 Creative industry performance(Z) -> Creative industry growth (Y) 0.446 0.434 0.108 4.129 0.000 Alternative hypothesis is accepted Ha4 Cultural heritage (X2) -> Access to capital (Z) 0.151 0.151 0.102 1,472 0.142 Alternative hypothesis is rejected Ha5 Cultural heritage (X2) -> Creative industry growth (Y) -0.005 0.001 0.062 0.084 0.933 Alternative hypothesis is rejected Ha6 Government programs (X1) - >Creative industry performance (Z) -0.147 -0.146 0.103 1,435 0.152 Alternative hypothesis is rejected Ha7 Government programs (X1) -> Creative industry growth (Y) -0.004 -0.006 0.065 0.054 0.957 Alternative hypothesis is rejected Ha8 Access to capital (X3) ->Creative industry performance(Z) -> Creative industry growth (Y) 0.379 0.367 0.092 4.117 0.000 Alternative hypothesis is accepted Ha9 Cultural heritage (X2) ->Creative industry performance(Z) -> Creative Industry growth (Y) 0.067 0.064 0.047 1,440 0.151 Alternative hypothesis is rejected Ha10 Government programs (X1) - >Creative Industry performance(Z) -> Creative industry growth (Y) -0.066 -0.062 0.046 1,423 0.155 Alternative hypothesis is rejected The test results show that there is a significant relationship between Access to Capital (X3) and Creative Industry Performance (Z) with an Original Sample value of 0.850 and T Statistics reaching 23.307. With a very low P Value (0.000), the alternative hypothesis is accepted, indicating that increasing access to capital significantly affects better access to capital. This confirms that available capital greatly contributes to increasing the availability of financial resources in the context of the creative industry. Furthermore, testing for the relationship between Access to Capital (X3) and Creative 3094 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate Industry Growth (Y) also shows significant results, with an Original Sample value of 0.494 and T Statistics of 5.213. The equally low P Value (0.000) confirms that the alternative hypothesis is accepted, meaning that access to capital not only plays a role in increasing access to capital, but also has a positive impact on the growth of the creative industry. This shows that companies or individuals who have better access to capital tend to be more able to invest in development and innovation, thus encouraging growth in this sector. The relationship between Creative Industry Performance (Z) and Creative Industry Growth (Y) is also proven to be significant. With the Original Sample value of 0.446 and T Statistics 4.129 and P Value 0.000, the alternative hypothesis is accepted, indicating that better access to capital has a positive effect on the growth of the creative industry. These results indicate the importance of access to capital in driving the growth of the creative sector, where business actors need to take advantage of existing opportunities to increase competitiveness and innovation. However, not all hypotheses were successfully accepted. The relationship between Cultural Heritage (X2) and Creative Industry Performance (Z) has an Original Sample value of 0.151 and T Statistics 1.472 with a P Value of 0.142. Since the P Value is greater than 0.05, the alternative hypothesis is rejected, indicating that cultural heritage does not have a significant effect on access to capital. This could indicate that cultural aspects may not be in line with the development of financial access needed to support the growth of the creative industry. The hypothesis testing the effect of Cultural Heritage (X2) on Creative Industry Growth (Y) also showed insignificant results, with an Original Sample of -0.005, T Statistics 0.084, and P Value 0.933. This indicates that cultural heritage does not contribute to the growth of the creative industry, which may be due to the mismatch between cultural values and the needs of a more dynamic creative industry market. Testing for the relationship between Government Programs (X1) and Access to Capital (Z) and Creative Industry Growth (Y) showed similar results. The Original Sample values for this relationship are -0.147 and -0.004, with P Values of 0.152 and 0.957 respectively, so the alternative hypotheses for both relationships are also rejected. This indicates that government programs do not have a significant impact on access to capital or creative industry growth, indicating the need for further evaluation of the effectiveness of these programs in supporting the creative sector. Finally, the test evaluating the mediation effect of Creative Industry Performance (Z) on the relationship between Access to Capital (X3) and Creative Industry Growth (Y) shows a positive result with an Original Sample of 0.379, T Statistics 4.117, and P Value 0.000. This indicates that better access to capital serves as a mediator that strengthens the relationship between access to capital and creative industry growth. However, the hypothesis testing the mediation of Cultural Heritage (X2) and Government Programs (X1) on Creative Industry Growth (Y) is rejected, indicating that both variables are ineffective as mediators. The results of this hypothesis test provide valuable insights into the factors that influence creative industry growth. Access to capital proved to be a key driver, while cultural heritage and government programs require further review to increase future impact. 4. Discussion The results of the hypothesis testing in this study provide in-depth insights into the factors that influence the performance and growth of the creative industry, especially in the context of access to capital. The test shows a significant relationship between Access to Capital (X3) and Creative Industry Performance (Z), as indicated by the Original Sample value of 0.850 and T Statistics reaching 23.307. With a very low P Value (0.000), the alternative hypothesis is accepted, indicating that increasing access to capital significantly affects the availability of financial resources in the context of the creative industry. This confirms that available capital greatly contributes to improving the performance of the creative industry, which is highly dependent on the availability of resources for investment and innovation. 4.1. Capital Access and Creative Industry Performance Good access to capital allows creative industry players to invest in new technologies, expand distribution networks, and improve the quality of their products. In an increasingly competitive world, the ability to innovate is key to survival and growth. With better access to capital, creative sector 3095 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate entrepreneurs can take the risks needed to explore new ideas and create unique products. The availability of these funds plays a vital role in supporting the development and implementation of creative projects that might not have been possible without financial support. Furthermore, the relationship between Access to Capital (X3) and Creative Industry Growth (Y) also shows significant results. With an Original Sample value of 0.494 and T Statistics of 5.213, and a P Value that is equally low (0.000), the alternative hypothesis is accepted. This confirms that access to capital not only plays a role in improving performance, but also has a positive impact on the growth of the creative industry. This shows that companies or individuals who have better access to capital tend to be more able to invest in development and innovation, thus driving growth in this sector. This condition reflects the importance of a supporting ecosystem that enables access to capital. Governments, financial institutions, and the private sector need to work together to create programs and policies that support access to capital for creative industry players. For example, low-interest loan programs or grants for innovative projects can provide the necessary impetus for businesses to capitalize on existing opportunities. 4.1.1. The Impact of Creative Industry Performance on Growth The relationship between Creative Industry Performance (Z) and Creative Industry Growth (Y) is also proven to be significant, with an Original Sample value of 0.446 and T Statistics of 4.129 and P Value of 0.000. This indicates that better performance in the creative industry contributes to the growth of the sector. In this context, industry players who succeed in improving their performance through innovation and product improvement not only gain financial benefits, but also create a broader impact on the growth of the industry as a whole. In other words, improved performance at the individual or company level can contribute to the development of the wider creative industry ecosystem. 4.1.2. Challenges in Cultural Relations and Government Programs Not all hypotheses are accepted. The relationship between Cultural Heritage (X2) and Creative Industry Performance (Z) has an Original Sample value of 0.151 and T Statistics 1.472 with a P Value of 0.142. Since the P Value is greater than 0.05, the alternative hypothesis is rejected, indicating that cultural heritage does not have a significant effect on creative industry performance. This indicates that cultural aspects may not be in line with the development of financial access needed to support the growth of the creative industry. It is important to consider that although cultural heritage is an important component of a society's identity, not all aspects of culture can be directly integrated in the context of the creative industry. Some industry players may face challenges in combining cultural elements with dynamic market demands. Therefore, a more adaptive approach is needed to combine cultural heritage with more contemporary industry practices. Furthermore, testing for the relationship between Cultural Heritage (X2) and Creative Industry Growth (Y) also shows insignificant results. With an Original Sample value of -0.005, T Statistics 0.084, and P Value 0.933, this indicates that cultural heritage does not contribute to the growth of the creative industry. The mismatch between cultural values and increasingly dynamic market needs may be the main cause of this result. Industry players must be able to adjust cultural values to market demand to create relevant and marketable products. 4.1.3. Evaluation of Government Programs The test results for the relationship between Government Programs (X1) and Access to Capital (Z) and Creative Industry Growth (Y) show similar results. The Original Sample values for this relationship are -0.147 and -0.004, with P Values of 0.152 and 0.957 respectively, so the alternative hypothesis for both relationships is also rejected. This indicates that government programs do not have a significant impact on access to capital or creative industry growth. This finding indicates the need for further evaluation of the effectiveness of government programs in supporting the creative sector. It is important to analyze why existing government programs do not provide the expected results. One possibility is that these programs do not match the specific needs of creative industry players. To increase the impact of these programs, direct involvement of industry players in program planning and implementation can help ensure that the initiatives taken are more relevant and in line with their needs. 3096 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate 4.1.4. Creative Industry Performance Mediation The test evaluating the mediation effect of Creative Industry Performance (Z) on the relationship between Access to Capital (X3) and Creative Industry Growth (Y) showed positive results. With an Original Sample of 0.379, T Statistics 4.117, and P Value 0.000, this indicates that better access to capital functions as a mediator that strengthens the relationship between access to capital and creative industry growth. This means that better performance in the creative industry can strengthen the positive effect of access to capital on growth, creating a positive cycle where increased access to capital not only has a direct impact on performance but also on future growth. However, the hypothesis testing the mediation of Cultural Heritage (X2) and Government Programs (X1) on Creative Industry Growth (Y) was rejected. This indicates that both variables are not effective as mediators. This finding suggests that although cultural heritage and government programs play an important role in the context of the creative industry, they are not strong enough to mediate the relationship between access to capital and growth. This confirms the importance of access to capital as a major driver in improving the performance and growth of the creative industry in Bojonegoro Regency 4.1.5. Relationship of Findings to Theoretical Review Government programs in Bojonegoro Regency do not necessarily have an impact on the growth of the creative industry in Bojonegoro Regency. Thus, the first hypothesis states that the Influence of Government Programs is positive and significant on the growth of the creative industry in Bojonegoro, which cannot be accepted. The results explain that government programs do not have a positive and significant effect on the growth of the creative industry in Bojonegoro Regency. However, this does not mean that government programs are not important in the context of the growth of the creative industry, but there are other factors that are more dominant in influencing the growth of the creative industry in Bojonegoro Regency. This result is inversely proportional to(Tao & Qi, 2014),(Liu, 2021),(Dörry et al., 2016), that government programs have an effect on the growth of the creative industry. government programs do not have a positive and significant effect on the performance of the creative industry in Bojonegoro Regency. However, this does not mean that government programs are not important in the context of creative industry performance, but there are other factors that are more dominant in influencing the performance of the creative industry in Bojonegoro Regency. This result is inversely proportional to the explanation in the book written by Throsby(Throsby, 2000),(Howkins, 2002), as well as research(Nainggolan, 2023)which explains that government programs can boost the creative industry. The higher the Creative Industry Performance in Bojonegoro Regency, it indicates that the growth of the creative industry in Bojonegoro Regency is getting higher. Thus, the first hypothesis states that the Influence of Creative Industry Performance is positive and significant on the growth of the creative industry in Bojonegoro, which can be accepted. The results of testing this hypothesis explain that Creative Industry Performance has a positive and significant effect on the growth of the creative industry in Bojonegoro Regency. These results are the same as the research conducted by(Putu Ayu Sita Laksmi, 2023),(Sidauruk, 2013),(Diana, 2017),(Chan & Jaja Raharja, 2018),(Zulida et al., 2022),(Ministry of Tourism and Creative Economy, 2014), that the research states that industrial performance influences the growth of the creative industry. 5. Conclusion and Suggestions This study shows that there is a significant relationship between access to capital, creative industry performance, and creative industry growth in Indonesia. Based on the results of the analysis, the alternative hypothesis Ha1 which states that access to capital (X3) has a positive effect on access to capital (Z) is accepted with a T-statistic value of 23.307 and P-values of 0.000. In addition, the hypothesis Ha2 which shows that access to capital also has a positive effect on creative industry growth (Y) is accepted with a T-statistic value of 5.213 and P-values of 0.000. Likewise, the hypothesis Ha3 which links creative industry performance (Z) with creative industry growth (Y) is also accepted, with a T-statistic value of 4.129 and P-values of 0.000. However, the hypotheses related to cultural heritage and government programs show different results. Hypothesis Ha4, which states that cultural heritage 3097 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 3081-3099, 2024 DOI: 10.55214/25768484.v8i6.2658 © 2024 by the authors; licensee Learning Gate (X2) has a positive effect on access to capital (Z), is rejected with a T-statistic value of 1.472 and P- values of 0.142. Likewise, hypothesis Ha5 linking cultural heritage (X2) with creative industry growth (Y) is rejected, with T-statistics of 0.084 and P-values of 0.933. In addition, hypotheses Ha6 and Ha7, linking government programs (X1) with creative industry performance (Z) and creative industry growth (Y), are also rejected, with T-statistics of 1.435 (P-values of 0.152) and 0.054 (P-values of 0.957), respectively. On the other hand, hypothesis Ha8 stating that access to capital (X3) has a positive effect through creative industry performance (Z) on creative industry growth (Y) is accepted with T-statistics of 4.117 and P-values of 0.000. However, hypotheses Ha9 and Ha10 linking cultural heritage and government programs with creative industry performance and creative industry growth are also rejected. Overall, the results of this study confirm the importance of access to capital in supporting the performance and growth of the creative industry, while the role of cultural heritage and government programs in this context needs to be further evaluated. Policy recommendations that focus on strengthening access to capital and preserving cultural heritage are expected to contribute to the development of a sustainable and inclusive creative industry in Indonesia. This study is important and provides direction for future research. First, strengthening capital access policies is crucial, because research shows a significant influence of capital access on the performance and growth of the creative industry. Therefore, the government needs to strengthen policies that facilitate access to capital, such as providing financial incentives and supporting financial institutions for the creative sector. In addition, although cultural heritage does not show a direct influence on capital access, its preservation is still important to create unique products that increase market appeal. This study also suggests the need for evaluation and improvement of government programs that currently have not made significant contributions. For future research, further studies on how cultural heritage can be utilized effectively in the creative industry are needed, including research that explores cases in various regions. A more in-depth evaluation of government programs is also needed to understand why the program did not show the expected results, so that more appropriate policies can be formulated to support sustainable creative economic growth. Copyright: © 2024 by the authors. 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