The decision to pursue entrepreneurship has been extensively studied across various countries, with numerous studies examining the internal, social, psychological, environmental, and cultural factors that influence this choice ((Antončič & Auer Antončič, 2023); (Kumar & Sudarsanam, 2022); (Lee et al., 2022); (Soltwisch et al., 2023)). However, contemporary economic trends and the rise of new economic sectors offer unique opportunities for non-entrepreneurs to consider venturing into these emerging areas. Among the most prominent of these is the creative economy, currently the fastest-growing sector globally, contributing 10 percent to the world's GDP ((Institute, 2022); F(Fernandez-Pol & Harvie, 2020)). This sector places a strong emphasis on creativity as a driver of innovation and economic advancement (Gouvea et al., 2021) (U.N.C.T.A.D., 2022).

Based on the percentage of tourism and creative economy enterprises by province in Indonesia, Central Java Province has the highest contribution at 13.62 percent, followed by East Java (13.39 percent) and West Java (12.1 percent), reflecting the dominance of Java Island in this sector. Outside Java, North Sumatra (5.14 percent) and Bali (3.43 percent) stand out as provinces with the highest contributions, highlighting the strategic roles of these regions as major tourist destinations. Conversely, provinces in eastern Indonesia, such as West Papua (0.51 percent), North Maluku (0.57 percent), and West Sulawesi (0.64 percent), show relatively low percentages, indicating significant potential for developing tourism and creative economy sectors in these areas. Additionally, provinces in Kalimantan display varying contributions, with South Kalimantan reaching the highest at 2.4 percent, while North Kalimantan ranks the lowest at 0.5 percent, reflecting regional disparities on the island (Ministry of Tourism and Creative Economy/Tourism and Creative Economy Agency, 2023).

In Indonesia, the creative economy employs 9.49 percent of the workforce, significantly outpacing the national employment growth rate of 3.2 percent in 2022 (Hendriyani, 2023). This suggests considerable potential for reducing unemployment in the country compared to other sectors. Notably, the majority of workers in this sector have an education level equivalent to high school or lower, with only around 7.2 percent holding higher education degrees (Rizaty, 2023). Furthermore, the sector offers promising opportunities to enhance the competitiveness of industries that remain underdeveloped in Indonesia. By prioritizing creativity, the sector has the potential to drive significant economic growth, boost competitiveness, and alleviate poverty (Rimbawati & Kustulasari, 2023).

Central Java stands out as one of Indonesia's leading provinces in terms of creative economy workforce, employing approximately 4.3 million people in this sector (Isnawati et al., 2023). In 2020, the province recorded 1.3 million creative businesses (Ikhsan, 2021). Among its regions, Magelang Regency is particularly noteworthy, exhibiting the highest creative economy growth in Central Java, accounting for 13.98 percent of the province's total (Durmasema et al., 2020). Magelang is also part of a National Tourism Strategic Area and hosts one of Indonesia's super-priority tourism destinations, Borobudur Temple (Kemenparekraf, 2018). This strategic positioning further strengthens the foundation for substantial growth in the creative economy. With the sector contributing 7.9 percent to Indonesia's national GDP (Barekraf, 2023), there is immense potential for local communities to capitalize on opportunities within the creative economy sector.

The creative economy’s vast potential can encourage non-entrepreneurs to explore entrepreneurial opportunities within this sector. The Neoclassical Model of Labor or Leisure Choice explains that individuals decide between work and leisure based on how they allocate their time (Gahramanov & Tang, 2016; Saczuk, 2012), with the total available hours being a key factor (Borjas, 2016). Utility theory further elucidates that individuals base their decisions on expected benefits or satisfaction derived from their choices (Mankiw, 2012). Thus, when determining whether to engage in entrepreneurship, particularly in the creative economy versus other sectors, individuals naturally weigh the expected benefits or satisfaction they anticipate from the creative economy.

Prior studies have demonstrated that the quality of educational institutions, the presence of mentoring programs, and exposure to diverse artistic and cultural experiences can significantly influence an individual’s decision to pursue entrepreneurial activities or careers ((Britto, 2016); (Kohn & Wewel, 2018); (Paulsen et al., 2021)). Additionally, factors such as institutional frameworks, public policies, organizational culture, and dynamic institutional processes have been shown to impact entrepreneurial decision-making, particularly during periods of innovation and institutional transitions((Audretsch, 2023); (Chowdhury et al., 2019); (Hjorth & Reay, 2022); (Hoogstraaten et al., 2020); (Sendra-pons et al., 2022); (Zhai et al., 2019)).

Research conducted in Vietnam revealed that entrepreneurship education, family background, entrepreneurial ecosystems, behavior, opportunities, attitudes, intentions, and efficiency all significantly influence entrepreneurial decisions, while social evaluation did not (Truong & My, 2022). Additionally, attitudes and motivation were found to have a direct correlation with entrepreneurial choices, especially regarding existing opportunities (Batz Liñeiro et al., 2024). A similar study in the Philippines, employing probit regression analysis, found that factors such as gender, family size, wealth, parental entrepreneurship, years of schooling, and risk tolerance significantly affect entrepreneurial decisions. However, variables like age, wages, initial capital, credit access, and administrative factors did not exhibit significant influence (Huang et al., 2016). These findings highlight the ongoing contradictions and inconsistencies in the determinants of entrepreneurial decision-making across different contexts.

While most research continues to focus on entrepreneurship in general or within distinct goal groups, few studies have specifically examined entrepreneurial decision-making within the creative economy. This gap is notable given the sector’s increasing significance and potential. In the context of the creative economy, creative entrepreneurship suggests that access to financial resources and the availability of support mechanisms, such as grants, loans, or crowdfunding platforms, play a crucial role in determining the viability of creative endeavors as a livelihood (Chang & Chen, 2020). Additionally, factors such as casualization, education levels, technology adoption, career interest, and job satisfaction can all influence an individual’s likelihood of entering the creative economy sector ((Comunian et al., 2015)(Gandini et al., 2017); (Jeong & Choi, 2017); (Taylor & Luckman, 2020)). Despite these insights, studies on entrepreneurship have yet to focus specifically on the creative economy, particularly within Indonesia’s super-priority tourism areas.

No prior research has specifically investigated the factors influencing the decision to pursue entrepreneurship within the creative economy sector, particularly in the context of National Tourism Strategic Areas. This study's unique contribution lies in its focused analysis of the determinants shaping entrepreneurial choices in the creative economy within such designated regions. Unlike previous studies, which typically examined broader influences, this research offers in-depth, empirical, and contextual insights into entrepreneurial dynamics within the creative economy sector in key tourism areas. The findings aim to enrich the existing literature on entrepreneurship and creative economy development while providing evidence-based policy recommendations to support sectoral growth in national tourism priority regions.

This section describes the study area, data collection methods, sample characteristics, variables, and analytical tools. A probit model is used to explore the factors affecting decision-making in the creative economy sector compared to other sectors. Sample selection followed relevant criteria to ensure representative data for robust and reliable analysis.

Magelang Regency, with Borobudur Temple as its cultural cornerstone, plays a critical role in fostering the growth of the creative sector in the region. The international prominence of Borobudur not only drives tourism but also energizes local creative industries that are essential to the region's economy. While Magelang Regency lacks a dominant architectural sector, the synergy between Borobudur's historical significance and the entrepreneurial activities it generates has transformed the surrounding area into a hub for creative sector development. This dynamic has created sustainable economic opportunities for the local population while preserving cultural heritage. As a designated Strategic National Tourism Area, Magelang demonstrates significant potential in the creative economy sector. It leads the creative economy landscape in Central Java Province, contributing 13.98% of the total creative economy actors in the region, further solidifying its critical role in driving local creative economic activities through its cultural and tourism assets.

The data on creative and non-creative economy actors used in this study is related to the results of the business actor survey conducted by the Department of Tourism, Youth, and Sports of Magelang Regency as the basis for calculating the sample size. The creative economy sector was defined according to Regional Regulation No. 3 of 2022, which governs the management of the creative economy in Magelang. Non-creative economy actors were defined as those involved in the broader tourism sector. The dataset includes 512 creative economy actors and 725 tourism business actors, representing the population for each sector and serving as the basis for sample selection. The sample size was calculated using the (Krejcie & Morgan, 1970) formula.

,

where: n is the sample size, N is the population, X is the chi-square value, P is the population proportion, and d is the sampling error (0.10).

The minimum sample size for each sector was determined to be 60 creative economy actors and 62 tourism business actors. Given the study's emphasis on the creative economy sector, additional efforts were made to ensure comprehensive representation across its subsectors. To exceed the minimum sample size, 64 creative economy actors and 69 tourism business actors were ultimately included, resulting in a total sample size of 131. The detailed breakdown of the sample is presented in Table 1.

This study utilized a purposive sampling method, selecting respondents based on specific criteria aligned with the research objectives. Data for this study was meticulously gathered through direct surveys employing a structured questionnaire instrument, conducted over the period from April to July 2024. The data processing protocol encompassed several critical stages, including validation, editing, cleaning, coding, and tabulation. This purposive sampling technique was strategically implemented to ensure the selection of respondents who are most pertinent to the research focus, thereby significantly enhancing the validity, specificity of findings, and contextual relevance of the study. Specifically, the rationale behind sample determination is anchored in the explicit focus and objectives of the research within the context of the creative economy sector. The intent is to ensure that the sample accurately reflects the diverse sub-sectors within the creative economy. This is achieved through the utilization of a comprehensive database of creative economy and tourism actors compiled by the Department of Tourism, Youth, and Sports of Magelang Regency. Furthermore, adjustments were made to account for the availability and completeness of data, prioritizing active business entities in the Borobudur temple area and its surroundings, as well as those businesses that remained operational during the year of study. The delineation of the creative economy sector is framed by Regional Regulation No. 3 of 2022 of Magelang Regency, which governs the organization of the creative economy. This regulatory framework is complemented by the Scope of Tourism and Creative Economy Activities as outlined in the Indonesian Business Field Classification (KBLI) 2020.

For the purposes of this study, cross-sectional data were collected during the research period. All data were sourced from entrepreneurs within the creative economy and tourism sectors. In addition to ensuring data availability, variable measurements were selected based on relevant theories, literature, and phenomena surrounding individual decision-making, particularly in relation to entrepreneurship in the creative economy and other sectors. Individual and business characteristics were analyzed as determinants influencing entrepreneurial decisions within these sectors. Individual characteristics included gender, age, and education level, while business characteristics were examined through workforce availability, capital, business longevity, raw material sources, competitiveness, economic institutions, technology, and financial inclusion. All variables are detailed in Table 2.

The study utilizes probit regression, an analytical approach suited for modeling limited dependent variables. The probit model was chosen for this study due to its ability to effectively analyze binary outcome variables, which aligns with the research objective of examining the factors influencing the decision to engage in the creative economy sector versus other sectors. This model is particularly suitable for limited dependent variables, where the outcome is categorical (creative economy sector vs. other sectors) and binary in nature (1 for engaging in the creative economy, 0 for not). Additionally, the probit model offers a robust framework for incorporating various individual and business characteristics as predictors, allowing for a nuanced understanding of how these factors influence entrepreneurial decisions. Unlike linear models, the probit model accounts for non-linear relationships between the independent variables and the likelihood of the outcome, providing more accurate estimates through the maximum likelihood estimation method. Moreover, the probit model’s use of cumulative distribution functions ensures the interpretation of results in terms of probability changes, particularly useful for assessing the marginal effects of different variables on the likelihood of choosing entrepreneurship in the creative economy sector. This approach also enhances the robustness and reliability of the analysis by testing for multicollinearity, model fit, and goodness of fit, ensuring that the model is well-suited to capture the complexities of entrepreneurial decision-making within the context of Magelang's creative economy.

The probit model is types of generalized linear models (GLMs) used to analyze the relationship between a binary dependent variable and one or more independent variables. The probit model is inherently non-linear and employs the maximum likelihood (ML) estimation method. Probit model is based on the cumulative distribution function (CDF) of the standard normal distribution. This can make it easier to interpret the coefficients, as they can be directly related to the standard normal distribution. Notably, probit coefficients are interpretable primarily in terms of their signs, rather than their magnitudes. The most informative way to interpret these coefficients is by calculating the marginal effect, which reflects the change in probability associated with a variation in the independent variable. Specifically, the marginal effect quantifies the shift in the probability of a successful outcome (Y=1) when an observation possesses a particular characteristic (X=1). In essence, it captures how the likelihood of a successful event (Y=1) is altered by the presence of the characteristic (X=1). A positive marginal effect signifies that an independent variable, particularly a categorical (dummy) variable with a specific characteristic (X=1), increases the probability of a successful event (Y=1) relative to the reference category, by the value of the marginal effect. For continuous independent variables, a positive marginal effect indicates that each one-unit increase in the independent variable leads to an increase in the probability of a successful outcome (Y=1), corresponding to the marginal effect's value.

The probit equation is grounded in Utility Theory and Neoclassical Labor Choice Theory, typically represented as Pi(Yi=1|Xi) = ⌀(β0 + β1Xi). To further assess the influence of individual entrepreneur characteristics and the attributes of creative and non-creative economic enterprises, the model is expanded to Pi(Yi=1|Xi)= In this context Pi(Yi = 1) takes the value of 1 if the probability of an individual choosing to engage in the creative economy sector is high, and 0 otherwise. The term are both individual and business characteristics, while β represents the parameters. To ensure the robustness of the probit model, three key diagnostic tests were conducted: multicollinearity analysis to check for independent variable correlation, the Overall Model Fit test with a null hypothesis positing model adequacy, and the Goodness of Fit Test using the chi-square statistic to evaluate the null hypothesis that no significant differences exist between the model and the observed data. Additionally, the model’s explanatory power was assessed through the coefficient of determination (Pseudo R²).

This study offers a comprehensive analysis of the factors shaping entrepreneurial decision-making within the creative economy sector, providing valuable insights for both scholars and practitioners. The findings emphasize partnership and resource availability as critical enablers of entrepreneurship. Partnership and improved access to resources significantly increase the likelihood of entrepreneurial engagement, underscoring their vital roles in supporting new business ventures. On the other hand, legality, competitiveness, technology and capital emerge as substantial deterrents, with heightened competitive pressures and significant capital serving as barriers to entry, discouraging potential entrepreneurs. Furthermore, the study indicates that variables such as age, labor, education, institutional and financial inclusion do not exhibit a statistically significant impact on entrepreneurial decisions within the creative economy. This finding challenges conventional assumptions and suggests that these factors may be less influential in this particular context, offering a revised perspective on their role in entrepreneurship.

The findings contribute to a deeper understanding of entrepreneurial decision-making and offer actionable recommendations for policy and practice. To foster entrepreneurship in the creative economy, the study recommends strengthening partnership initiatives, improving access to resources, and encouraging the adoption of advanced technologies, while also addressing barriers related to legality, competitiveness and capital investment. The Integrated Business Service Center (PLUT-KUMKM) of Magelang Regency in Indonesia has successfully facilitated business licensing for MSME actors, provided technical guidance, partnership and enhanced and mentored entrepreneurial capacity through training programs and collaborations with universities, such as digital capacity building and marketing programs for creative MSMEs. In doing so, the research enriches the existing literature on entrepreneurship by providing practical insights and proposing strategies for cultivating a more supportive entrepreneurial ecosystem.

The limitations of our study include the availability of data for analysis at a broader regional level and challenges in controlling intern variables from individuals that may influence the research outcomes. Future research should investigate how context-specific factors, such as regional economic conditions or industry characteristics, influence entrepreneurial decisions within the creative economy. Longitudinal studies could offer valuable insights into how these determinants evolve over time and affect entrepreneurial engagement. Additionally, examining specific sub-sectors within the creative economy may reveal unique challenges and opportunities. Assessing the effectiveness of targeted policy interventions could provide practical guidance for addressing barriers like high capital requirements and competitive pressures. Comparative studies across different countries could also shed light on both global and localized variations in entrepreneurial determinants. Finally, exploring the impact of emerging technologies, such as artificial intelligence and blockchain, on entrepreneurship could enhance our understanding of how these innovations are shaping business formation and success.

The authors wish to express their profound gratitude to BRIN CWS Magelang, and the Regional Development Planning, Research, and Development Agency of Magelang Regency for their invaluable support and collaboration throughout this research. Their expertise and partnership were instrumental in ensuring the successful completion of this study. We also extend our sincere appreciation to Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Tidar University for its financial support, without which this research would not have been possible. The contributions of all parties have greatly enriched the depth and breadth of the study, and their unwavering commitment to advancing academic inquiry is deeply acknowledged.