Central Bank Digital Currency as a New Form of Legal Tender: Analysing the Determinants of Indonesia's CBDC Research Progress

Central Bank Digital Currency as a New Form of Legal Tender: Analysing the Determinants of Indonesia's CBDC Research Progress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Central Bank Digital Currency as a New Form of Legal Tender: Analysing the Determinants of Indonesia's CBDC Research Progress Ruben Raditya Eddon Lydda, Raden Aswin Rahadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6935405/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bank Indonesia’s white paper on the digital rupiah places shows the central bank’s intention of contributing to global central bank digital currency (CBDC) research and following the steps of other central banks in advanced stages of CBDC research. This study looks to investigate the factors which affects CBDC research progress in Indonesia on a provincial level using a quantitative method analysis. The quantitative method uses a latent variable to determine which provinces are suitable for CBDC research advancement and a binary logistic regression method to analyse which variables are determinants of Indonesia’s readiness for CBDC research progress. The results show that mostly western provinces in Sumatera and Java, with a few in the eastern provinces, are suitable for the pilot stage of CBDC research and variables with statistically significant results are the EDC machine usage and the integrity index of each province. These results could aid Bank Indonesia's eventual introduction of CBDC within Indonesia, starting off from a provincial level to adjust the system as needed. Business and commerce/Economics Business and commerce/Finance Figures Figure 1 Figure 2 Introduction With the increased use of digital payment methods including but not limited to the Quick Response Code Indonesian Standard (QRIS), GoPay, OVO, and ShopeePay, anticipating a future where currency is not only digital but also centralised and securely managed by the country's central bank will not be too far off. With the release of Bank Indonesia’s white paper on the digital rupiah, Indonesia’s CBDC project, followed by the release of the proof-of-concept on wholesale CBDCs, this new form of digital legal tender will play a part in reshaping the financial landscape of Indonesia currency transactions. In the current age dominated by digital transformations, Indonesia finds itself contemplating the adoption of its own CBDC, offering both unprecedented opportunities and significant challenges. Many central banks of countries that represent almost 98% of the World’s GDP are exploring the potential of CBDCs to modernise financial systems, enhance payment efficiency, and bolster financial inclusion (The Atlantic Council, 2023). Bank Indonesia itself is no stranger to innovating payment systems, with the introduction of the QRIS seeing high implementation rates amongst merchants and users alike along with plans to implement cross-border transactions for the system (Bank Indonesia, 2020 ). The growing interest in cryptocurrency as another form of digital currency also contributes to the interest in CBDC, especially within the younger generations of Indonesians, in which cryptocurrencies act as a decentralised payment system without a central authority such as a central bank (Fadli et al., 2025 ). Like with other central banks around the world, the unpredictable and uncontrollable nature of cryptocurrencies have prompted Bank Indonesia to attempt to reduce the growing reliance of cryptocurrencies as a digital currency (Febriyanto et al., 2025 ). Based on Bank Indonesia’s Project Garuda White Paper on Indonesia’s efforts to study and implement CBDCs in the future, factors that are identified to have an importance in the consideration of CBDC implementation include technological advances through the rise of fintech, mobile banking, and cryptocurrencies in redefining how financial transactions occur; financial inclusion and providing access to digital financial services for all, particularly in remote or underserved areas, as Indonesia is a country with diverse geographic and socioeconomic disparities; analysis of Indonesia's existing regulatory environment and digital infrastructure is essential for evaluating the feasibility of CBDC research progress; public and private sector stakeholders in playing a crucial role in shaping the CBDC landscape; and lastly unique challenges and opportunities from the research of a relatively new topic in digital currencies. This study addresses existing gaps in CBDC research, with many having been conducted as early as 2020 as it is a new concept that has been recently gaining attention from central banks all over the world due to its perceived usefulness for each economy’s digital payment systems. Thus, there is always the assumption that new findings and therefore new variables to be included when conducting the research method. Within an Indonesian context, previous researchers have suggested to further explore the implications within the Indonesian economy and effect on variables including financial inclusion, cybersecurity, technological infrastructure, regulatory quality, or system coexistence along with a comparative analysis on central banks that have successfully launched their own CBDC systems. The remainder of this research consists of covering the literature review of CBDCs in general and Indonesia, establishing the methodology of the research, analysing the results of the research, and lastly discussing the implications and recommendations for future research. Literature Review The emergence of CBDC represents a response to the rapid digital transformation of the financial industry (Auer et al., 2023 ). As per the CBDC Tracker website during the time this study was conducted, central banks which have officially launched their CBDC system include Jamaica, the Bahamas, Zimbabwe, and Nigeria, with major economies such as China, Russia, and the European Union reaching the pilot stages of their project. CBDCs can be categorised into two types, which are wholesale and retail CBDC. Wholesale CBDC is typically reserved for financial institutions and interbank settlements and typically preferred in economies with more developed financial markets and greater cross-border transactions, whereas retail CBDC is aimed at the public for everyday transactions and preferred in economies with lower levels of financial inclusion, large informal economies, and a higher degree of innovation (Maryaningsih et al., 2022 ). The coexistence of both forms of CBDC is essential and a country is considered to have implemented an economic-wide CBDC adoption should both forms be adopted. Indonesia is examining the potential integration of both forms of CBDC (Bank Indonesia, 2022 ), which necessitates careful consideration of the unique regulatory, technological, and financial inclusion aspects for each category. International perspective. To highlight the increasing interest in CBDC research, several studies on central banks which have launched their CBDC systems have been analysed in varying degrees, namely The Bahamas and Nigeria. In the case of The Bahamas, the Sand Dollar serves as an incentive to combat the financial inclusion gap within the country, which it has indeed slowly achieved. However, the launching of the sand dollar, implementation has seen a slow start due to the Central Bank of The Bahamas focusing more on expanding the system by inviting new financial groups to join the system rather than encouraging its use (Bilgen & Colberg, 2024), with cryptocurrencies having more benefit due to their anonymity and unregulated nature being more aligned towards The Bahamas’ economic environment of being a tax haven, with CBDC thus serving as a payment system that promotes a payment system which promotes geopolitical purposes or to adhere to international standards instead (Wenker, 2022 ). In the case of Nigeria, several studies similarly show after the implementation of the eNaira, although a positive impact is shown on financial inclusion due to the introduction of the CBDC system, Nigerians are reluctant in the adoption of the eNaira due to factors concerning usage privacy, digital literacy on the knowledge of how to use the new system, and the lack of technological infrastructure in aiding a simplified use of the system (Akpan & Umaru, 2024 ; Omotubora, 2024 ). Many Nigerians feel that factors which help increase the positive intention to adopt CBDCs include performance expectancy, effort expectancy, social influence, trust, government regulation, and behavioural intention (Marzuk & Abdullah, 2024). Research by Osakwe et al. (2025) show that one of Nigeria’s main determinants of CBDC adoption include financial inclusion in order to address the issue of the underbanked in the country, a common theme prevalent in developing countries, whilst perceived financial cost serves as an obstacle to individuals’ behaviour to adoption and thus linking it to digital literacy. In Indonesia, this shift towards CBDC adoption is influenced by both global trends and local imperatives. For Bank Indonesia, factors that contributes to a digital disruption on banking practices, such as an increased use of blockchain assets, the preferred use of financial transactions due to the COVID-19 pandemic outbreak, as well as other major economies taking considerable action on CBDC research, have made Bank Indonesia take their first steps in CBDC research. The release of Bank Indonesia’s white paper on the Digital Rupiah provides information and outlines the central bank’s vision and strategy for CBDC adoption in Indonesia, including research conducted to support the information provided (Bank Indonesia, 2022 ). Financial inclusio n. Fabris ( 2019 ) highlights that CBDC could play a pivotal role in advancing digital transactions by closing the financial inclusion gap for people who do not have ease of access to banking, specifically mentioning groups that are poor or elderly. This study can be applied to Indonesia in which CBDC can extend financial services to remote and underserved areas as well as those who are considered lacking in technological literacy. However, it should be worth noting that although the aim for financial inclusion is more economic than monetary, the adoption of CBDCs should also be aligned with existing or potential regulations in order to ensure that the digital turnover is compliant with the relevant regulations, which is discussed by Mooij (2022) in the context of the European Central Bank’s potential implementation of the digital euro with regards to the ECB’s economic mandate. Furthermore, issuance aligns with Project Garuda’s objective of ensuring the population’s financially vulnerable individuals are protected through CBDCs, with several studies concluding that CBDC reduces the share of unbanked individuals and thus promotes financial inclusion due to changes in lending schemes from lower liquidity risks and efficient payment systems (Tan, 2024 ) with significant impact amongst the sample’s vulnerable individuals (Dunbar & Treku, 2024 ). Cybersecurity . Bank Indonesia ( 2022 ) outlines the key pillars of regulatory oversight and cybersecurity infrastructure necessary for secure CBDC deployment, emphasising the role of the central bank in regulating the currency. Indonesia’s regulatory framework on cybersecurity for money laundering activities was seen as far from capable of protecting against such threats, with cryptocurrencies serving as a precedent (Putri et al., 2023 ) and Santoso et al. ( 2023 ) going as far to conclude that current applicable regulations for cybersecurity only allows retail CBDC to operate as a payment instrument rather than a currency. Firdaus ( 2023 ) compares Indonesia’s regulatory framework on cybersecurity compared to other countries currently undergoing CBDC research, namely Malaysia and Australia, and concludes that Indonesia is lacking in its existing money laundering laws, referred to as the PPTPPU Law in Indonesia, and would need to be reformed for harmonisation purposes with the implementation of CBDC. A study conducted by the IMF analyses several cybersecurity conditions that should be taken into consideration based on the CBDC research phase of each countries’ central banks, with Bank Indonesia’s proof-of-concept stage CBDC research being advised to consider prioritising trust by assessing risk mitigation strategies for all stakeholders and to start formulating a governance framework to ensure the security components of the potential CBDC to be aligned with existing payment systems and updated technological functions (Bharath et al., 2024 ). Digital literacy serves as a potential to mitigate these arising cyber threats as although the system is relatively new for many Indonesians, similar forms of financial technology has been introduced in the country and is now being utilised by most of the population, thus serving as a precedent CBDC adoption (Putri et al. 2023 ). In ensuring a more robust cybersecurity framework, e-governance measures are said to assist in technological advances by ensuring mutual trust between the government and their citizens, which includes CBDC as well (Grigalashvili, 2022 ; Malodia et al., 2021 ). Indonesian government agencies have already implemented measures and standards to ensure smoother e-governance as top-level support would be beneficial in preparing the population for CBDC adoption (Durigan Junior et al., 2022 ). Technological infrastructure. Indonesia's advancement in CBDC research must consider the existing digital infrastructure and the extent to which the population is equipped to engage with digital currency systems, with the relatively new topic facing the challenge of needing to be understood by the population to ensure proper digital literacy and awareness of the to-be introduced system with newer functions such as offline functionality being considered (Chu et al. 2022 ). As such, Maryaningsih et al. ( 2022 ) categorises infrastructure in the form of access to electricity, quality of electric output, mobile subscription, and internet users, in which Fadli et al. ( 2025 ) suggests is contributed by the younger generations in Generation Y and Z. Regulatory quality. Serving as a precedent to CBDCs, cryptocurrencies have been observed to have a negative impact on financial system stability due to their price differences and volatility as a result of their independence from monetary policy (Liu & Serletis, 2019 ). Bank Indonesia’s White Paper (2022) further emphasises the cryptocurrency problem and realises the need for effective monetary policy to use CBDC adoption to combat financial system instability from the use of alternative and unauthorised digital currencies, thus requiring the need to evaluate the country’s current regulatory framework from the top level, namely monetary, fiscal, and legal policies. China’s central bank, the People’s Bank of China, is shown to undergo towards a more proactive approach in the implementation of their CBDC project, the digital yuan, through the introduction of several drafts that cater towards a digitalised currency and ensuring a secure circulation of the currency whilst also mitigating potential cross-border crimes due to the ease of access of the system (Xu and Jin, 2022 ). Aginta & Someya ( 2022 ) mentions the importance of monetary policy on a per province basis and emphasizes the importance of interest rate, bank lending, and exchange rate channels, with Muduli & Behera ( 2025 ) using credit-deposit ratio as a measure of not only measuring monetary policy in a per province manner, but also to analyse the implications on financial inclusion measures initiated by India’s central bank, the Reserve Bank of India. Similarly, the importance of fiscal policy is mentioned by Lewis & Oosterman ( 2011 ) as a driver for economic growth, and thus innovation for new systems such as CBDC, as sub-national support is not enough compared to central government support. Furthermore, Ramadhani et al. ( 2025 ) suggest implementing AEOI procedures for the digital rupiah to harmonise the ongoing changes in the Indonesian tax law environment. System coexistence. Bank Indonesia’s real-time payment system QRIS released in 2019 has revolutionised the payment ecosystem in the country (Bank Indonesia, 2020 ) and is quickly being adopted by many transaction methods in the country, with users emphasizing its ease of use and usefulness as a major reason to adopt it (Nurqamarani et al., 2024 ; Usman et al., 2025 ). Sonjaya et al. ( 2025 ) demonstrates the positive impact it has on countries in ASEAN as it serves as an option for integrated payment systems in cross-border transactions. As Indonesia is home to one of the fastest growing fintech industries, Zams et al. ( 2020 ) observes that a “cash-like” retail CBDC model is a suitable match for Indonesia as it not only has characteristics of a traditional currency but is similar to financial services that many Indonesians are familiar with, including both card and digital methods of transaction. Using the European Union’s CBDC project, the digital euro, as an example, Mooij (2021) states that due to the complex legal framework of the European Union Central Bank, a specified of the digital euro must be implemented towards the legal framework instead of vice versa, meaning the digital euro should be tailored to be aligned with the ECB’s current regulatory framework due to the ECB’s mandates of promoting competitiveness with commercial banks for resource efficiency purposes. Figure 1 thus represents the conceptual framework that includes the variables used in this research, with each variable grouped in accordance with the research objectives, and each variable’s relationship towards the independent variable, which is the CBDC Readiness Index. This framework is derived from the digital rupiah framework by Lydda & Rahadi (2025). Methodology The quantitative analysis involves collecting secondary data to be used for a statistical model in determining the appropriate variables to be used and to be interpreted. The dataset will be taken from on 34 provinces instead of the current 38, as complete data is mostly available until 2023 or 2024. Collection of data will be sourced from various locations, which includes but are not limited to LPS, Bank Indonesia, APJIII, BPS, OJK, KPK, ASPI, and government ministries such as the Kemen PANRB, Kemen ESDM, Kemenkeu, Kemendagri, and Komdigi. The dependent variable is represented by the province’s CBDC Readiness Index which represents whether the province is suitable to advance to the next research project based on the stages of CBDC research of worldwide central banks in accordance with the CBDC Tracker website (Alfar et al., 2023 ). Thus, the CBDC Readiness Index is represented by either 0 or 1, where 0 is for provinces who are not yet ready to advance to the next stage of CBDC research and 1 is for provinces that are ready to advance to the next stage of CBDC research, specifically the pilot stage as it is the stage that proceeds Indonesia’s current proof of concept stage. Independent variables are collected between the years of 2020 to 2024, with Table 1 listing further details on the literature review and previous research in which the indicators are derived from. Table 1 List of variables. ID Indicators Description Period Relevant Literature CBDC Readiness Index (Dependent Variable) FII Financial Inclusion Index Index of adults above the age of 15 with access to banking services 2023 Dunbar & Treku ( 2024 ); Tan ( 2024 ) DLI Digital Literacy Index Index of individuals based on understanding of digital services 2021 Putri et al. ( 2023 ) POP Population Number of individuals based on consensus, logged 2024 Maryaningsih et al. ( 2022 ) QRIS QRIS Usage Number of transactions using the Quick Response Code Indonesian Standard system, logged 2024 Nurqamarani et al. ( 2024 ); Usman et al. ( 2025 ) GDP GDP per Capita Gross domestic product using 2010 as the benchmark year divided by population consensus, logged 2024 Maryaningsih et al. ( 2022 ); Koparan ( 2025 ) FI – Financial Inclusion BANK Bank Account Ownership Amount of bank account owned by adults above the age of 15, logged 2020–2024 Maryaningsih et al. ( 2022 ); Koparan ( 2025 ) CO ATM/Debit Card Ownership Amount of ATM and debit cards owned by adults above the age of 15, logged Demirgüç-Kunt & Klapper ( 2022 ). PAY Digital Payment Usage Number of transactions using Real Time Gross Settlement (RTGS), logged Nurqamarani et al. ( 2024 ); Usman et al. ( 2025 ) CS – Cybersecurity GBES Government-Based Electronic System Index Index of adherence to electronic governance systems 2020–2024 Malodia et al., ( 2021 ); Durigan Junior et al. ( 2022 ); Grigalashvili ( 2022 ); Bharath et al., ( 2024 ) TI – Technological Infrastructure INTP Internet Penetration Percentage of online population 2020–2024 Maryaningsih et al. ( 2022 ); Koparan ( 2025 ) ELEC Electricity Access Percentage of households with access to electricity services Maryaningsih et al. ( 2022 ) MC Mobile Cell Phone Proficiency Percentage of population with proficient use of mobile cell phones Maryaningsih et al. ( 2022 ); Koparan ( 2025 ) INTA Internet Access Percentage of population with access to internet services Maryaningsih et al. ( 2022 ); Koparan ( 2025 ) RQ – Regulatory Quality CDR Credit-Deposit Ratio Percent variation of ratio between bank’s outstanding credits to third party funds 2020–2024 Aginta & Someya ( 2022 ); Muduli & Behera ( 2025 ) RFC Regional Fiscal Capacity Index of fiscal capacity of local governments Lewis & Oosterman ( 2011 ) II Integrity Index Index of measures to combat corruption Koparan ( 2025 ) SC – System Coexistence INOV Innovativeness Index Index of innovation 2020–2024 Maryaningsih et al. ( 2022 ) EM Electronic Money Usage Number of transactions using electronic money, logged Nurqamarani et al. ( 2024 ); Usman et al. ( 2025 ) EDC EDC Machine Usage Number of transactions through EDC machines, logged Saputra et al. ( 2024 ) The quantitative research will utilise a binary logistic regression model and with reference to the model equations used by Maryaningsih et al. ( 2022 ) and Koparan ( 2025 ). Starting with the dependent variable, unlike the previously mentioned studies, the Indonesian provinces do not have an official CBDC research stage and will therefore be measured through a latent variable using PCA. The latent variable will thus be represented as follows: $$\:{{y}_{i}}^{*}={\beta\:}_{1}{FII}_{2023}+{\beta\:}_{2}{DLI}_{2021}+{\beta\:}_{3}{POP}_{2024}+{\beta\:}_{4}{QRIS}_{2024}+{\beta\:}_{5}{GDP}_{2024}+{\epsilon\:}_{i}$$ 1 Where y i * represents the latent variable for each province, β 1 , … β 5 are the coefficients to be estimated, and ε i is a standard normal error capturing unobserved factors. Since the purpose of the latent variable is to determine the current stage of CBDC research a province has the potential to be in, only the latest available year of the indicators will used, hence the use of PCA. From the results of the latent variables for each province the following rule is thus applied to determine which provinces are categorised as 0 or 1: $$\:{Readiness}_{i}\:\left\{\:\:\begin{array}{c}1\:if\:{{y}_{i}}^{*}>0,\\\:0\:if\:{{y}_{i}}^{*}\le\:0.\end{array}\right.\:$$ 2 Once the dependent variable of CBDC Readiness Index is secured, the binary logistic regression model is thus represented by the following expanded equation: $$\:{Readiness}_{i}\in\:\left\{\text{0,1}\right\}={\beta\:}_{0}+{\beta\:}_{1}{BANK}_{i}+{\beta\:}_{2}{CO}_{i}+{\beta\:}_{3}{PAY}_{i}+{\beta\:}_{4}{GBES}_{i}+{\beta\:}_{5}{INTP}_{i}+{\beta\:}_{6}{ELEC}_{i}+{\beta\:}_{7}{MC}_{i}+{\beta\:}_{8}{INTA}_{i}+{\beta\:}_{9}{CDR}_{i}+{\beta\:}_{10}{RFC}_{i}+{\beta\:}_{11}{II}_{i}+{\beta\:}_{12}{INOV}_{i}+{\beta\:}_{13}{EM}_{i}+{\beta\:}_{14}{EDC}_{i}$$ 3 Where Readiness i is the CBDC Readiness Index represented by either 0 or 1 which indicated if the province progresses to the next stage of CBDC research or not and β 1 , … β 14 are the coefficients to be estimated and β 0 is the intercept. With reference to the formulated research questions, BANK, CO , and PAY represents the indicators falling into the category of financial inclusion (FI); GBES represents the indicators under cybersecurity measures (CS); INTP, ELEC, MC , and INTA represents the indicators categorised under technological infrastructure (TI); CDR, RFC , and II represents the indicators under regulatory quality (RQ); and INOV, EM , and EDC as indicators under system coexistence (SC). Using statistical tools such as EViews 13 and the Jupyter Notebook on Visual Studios Code, a binary logistic regression model will be used for this research to determine the influence of various factors on the CBDC Readiness Index of provinces in Indonesia. First, the research objectives and questions regarding factors that influence CBDC research progress in Indonesia are identified, with independent and dependent variables relevant to the objective being defined as well. Data is then collected and checked for completeness, accuracy, and reliability, with missing values and outliers addressed. The model equation that estimates the effect of each variable to CBDC Readiness Index is defined. Using the data collected, descriptive statistics is presented to understand the characteristics of the data and used to calculate means, standard deviations, and other summary statistics for continuous variables. Once the latent variable is calculated to determine the dependent variable for each province is calculated, the independent variables are tested and the results are presented, and the implications of the research are discussed with regards to CBDC research progress in Indonesia by referring to the defined research objectives. To ensure the validity of the data, a robustness check through the exclusion of data with a significant p-value will be conducted. This is done to check whether removing a certain variable will significantly alter the results of the findings and to determine the reasons for the change. Scenario-based simulations will also be conducted similarly to determine which variables have a larger impact on the dependent variable. For each of the variables, the number of observers from 34 provinces are recorded with the mean data from 2020 to 2024 as the data for the mentioned period will prove to have continued relevance due to modern studies which still utilises this data (Maryaningsih et al., 2022 ) and the availability of complete data is up to this period. Additionally, data for the year 2020 for the provinces of Aceh, Jambi, Bangka Belitung Islands, Riau Islands, Lampung, West Sulawesi, and West Papua are dropped due to the large amount of missing data for said provinces. On the other hand, indicators with one year of missing data are forecasted using mixed-effect imputation to impute realistic forecasts based on patterns. Results The dataset represents both the dependent variables, each with 34 observations, and independent variables, with 156 observations, which includes 34 provinces between the years 2020 to 2024, barring a few provinces for the year 2020, and shows the disparity between provinces at certain time periods. Table 2 shows the descriptive statistics of the indicators that will be used. Variables CO , CDR , RFC , and EM are shown to have a minimum of less than 1, indicating that several provinces are lacking when it comes to certain indicators. INTA and INOV shows the highest standard deviations amongst all the indicators, portraying several provinces have a huge gap when it comes to internet access and innovativeness between provinces. Table 2 Descriptive statistics. ID Indicators N Mean Median S.D. Min Max FII Financial Inclusion Index 34 9.58 89.61 5.44 79.08 99.52 DLI Digital Literacy Index 34 3.51 3.51 0.10 3.18 3.71 POP Population 34 15.07 14.99 1.01 13.24 17.47 QRIS QRIS Usage 34 13.03 12.84 1.21 11.30 15.86 GDP GDP per Capita 34 10.63 10.60 0.55 9.53 12.21 BANK Bank Account Ownership 156 15.19 15.24 1.52 12.12 19.67 CO ATM/Debit Card Ownership 156 14.77 15.18 2.89 0 17.80 PAY Digital Payment Usage 156 10.69 10.51 1.52 8.07 15.33 GBES Government-Based Electronic System Index 156 2.94 3.01 0.78 1.00 4.73 INTP Internet Penetration 156 75.01 75.27 6.84 49.80 90.50 ELEC Electricity Access 156 99.28 99.99 1.89 87.62 100.00 MC Mobile Cell Phone Proficiency 156 66.81 67.66 7.78 35.33 82.47 INTA Internet Access 156 84.12 86.52 10.63 35.14 100.00 CDR Credit-Deposit Ratio 156 2.91 2.67 1.75 0 13.20 RFC Regional Fiscal Capacity 156 1.65 1.49 1.23 0.10 11.39 II Integrity Index 156 69.54 70.33 5.09 56.42 82.81 INOV Innovativeness Index 156 51.39 50.42 14.48 7.74 88.92 EM Electronic Money Usage 156 13.71 13.70 2.55 0.69 20.32 EDC EDC Machine Usage 156 9.59 9.49 1.78 2.30 13.62 CBDC Readiness Index. The latent variable for each province must thus be identified to assign the dependent variable of CBDC Readiness Index for each province. Table 3 shows the PCA Model of the indicators that will be used to determine the latent variable, which are FII in 2023, DLI in 2021, POP in 2024, QRIS in 2024, and GDP in 2024. Once the dataset has been converted into their Z-scores, the PCA Model will utilise dataset from the latest available year as the CBDC Readiness Index is determined during the latest year regardless of historical trends. Table 3 PCA model results. Eigenvalues: (Sum = 5, Average = 1) Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 1.9899 0.3864 0.3980 1.9899 0.3980 Eigenvectors (loadings) Indicator PC 1 FII (2023) − 0.2581 DLI (2021) − 0.2723 POP (2024) 0.6652 QRIS (2024) 0.6413 GDP (2024) 0.0737 Ordinary Correlations FII (2023) DLI (2021) POP (2024) QRIS (2024) GDP (2024) FII (2023) 1 DLI (2021) 0.5567 1 POP (2024) -0.1760 -0.1413 1 QRIS (2024) -0.0066 -0.0545 0.9002 1 GDP (2024) 0.2362 0.0409 -0.0358 0.2634 1 As per Table 3 , PC 1 thus shows the results of the loadings of each indicator at a total variance of 39.8% as per the proportion value. The loadings can thus be inputted into the latent variable equation, resulting in the rankings of each province shown in Table 4 , and a visualisation using the map of Indonesia’s provinces on Fig. 2 . Table 4 CBDC Readiness Index per province. Province y i * CBDC Readiness Index Aceh -1.0934 0 North Sumatra 1.5600 1 West Sumatra 0.0091 1 Riau 1.2910 1 Jambi 0.4918 1 South Sumatra 1.2739 1 Bengkulu -1.0404 0 Lampung 0.7918 1 Bangka Belitung Islands -1.4693 0 Riau Islands -0.9949 0 Jakarta 1.6815 1 West Java 3.1546 1 Central Java 2.5874 1 Yogyakarta -0.6901 0 East Java 2.7897 1 Banten 1.8369 1 Bali 0.3244 1 West Nusa Tenggara 0.1474 1 East Nusa Tenggara -0.6634 0 West Kalimantan 0.1143 1 Central Kalimantan -0.2293 0 South Kalimantan 0.1036 1 East Kalimantan -0.3097 0 North Kalimantan -2.4091 0 North Sulawesi -0.8239 0 Central Sulawesi -0.6334 0 South Sulawesi 0.8783 1 Southeast Sulawesi -0.8604 0 Gorontalo -2.2521 0 West Sulawesi -2.1860 0 Maluku -1.1653 0 North Maluku -0.2618 0 Papua 0.0752 1 West Papua -2.0284 0 The binary dependent variable is classified into two opposite distinctions, namely 0 and 1. The resulting CBDC Readiness Index show a clear pattern where provinces considered to be on the so called western side of Indonesia, which includes the islands of Sumatera and Java with the addition of Bali, considered to be ready for the next stage of CBDC research, with additional provinces outside of the mentioned area including West Nusa Tenggara, South Sulawesi, West and South Kalimantan, and Papua. Correlation test. To better visualise the links between pairs of indicators and to test for multicollinearity, Table 5 shows a correlation matrix comparing the Pearson correlation coefficient between all pairs of the indicators. As a general rule, indicators with a correlation coefficient ranging from less than or greater than − 0.85 or 0.85 respectively should be further observed for multicollinearity. CO and EM with a coefficient of 0.9026 along with EDC and PAY with a coefficient of 0.8663 fits into this category. This could be explained by the fact that these indicators represent a similar form of payment system that form as a complement to each other and will be a point of discussion in the next chapter. Nonetheless, these findings will serve as the basis for the robustness check. Table 5 Correlation matrix of variables. BANK CO PAY GBES INTP ELEC MC INTA CDR RFC II INOV EM EDC BANK 1 0.2809 0.3852 0.5269 0.1811 0.1893 0.2153 0.3531 -0.2134 0.2030 0.1536 0.4586 0.3770 0.3854 CO 0.2809 1 0.5127 0.2855 0.2598 0.0262 -0.1304 -0.0049 0.1692 0.2003 0.1205 0.3335 0.9026 0.7405 PAY 0.3852 0.5127 1 0.3919 0.4977 0.1904 0.2682 0.2814 -0.1406 0.4989 0.3354 0.5282 0.7296 0.8663 GBES 0.5269 0.2855 0.3919 1 0.2729 0.2145 0.2104 0.4409 -0.2892 0.2661 0.2664 0.5708 0.4303 0.4123 INTP 0.1811 0.2598 0.4977 0.2729 1 0.1463 0.3664 0.3498 -0.1626 0.2327 0.2928 0.2860 0.3992 0.4871 ELEC 0.1893 0.0262 0.1904 0.2145 0.1463 1 0.4374 0.4116 -0.0620 0.1725 0.0863 0.1560 0.1414 0.1989 MC 0.2153 -0.1304 0.2682 0.2104 0.3664 0.4374 1 0.8873 -0.2367 0.2274 0.1507 0.0819 0.1153 0.1728 INTA 0.3531 -0.0049 0.2814 0.4409 0.3498 0.4116 0.8873 1 -0.3301 0.2934 0.1322 0.2517 0.2363 0.2343 CDR -0.2134 0.1692 -0.1406 -0.2892 -0.1626 -0.0620 -0.2367 -0.3301 1 -0.0352 -0.1096 -0.1492 0.0626 -0.0565 RFC 0.2030 0.2003 0.4989 0.2661 0.2327 0.1725 0.2274 0.2934 -0.0352 1 -0.0141 0.2069 0.4194 0.3587 II 0.1536 0.1205 0.3354 0.2664 0.2928 0.0863 0.1507 0.1322 -0.1096 -0.0141 1 0.2096 0.2174 0.3565 INOV 0.4586 0.3335 0.5282 0.5708 0.2860 0.1560 0.0819 0.2517 -0.1492 0.2069 0.2096 1 0.4552 0.5007 EM 0.3770 0.9026 0.7296 0.4303 0.3992 0.1414 0.1153 0.2363 0.0626 0.4194 0.2174 0.4552 1 0.8476 EDC 0.3854 0.7405 0.8663 0.4123 0.4871 0.1989 0.1728 0.2343 -0.0565 0.3587 0.3565 0.5007 0.8476 1 Logistic regression results. Table 6 shows the results of the logistic regression, where the coefficient represents the change in the log-odds due to a one-unit increase in the independent variable ceteris paribus whereas the odds ratio represents the multiplier of the odds of the dependent variable, CBDC Readiness Index, increasing to Ready (1) if the independent variable experiences a one-unit increase ceteris paribus . The result logit marginal effects represent the average marginal effect (dy/dx) in which a one-unit increase of an independent variable changes the probability of the dependent variable by said amount ceteris paribus . PAY , MC , II , and EDC are statistically significant at a 1% interval, with EM being statistically significant at a 10% interval. An increase a province’s digital payment usage through RTGS ( PAY ) and its EDC system usage ( EDC ) increases the odds of the province being ready for CBDC research progress by 10.8158 times and 43.6648 times respectively with the probability of the province being ready for CBDC research progress due to the increase at a chance of 50.96% and 88.02% respectively. On the other hand, an increase in a province’s mobile cell phone proficiency ( MC ) and integrity index ( II ) reduces the odds of the province being ready for CBDC research progress by 0.6431 times and 0.7182 times respectively with the probability of the province’s readiness being lowered by 9.45% and 7.08% respectively. Table 6 Logistic Regression and Logit Marginal Effects results Logistic Regression Logit Marginal Effects Coef. Odds Ratio S.E. z P>|z| dy/dx S.E. z P>|z| BANK 0.2292 1.2576 0.4494 0.5101 0.6100 0.0491 0.0950 0.5170 0.605 CO 0.6100 1.8405 0.7216 0.8453 0.3979 0.1305 0.169 0.771 0.441 PAY 2.3810*** 10.8158 0.8619 2.7625 0.0057 0.5096*** 0.1700 3.0060 0.003 GBES -1.3393 0.2620 0.9155 -1.4629 0.1435 -0.2866 0.1870 -1.534 0.125 INTP -0.0085 0.9915 0.0604 -0.1411 0.8878 -0.0018 0.013 -0.1410 0.8880 ELEC -0.0956 0.9088 0.0901 -1.0612 0.2886 -0.0205 0.0200 -1.0240 0.306 MC -0.4415*** 0.6431 0.1569 -2.8136 0.0049 -0.0945*** 0.033 -2.8860 0.004 INTA 0.1498 1.1616 0.1187 1.2621 0.2069 0.0321 0.026 1.2190 0.223 CDR -0.4286 0.6514 0.2763 -1.5513 0.1208 -0.0917 0.062 -1.4710 0.141 RFC -0.1671 0.8461 0.5456 -0.3063 0.7594 -0.0358 0.118 -0.302 0.762 II -0.3310*** 0.7182 0.1080 -3.0648 0.0022 -0.0708*** 0.022 -3.1870 0.001 INOV 0.0480 1.0492 0.0331 1.4514 0.1467 0.0103 0.007 1.438 0.15 EM -1.4805* 0.2275 0.8843 -1.6743 0.0941 -0.3168 0.221 -1.436 0.151 EDC 3.7765*** 43.6648 1.1378 3.3192 0.0009 0.8802*** 0.258 3.131 0.002 Pseudo R 2 0.7002 *, **, and *** are significance at 10%, 5%, and 1% respectively. Robustness check. With reference to Table 5 , several variables have shown to be highly multicollinear as per their Pearson correlation coefficients. A robustness check shown in Table 7 will thus be conducted in order to determine the validity of the results of the model by dropping at least one variable of the indicator pairs with the highest correlation coefficient, namely at least one of CO , EM , PAY , or EDC . Firstly, CO will be chosen to be dropped as its p-value does not allow it to reject the null hypothesis, followed by EDC chosen to be dropped as although the model shows the statistical significance of the variable, a theoretical standpoint suggests the growing importance of EDC over PAY due to EDC machines signifying innovativeness of payment systems (Saputra et al., 2024 ) and RTGS systems being more associated with wholesale CBDCs, which emerging countries such as Indonesia are found to not be as compatible with when compared to retail CBDCs (Maryaningsih et al., 2022 ). Several observations can be made due to the robustness check. Dropping CO only resulted in less correlation between its most collinear pair EM and thus resulted EM gaining statistical significance over the 1% threshold. However, further robustness checks through the additional omission of PAY resulted in not only EM losing statistical significance back to the 5% threshold, but MC as well to the same statistical significance. This suggests that the previous results for MC were partially driven by the presence of CO and PAY , with EDC and II staying consistent in their statistical significance and thus confirming the robustness of its results. Additionally, the omission of CO and PAY resulted in CDR gaining statistical significance at the 5% threshold. This implies that due to the removal of CO and PAY , the lower collinearity between each of the two variables to CDR resulted in a lower standard of error and somewhat raised its statistical significance. Thus, the robustness check indicates that EDC and II are consistent factors that influence CBDC research progress. Table 7 Robustness check through omission of variables. Complete Model No CO No CO + PAY Coef. P>|z| Coef. P>|z| Coef. P>|z| PAY 2.3810*** 0.0057 2.5237*** 0.0030 Omitted MC -0.4415*** 0.0049 -0.4373*** 0.0050 -0.2659** 0.0260 CDR -0.4286 0.1208 -0.4060 0.1410 -0.4729** 0.0470 II -0.3310*** 0.0022 -0.3402*** 0.0020 -0.2959*** 0.0010 EM -1.4805* 0.0941 -0.7875*** 0.0080 -0.5092** 0.0480 EDC 3.7765*** 0.0009 3.5872*** 0.0010 4.0675*** 0.0000 Pseudo R 2 0.7002 0.6968 0.6419 *, **, and *** are significance at 10%, 5%, and 1% respectively. Conclusion The CBDC Readiness Index for the 34 provinces show 17 provinces which are considered ready to move to the next stage of CBDC research, which is the pilot stage. A noticeable trend of the provinces that are ready is that it consists of a majority of provinces that are on the western side of the archipelago. This provides a clear picture of the divide in development of most parts of the country, with Indonesia’s western provinces on the islands of Sumatera and Java considered to be more developed in many ways possible, thus facilitating a better environment for CBDC pilot testing (Kurniawan et al., 2019 ). Another province which has proven to be ready is South Sulawesi, whose provincial capital of Makassar serves as a testament to this result due to its position as the gateway to eastern Indonesia with a rapidly increasing economic growth during the post-COVID era (Arifin, 2024) and its relatively fast digital transformation (Abduh et al., 2024 ). The rest of the provinces which are ready for the pilot stage CBDC have shown a growth in their digital transformation, such as through the adoption of digital technologies in MSMEs in West Nusa Tenggara (Armiani et al., 2021 ), electronic governance systems to promote regional development in Papua (Santiko & Suhendra, 2022 ), or the preparation of an increasing service sector in the traditionally resource-dominated economy of Kalimantan as the region prepares itself to be the seat of the new national capital (Yusuf et al., 2023 ), with West Kalimantan also being on the border of Malaysia being a potential driver for cross-border transactions through trade (Messa et al., 2023 ) and an increased digital transformation in the province (Yutika, 2024 ). The decision to implement pilot CBDCs across only a handful of provinces is supported by China’s decision to introduce their pilots in major cities only, with several cities gradually joining once certain issues with the system are addressed (Wu et al., 2024 ). As provinces increase their usage of EDC machines, their likelihood of being ready to proceed to the pilot stage of CBDC research increases. This result is backed by Nurqamarani et al. ( 2024 ) and Saputra et al. ( 2024 ) as EDC machines in Indonesia represents payment system coexistence due its use multipurpose use for payments using e-money, internet banking, debit, and credit cards. EDC machines have also implemented QRIS as a payment method, thus signifying how new payment systems are able to be easily integrated due to EDC machines. On the other hand, the more a province’s integrity index, or level of corruption within the provincial government, rises, the likelihood of advancing to the CBDC pilot stage decreases. As of now, Indonesia’s regulatory environment does not have the proper policies to ensure proper implementation for CBDC systems (Firdaus, 2023 ; Santoso et al., 2023 ). Proper regulatory reforms require careful consideration through due process of the legal sector, of which a system with proper integrity would follow. This is proven by China’s central bank, the People’s Bank of China, which has developed a five-year plan that follows due process before ensuring implementation (Xu & Jin, 2022 ). In other words, a provincial government with high integrity index and lower levels of corruption would be discouraged from moving on to the next stage of CBDC research until a regulatory reform, and vice versa. Due to the ongoing research nature of CBDCs, especially as Indonesia is currently in the proof of concept as of this moment, future research should consider updating indicators to ensure the accuracy and stability of the results. The latent variables to determine CBDC Readiness Index should consider expanding the criteria for the index in order to accurately determine which provinces are truly ready for the pilot stage of CBDC implementation, which can thus be used by Bank Indonesia to launch pilots in said provinces. As with many other countries within the same stage of CBDC research, Bank Indonesia is recommended to use countries which has advanced in their CBDC research stage as case study comparisons to determine the best practices used and challenges faced. Thus, future studies should implement a qualitative method or mixed-method study in order to determine the best course of action from both a theoretical and practical standpoint, such as once the system advances to the pilot stage of CBDC research, interviews should be conducted to determine the specifications of CBDC system Indonesia needs, as several studies emphasised that no CBDC system should be uniform as a country’s payment system has their own specific needs. Declarations Competing Interests: The author(s) declare no competing interests. Ethical Approval: This article does not contain any studies with human participants performed by any of the authors. Informed Consent: This article does not contain any studies with human participants performed by any of the authors. Funding Declaration The author(s) declare no funding was provided for this research. Author Contribution All authors contributed equally to this manuscript. RREL wrote the main manuscript text and prepared all figures and tables. RAR supervised the process and reviewed the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Abduh T, Remmang H, Abubakar H, Karim A (2024) Entrepreneurship and MSME market orientation toward creative industries: Society Era 5.0 in Makassar city. Asian Economic Financial Rev 14(2):76–87. https://doi.org/10.55493/5002.v14i2.4964 Aginta H, Someya M (2022) Regional economic structure and heterogeneous effects of monetary policy: Evidence from Indonesian provinces. J Economic Struct 11(1):1. https://doi.org/10.1186/s40008-021-00260-6 Akpan U, Umaru A (2024) Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from. https://doi.org/10.21203/rs.3.rs-3861545/v1 . Structural Equation Model Alfar AJK, Kumpamool C, Nguyen DTK, Ahmed R (2023) The determinants of issuing central bank digital currencies. Res Int Bus Finance 64:101884. https://doi.org/10.1016/j.ribaf.2023.101884 Arifin (n.d.). Analysis of economic growth and policy impact of Makassar city priority program (case study of 2018–2022). Jurnal Ekonomi, 13(01) Armiani A, Basuki B, Suwarno N, DAMPAK STRATEGI BISNIS TERHADAP KINERJA UMKM DI NUSA TENGGARA BARAT (2021) EKUITAS (Jurnal Ekonomi dan Keuangan) 5(3). https://doi.org/10.24034/j25485024.y2021.v5.i3.4892 . TEKNOLOGI DIGITAL MEMEDIASI Auer R, Cornelli G, Frost J (2023) Rise of the Central Bank Digital Currencies. Int J Cent Bank, 19(4) Bank Indonesia (2020) Quick Response Code Indonesian Standard (QRIS). https://www.bi.go.id/en/fungsi-utama/sistem-pembayaran/ritel/kanal-layanan/QRIS/default.aspx Bank Indonesia (2022) Project Garuda: Navigating the Architecture of Digital Rupiah Bharath A, Paduraru A, Gaidosch T (2024) Cyber Resilience of the Central Bank Digital Currency Ecosystem. Fintech Notes, 2024(003), 1. https://doi.org/10.5089/9798400286995.063 Bilgen C, Dutto M, Colberg T (2024) Analyzing the CBDC Tree: The Case of the Bahamian Sand Dollar Using Three-. Level Central Bank Digital Currency Design Framework Chu Y, Lee J, Kim S, Kim H, Yoon Y, Chung H (2022) Review of Offline Payment Function of CBDC Considering Security Requirements. Appl Sci 12(9):4488. https://doi.org/10.3390/app12094488 Demirgüç-Kunt A, Klapper L (2022) The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. The World Bank. https://doi.org/10.1596/978-1-4648-1897-4 Dunbar K, Treku DN (2024) Examining the impact of a central bank digital currency on the access to banking. Int Rev Financial Anal 93:103220. https://doi.org/10.1016/j.irfa.2024.103220 Durigan Junior CA, De Mesquita Spinola M, Goncalves F, R., Barbin Laurindo FJ, CENTRAL BANK DIGITAL CURRENCIES: THE ADVENT OF ITS IT GOVERNANCE IN THE FINANCIAL MARKETS (2022). 19th CONTECSI International Conference on Information Systems and Technology Management. 19th CONTECSI International Conference on Information Systems and Technology Management. https://doi.org/10.5748/19CONTECSI/PSE/ITM/6914 Fabris N (2019) Cashless Society – The Future of Money or a Utopia? J Cent Bank Theory Pract 8(1):53–66. https://doi.org/10.2478/jcbtp-2019-0003 Fadli JA, Rusmanto T, Kurniawan Y, Hutagaol-Martowidjojo Y (2025) The Interplay of Financial Availability, Herding Behavior, and Cryptocurrency Investment Experience Moderated by Government Policy: A Study from Indonesia. Qubahan Acad J 4(4):509–527. https://doi.org/10.48161/qaj.v4n4a1144 Febriyanto A, Adenia N, Annabila I, Yusfiarto R (2025) CBDC ADOPTION: ALIGNING MODEL TECHNOSTRESS INHIBITORS AND PERCEIVED VALUE AMONG INDONESIAN GENERATION Z. J Cent Bank Law Institutions 4(1):49–78. https://doi.org/10.21098/jcli.v4i1.258 Firdaus SP (2023) Urgency of Money Laundering Policy Reform for Digital Rupiah Implementation. AML/CFT Journal The Journal of Anti Money Laundering and Countering the Financing of Terrorism. 2(1):58–82. https://doi.org/10.59593/amlcft.2023.v2i1.118 Grigalashvili V (2022) E-government and E-governance: Various or Multifarious Concepts. Int J Sci Manage Res 05(01):183–196. https://doi.org/10.37502/IJSMR.2022.5111 International Monetary Fund (2023), November 14 Central Bank Digital Currency—Initial Considerations. IMF. https://www.imf.org/en/Publications/Policy-Papers/Issues/2023/11/14/Central-Bank-Digital-Currency-Initial-Considerations-541466 Koparan A (2025) Central Bank Digital Currencies: A review of global trends in adoption, financial inclusion, and the role of country characteristics. Invest Manage Financial Innovations 22(1):107–121. https://doi.org/10.21511/imfi.22(1).2025.09 Kurniawan H, De Groot HLF, Mulder P (2019) Are poor provinces catching-up the rich provinces in Indonesia? Reg Sci Policy Pract 11(1):89–109. https://doi.org/10.1111/rsp3.12160 Lewis BD, Oosterman A (2011) SUB-NATIONAL GOVERNMENT CAPITAL SPENDING IN INDONESIA: LEVEL, STRUCTURE, AND FINANCING. Public Adm Dev 31(3):149–158. https://doi.org/10.1002/pad.582 Liu J, Serletis A (2019) Volatility in the Cryptocurrency Market. Open Econ Rev 30(4):779–811. https://doi.org/10.1007/s11079-019-09547-5 Lydda RRE, Rahadi RA (n.d.). Central Bank Digital Currency and a Digital Rupiah Framework for Indonesia’s Implementation: A Literature Review Malodia S, Dhir A, Mishra M, Bhatti ZA (2021) Future of e-Government: An integrated conceptual framework. Technol Forecast Soc Chang 173:121102. https://doi.org/10.1016/j.techfore.2021.121102 Maryaningsih N, Nazara S, Kacaribu FN, Juhro SM (2022) CENTRAL BANK DIGITAL CURRENCY: WHAT FACTORS DETERMINE ITS ADOPTION? Buletin Ekonomi Moneter Dan Perbankan 25(1):1–24. https://doi.org/10.21098/bemp.v25i1.1979 Messa MY, Bariyah N, Kurniasih EP (2023) Determining Factors of Economic Growth and Poverty in West Kalimantan. Jurnal Ilmu Sosial Dan Humaniora 12(3):530–546. https://doi.org/10.23887/jish.v12i3.67197 Mooij AM (2023) The role of the European Central Bank in response to COVID19. An evaluation of its mandate. J Eur Integr 45(4):649–663. https://doi.org/10.1080/07036337.2022.2120479 Muduli S, Behera T (2025) An Anatomy of Credit-Deposit Ratios Among Indian States Nurqamarani AS, Fadilla S, Juliana A (2024) Revolutionizing Payment Systems: The Integration of TRAM and Trust in QRIS Adoption for Micro, Small, and Medium Enterprises in Indonesia. J Inform Syst Eng Bus Intell 10(3):314–327. https://doi.org/10.20473/jisebi.10.3.314-327 Omotubora A (2024) Same Naira, More Possibilities! Assessing the Legal Status of the eNaira and Its Potential for Privacy and Inclusion. J Afr Law 68(2):245–262. https://doi.org/10.1017/S0021855324000044 Putri AM, Wiryono SK, Damayanti SM, Rahadi RA (2023) Framework of Digital Financial Literacy Dimensions in Indonesia. https://kurdishstudies.net/menu-script/index.php/KS/article/view/1428 Putri T, Amiludin A, Ahmad DN, Hidayatulloh H (2023) Inadequate Cryptocurrency and Money Laundering Regulations in Indonesia (Comparative Law of US and Germany). Yustisia Jurnal Hukum 12(2):129. https://doi.org/10.20961/yustisia.v12i2.71835 Ramadhani R, Farisy Z, Puteri DS, OF CBDC AND TAX LAW ENFORCEMENT IN SELECTED COUNTRIES (2025) J Cent Bank Law Institutions 4(1):141–180. https://doi.org/10.21098/jcli.v4i1.275 . COMPARATIVE ANALYSIS Ramayanti R, Azhar Z, Nik Azman NH (2025) Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach. Telematics Inf Rep 17:100185. https://doi.org/10.1016/j.teler.2024.100185 Santiko A, Suhendra A (2022) Governance Through Electronic-based Information System by Papua Provincial Government. Int J Reg Innov 2(4):43–50. https://doi.org/10.52000/ijori.v2i3.65 Santoso WY, Putra AA, Susanti L, Rahman F, DESIGN ELEMENTS AND RISKS OF CENTRAL BANK DIGITAL CURRENCY IN TAILORING A PRUDENT (2023) Diponegoro Law Rev 8(2):141–158. https://doi.org/10.14710/dilrev.8.2.2023.141-158 Saputra RA, Fihartini Y, Husna N (2024) Influence of Easy, Speed and Security of Transactions on Decision to Use BRI EDC Machine in Indonesia. J Bus Manage Economic Dev 2(03):1335–1345. https://doi.org/10.59653/jbmed.v2i03.978 Sonjaya A, Ragimun, Basmar E, Ermawati T, Kurniadi AP, Dasilva H, Sabilla K, Hasan, Takhim M, Pratiwi R, Mutaqin, Yosepha SY (2025) How the Integration of Payment Systems Through QRIS Accelerates Economic and Financial Cooperation in the ASEAN Region. Int J Sustainable Dev Plann 20(3). https://doi.org/10.18280/ijsdp.200305 Tan BJ (2024) Central bank digital currency and financial inclusion. J Macroecon 81:103620. https://doi.org/10.1016/j.jmacro.2024.103620 Usman O, Hardini IR, Kasofi A (2025) Integrated QR payment system: Cashless payment solution in developing countries from the perspective of MSMEs. Int J Innovative Res Sci Stud 8(2):1424–1432. https://doi.org/10.53894/ijirss.v8i2.5470 Wenker K (2022) Retail Central Bank Digital Currencies (CBDC), Disintermediation and Financial Privacy: The Case of the Bahamian Sand Dollar. FinTech 1(4) Article 4. https://doi.org/10.3390/fintech1040026 Wu J, Liu X, Zhang C (2024) Unveiling the influencing mechanism underlying users’ adoption and recommend intentions of central bank digital currency: A behavioral reasoning theory perspective. J Retailing Consumer Serv 81:104050. https://doi.org/10.1016/j.jretconser.2024.104050 Xu C, Jin B (2022) Digital currency in China: Pilot implementations,legal challenges and prospects. Juridical Tribune 12(2). https://doi.org/10.24818/TBJ/2022/12/2.02 Yusuf AA, Roos EL, Horridge JM, Hartono D (2023) Indonesian capital city relocation and regional economy’s transition toward less carbon-intensive economy: An inter-regional CGE analysis. Japan World Econ 68:101212. https://doi.org/10.1016/j.japwor.2023.101212 Yutika F (2024) THE ROLE OF KNOWLEDGE MANAGEMENT IN ENHANCING DIGITAL CAPABILITIES AND DRIVING INNOVATION IN SMES. 26 Zams BM, Indrastuti R, Pangersa AG, Hasniawati NA, Zahra FA, Fauziah IA (2020) DESIGNING CENTRAL BANK DIGITAL CURRENCY FOR INDONESIA: THE DELPHI–ANALYTIC NETWORK PROCESS. Buletin Ekonomi Moneter Dan Perbankan 23(3):413–440. https://doi.org/10.21098/bemp.v23i3.1351 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":101314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework: The figure depicts a conceptual framework of the research.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6935405/v1/0d7d56cb3e302b93bc670135.png"},{"id":94196961,"identity":"46938d2b-3178-4ebc-b633-668fc891a716","added_by":"auto","created_at":"2025-10-23 13:14:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCBDC Readiness Index visualised: The figure depicts a map visualisation of Indonesia based on CBDC Readiness Index.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6935405/v1/59f6fd4fa5f0fd493fb86dbc.jpeg"},{"id":105035243,"identity":"3cd4f1d5-9b25-415c-abfe-8cc3f36972dc","added_by":"auto","created_at":"2026-03-20 07:25:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1992281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6935405/v1/0e3d77b0-89ee-42bd-a110-6c907ef86873.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Central Bank Digital Currency as a New Form of Legal Tender: Analysing the Determinants of Indonesia's CBDC Research Progress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the increased use of digital payment methods including but not limited to the Quick Response Code Indonesian Standard (QRIS), GoPay, OVO, and ShopeePay, anticipating a future where currency is not only digital but also centralised and securely managed by the country's central bank will not be too far off. With the release of Bank Indonesia\u0026rsquo;s white paper on the digital rupiah, Indonesia\u0026rsquo;s CBDC project, followed by the release of the proof-of-concept on wholesale CBDCs, this new form of digital legal tender will play a part in reshaping the financial landscape of Indonesia currency transactions. In the current age dominated by digital transformations, Indonesia finds itself contemplating the adoption of its own CBDC, offering both unprecedented opportunities and significant challenges.\u003c/p\u003e\u003cp\u003eMany central banks of countries that represent almost 98% of the World\u0026rsquo;s GDP are exploring the potential of CBDCs to modernise financial systems, enhance payment efficiency, and bolster financial inclusion (The Atlantic Council, 2023). Bank Indonesia itself is no stranger to innovating payment systems, with the introduction of the QRIS seeing high implementation rates amongst merchants and users alike along with plans to implement cross-border transactions for the system (Bank Indonesia, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The growing interest in cryptocurrency as another form of digital currency also contributes to the interest in CBDC, especially within the younger generations of Indonesians, in which cryptocurrencies act as a decentralised payment system without a central authority such as a central bank (Fadli et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Like with other central banks around the world, the unpredictable and uncontrollable nature of cryptocurrencies have prompted Bank Indonesia to attempt to reduce the growing reliance of cryptocurrencies as a digital currency (Febriyanto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on Bank Indonesia\u0026rsquo;s Project Garuda White Paper on Indonesia\u0026rsquo;s efforts to study and implement CBDCs in the future, factors that are identified to have an importance in the consideration of CBDC implementation include technological advances through the rise of fintech, mobile banking, and cryptocurrencies in redefining how financial transactions occur; financial inclusion and providing access to digital financial services for all, particularly in remote or underserved areas, as Indonesia is a country with diverse geographic and socioeconomic disparities; analysis of Indonesia's existing regulatory environment and digital infrastructure is essential for evaluating the feasibility of CBDC research progress; public and private sector stakeholders in playing a crucial role in shaping the CBDC landscape; and lastly unique challenges and opportunities from the research of a relatively new topic in digital currencies.\u003c/p\u003e\u003cp\u003eThis study addresses existing gaps in CBDC research, with many having been conducted as early as 2020 as it is a new concept that has been recently gaining attention from central banks all over the world due to its perceived usefulness for each economy\u0026rsquo;s digital payment systems. Thus, there is always the assumption that new findings and therefore new variables to be included when conducting the research method. Within an Indonesian context, previous researchers have suggested to further explore the implications within the Indonesian economy and effect on variables including financial inclusion, cybersecurity, technological infrastructure, regulatory quality, or system coexistence along with a comparative analysis on central banks that have successfully launched their own CBDC systems. The remainder of this research consists of covering the literature review of CBDCs in general and Indonesia, establishing the methodology of the research, analysing the results of the research, and lastly discussing the implications and recommendations for future research.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe emergence of CBDC represents a response to the rapid digital transformation of the financial industry (Auer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As per the CBDC Tracker website during the time this study was conducted, central banks which have officially launched their CBDC system include Jamaica, the Bahamas, Zimbabwe, and Nigeria, with major economies such as China, Russia, and the European Union reaching the pilot stages of their project. CBDCs can be categorised into two types, which are wholesale and retail CBDC. Wholesale CBDC is typically reserved for financial institutions and interbank settlements and typically preferred in economies with more developed financial markets and greater cross-border transactions, whereas retail CBDC is aimed at the public for everyday transactions and preferred in economies with lower levels of financial inclusion, large informal economies, and a higher degree of innovation (Maryaningsih et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The coexistence of both forms of CBDC is essential and a country is considered to have implemented an economic-wide CBDC adoption should both forms be adopted. Indonesia is examining the potential integration of both forms of CBDC (Bank Indonesia, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which necessitates careful consideration of the unique regulatory, technological, and financial inclusion aspects for each category.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternational perspective.\u003c/b\u003e To highlight the increasing interest in CBDC research, several studies on central banks which have launched their CBDC systems have been analysed in varying degrees, namely The Bahamas and Nigeria. In the case of The Bahamas, the Sand Dollar serves as an incentive to combat the financial inclusion gap within the country, which it has indeed slowly achieved. However, the launching of the sand dollar, implementation has seen a slow start due to the Central Bank of The Bahamas focusing more on expanding the system by inviting new financial groups to join the system rather than encouraging its use (Bilgen \u0026amp; Colberg, 2024), with cryptocurrencies having more benefit due to their anonymity and unregulated nature being more aligned towards The Bahamas’ economic environment of being a tax haven, with CBDC thus serving as a payment system that promotes a payment system which promotes geopolitical purposes or to adhere to international standards instead (Wenker, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the case of Nigeria, several studies similarly show after the implementation of the eNaira, although a positive impact is shown on financial inclusion due to the introduction of the CBDC system, Nigerians are reluctant in the adoption of the eNaira due to factors concerning usage privacy, digital literacy on the knowledge of how to use the new system, and the lack of technological infrastructure in aiding a simplified use of the system (Akpan \u0026amp; Umaru, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Omotubora, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Many Nigerians feel that factors which help increase the positive intention to adopt CBDCs include performance expectancy, effort expectancy, social influence, trust, government regulation, and behavioural intention (Marzuk \u0026amp; Abdullah, 2024). Research by Osakwe et al. (2025) show that one of Nigeria’s main determinants of CBDC adoption include financial inclusion in order to address the issue of the underbanked in the country, a common theme prevalent in developing countries, whilst perceived financial cost serves as an obstacle to individuals’ behaviour to adoption and thus linking it to digital literacy.\u003c/p\u003e\u003cp\u003eIn Indonesia, this shift towards CBDC adoption is influenced by both global trends and local imperatives. For Bank Indonesia, factors that contributes to a digital disruption on banking practices, such as an increased use of blockchain assets, the preferred use of financial transactions due to the COVID-19 pandemic outbreak, as well as other major economies taking considerable action on CBDC research, have made Bank Indonesia take their first steps in CBDC research. The release of Bank Indonesia’s white paper on the Digital Rupiah provides information and outlines the central bank’s vision and strategy for CBDC adoption in Indonesia, including research conducted to support the information provided (Bank Indonesia, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFinancial inclusio\u003c/b\u003en. Fabris (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlights that CBDC could play a pivotal role in advancing digital transactions by closing the financial inclusion gap for people who do not have ease of access to banking, specifically mentioning groups that are poor or elderly. This study can be applied to Indonesia in which CBDC can extend financial services to remote and underserved areas as well as those who are considered lacking in technological literacy. However, it should be worth noting that although the aim for financial inclusion is more economic than monetary, the adoption of CBDCs should also be aligned with existing or potential regulations in order to ensure that the digital turnover is compliant with the relevant regulations, which is discussed by Mooij (2022) in the context of the European Central Bank’s potential implementation of the digital euro with regards to the ECB’s economic mandate. Furthermore, issuance aligns with Project Garuda’s objective of ensuring the population’s financially vulnerable individuals are protected through CBDCs, with several studies concluding that CBDC reduces the share of unbanked individuals and thus promotes financial inclusion due to changes in lending schemes from lower liquidity risks and efficient payment systems (Tan, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with significant impact amongst the sample’s vulnerable individuals (Dunbar \u0026amp; Treku, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCybersecurity\u003c/b\u003e. Bank Indonesia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) outlines the key pillars of regulatory oversight and cybersecurity infrastructure necessary for secure CBDC deployment, emphasising the role of the central bank in regulating the currency. Indonesia’s regulatory framework on cybersecurity for money laundering activities was seen as far from capable of protecting against such threats, with cryptocurrencies serving as a precedent (Putri et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Santoso et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) going as far to conclude that current applicable regulations for cybersecurity only allows retail CBDC to operate as a payment instrument rather than a currency. Firdaus (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) compares Indonesia’s regulatory framework on cybersecurity compared to other countries currently undergoing CBDC research, namely Malaysia and Australia, and concludes that Indonesia is lacking in its existing money laundering laws, referred to as the PPTPPU Law in Indonesia, and would need to be reformed for harmonisation purposes with the implementation of CBDC. A study conducted by the IMF analyses several cybersecurity conditions that should be taken into consideration based on the CBDC research phase of each countries’ central banks, with Bank Indonesia’s proof-of-concept stage CBDC research being advised to consider prioritising trust by assessing risk mitigation strategies for all stakeholders and to start formulating a governance framework to ensure the security components of the potential CBDC to be aligned with existing payment systems and updated technological functions (Bharath et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Digital literacy serves as a potential to mitigate these arising cyber threats as although the system is relatively new for many Indonesians, similar forms of financial technology has been introduced in the country and is now being utilised by most of the population, thus serving as a precedent CBDC adoption (Putri et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In ensuring a more robust cybersecurity framework, e-governance measures are said to assist in technological advances by ensuring mutual trust between the government and their citizens, which includes CBDC as well (Grigalashvili, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Malodia et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indonesian government agencies have already implemented measures and standards to ensure smoother e-governance as top-level support would be beneficial in preparing the population for CBDC adoption (Durigan Junior et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTechnological infrastructure.\u003c/b\u003e Indonesia's advancement in CBDC research must consider the existing digital infrastructure and the extent to which the population is equipped to engage with digital currency systems, with the relatively new topic facing the challenge of needing to be understood by the population to ensure proper digital literacy and awareness of the to-be introduced system with newer functions such as offline functionality being considered (Chu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As such, Maryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) categorises infrastructure in the form of access to electricity, quality of electric output, mobile subscription, and internet users, in which Fadli et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggests is contributed by the younger generations in Generation Y and Z.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegulatory quality.\u003c/b\u003e Serving as a precedent to CBDCs, cryptocurrencies have been observed to have a negative impact on financial system stability due to their price differences and volatility as a result of their independence from monetary policy (Liu \u0026amp; Serletis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bank Indonesia’s White Paper (2022) further emphasises the cryptocurrency problem and realises the need for effective monetary policy to use CBDC adoption to combat financial system instability from the use of alternative and unauthorised digital currencies, thus requiring the need to evaluate the country’s current regulatory framework from the top level, namely monetary, fiscal, and legal policies. China’s central bank, the People’s Bank of China, is shown to undergo towards a more proactive approach in the implementation of their CBDC project, the digital yuan, through the introduction of several drafts that cater towards a digitalised currency and ensuring a secure circulation of the currency whilst also mitigating potential cross-border crimes due to the ease of access of the system (Xu and Jin, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Aginta \u0026amp; Someya (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) mentions the importance of monetary policy on a per province basis and emphasizes the importance of interest rate, bank lending, and exchange rate channels, with Muduli \u0026amp; Behera (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) using credit-deposit ratio as a measure of not only measuring monetary policy in a per province manner, but also to analyse the implications on financial inclusion measures initiated by India’s central bank, the Reserve Bank of India. Similarly, the importance of fiscal policy is mentioned by Lewis \u0026amp; Oosterman (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) as a driver for economic growth, and thus innovation for new systems such as CBDC, as sub-national support is not enough compared to central government support. Furthermore, Ramadhani et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggest implementing AEOI procedures for the digital rupiah to harmonise the ongoing changes in the Indonesian tax law environment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSystem coexistence.\u003c/b\u003e Bank Indonesia’s real-time payment system QRIS released in 2019 has revolutionised the payment ecosystem in the country (Bank Indonesia, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and is quickly being adopted by many transaction methods in the country, with users emphasizing its ease of use and usefulness as a major reason to adopt it (Nurqamarani et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Usman et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Sonjaya et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrates the positive impact it has on countries in ASEAN as it serves as an option for integrated payment systems in cross-border transactions. As Indonesia is home to one of the fastest growing fintech industries, Zams et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observes that a “cash-like” retail CBDC model is a suitable match for Indonesia as it not only has characteristics of a traditional currency but is similar to financial services that many Indonesians are familiar with, including both card and digital methods of transaction. Using the European Union’s CBDC project, the digital euro, as an example, Mooij (2021) states that due to the complex legal framework of the European Union Central Bank, a specified of the digital euro must be implemented towards the legal framework instead of vice versa, meaning the digital euro should be tailored to be aligned with the ECB’s current regulatory framework due to the ECB’s mandates of promoting competitiveness with commercial banks for resource efficiency purposes.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e thus represents the conceptual framework that includes the variables used in this research, with each variable grouped in accordance with the research objectives, and each variable’s relationship towards the independent variable, which is the CBDC Readiness Index. This framework is derived from the digital rupiah framework by Lydda \u0026amp; Rahadi (2025).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe quantitative analysis involves collecting secondary data to be used for a statistical model in determining the appropriate variables to be used and to be interpreted. The dataset will be taken from on 34 provinces instead of the current 38, as complete data is mostly available until 2023 or 2024. Collection of data will be sourced from various locations, which includes but are not limited to LPS, Bank Indonesia, APJIII, BPS, OJK, KPK, ASPI, and government ministries such as the Kemen PANRB, Kemen ESDM, Kemenkeu, Kemendagri, and Komdigi.\u003c/p\u003e\u003cp\u003eThe dependent variable is represented by the province’s CBDC Readiness Index which represents whether the province is suitable to advance to the next research project based on the stages of CBDC research of worldwide central banks in accordance with the CBDC Tracker website (Alfar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the CBDC Readiness Index is represented by either 0 or 1, where 0 is for provinces who are not yet ready to advance to the next stage of CBDC research and 1 is for provinces that are ready to advance to the next stage of CBDC research, specifically the pilot stage as it is the stage that proceeds Indonesia’s current proof of concept stage. Independent variables are collected between the years of 2020 to 2024, with Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e listing further details on the literature review and previous research in which the indicators are derived from.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRelevant Literature\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCBDC Readiness Index (Dependent Variable)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinancial Inclusion Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of adults above the age of 15 with access to banking services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDunbar \u0026amp; Treku (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Tan (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Literacy Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of individuals based on understanding of digital services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePutri et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of individuals based on consensus, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQRIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQRIS Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of transactions using the Quick Response Code Indonesian Standard system, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurqamarani et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Usman et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP per Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGross domestic product using 2010 as the benchmark year divided by population consensus, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFI – Financial Inclusion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBANK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBank Account Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmount of bank account owned by adults above the age of 15, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2020–2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATM/Debit Card Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmount of ATM and debit cards owned by adults above the age of 15, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDemirgüç-Kunt \u0026amp; Klapper (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Payment Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of transactions using Real Time Gross Settlement (RTGS), logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurqamarani et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Usman et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCS – Cybersecurity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernment-Based Electronic System Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of adherence to electronic governance systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020–2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMalodia et al., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e);\u003c/p\u003e\u003cp\u003eDurigan Junior et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e);\u003c/p\u003e\u003cp\u003eGrigalashvili (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Bharath et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTI – Technological Infrastructure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Penetration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of online population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2020–2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectricity Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of households with access to electricity services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobile Cell Phone Proficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of population with proficient use of mobile cell phones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of population with access to internet services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRQ – Regulatory Quality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCredit-Deposit Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent variation of ratio between bank’s outstanding credits to third party funds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2020–2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAginta \u0026amp; Someya (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e);\u003c/p\u003e\u003cp\u003eMuduli \u0026amp; Behera (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Fiscal Capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of fiscal capacity of local governments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLewis \u0026amp; Oosterman (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegrity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of measures to combat corruption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKoparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSC – System Coexistence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovativeness Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex of innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2020–2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectronic Money Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of transactions using electronic money, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurqamarani et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Usman et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDC Machine Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of transactions through EDC machines, logged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSaputra et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe quantitative research will utilise a binary logistic regression model and with reference to the model equations used by Maryaningsih et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Koparan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Starting with the dependent variable, unlike the previously mentioned studies, the Indonesian provinces do not have an official CBDC research stage and will therefore be measured through a latent variable using PCA. The latent variable will thus be represented as follows:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{{y}_{i}}^{*}={\\beta\\:}_{1}{FII}_{2023}+{\\beta\\:}_{2}{DLI}_{2021}+{\\beta\\:}_{3}{POP}_{2024}+{\\beta\\:}_{4}{QRIS}_{2024}+{\\beta\\:}_{5}{GDP}_{2024}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere y\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e represents the latent variable for each province, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, … \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e are the coefficients to be estimated, and ε\u003csub\u003ei\u003c/sub\u003e is a standard normal error capturing unobserved factors. Since the purpose of the latent variable is to determine the current stage of CBDC research a province has the potential to be in, only the latest available year of the indicators will used, hence the use of PCA. From the results of the latent variables for each province the following rule is thus applied to determine which provinces are categorised as 0 or 1:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Readiness}_{i}\\:\\left\\{\\:\\:\\begin{array}{c}1\\:if\\:{{y}_{i}}^{*}\u0026gt;0,\\\\\\:0\\:if\\:{{y}_{i}}^{*}\\le\\:0.\\end{array}\\right.\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOnce the dependent variable of CBDC Readiness Index is secured, the binary logistic regression model is thus represented by the following expanded equation:\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Readiness}_{i}\\in\\:\\left\\{\\text{0,1}\\right\\}={\\beta\\:}_{0}+{\\beta\\:}_{1}{BANK}_{i}+{\\beta\\:}_{2}{CO}_{i}+{\\beta\\:}_{3}{PAY}_{i}+{\\beta\\:}_{4}{GBES}_{i}+{\\beta\\:}_{5}{INTP}_{i}+{\\beta\\:}_{6}{ELEC}_{i}+{\\beta\\:}_{7}{MC}_{i}+{\\beta\\:}_{8}{INTA}_{i}+{\\beta\\:}_{9}{CDR}_{i}+{\\beta\\:}_{10}{RFC}_{i}+{\\beta\\:}_{11}{II}_{i}+{\\beta\\:}_{12}{INOV}_{i}+{\\beta\\:}_{13}{EM}_{i}+{\\beta\\:}_{14}{EDC}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eReadiness\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the CBDC Readiness Index represented by either 0 or 1 which indicated if the province progresses to the next stage of CBDC research or not and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, … \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e14\u003c/em\u003e\u003c/sub\u003e are the coefficients to be estimated and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the intercept. With reference to the formulated research questions, \u003cem\u003eBANK, CO\u003c/em\u003e, and \u003cem\u003ePAY\u003c/em\u003e represents the indicators falling into the category of financial inclusion (FI); \u003cem\u003eGBES\u003c/em\u003e represents the indicators under cybersecurity measures (CS); \u003cem\u003eINTP, ELEC, MC\u003c/em\u003e, and \u003cem\u003eINTA\u003c/em\u003e represents the indicators categorised under technological infrastructure (TI); \u003cem\u003eCDR, RFC\u003c/em\u003e, and \u003cem\u003eII\u003c/em\u003e represents the indicators under regulatory quality (RQ); and \u003cem\u003eINOV, EM\u003c/em\u003e, and \u003cem\u003eEDC\u003c/em\u003e as indicators under system coexistence (SC).\u003c/p\u003e\u003cp\u003eUsing statistical tools such as EViews 13 and the Jupyter Notebook on Visual Studios Code, a binary logistic regression model will be used for this research to determine the influence of various factors on the CBDC Readiness Index of provinces in Indonesia. First, the research objectives and questions regarding factors that influence CBDC research progress in Indonesia are identified, with independent and dependent variables relevant to the objective being defined as well. Data is then collected and checked for completeness, accuracy, and reliability, with missing values and outliers addressed. The model equation that estimates the effect of each variable to CBDC Readiness Index is defined. Using the data collected, descriptive statistics is presented to understand the characteristics of the data and used to calculate means, standard deviations, and other summary statistics for continuous variables. Once the latent variable is calculated to determine the dependent variable for each province is calculated, the independent variables are tested and the results are presented, and the implications of the research are discussed with regards to CBDC research progress in Indonesia by referring to the defined research objectives.\u003c/p\u003e\u003cp\u003eTo ensure the validity of the data, a robustness check through the exclusion of data with a significant p-value will be conducted. This is done to check whether removing a certain variable will significantly alter the results of the findings and to determine the reasons for the change. Scenario-based simulations will also be conducted similarly to determine which variables have a larger impact on the dependent variable.\u003c/p\u003e\u003cp\u003eFor each of the variables, the number of observers from 34 provinces are recorded with the mean data from 2020 to 2024 as the data for the mentioned period will prove to have continued relevance due to modern studies which still utilises this data (Maryaningsih et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the availability of complete data is up to this period. Additionally, data for the year 2020 for the provinces of Aceh, Jambi, Bangka Belitung Islands, Riau Islands, Lampung, West Sulawesi, and West Papua are dropped due to the large amount of missing data for said provinces. On the other hand, indicators with one year of missing data are forecasted using mixed-effect imputation to impute realistic forecasts based on patterns.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe dataset represents both the dependent variables, each with 34 observations, and independent variables, with 156 observations, which includes 34 provinces between the years 2020 to 2024, barring a few provinces for the year 2020, and shows the disparity between provinces at certain time periods. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the descriptive statistics of the indicators that will be used. Variables \u003cem\u003eCO\u003c/em\u003e, \u003cem\u003eCDR\u003c/em\u003e, \u003cem\u003eRFC\u003c/em\u003e, and \u003cem\u003eEM\u003c/em\u003e are shown to have a minimum of less than 1, indicating that several provinces are lacking when it comes to certain indicators. \u003cem\u003eINTA\u003c/em\u003e and \u003cem\u003eINOV\u003c/em\u003e shows the highest standard deviations amongst all the indicators, portraying several provinces have a huge gap when it comes to internet access and innovativeness between provinces.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS.D.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinancial Inclusion Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e99.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Literacy Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQRIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQRIS Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP per Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBANK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBank Account Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATM/Debit Card Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Payment Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernment-Based Electronic System Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Penetration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e90.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectricity Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobile Cell Phone Proficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCredit-Deposit Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Fiscal Capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegrity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovativeness Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e88.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectronic Money Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDC Machine Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCBDC Readiness Index.\u003c/b\u003e The latent variable for each province must thus be identified to assign the dependent variable of CBDC Readiness Index for each province. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the PCA Model of the indicators that will be used to determine the latent variable, which are \u003cem\u003eFII\u003c/em\u003e in 2023, \u003cem\u003eDLI\u003c/em\u003e in 2021, \u003cem\u003ePOP\u003c/em\u003e in 2024, \u003cem\u003eQRIS\u003c/em\u003e in 2024, and \u003cem\u003eGDP\u003c/em\u003e in 2024. Once the dataset has been converted into their Z-scores, the PCA Model will utilise dataset from the latest available year as the CBDC Readiness Index is determined during the latest year regardless of historical trends.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePCA model results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eEigenvalues: (Sum\u0026thinsp;=\u0026thinsp;5, Average\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCumulative\u003c/p\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative\u003c/p\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.9899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.9899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEigenvectors (loadings)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndicator\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePC 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eFII (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;0.2581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDLI (2021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;0.2723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePOP (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e0.6652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eQRIS (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e0.6413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGDP (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e0.0737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOrdinary Correlations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFII (2023)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDLI (2021)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePOP (2024)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQRIS (2024)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eGDP (2024)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFII (2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDLI (2021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOP (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQRIS (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs per Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, PC 1 thus shows the results of the loadings of each indicator at a total variance of 39.8% as per the proportion value. The loadings can thus be inputted into the latent variable equation, resulting in the rankings of each province shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and a visualisation using the map of Indonesia\u0026rsquo;s provinces on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCBDC Readiness Index per province.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCBDC Readiness Index\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAceh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.0934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNorth Sumatra\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.5600\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWest Sumatra\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.0091\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRiau\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.2910\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJambi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.4918\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSouth Sumatra\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.2739\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBengkulu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.0404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLampung\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.7918\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangka Belitung Islands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.4693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiau Islands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJakarta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.6815\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWest Java\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3.1546\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCentral Java\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.5874\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYogyakarta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEast Java\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.7897\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBanten\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.8369\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBali\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.3244\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWest Nusa Tenggara\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.1474\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Nusa Tenggara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWest Kalimantan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.1143\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Kalimantan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.2293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSouth Kalimantan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.1036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Kalimantan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Kalimantan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.4091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Sulawesi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.8239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Sulawesi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSouth Sulawesi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.8783\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Sulawesi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.8604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGorontalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.2521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Sulawesi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.1860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaluku\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.1653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Maluku\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.2618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePapua\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.0752\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Papua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.0284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe binary dependent variable is classified into two opposite distinctions, namely 0 and 1. The resulting CBDC Readiness Index show a clear pattern where provinces considered to be on the so called western side of Indonesia, which includes the islands of Sumatera and Java with the addition of Bali, considered to be ready for the next stage of CBDC research, with additional provinces outside of the mentioned area including West Nusa Tenggara, South Sulawesi, West and South Kalimantan, and Papua.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation test.\u003c/b\u003e To better visualise the links between pairs of indicators and to test for multicollinearity, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows a correlation matrix comparing the Pearson correlation coefficient between all pairs of the indicators. As a general rule, indicators with a correlation coefficient ranging from less than or greater than \u0026minus;\u0026thinsp;0.85 or 0.85 respectively should be further observed for multicollinearity. \u003cem\u003eCO\u003c/em\u003e and \u003cem\u003eEM\u003c/em\u003e with a coefficient of 0.9026 along with \u003cem\u003eEDC\u003c/em\u003e and \u003cem\u003ePAY\u003c/em\u003e with a coefficient of 0.8663 fits into this category. This could be explained by the fact that these indicators represent a similar form of payment system that form as a complement to each other and will be a point of discussion in the next chapter. Nonetheless, these findings will serve as the basis for the robustness check.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation matrix of variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBANK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGBES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eINTP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eELEC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eINTA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eINOV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eEDC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBANK\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.2134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.3770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.3854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.1304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.3335\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c10\"\u003e\u003cp\u003e-0.1096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.0141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.2096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.2174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.3565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eINOV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c15\"\u003e\u003cp\u003e0.5007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.4194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.2174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.8476\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEDC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.3587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.3565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.5007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.8476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLogistic regression results.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results of the logistic regression, where the coefficient represents the change in the log-odds due to a one-unit increase in the independent variable \u003cem\u003eceteris paribus\u003c/em\u003e whereas the odds ratio represents the multiplier of the odds of the dependent variable, CBDC Readiness Index, increasing to Ready (1) if the independent variable experiences a one-unit increase \u003cem\u003eceteris paribus\u003c/em\u003e. The result logit marginal effects represent the average marginal effect (dy/dx) in which a one-unit increase of an independent variable changes the probability of the dependent variable by said amount \u003cem\u003eceteris paribus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePAY\u003c/em\u003e, \u003cem\u003eMC\u003c/em\u003e, \u003cem\u003eII\u003c/em\u003e, and \u003cem\u003eEDC\u003c/em\u003e are statistically significant at a 1% interval, with \u003cem\u003eEM\u003c/em\u003e being statistically significant at a 10% interval. An increase a province\u0026rsquo;s digital payment usage through RTGS (\u003cem\u003ePAY\u003c/em\u003e) and its EDC system usage (\u003cem\u003eEDC\u003c/em\u003e) increases the odds of the province being ready for CBDC research progress by 10.8158 times and 43.6648 times respectively with the probability of the province being ready for CBDC research progress due to the increase at a chance of 50.96% and 88.02% respectively. On the other hand, an increase in a province\u0026rsquo;s mobile cell phone proficiency (\u003cem\u003eMC\u003c/em\u003e) and integrity index (\u003cem\u003eII\u003c/em\u003e) reduces the odds of the province being ready for CBDC research progress by 0.6431 times and 0.7182 times respectively with the probability of the province\u0026rsquo;s readiness being lowered by 9.45% and 7.08% respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression and Logit Marginal Effects results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eLogistic Regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" 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colname=\"c9\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePAY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3810***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.7625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5096***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.0060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGBES\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.3393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.4629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eINTP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.1411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.1410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.8880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eELEC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.0612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.0240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4415***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.8136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0945***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-2.8860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eINTA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.2190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.5513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.4710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRFC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.3063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3310***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.0648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.0708***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-3.1870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eINOV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.4514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.4805*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.6743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.3168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEDC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.7765***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.6648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8802***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePseudo R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e*, **, and *** are significance at 10%, 5%, and 1% respectively.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRobustness check.\u003c/b\u003e With reference to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, several variables have shown to be highly multicollinear as per their Pearson correlation coefficients. A robustness check shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e will thus be conducted in order to determine the validity of the results of the model by dropping at least one variable of the indicator pairs with the highest correlation coefficient, namely at least one of \u003cem\u003eCO\u003c/em\u003e, \u003cem\u003eEM\u003c/em\u003e, \u003cem\u003ePAY\u003c/em\u003e, or \u003cem\u003eEDC\u003c/em\u003e. Firstly, \u003cem\u003eCO\u003c/em\u003e will be chosen to be dropped as its p-value does not allow it to reject the null hypothesis, followed by \u003cem\u003eEDC\u003c/em\u003e chosen to be dropped as although the model shows the statistical significance of the variable, a theoretical standpoint suggests the growing importance of \u003cem\u003eEDC\u003c/em\u003e over \u003cem\u003ePAY\u003c/em\u003e due to EDC machines signifying innovativeness of payment systems (Saputra et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and RTGS systems being more associated with wholesale CBDCs, which emerging countries such as Indonesia are found to not be as compatible with when compared to retail CBDCs (Maryaningsih et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral observations can be made due to the robustness check. Dropping \u003cem\u003eCO\u003c/em\u003e only resulted in less correlation between its most collinear pair \u003cem\u003eEM\u003c/em\u003e and thus resulted \u003cem\u003eEM\u003c/em\u003e gaining statistical significance over the 1% threshold. However, further robustness checks through the additional omission of \u003cem\u003ePAY\u003c/em\u003e resulted in not only \u003cem\u003eEM\u003c/em\u003e losing statistical significance back to the 5% threshold, but \u003cem\u003eMC\u003c/em\u003e as well to the same statistical significance. This suggests that the previous results for \u003cem\u003eMC\u003c/em\u003e were partially driven by the presence of \u003cem\u003eCO\u003c/em\u003e and \u003cem\u003ePAY\u003c/em\u003e, with \u003cem\u003eEDC\u003c/em\u003e and \u003cem\u003eII\u003c/em\u003e staying consistent in their statistical significance and thus confirming the robustness of its results. Additionally, the omission of \u003cem\u003eCO\u003c/em\u003e and \u003cem\u003ePAY\u003c/em\u003e resulted in \u003cem\u003eCDR\u003c/em\u003e gaining statistical significance at the 5% threshold. This implies that due to the removal of \u003cem\u003eCO\u003c/em\u003e and \u003cem\u003ePAY\u003c/em\u003e, the lower collinearity between each of the two variables to \u003cem\u003eCDR\u003c/em\u003e resulted in a lower standard of error and somewhat raised its statistical significance. Thus, the robustness check indicates that \u003cem\u003eEDC\u003c/em\u003e and \u003cem\u003eII\u003c/em\u003e are consistent factors that influence CBDC research progress.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness check through omission of variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eComplete Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eNo CO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eNo CO\u0026thinsp;+\u0026thinsp;PAY\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePAY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3810***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5237***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eOmitted\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4415***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.4373***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.2659**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.4060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.4729**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.3310***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.0022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.3402***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.2959***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.0010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.4805*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.7875***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.5092**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEDC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3.7765***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.0009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.5872***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4.0675***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePseudo R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.6419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e*, **, and *** are significance at 10%, 5%, and 1% respectively.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe CBDC Readiness Index for the 34 provinces show 17 provinces which are considered ready to move to the next stage of CBDC research, which is the pilot stage. A noticeable trend of the provinces that are ready is that it consists of a majority of provinces that are on the western side of the archipelago. This provides a clear picture of the divide in development of most parts of the country, with Indonesia\u0026rsquo;s western provinces on the islands of Sumatera and Java considered to be more developed in many ways possible, thus facilitating a better environment for CBDC pilot testing (Kurniawan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another province which has proven to be ready is South Sulawesi, whose provincial capital of Makassar serves as a testament to this result due to its position as the gateway to eastern Indonesia with a rapidly increasing economic growth during the post-COVID era (Arifin, 2024) and its relatively fast digital transformation (Abduh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The rest of the provinces which are ready for the pilot stage CBDC have shown a growth in their digital transformation, such as through the adoption of digital technologies in MSMEs in West Nusa Tenggara (Armiani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), electronic governance systems to promote regional development in Papua (Santiko \u0026amp; Suhendra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or the preparation of an increasing service sector in the traditionally resource-dominated economy of Kalimantan as the region prepares itself to be the seat of the new national capital (Yusuf et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with West Kalimantan also being on the border of Malaysia being a potential driver for cross-border transactions through trade (Messa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and an increased digital transformation in the province (Yutika, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The decision to implement pilot CBDCs across only a handful of provinces is supported by China\u0026rsquo;s decision to introduce their pilots in major cities only, with several cities gradually joining once certain issues with the system are addressed (Wu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs provinces increase their usage of EDC machines, their likelihood of being ready to proceed to the pilot stage of CBDC research increases. This result is backed by Nurqamarani et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Saputra et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as EDC machines in Indonesia represents payment system coexistence due its use multipurpose use for payments using e-money, internet banking, debit, and credit cards. EDC machines have also implemented QRIS as a payment method, thus signifying how new payment systems are able to be easily integrated due to EDC machines.\u003c/p\u003e\u003cp\u003eOn the other hand, the more a province\u0026rsquo;s integrity index, or level of corruption within the provincial government, rises, the likelihood of advancing to the CBDC pilot stage decreases. As of now, Indonesia\u0026rsquo;s regulatory environment does not have the proper policies to ensure proper implementation for CBDC systems (Firdaus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Santoso et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Proper regulatory reforms require careful consideration through due process of the legal sector, of which a system with proper integrity would follow. This is proven by China\u0026rsquo;s central bank, the People\u0026rsquo;s Bank of China, which has developed a five-year plan that follows due process before ensuring implementation (Xu \u0026amp; Jin, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In other words, a provincial government with high integrity index and lower levels of corruption would be discouraged from moving on to the next stage of CBDC research until a regulatory reform, and vice versa.\u003c/p\u003e\u003cp\u003eDue to the ongoing research nature of CBDCs, especially as Indonesia is currently in the proof of concept as of this moment, future research should consider updating indicators to ensure the accuracy and stability of the results. The latent variables to determine CBDC Readiness Index should consider expanding the criteria for the index in order to accurately determine which provinces are truly ready for the pilot stage of CBDC implementation, which can thus be used by Bank Indonesia to launch pilots in said provinces. As with many other countries within the same stage of CBDC research, Bank Indonesia is recommended to use countries which has advanced in their CBDC research stage as case study comparisons to determine the best practices used and challenges faced. Thus, future studies should implement a qualitative method or mixed-method study in order to determine the best course of action from both a theoretical and practical standpoint, such as once the system advances to the pilot stage of CBDC research, interviews should be conducted to determine the specifications of CBDC system Indonesia needs, as several studies emphasised that no CBDC system should be uniform as a country\u0026rsquo;s payment system has their own specific needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFunding Declaration\u003c/h2\u003e\u003cp\u003eThe author(s) declare no funding was provided for this research.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally to this manuscript. RREL wrote the main manuscript text and prepared all figures and tables. RAR supervised the process and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbduh T, Remmang H, Abubakar H, Karim A (2024) Entrepreneurship and MSME market orientation toward creative industries: Society Era 5.0 in Makassar city. Asian Economic Financial Rev 14(2):76\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.55493/5002.v14i2.4964\u003c/span\u003e\u003cspan address=\"10.55493/5002.v14i2.4964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAginta H, Someya M (2022) Regional economic structure and heterogeneous effects of monetary policy: Evidence from Indonesian provinces. J Economic Struct 11(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40008-021-00260-6\u003c/span\u003e\u003cspan address=\"10.1186/s40008-021-00260-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkpan U, Umaru A (2024) Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-3861545/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3861545/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Structural Equation Model\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlfar AJK, Kumpamool C, Nguyen DTK, Ahmed R (2023) The determinants of issuing central bank digital currencies. Res Int Bus Finance 64:101884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ribaf.2023.101884\u003c/span\u003e\u003cspan address=\"10.1016/j.ribaf.2023.101884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArifin (n.d.). Analysis of economic growth and policy impact of Makassar city priority program (case study of 2018\u0026ndash;2022). Jurnal Ekonomi, 13(01)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmiani A, Basuki B, Suwarno N, DAMPAK STRATEGI BISNIS TERHADAP KINERJA UMKM DI NUSA TENGGARA BARAT (2021) EKUITAS (Jurnal Ekonomi dan Keuangan) 5(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24034/j25485024.y2021.v5.i3.4892\u003c/span\u003e\u003cspan address=\"10.24034/j25485024.y2021.v5.i3.4892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. TEKNOLOGI DIGITAL MEMEDIASI\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAuer R, Cornelli G, Frost J (2023) Rise of the Central Bank Digital Currencies. Int J Cent Bank, 19(4)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBank Indonesia (2020) Quick Response Code Indonesian Standard (QRIS). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bi.go.id/en/fungsi-utama/sistem-pembayaran/ritel/kanal-layanan/QRIS/default.aspx\u003c/span\u003e\u003cspan address=\"https://www.bi.go.id/en/fungsi-utama/sistem-pembayaran/ritel/kanal-layanan/QRIS/default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBank Indonesia (2022) Project Garuda: Navigating the Architecture of Digital Rupiah\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBharath A, Paduraru A, Gaidosch T (2024) Cyber Resilience of the Central Bank Digital Currency Ecosystem. Fintech Notes, 2024(003), 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5089/9798400286995.063\u003c/span\u003e\u003cspan address=\"10.5089/9798400286995.063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBilgen C, Dutto M, Colberg T (2024) Analyzing the CBDC Tree: The Case of the Bahamian Sand Dollar Using Three-. Level Central Bank Digital Currency Design Framework\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu Y, Lee J, Kim S, Kim H, Yoon Y, Chung H (2022) Review of Offline Payment Function of CBDC Considering Security Requirements. Appl Sci 12(9):4488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app12094488\u003c/span\u003e\u003cspan address=\"10.3390/app12094488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemirg\u0026uuml;\u0026ccedil;-Kunt A, Klapper L (2022) The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. The World Bank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1596/978-1-4648-1897-4\u003c/span\u003e\u003cspan address=\"10.1596/978-1-4648-1897-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunbar K, Treku DN (2024) Examining the impact of a central bank digital currency on the access to banking. Int Rev Financial Anal 93:103220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.irfa.2024.103220\u003c/span\u003e\u003cspan address=\"10.1016/j.irfa.2024.103220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurigan Junior CA, De Mesquita Spinola M, Goncalves F, R., Barbin Laurindo FJ, CENTRAL BANK DIGITAL CURRENCIES: THE ADVENT OF ITS IT GOVERNANCE IN THE FINANCIAL MARKETS (2022). 19th CONTECSI International Conference on Information Systems and Technology Management. 19th CONTECSI International Conference on Information Systems and Technology Management. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5748/19CONTECSI/PSE/ITM/6914\u003c/span\u003e\u003cspan address=\"10.5748/19CONTECSI/PSE/ITM/6914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFabris N (2019) Cashless Society \u0026ndash; The Future of Money or a Utopia? J Cent Bank Theory Pract 8(1):53\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/jcbtp-2019-0003\u003c/span\u003e\u003cspan address=\"10.2478/jcbtp-2019-0003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFadli JA, Rusmanto T, Kurniawan Y, Hutagaol-Martowidjojo Y (2025) The Interplay of Financial Availability, Herding Behavior, and Cryptocurrency Investment Experience Moderated by Government Policy: A Study from Indonesia. Qubahan Acad J 4(4):509\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48161/qaj.v4n4a1144\u003c/span\u003e\u003cspan address=\"10.48161/qaj.v4n4a1144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFebriyanto A, Adenia N, Annabila I, Yusfiarto R (2025) CBDC ADOPTION: ALIGNING MODEL TECHNOSTRESS INHIBITORS AND PERCEIVED VALUE AMONG INDONESIAN GENERATION Z. J Cent Bank Law Institutions 4(1):49\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21098/jcli.v4i1.258\u003c/span\u003e\u003cspan address=\"10.21098/jcli.v4i1.258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFirdaus SP (2023) Urgency of Money Laundering Policy Reform for Digital Rupiah Implementation. AML/CFT Journal The Journal of Anti Money Laundering and Countering the Financing of Terrorism. 2(1):58\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59593/amlcft.2023.v2i1.118\u003c/span\u003e\u003cspan address=\"10.59593/amlcft.2023.v2i1.118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrigalashvili V (2022) E-government and E-governance: Various or Multifarious Concepts. Int J Sci Manage Res 05(01):183\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.37502/IJSMR.2022.5111\u003c/span\u003e\u003cspan address=\"10.37502/IJSMR.2022.5111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Monetary Fund (2023), November 14 Central Bank Digital Currency\u0026mdash;Initial Considerations. IMF. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imf.org/en/Publications/Policy-Papers/Issues/2023/11/14/Central-Bank-Digital-Currency-Initial-Considerations-541466\u003c/span\u003e\u003cspan address=\"https://www.imf.org/en/Publications/Policy-Papers/Issues/2023/11/14/Central-Bank-Digital-Currency-Initial-Considerations-541466\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoparan A (2025) Central Bank Digital Currencies: A review of global trends in adoption, financial inclusion, and the role of country characteristics. Invest Manage Financial Innovations 22(1):107\u0026ndash;121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21511/imfi.22(1).2025.09\u003c/span\u003e\u003cspan address=\"10.21511/imfi.22(1).2025.09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurniawan H, De Groot HLF, Mulder P (2019) Are poor provinces catching-up the rich provinces in Indonesia? Reg Sci Policy Pract 11(1):89\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/rsp3.12160\u003c/span\u003e\u003cspan address=\"10.1111/rsp3.12160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewis BD, Oosterman A (2011) SUB-NATIONAL GOVERNMENT CAPITAL SPENDING IN INDONESIA: LEVEL, STRUCTURE, AND FINANCING. Public Adm Dev 31(3):149\u0026ndash;158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pad.582\u003c/span\u003e\u003cspan address=\"10.1002/pad.582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Serletis A (2019) Volatility in the Cryptocurrency Market. Open Econ Rev 30(4):779\u0026ndash;811. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11079-019-09547-5\u003c/span\u003e\u003cspan address=\"10.1007/s11079-019-09547-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLydda RRE, Rahadi RA (n.d.). Central Bank Digital Currency and a Digital Rupiah Framework for Indonesia\u0026rsquo;s Implementation: A Literature Review\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalodia S, Dhir A, Mishra M, Bhatti ZA (2021) Future of e-Government: An integrated conceptual framework. Technol Forecast Soc Chang 173:121102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2021.121102\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2021.121102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaryaningsih N, Nazara S, Kacaribu FN, Juhro SM (2022) CENTRAL BANK DIGITAL CURRENCY: WHAT FACTORS DETERMINE ITS ADOPTION? Buletin Ekonomi Moneter Dan Perbankan 25(1):1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21098/bemp.v25i1.1979\u003c/span\u003e\u003cspan address=\"10.21098/bemp.v25i1.1979\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessa MY, Bariyah N, Kurniasih EP (2023) Determining Factors of Economic Growth and Poverty in West Kalimantan. Jurnal Ilmu Sosial Dan Humaniora 12(3):530\u0026ndash;546. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23887/jish.v12i3.67197\u003c/span\u003e\u003cspan address=\"10.23887/jish.v12i3.67197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMooij AM (2023) The role of the European Central Bank in response to COVID19. An evaluation of its mandate. J Eur Integr 45(4):649\u0026ndash;663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07036337.2022.2120479\u003c/span\u003e\u003cspan address=\"10.1080/07036337.2022.2120479\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuduli S, Behera T (2025) An Anatomy of Credit-Deposit Ratios Among Indian States\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNurqamarani AS, Fadilla S, Juliana A (2024) Revolutionizing Payment Systems: The Integration of TRAM and Trust in QRIS Adoption for Micro, Small, and Medium Enterprises in Indonesia. J Inform Syst Eng Bus Intell 10(3):314\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20473/jisebi.10.3.314-327\u003c/span\u003e\u003cspan address=\"10.20473/jisebi.10.3.314-327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmotubora A (2024) Same Naira, More Possibilities! Assessing the Legal Status of the eNaira and Its Potential for Privacy and Inclusion. J Afr Law 68(2):245\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0021855324000044\u003c/span\u003e\u003cspan address=\"10.1017/S0021855324000044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutri AM, Wiryono SK, Damayanti SM, Rahadi RA (2023) Framework of Digital Financial Literacy Dimensions in Indonesia. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kurdishstudies.net/menu-script/index.php/KS/article/view/1428\u003c/span\u003e\u003cspan address=\"https://kurdishstudies.net/menu-script/index.php/KS/article/view/1428\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutri T, Amiludin A, Ahmad DN, Hidayatulloh H (2023) Inadequate Cryptocurrency and Money Laundering Regulations in Indonesia (Comparative Law of US and Germany). Yustisia Jurnal Hukum 12(2):129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20961/yustisia.v12i2.71835\u003c/span\u003e\u003cspan address=\"10.20961/yustisia.v12i2.71835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamadhani R, Farisy Z, Puteri DS, OF CBDC AND TAX LAW ENFORCEMENT IN SELECTED COUNTRIES (2025) J Cent Bank Law Institutions 4(1):141\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21098/jcli.v4i1.275\u003c/span\u003e\u003cspan address=\"10.21098/jcli.v4i1.275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. COMPARATIVE ANALYSIS\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamayanti R, Azhar Z, Nik Azman NH (2025) Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach. Telematics Inf Rep 17:100185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.teler.2024.100185\u003c/span\u003e\u003cspan address=\"10.1016/j.teler.2024.100185\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantiko A, Suhendra A (2022) Governance Through Electronic-based Information System by Papua Provincial Government. Int J Reg Innov 2(4):43\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.52000/ijori.v2i3.65\u003c/span\u003e\u003cspan address=\"10.52000/ijori.v2i3.65\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantoso WY, Putra AA, Susanti L, Rahman F, DESIGN ELEMENTS AND RISKS OF CENTRAL BANK DIGITAL CURRENCY IN TAILORING A PRUDENT (2023) Diponegoro Law Rev 8(2):141\u0026ndash;158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14710/dilrev.8.2.2023.141-158\u003c/span\u003e\u003cspan address=\"10.14710/dilrev.8.2.2023.141-158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaputra RA, Fihartini Y, Husna N (2024) Influence of Easy, Speed and Security of Transactions on Decision to Use BRI EDC Machine in Indonesia. J Bus Manage Economic Dev 2(03):1335\u0026ndash;1345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59653/jbmed.v2i03.978\u003c/span\u003e\u003cspan address=\"10.59653/jbmed.v2i03.978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSonjaya A, Ragimun, Basmar E, Ermawati T, Kurniadi AP, Dasilva H, Sabilla K, Hasan, Takhim M, Pratiwi R, Mutaqin, Yosepha SY (2025) How the Integration of Payment Systems Through QRIS Accelerates Economic and Financial Cooperation in the ASEAN Region. Int J Sustainable Dev Plann 20(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18280/ijsdp.200305\u003c/span\u003e\u003cspan address=\"10.18280/ijsdp.200305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan BJ (2024) Central bank digital currency and financial inclusion. J Macroecon 81:103620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmacro.2024.103620\u003c/span\u003e\u003cspan address=\"10.1016/j.jmacro.2024.103620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUsman O, Hardini IR, Kasofi A (2025) Integrated QR payment system: Cashless payment solution in developing countries from the perspective of MSMEs. Int J Innovative Res Sci Stud 8(2):1424\u0026ndash;1432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.53894/ijirss.v8i2.5470\u003c/span\u003e\u003cspan address=\"10.53894/ijirss.v8i2.5470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWenker K (2022) Retail Central Bank Digital Currencies (CBDC), Disintermediation and Financial Privacy: The Case of the Bahamian Sand Dollar. FinTech 1(4) Article 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/fintech1040026\u003c/span\u003e\u003cspan address=\"10.3390/fintech1040026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu J, Liu X, Zhang C (2024) Unveiling the influencing mechanism underlying users\u0026rsquo; adoption and recommend intentions of central bank digital currency: A behavioral reasoning theory perspective. J Retailing Consumer Serv 81:104050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2024.104050\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2024.104050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu C, Jin B (2022) Digital currency in China: Pilot implementations,legal challenges and prospects. Juridical Tribune 12(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24818/TBJ/2022/12/2.02\u003c/span\u003e\u003cspan address=\"10.24818/TBJ/2022/12/2.02\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusuf AA, Roos EL, Horridge JM, Hartono D (2023) Indonesian capital city relocation and regional economy\u0026rsquo;s transition toward less carbon-intensive economy: An inter-regional CGE analysis. Japan World Econ 68:101212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.japwor.2023.101212\u003c/span\u003e\u003cspan address=\"10.1016/j.japwor.2023.101212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYutika F (2024) THE ROLE OF KNOWLEDGE MANAGEMENT IN ENHANCING DIGITAL CAPABILITIES AND DRIVING INNOVATION IN SMES. 26\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZams BM, Indrastuti R, Pangersa AG, Hasniawati NA, Zahra FA, Fauziah IA (2020) DESIGNING CENTRAL BANK DIGITAL CURRENCY FOR INDONESIA: THE DELPHI\u0026ndash;ANALYTIC NETWORK PROCESS. Buletin Ekonomi Moneter Dan Perbankan 23(3):413\u0026ndash;440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21098/bemp.v23i3.1351\u003c/span\u003e\u003cspan address=\"10.21098/bemp.v23i3.1351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6935405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6935405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBank Indonesia\u0026rsquo;s white paper on the digital rupiah places shows the central bank\u0026rsquo;s intention of contributing to global central bank digital currency (CBDC) research and following the steps of other central banks in advanced stages of CBDC research. This study looks to investigate the factors which affects CBDC research progress in Indonesia on a provincial level using a quantitative method analysis. The quantitative method uses a latent variable to determine which provinces are suitable for CBDC research advancement and a binary logistic regression method to analyse which variables are determinants of Indonesia\u0026rsquo;s readiness for CBDC research progress. The results show that mostly western provinces in Sumatera and Java, with a few in the eastern provinces, are suitable for the pilot stage of CBDC research and variables with statistically significant results are the EDC machine usage and the integrity index of each province. These results could aid Bank Indonesia's eventual introduction of CBDC within Indonesia, starting off from a provincial level to adjust the system as needed.\u003c/p\u003e","manuscriptTitle":"Central Bank Digital Currency as a New Form of Legal Tender: Analysing the Determinants of Indonesia's CBDC Research Progress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 13:06:08","doi":"10.21203/rs.3.rs-6935405/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"002b44ed-bef5-45ae-82f6-687f6a8358fa","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56547272,"name":"Business and commerce/Economics"},{"id":56547273,"name":"Business and commerce/Finance"}],"tags":[],"updatedAt":"2026-03-20T00:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 13:06:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6935405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6935405","identity":"rs-6935405","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}