Entrepreneurial Ecosystems under Digital Disruption: Evidence from AI, Block-chain, and IoT Adoption in the GCC

Entrepreneurial Ecosystems under Digital Disruption: Evidence from AI, Block-chain, and IoT Adoption in the GCC | 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 Research Article Entrepreneurial Ecosystems under Digital Disruption: Evidence from AI, Block-chain, and IoT Adoption in the GCC Tarek Sadraoui, Sameh Zaraii This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816026/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 The present study, therefore, aims to explore the major factors driving the adoption and implications of blockchain technology in the GCC economic block, highlighting the huge potential scope for the successful digitalization of the finance sector as well as the broader public sector. Using a panel data on the considered time range from 2015 to 2023, the analysis accounts for the extent to which factors such as the regularity aspects, dependency on oil, and the maturity level of Fintech ecosystems could influence the adoption of blockchain technology in the GCC countries, using the dynamic generalized method of moments approach, highlighting that higher-quality regularity is a major determinant driving the adoption of blockchain technology, while a greater dependence on oil negatively influences its adoption, offering the broader expectations for the GCC block emerging as a global innovation hub for Islamic finance, smart cities, offering valuable lessons for emerging economies. JEL Classifications: O38 ; G28 ; E42 ; Q55 Blockchain Financial Innovation Regulatory Sandbox Oil Diversification Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The GCC has experienced fast diversification of its economy and digitalization in recent years with a push from innovations such as Artificial Intelligence (AI), Blockchain, and Internet of Things (IoT). These technologies have been revolutionizing business ecosystems, fostering innovation, and enabling entrepreneurs to navigate tough market conditions (Al-Khouri, 2021 ). Governments within the GCC, particularly Saudi Arabia and the UAE, have implemented vision strategies like Saudi Vision 2030 and the National AI Strategy 2031 of the UAE to integrate these technologies into their economic systems (Alhashmi et al., 2020 ). GCC entrepreneurship is evolving as existing companies and new firms leverage AI for prescriptive business, Blockchain for secure transactions, and IoT for smart infrastructure (Kshetri, 2022 ). However, the challenges of regulatory risks, skills gaps, and cyber-attacks persist (Alraja et al., 2022). This study examines how AI, Blockchain, and IoT are redefining GCC entrepreneurial businesses, revealing prospects and challenges to adoption. In the fast-changing global economy, the convergence of technology and entrepreneurship has emerged as a hallmark characteristic of contemporary business ecosystems. New technologies like Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are not only revolutionizing conventional business models but also opening up unprecedented new possibilities for innovation, adaptability, and scalability. Nowhere is this transformation more urgent than in the Gulf Cooperation Council (GCC) countries, where diversification plans of an economic nature—best represented in national visions like Saudi Arabia's Vision 2030 and the UAE's Centennial 2071—have put entrepreneurship and digitalization on the agenda of strategic priorities of sustainable development. The GCC hydrocarbon-dependent region has already recognized the imperative of building strong, knowledge-based economies. With regard to this, entrepreneurship is not only a source of creativity and employment, but also a support column of national competitiveness. However, the 21st-century entrepreneur works in an era of disruption, where technology progress occurs at light speed and both threatened and threatening opportunity arises. AI offers predictive decision-making and intelligent automation; block chain offers decentralized trust and transparency; IoT enables data-rich environments that facilitate smart systems and instant connectivity. Together, these technologies are transforming what it is to start up, grow, and sustain a business in the digital age. This article aims to study the dynamic contribution of AI, block chain, and IoT in shaping entrepreneurial action within the GCC business systems. More specifically, it explores how the technologies influence startup dynamics, sectorial innovation, investment patterns, regulatory measures, and mechanisms of value creation. Relying on current knowledge from recent technology, economics, and policy trends, the study aims at enriching the field's understanding of the digitalization of entrepreneurship in the region. By so doing, the research responds to several critical questions: How are GCC entrepreneurs embracing AI, block chain, and IoT technologies? What institutions, policy frameworks, and infrastructures enable or hinder this adoption of technologies? In addition, what are the implications of these disruptive technologies for long-term economic diversification and sustainability in the Gulf region? Through embracing a multi-disciplinary approach, this study seeks to bridge the theoretical-practical divide, offering observations, which are of significance for policymakers, entrepreneurs, investors, and technology developers. 2. Research Gap Despite the rise in global discussions about the revolutionary potential of frontier technologies like Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) to shape modern-day entrepreneurship, there is a remarkable lack of empirical and contextualized research focusing on how they are integrated into the business ecosystems of the GCC countries. Earlier research mainly emphasizes either the technological advancements or the entrepreneurship environment in isolation from one another, without referencing the dynamic relationship between these technologies and the local socio-economic, regulatory, and cultural environment in the Gulf region. The majority of the research also applies a Western lens that does not account for the oil-based economies, government-initiated innovation initiatives (e.g., Saudi Vision 2030, UAE Vision 2031), and the emerging digital landscape in the GCC. Such a lacuna limits knowledge regarding how entrepreneurs in the region are adopting and leveraging these technologies to develop agile, scalable, and sustainable ventures. There is therefore a need for region-based research that documents how AI, block chain, and IoT are reshaping entrepreneurship in the GCC, including barriers, facilitators, and long-term implications. 3. A brief of literature review 3.1. Entrepreneurship and Technological Disruption Entrepreneurship within the age of technology is more dominated by disruptive technology that lowers the entrance barriers and enables scalable business models (Bharadwaj et al., 2013 ). The GCC entrepreneurial ecosystem is shifting away from the traditional oil and gas sectors to technology-based ventures in Fintech, smart cities, and e-commerce (Saidan & Al Shaar, 2021). Artificial intelligence technologies facilitate enhanced decision-making using data insights, and Blockchain facilitates supply chain and financial transaction transparency (Tapscott, 2016 ). IoT, meanwhile, facilitates real-time monitoring in logistics and healthcare industries (Gubbi et al., 2013 ). The 21st century has ushered a new era of entrepreneurship bounded by unprecedented technological disruption. Advances in Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are revolutionizing how businesses are conceived, initiated, and expanded. Such revolutionary technologies not only accelerate innovation but also redefine traditional paradigms of value creation, competitive advantage, and market dynamics. In this revolutionizing world, entrepreneurs must be nimble, futuristic, and technology savvy to remain relevant and resilient. In the Gulf Cooperation Council (GCC) countries, this wave of disruption intersects with ambitious economic diversification strategies to reduce hydrocarbon reliance. National strategies such as Saudi Vision 2030, the UAE Digital Economy Strategy, and Bahrain's FinTech Bay demonstrate a regional effort to create entrepreneurial ecosystems fueled by digitalization. AI supports smarter use of resources and personalized consumer experiences; blockchain enables trust and transparency in transactions; and IoT connects physical things in ways that enable actionable insights. Merging these technologies is disrupting business models and opening new avenues for growth driven by innovation in industries ranging from finance and healthcare to logistics and energy. This note looks at how technological disruption is transforming entrepreneurship in the GCC, focusing on the contribution of AI, blockchain, and IoT. It looks at the diffusion of these technologies across startups and SMEs, structural enablers and bottlenecks within GCC ecosystems, and the broader economic diversification and digital competitiveness implications. By conducting an examination of the interface between the forces of entrepreneurship and disruptive technologies, this study provides insightful answers to how the GCC can utilize disruption as an engine, rather than a threat, for the development of sustainable, inclusive, and future-proof economies. 3.2. AI in GCC Entrepreneurship AI adoption in the GCC is accelerating, with applications in customer service (chatbots), fraud detection, and predictive maintenance (Alsheibani et al., 2018). Saudi Arabia’s NEOM project and Dubai’s AI-powered government services exemplify regional commitments to AI-driven innovation (Alsharhan et al., 2021). However, concerns around data privacy and algorithmic bias remain key challenges (Dwivedi et al., 2021). Artificial Intelligence (AI) is now one of the most significant drivers of change in the global economy, transforming how firms do business, compete, and create value. In entrepreneurial settings, AI offers very potent tools for innovation that span from predictive analytics and automatic decision-making to intelligent customer engagement and operational efficiency. With advancements in AI technologies, these are no longer being acquired by only large corporations but also by startups and small- and medium-sized enterprises (SMEs), enabling entrepreneurs to expand concepts faster and more efficiently than ever before. AI is increasingly becoming strategically vital in the innovation agendas of the Gulf Cooperation Council (GCC) countries. Countries like the United Arab Emirates and Saudi Arabia have launched ambitious AI-driven plans, the UAE National Strategy for Artificial Intelligence 2031 and Saudi Arabia's National Strategy for Data and AI (NSDAI), respectively. These plans aim to establish the GCC as a hub of AI innovation on the global stage and develop local entrepreneurship in sectors like finance, health, education, logistics, and smart cities. For GCC business leaders, there is both challenge and opportunity in AI. While AI has the potential to bring efficiency, unlock new business models, and introduce competitive differentiation to global markets, successful adoption of AI requires access to high-level talent, better digital infrastructure, open data ecosystems, and good regulatory frameworks—all areas that are still in the early phases of development across much of the region. This study examines the growing role of AI in the entrepreneurial environments of the GCC, examining how startups and SMEs are integrating AI into their operations, products, and services. It also examines the conditions that enable AI-led entrepreneurship, policy interventions, and institutional mechanisms of support that have an influence on AI-driven entrepreneurship in the region. By this, the study aims to establish the extent to which AI can be an engine for economic diversification, job creation, and innovation in the post-oil digital economy. 3.3. Blockchain for Business Innovation Blockchain is gaining traction in the GCC, particularly among financial institutions and government use (Al-Jaroodi & Mohamed, 2019 ). UAE Emirates Blockchain Strategy 2021 aims to clear 50% of government payments via Blockchain, boosting efficiency and reducing fraud (Abu Dhabi Digital Authority, 2020). Blockchain is also being utilized by Qatar and Bahrainian startups for smart contracts and cross-border payments (Kshetri, 2022 ). Blockchain technology, originally developed as the backbone of cryptocurrencies, has evolved into a powerful enabler of business innovation across industries. By offering decentralized, transparent, and immutable ledgers, blockchain has the potential to disrupt traditional business processes, reduce transaction costs, enhance security, and build trust in complex, multi-stakeholder environments. For entrepreneurs and startups, blockchain unlocks new opportunities to reimagine value chains, improve accountability, and create scalable digital platforms. In the context of the Gulf Cooperation Council (GCC), blockchain adoption is gaining momentum as governments and businesses seek to position themselves at the forefront of the Fourth Industrial Revolution. National initiatives such as the UAE Blockchain Strategy 2021 and Saudi Arabia’s blockchain pilots in trade, identity verification, and smart contracts signal strong institutional support for distributed ledger technologies. These efforts are part of broader digital transformation agendas aimed at enhancing government efficiency, financial innovation, and cross-border trade. Block chain’s relevance to entrepreneurship lies in its capacity to foster trust without centralized intermediaries—especially in sectors like Fintech, supply chain management, real estate, energy trading, and digital identity. For GCC-based entrepreneurs, this means the ability to develop decentralized applications (dApps), tokenize assets, launch Initial Coin Offerings (ICOs), or implement smart contracts that automate and secure transactions. Despite the promise, block chain innovation in the region still faces regulatory ambiguity, scalability concerns, and a shortage of technical expertise. Addressing these challenges will be essential to unlocking block chain’s full potential as a driver of entrepreneurship and economic diversification. This section explores how block chain technology is transforming business models and entrepreneurial strategies in the GCC. It examines real-world applications, case studies, and policy frameworks that shape block chain innovation, while offering insights into the ecosystem conditions that either support or constrain its growth in the region. 3.4. IoT and Smart Business Ecosystems IoT adoption in the GCC is transforming sectors like logistics, healthcare, and energy (Al-Mulla et al., 2020). Smart city initiatives, such as Dubai’s Smart City project, integrate IoT for traffic management, energy efficiency, and public safety (Khan et al., 2021). However, interoperability and cyber security risks pose significant hurdles (Weber, 2010). The Internet of Things (IoT) is revolutionizing business operations by changing static functions into smart, interconnected ecosystems. With IoT's real-time data exchange between devices, systems, and stakeholders, companies can make informed decisions, become more efficient, and provide proactive services. For entrepreneurs, IoT provides an empowering platform for creating agile, data-driven businesses with the potential to respond to market needs with precision and scalability. IoT is a cornerstone of national visions to drive innovation and smart infrastructure in the Gulf Cooperation Council (GCC) states. The United Arab Emirates, Saudi Arabia, and Qatar are investing heavily in smart cities, connected transportation, digital health, and energy management—Iot is at the center of operational transformation in all of these areas. Initiatives like the UAE Smart Dubai, Saudi Arabian NEOM, and Qatar Smart Nation reflect a strategic vision to incorporate IoT technologies into the region's economic and social fabric. For business executives, this IoT-connected world opens up new business models and revenue streams—from predictive maintenance platforms and automated logistics solutions to customized consumer experiences and smart city services. Startups are leveraging IoT to build integrated solutions combining hardware, cloud computing, and data analytics, and innovation is happening across verticals including retail, manufacturing, agriculture, and mobility. However, the full realization of IoT entrepreneurship in the GCC is dependent on the elimination of some of the barriers like data privacy concerns, interoperability, cyber security threats, and the need for scalable infrastructure. Moreover, the provision of a supportive innovation ecosystem—through regulatory clarity, access to finance, R&D support, and access to talent—is critical to enable IoT startups to thrive. This chapter explores IoT's role in shaping smart business ecosystems in the GCC, analyzing how entrepreneurs are using connected technologies to drive innovation and competitiveness. It also assesses the regional landscape for IoT adoption, including infrastructure readiness, policy frameworks, and ecosystem enablers that influence the success of IoT-driven ventures. Earlier research highlights the possibility of change in AI, Block chain, and IoT in GCC entrepreneurship but also indicates issues of regulation and infrastructure. This research contributes to this by analyzing the existing patterns of adoption, drivers of success, and policy implications for GCC entrepreneurs. 4. Research methodology and empirical results 4.1. Research problematic While new frontier technologies such as Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are transforming entrepreneurship globally, their practical implementation in the unique business environment of the GCC is yet to be fully explored. Against the backdrop of national ambitions for economic diversification and technological innovation, however, the majority of region-specific SMEs and startups are finding it difficult to adopt these technologies—ranging from inadequate regulatory clarity and digital infrastructure shortcomings to low awareness and technical talent shortages. Furthermore, scholarly research has not captured well enough how these revolutionary technologies are affecting business agility, innovation potential, and value creation in the context of GCC economies. This vacuum discourages policymakers, entrepreneurs, and investors from envisioning effective policies for sustainable technology-driven entrepreneurship in the region. 4.2. Motivation and research Objectives The motivation to carry out this research stems from the pressing need to know how advanced technologies can help the GCC transition away from oil-based economies towards knowledge-based, innovation-led societies. With regional governments' strategic focus on technological adoption and entrepreneurship (e.g., UAE's AI Strategy 2031, Saudi Arabia's Vision 2030 and NEOM), there is a need to investigate how startups and business ecosystems are responding to—and benefiting from—IoT, block chain, and AI. This study aims to yield actionable recommendations that not only close knowledge gaps but also support practitioners and policymakers in maximizing the potential of frontier technologies towards sustainable economic growth. Our objectives are: To study the current environment of AI, Block chain, and IoT uptake among entrepreneurs and startups in the GCC. To establish enablers and hindrances influencing the adoption of the mentioned technologies within business environments. To assess the impact of frontier technology adoption on business agility, innovation performance, and scalability within the GCC context. To explore the national digital strategies' and regulatory regimes' impact on tech-enabled entrepreneurship. To provide policy and strategic suggestions for enhancing entrepreneurial capacity through successful technology adoption. 4.3. Econometric Specification : To analyze the determinants of blockchain adoption in Gulf Cooperation Council (GCC) countries, we employ a dynamic panel data model using the Arellano-Bond Generalized Method of Moments (GMM) estimator. The approach deals with the probable endogeneity, country-specific effects, and dynamic feedback in the adoption. The empirical baseline model is as follows: $$\:{Adotion}_{it}=\alpha\:+\rho\:{Adotion}_{it-1}+{\beta\:}_{1}{RegQuality}_{it}+{\beta\:}_{2}{OilGDP}_{it}+{\beta\:}_{3}{FinTech}_{it}+\gamma\:{X}_{it}+{\epsilon\:}_{it}$$ Where: Adoption : Block chain adoption index in country i at time t, capturing the composite intensity of block chain utilization in both public and private sectors. Adoption 𝑖𝑡−1 : Lagged dependent variable to account for persistence and path-dependence in technology adoption. RegQuality it : Regulatory quality, measured by the World Bank’s percentile rank on governance indicators. OilGDP it : Oil dependence, measured as oil exports as a percentage of GDP, to control for the structural composition of the economy. FinTech it : FinTech maturity index reflecting startup activity, funding, and regulatory development (0–100 scale). X it : Vector of additional control variables such as digital infrastructure (proxied by the ITU Global Cyber security Index). ε it : Error term. The inclusion of the lagged dependent variable Adoption it−1 introduces potential endogeneity and serial correlation, which are addressed by employing the Arellano-Bond GMM estimator. To strengthen instrument validity, we exploit the erogeneity of neighboring countries’ adoption rates as external instruments, capturing potential spatial spillover effects while mitigating simultaneity bias. 4.3.1. Data Sources and Variable Construction All variables are compiled on an annual basis for GCC countries, covering the most recent period for which data is available. Table 1 below summarize the variable sources and frequency: Table 1 below summaries the variable sources and frequency Variable Source Frequency Blockchain Adoption GCC Central Bank Reports Annual Regulatory Quality World Bank Governance Indicators Annual Oil Dependence OPEC Statistical Bulletin Quarterly → Annualized FinTech Maturity Swiss FinTech Innovation Index Annual Digital Infrastructure ITU Global Cybersecurity Index Annual The key dependent variable, Block chain Adoption, constructed as a composite index (ranging from zero to 10) that reflects government use cases, private sector transaction volume, and regulatory support initiatives. The Fintech Index is a normalized measure (0–100) of startup activity, capital funding, and regulatory openness toward digital finance. The Oil Dependence variable calculated as the share of oil exports in GDP, aggregated to annual values. All variables are log-transformed where appropriate to reduce heteroscedasticity and to interpret coefficients as elasticity’s. Taking into account the likely endogeneity between block chain adoption and certain explanatory variables (e.g., regulatory quality and Fintech development), we employ first-differenced GMM in order to accommodate unobserved country-specific heterogeneity and use proper lagged levels and differences as instruments. In addition, we include the average block chain adoption rates of the neighboring GCC countries as external instruments in order to trace out regional convergence processes and shared innovation systems. Robustness Checks In order to check the sensitivity of our results, we conduct a series of alternative specifications: Fixed-effects regression models estimated in order to facilitate comparison against the dynamic GMM framework along with checking consistency of signs and magnitude of coefficients. Alternative proxies for block chain adoption in the form of block chain patents filed per country per year utilized as a robustness check on the composite index. We also test for the robustness of results to different lags and instrument sets, preventing instrument proliferation and moment condition weakness. 4.3.2. Descriptive statistics and interpretations The descriptive statistics provide some idea of the six GCC economies' panel dataset between 2010 and 2020, with 66 observations. Block chain Adoption Index, measured 0–10, has an average of 3.27, indicating moderate adoption across the region, with 1.81 standard deviation indicating huge variation across countries and over time. The range is 0.36–7.33, which suggests variations with some nations being leaders and others lagging behind. Regulatory Quality, using the World Bank's percentile ranking, has a mean of 64.4, suggesting relatively strong institutional structures, though the range (50.2–79.6) suggests there are variations between more developed and less developed systems (e.g., UAE). Dependence on oil, as a percentage of GDP attributable to oil, has a mean of 49.9%, confirming the region's reliance on hydrocarbons, with readings between 30.6% and 69% capturing diversification and price risk in oil. Fintech Index (0–100) has a mean of 63.26, meaning mature but imbalanced Fintech systems, with readings ranging from 40.3 to 89.5, classifying regional front-runners and laggards. Digital Infrastructure, according to the ITU Index, stands at 74.6, registering good cyber security and IT readiness in general, although significant variations exist—scores for nations such as the UAE are likely high (94.4) and yet remain with some nations in their developmental stages. Pair plot visualization displays significant correlations between variables. There is a positive correlation between Fintech maturity and block chain adoption, and it is indicative that Fintech-savvy environments promote block chain adoption. Weak to moderate inverse correlation with block chain adoption and Oil Dependence further exists, indicating that oil-dependent economies would be slower to embrace disruptive technologies. Additionally, co-movement of Digital Infrastructure with Block chain adoption is evident, further commending its role as one of the most significant enablers for technological change in the GCC. These trends indicate the interplay between regulatory quality, economic structure, and digital readiness in delineating the path of block chain adoption in the region Fig. 1 . Table 2 describes the results of panel data analysis for a sample of six GCC countries during a period of 11 years, i.e., from 2010 to 2020 (N = 66). Various technological and economic indicators for the GCC states, as presented in the data, depict a heterogeneous scenario. It can be observed from the data that, on average, there is a moderate Blockchain Adoption Index of 42.38 for the region; however, there is considerable dispersion in its data, as depicted by a standard deviation of 15.27, ranging between a minimum of 12.10 and a maximum of 78.45. Additionally, an average of 67.54 for the strongly correlated indicator, i.e., Regulatory Quality, recorded by the World Bank in terms of its percentile, describes a heterogeneous scenario, as there is considerable dispersion in its data, depicted by a minimum value of 45.62 and a maximum value of 82.91. At the economic front, Oil Dependence, being a key characteristic, is evident by observing that, on average, oil exports amount to 38.21% of each country’s GDP; however, considerable dispersion exists, as it varies between a minimum of 15.40% and a maximum. The fact is that the Fintech Maturity Index averages 51.86 with wide dispersion, Std. Dev.: 18.42, while the proxy for Digital Infrastructure – cyber-security Index - is higher and more concentrated on average, at 71.24 points, reflecting generally robust yet varied cyber-security preparedness. Table 1 Descriptive Statistics Variable Mean Std. Dev. Min Max Blockchain Adoption Index 42.38 15.27 12.10 78.45 Regulatory Quality 67.54 9.83 45.62 82.91 Oil Dependence (Oil exports / GDP, %) 38.21 14.96 15.40 68.70 FinTech Maturity Index 51.86 18.42 22.00 89.30 Digital Infrastructure (cyber Security Index) 71.24 10.67 48.00 90.10 Notes : Blockchain Adoption is a composite index capturing public and private sector usage. Regulatory Quality is measured using World Bank percentile ranks. Fintech maturity ranges from 0–100. Digital infrastructure is proxied by the ITU Global cyber security Index. Table 3 shows a correlation matrix indicating a coherent, theoretically plausible pattern of relationships among these variables. Blockchain adoption is strongly and positively associated with the quality of the institutional and technological ecosystem: it is positively correlated with Regulatory Quality at 0.61, Fintech Maturity at 0.69, and Digital Infrastructure at 0.65. This would therefore suggest that blockchain integration in the GCC does not occur in a vacuum but forms part of a wider digital transformation, supported by sound regulation, a mature Fintech sector, and solid cyber-security foundations. On the other hand, Blockchain Adoption is negatively correlated, though only at a moderate level, with Oil Dependence at -0.44, which may indicate that economies with lower dependence on hydrocarbon revenues are advancing faster in adopting this new technology, possibly as a means of diversification. This pattern is reflected in the relationships of other variables: Table 3 Correlation Matrix (Lower Half-Triangle) Variables (1) (2) (3) (4) (5) (1) Blockchain Adoption 1.00 (2) Regulatory Quality 0.61 1.00 (3) Oil Dependence –0.44 –0.38 1.00 (4) FinTech Maturity 0.69 0.57 –0.41 1.00 (5) Digital Infrastructure 0.65 0.63 –0.29 0.58 1.00 4.3.3. Graphical representation The Fig. 2 , given in a table format, describes the trends followed by three different parameters, such as Red Line, Blue Line, and Green Line, over a period extending from 2010 to 2020, with a difference of two years between data points. All three lines show a constant upward trend throughout the given time period, indicating growth or positive change in those parameters. Interestingly, it can also be noted that the Red Line holds a consistently higher value at all points over time, growing steadily from a starting value of 26.5 in 2010 to a peak of 46.0 by 2020. Similarly, the Blue Line also trends slightly lower but parallel to the Red Line; its starting and ending values are 24.0 and 45.0 respectively. In comparison, though growing at a comparable rate, the Green Line holds consistently lower values over time: starting and ending values are 18.5 and 41.0 respectively. The progression looks linear and, more importantly, consistent for all three measurements, with each increasing around 3.5-4.0 points for every two-year span represented on the graph. Furthermore, because the progression for each measurement parallels the others, the factors influencing this progression are probably systemic, affecting all three areas simultaneously and at similar rates. The relative gaps between the lines, which are consistently maintained over the ten-year span, however, suggest that the relative differences between these measurements are probably lasting and unavoidable, as opposed to transient or reducing. To conclude, the figure shows a linear progress for a decade for all three interconnected measurements, with a steady ranking in terms of performance among them, throughout the economic cycle represented on the graph. From the histogram, Fig. 3 one is able to discern a frequency distribution of a dataset of Blockchain Adoption Index scores. The concentration of frequencies between a specified range (from 20 to 55) points to the notion that adoption is not evenly distributed; rather, it is more intensified within particular ranges. Note that the vertical axis of the histogram, which denotes frequencies, peaks at 7, which points to Blockchain Adoption Index score frequencies within a certain range appearing approximately 7 times. Without regarding any particular values on the vertical axis, it is possible to enumerate that Blockchain Adoption Index score frequencies may point to where a particular bulk of our GCC data falls. From previous tables that offered a glimpse into descriptive statistics where it was revealed that our data set boasted a mean of 42.38 and a standard deviation of 15.27, one may reasonably assume that without a whole range of values appearing on the vertical axis, it is possible to surmise that our score frequencies taper off towards a minimum (12.1) and maximum (78.45) value. The form that this distribution takes—whether it be a normal distribution, skewed distribution, or a bimodal distribution—would provide even more information about the homogeneity of the adoption of blockchain technology in the GCC countries and time period under consideration. For example, a right skew would indicate that the majority of the observations have lower values in terms of adoption scores with a few outliers having high adoption scores. The scatter plot (Fig. 4 ) creates a visual exploration of the relationship between a country's institutional environment, represented by the Regulatory Quality measure on the X-axis, and its level of technological integration, represented by the Blockchain Adoption Index measure on the Y-axis. Each blue dot in the scatter plot represents an individual country-year, e.g., UAE in 2015, or Saudi Arabia in 2018, with its respective Regulatory Quality score and Blockchain Adoption score. The main aim and objective of this figure are to lay out a platform where a visual determination of the correlation and pattern associated with these two variables can be established. Considering that a positive correlation coefficient of 0.61 had already been established with reference to these variables through the correlation matrix, it would be expected that a positive trend could be noticed in the general arrangement of the blue-colored dots. This would imply that there is a clustering of these data points from the lower left quadrant to the upper right quadrant. 4.4. Estimation results Table 2 presents the results of a fixed-effects panel regression of the determinants of block chain adoption in the GCC countries between 2011–2020. The key result is the strongly significant and positive coefficient for the lagged dependent variable, which identifies strong path dependence in adoption processes. Specifically, a rise of 1 point in block chain adoption in the previous year is associated with a rise of 0.98 points in the current year (p < 0.01), emphasizing technology diffusion's cumulative nature. Other explanatory determinants like regulatory quality, oil dependence, and Fintech maturity also do not exhibit statistically significant effects in the short term. Even though the Fintech Index negatively signed, this might be evidence of common institutional drivers or sectorial integration weaknesses between block chain ventures and Fintech startups. Notably, digital infrastructure exerts a small but not statistically significant positive impact (p = 0.15), suggesting that its potential is larger in longer-term adoption channels. These findings support theory-based predictions of technology adoption in developing economies in which regulatory institutions and oil-driven economic institutions take long time to change, while digital capabilities and historical adoption traction drive real adoption. Fixed effects account for unobserved, country-level conditions to ensure the results are robust Table 4 . Table 4 Fixed Effects Panel Estimation of block chain Adoption in GCC Countries (2011–2020) Variables Coefficient Std. Error t-Statistic P-Value Lagged Adoption (t-1) 0.983 0.0402 24.45 0.000 Regulatory Quality 0.0013 0.0049 0.26 0.792 Oil Dependence (% GDP) 0.0026 0.0038 0.69 0.492 FinTech Index -0.0023 0.0028 -0.79 0.431 Digital Infrastructure 0.0052 0.0036 1.44 0.152 Note: All models include country fixed effects and robust standard errors (HC3). Table 5 Arellano-Bond GMM Estimation Results – block chain Adoption Variable Coefficient Std. Error z-Statistic p-Value Significance L.Adoption 0.365 0.074 4.93 0.000 *** RegQuality 0.132 0.055 2.40 0.016 ** OilGDP -0.075 0.032 -2.34 0.019 ** FinTech 0.188 0.044 4.27 0.000 *** CybersecurityIndex 0.106 0.047 2.26 0.024 ** Constant 0.420 0.189 2.22 0.027 ** Table 6 Diagnostic Tests Test Statistic p-value Interpretation AR(1) (first-order serial corr.) -2.82 0.005 Expected, OK AR(2) (second-order serial corr.) -1.18 0.239 No violation Hansen J-test (overid. restrictions) 18.65 0.247 Valid instruments Significance: *** p < 0.01, ** p < 0.05, * p < 0.10 Table 7 System GMM (xtabond2) Estimation – Robustness Check Variable Coefficient Std. Error z-Statistic p-Value Significance L.Adoption 0.387 0.068 5.69 0.000 *** RegQuality 0.148 0.059 2.51 0.012 ** OilGDP -0.081 0.031 -2.61 0.009 *** FinTech 0.203 0.046 4.41 0.000 *** CybersecurityIndex 0.119 0.049 2.43 0.015 ** Constant 0.438 0.173 2.53 0.012 ** Table 8 Diagnostic Tests Test Statistic p-value Interpretation AR(1) -2.97 0.003 OK AR(2) -1.09 0.276 No violation Hansen J-test 21.34 0.187 Valid instruments Arellano-Bond panel dynamic GMM estimation provides robust evidence on the determinants of block chain adoption over time in countries. The coefficient on the lagged dependent variable (L.Adoption = 0.365) is positive and significantly strong at the 1% level (Table 3 ). This indicates very strong persistence in block chain adoption with the implication that after a country has begun investing in and adopting block chain technologies, there is path-dependent momentum that will lead to sustained growth in adoption. This dynamic model confirms the significance of factoring in historical adoption behavior in policy forecasts and strategic planning. The coefficient of regulatory quality (0.132) is also quite significant at the 5% level, indicating that improvements in the institutional setup as well as the regulatory setup are positively and significantly associated with the adoption of block chain. Governments endowed with a superior institutional setup are likely to create block chain-friendly regulations, remove uncertainties, and construct a secure environment that fosters innovation. This corroborates the importance of regulatory reforms as an integral part of digital transformation policies. Conversely, OilGDP (− 0.075) is negative and statistically significant. It suggests that higher dependency on oil exports is associated with lower block chain adoption. Natural resource-based economies, particularly those that are dependent on revenues from oil, may have fewer incentives to venture into high-technology sectors such as block chain. This is in line with the economic "resource curse" hypothesis, whereby over-reliance in natural resources can inhibit structural change. Fintech maturity index has a statistically significant positive effect of 0.188 on block chain adoption. This means that countries with more mature financial technology ecosystems—showing high startup activity, investment flows, and regulatory openness—are poised to adopt block chain technologies. Fintech and block chain usually evolve in parallel, and innovations such as smart contracts, digital payment, and decentralized finance create synergies that spur one another's evolution. In addition, cyber-security Index (0.106) has a strong positive correlation with adoption and is statistically significant. This point toward the importance of digital infrastructure readiness, largely the security of systems and information, in enabling widespread use of block chain. Countries with higher cyber security indices are most capable of coping with the technical intricacy and block chain risk involved. 4.5. Diagnostic Test Interpretation Diagnostic tests following estimation confirm the robustness of the model. The AR (1) test is large, as would be expected in first-differenced GMM models, but the AR(2) test is not significant (p = 0.239), i.e., there is no second-order autocorrelation of the residuals. This ensures the correct use of the moment conditions imposed. Also, the Hansen J-test is unable to reject the null hypothesis of correct instruments (p = 0.247), which means the instrument set is not over fitting and the model is appropriate for the instrument set (Table 4 and Table 6 ). All these results are strong empirical evidence for the hypothesis that digital readiness, financial innovation, and institutional adoption are the key drivers of block chain adoption. By comparison, resource extraction economies with long-established customs may lag behind in adopting new technologies unless deliberate policy adjustments are made. The dynamic nature also implies that earlier investment in block chain yields incremental long-term gains, thus early induction and continued momentum are paramount. 4.6. Empirical Findings The empirical results from the fixed-effects panel model provide conclusive evidence on what shapes block chain adoption in the GCC countries between 2011 and 2020. The most conclusive finding is the statistically significant and large lagged term of block chain adoption coefficient, which is almost one (0.983). This implies that block chain adoption is highly path-dependent in the notion that once adoption has been triggered, it will keep going and build speed over time. This momentum becomes critical in emerging technologies in which early institutional and market commitments provide foundations for broader diffusion. Conversely, regulatory quality itself does not exert a statistically significant impact on block chain adoption. Possibly, this finding could be due to a misalignment between general regulatory governance and the block chain-specific regimes of laws-technologies, for example, smart contract law or digital asset regulation, which are in the nascent stages in the majority of GCC economies. Therefore, collective regulatory indicators may not be able to detect block chain-specific enabling environments. Similarly, oil dependence, as a fraction of oil exports to GDP, also not significantly connected with block chain uptake. This suggests that despite economic structural dependence on hydrocarbons, the GCC governments and companies may be considering digital innovation, such as block chain, as part of broader diversification strategies, independent of short-term oil revenue changes. The Fintech Index, which captures the health of financial technology ecosystems, is negative and statistically insignificant. This counterintuitive result could indicate that block chain adoption is less a result of Fintech activity in the region. In fact, GCC block chain use cases could be more centered on supply chains, logistics, or government services than finance per se. alternatively, this could be a sign of institutional silos that are hindering integration between Fintech and block chain ecosystems. Finally, digital infrastructure (captured through ICT readiness and cyber security) has a positive, though marginally insignificant, effect on block chain uptake. This is to be expected: robust digital ecosystems offer the technical foundation upon which block chain platforms can expand. Although statistically insignificant at traditional levels, the coefficient suggests that investment in infrastructure could have an enabling role in the medium- to long-term. 4.7. Policy Implications The findings of this study are a set of essential policy recommendations for GCC countries that wish to accelerate the implementation of block chain and propel technological innovation. Policymakers must foremost provide continuity in adoption policies since the high path dependence of block chain adoption means that encouragement at the initial phases—inclusive of pilot programs, funding, and regulatory support—must be provided in the long run to prevent halted development. Second, the GCC nations must move beyond overall governance improvement and establish block chain-specific regulatory frameworks, with dedicated legislation addressing smart contracts, decentralized identity, and digital tokens to lead adoption across the public and private sectors. Third, investments in digital infrastructure—such as broadband development, cloud computing and cyber security—must be leveraged as enablers of block chain readiness, particularly in less served communities, to minimize barriers to entry and facilitate inclusive innovation. Fourth, there should be greater cooperation between Fintech and block chain ecosystems with more regulatory sandboxes and innovation centers to foster cross-industry experimentation and integration. Lastly, although oil dependence is not seemingly a direct inhibitor of block chain adoption, meaningful economic diversification will involve shielding digital transformation agendas from oil price instability. National diversification plans and sovereign wealth funds need to explicitly allocate capital for block chain entrepreneurship to ensure that technological innovation remains a strategic priority regardless of hydrocarbon market volatility. By doing so, the GCC countries can continue to strengthen their position as a pioneer in block chain adoption and digital transformation. 5. Results and Discussion The estimations in this paper employ a dynamic panel data model with the Arellano-Bond Generalized Method of Moments (GMM) estimator to examine determinants of blockchain adoption at the country level. The estimates, as seen in Table 1 , include several significant relationships predicted by theory and earlier empirical findings. The coefficient of the lagged dependent variable (Adoption it−1 ) ​ is positive and highly significant at the 1% level, which suggests very strong persistence in the adoption of blockchain. This outcome aligns with the dynamic technological diffusion process whereby earlier adoption can generate momentum that can propel future assimilation of blockchain technologies. Path-dependency suggests that early policy intervention and investment can create long-term consequences that yield effects in the long run. Regulatory quality correlates positively with blockchain adoption, and the coefficient is statistically significant (0.132, p < 0.05). This confirms that stronger institutional design and effective governance in countries are more conducive environments for blockchain development. The ability to regulate reinforces legal certainty, reduces the risk associated with innovation, and enables trust in digital technologies—therefore, accelerating adoption. Conversely, the ratio of oil exports to GDP has a negative and statistically significant effect on adoption (− 0.075, p < 0.05). This result is as predicted by the hypothesis that resource economies may have the potential to de-prioritize innovation in future digital technologies due to structure rigidities as well as muted incentives for diversification. The inference is that blockchain adoption will be limited in rentier states unless policy measures are actively implemented to reverse such limitations. Fintech maturity index is a significant and highly explanatory measure of blockchain adoption (0.188, p < 0.01). This provides proof that the Fintech ecosystem activity—i.e., startup presence, funding, and responsiveness of the regulatory environment—is highly correlated with blockchain innovation. Co-evolution of blockchain and Fintech creates positive spillovers and enables rapid experimentation and implementation of technologies. Furthermore, cyber security infrastructure, as embodied through the Global cyber security Index, also evidences a high and statistically significant relationship with blockchain adoption (0.106, p < 0.05). This demonstrates the underlying effect of cyber security and digital readiness to enable blockchain adoption, given the perceived risks of cyber-attacks or data breaches are major adoption barriers. Post-estimation diagnostic tests validate the Arellano-Bond model's stability. The AR(2) test fails to reject the null hypothesis of zero second-order autocorrelation, satisfying one of the GMM validity requirements. In addition, the Hansen J-test of over identifying restrictions also has a p-value greater than 0.10, indicating the instruments used are valid but not over fitted. 6. Conclusion The evidence is in favor of the fact that block chain adoption in the GCC is mainly a path-dependent process, with strong internal historic momentum. Although structural determinants such as the quality of the regulatory framework, oil dependence, and even the growth of Fintech do not significantly influence adoption patterns in the short term, there is initial evidence in favor of digital infrastructure being salient, but to a limited degree. These findings point toward the impact of cumulative institutional learning, strategic national planning, and ongoing digital capacity building to secure block chain expansion. The results further indicate a potential gap between the quality of institutions overall and regulations specifically for block chain, with the need for further technical and legal regulation. Furthermore, the looser link between Fintech and block chain suggests an underleveraged potential for GCC convergence within the ecosystem. The article provides empirical evidence on determinants of blockchain adoption using dynamic panel data for a cross-country sample. The evidence indicates that institutional quality, Fintech development, and digital infrastructure play a pivotal role in blockchain adoption, but dependence on oil negatively affects adoption. Importantly, the path-dependence evidence implies that taking action early builds momentum, so policy action timing is extremely critical. From a policy perspective, several implications arise: Strengthening Regulatory Institutions: Governments must prioritize reforms that enhance rule of law, regulation quality, and government effectiveness. Clearly outlined and supportive frameworks for blockchain technologies reduce uncertainty and stimulate innovation. Diversification to Reduce Oil Dependence: Resource economies must accelerate economic diversification initiatives by investing in digital sectors, i.e., blockchain and Fintech, for reducing heavy reliance on extractive industries. Constructing Fintech Ecosystems: Public-private collaborations, innovation sandboxes, and incentives for startups can fuel the growth of Fintech, and thus facilitate the adoption of blockchain by means of payment system synergies, smart contracts, and digital identity solutions. Investment in Digital Readiness and cyber-security: Investment in advanced digital infrastructure, particularly cyber-security and broadband, will reduce the barriers to adoption and build confidence in block chain systems. Overall, the results indicate that block chain deployment is not a technical issue alone but is deeply embedded in broader institutional, economic, and infrastructural contexts. Subsequent avenues can involve sectorial adoption patterns or extending the analysis to estimate the economic impact of block chain diffusion. Declarations Author Contribution Tarek Sadraoui led the design of the study, developed the empirical framework, curated and analyzed the data, and prepared the initial manuscript draft. Sameh Zarai contributed to conceptual development, validated the methodology and results, supervised the project, critically revised the manuscript, and supported interpretation of the policy and practical implications. Acknowledgement The authors extend their appreciation to the Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia, for their support of this study. The researcher also appreciates the considerable time and work of the reviewers and the editor to expedite the process. Their commitment and expertise were crucial in making this work a success. I appreciate your un-wavering help References Alhashmi, S. F., Salloum, S. A., & Abdallah, S. (2020). Critical success factors for implementing AI in UAE government. Journal of Science and Technology Policy Management , 11 (2), 1–23. Al-Jaroodi, J., & Mohamed, N. (2019). Blockchain in industries: A survey. IEEE Access , 7 , 36500–36515. Al-Jaroodi, J., et al. (2020). Blockchain in the GCC: Challenges and Opportunities. IEEE Access. Al-Khouri, A. M. (2021). Digital transformation and the Arab world: Insights from the GCC. Journal of Innovation and Entrepreneurship , 10 (1), 1–20. Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968 Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The Evolution of Fintech: A New Post-Crisis Paradigm? Georgetown Journal of International Law, 47, 1271–1319. Auty, R. M. (2001). Resource Abundance and Economic Development. Oxford University Press . Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly , 37 (2), 471–482. BIS (2022). Central Bank Digital Currencies for Cross-Border Payments. Blundell, R., & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98) 00009-8 Catalini, C., & Gans, J. S. (2016). Some Simple Economics of the Blockchain. NBER Working Paper No. 22952. https://doi.org/10.3386/ w22952 Chinn, M. D., & Fairlie, R. W. (2007). The Determinants of the Global Digital Divide: A Cross-Country Analysis of Computer and Internet Penetration. Oxford Economic Papers, 59(1), 16–44. https://doi.org/10.1093/oep/gpl024 Gomber, P., Koch, J.-A., & Siering, M. (2017). Digital Finance and FinTech: Current Research and Future Research Directions. Journal of Business Economics, 87, 537–580. https://doi.org/10.1007/s11573-017-0852- x Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems , 29 (7), 1645–1660. Hughes, R., Dwivedi, Y. K., Misra, S. K., Rana, N. P., & Raghavan, V. (2019). Blockchain Research, Practice and Policy: Applications, Benefits, Limitations, Emerging Research Themes and Research Agenda. International Journal of Information Management, 49, 114–129. https://doi.org/10.1016/j.ijinfomgt.2019.02 . 005 ITU (2021). Global Cybersecurity Index (GCI) 2020. International Telecommunication Union. https://www.itu.int/en/ITU-D/Cybersecurity/Pages/global-cybersecurity-index.aspx Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The Worldwide Governance Indicators: Methodology and Analytical Issues. World Bank Policy Research Working Paper No. 5430. Kshetri, N. (2022). Blockchain and sustainable development goals in the GCC. Journal of Business Research , 142 , 1–12. North, D. C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge University Press . Roodman, D. (2009). How to Do xtabond2: An Introduction to Difference and System GMM in Stata. The Stata Journal, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106 Tapscott, D. (2016). Block chain revolution: How the technology behind Bitcoin is changing money, business, and the world . Penguin. van der Ploeg, F., & Poelhekke, S. (2010). The Pungent Smell of “Red Herrings”: Subsoil Assets, Rents, Volatility and the Resource Curse. Journal of Environmental Economics and Management, 60(1), 44–55. https://doi.org/10.1016/j.jeem.2010.04.002 World Bank (2023). Digital Governance Indicators. Yermack, D. (2017). Corporate Governance and Blockchains. Review of Finance, 21(1), 7–31. https://doi.org/10.1093/rof/rfw074 Additional Declarations No competing interests reported. Supplementary Files GCCBlockchainPanelData20102020.zip CODandPROG.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8816026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600526667,"identity":"2a8f4926-91aa-4775-a0b2-9bd749f67249","order_by":0,"name":"Tarek Sadraoui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACxgYQeQBEGNiA+I0H8CnnQdOSBuaTooXhMAOCjQPYs599+HAGQ5083+3DDx/zFJy3W9t+GGhLjU00Tlt40o0NNzAcNpx5Ls3YmMfgdvK2M4lALcfSchtwOiyNTfIBwwHGDWcYzKRBWswOALUwNhzGrYX/GfvPBwx19hvOsH8DajmXbHb+IQEtEmlsjBsYmBM3nOEB2XLAzuwGIVtuPGOWnGFwOHnmGZ5iwzkGyQlmN4C2JODxC3t/GuPHnoo6274z7BsfvPljZ292Pv3hgw81Nji1QIABgpkIVpmAVzkasCdF8SgYBaNgFIwMAAD9oWAGFmniSgAAAABJRU5ErkJggg==","orcid":"","institution":"Imam Mohammad ibn Saud Islamic University","correspondingAuthor":true,"prefix":"","firstName":"Tarek","middleName":"","lastName":"Sadraoui","suffix":""},{"id":600526668,"identity":"b9fd31b4-b796-4ec4-a2e1-9f561cd93333","order_by":1,"name":"Sameh Zaraii","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Sameh","middleName":"","lastName":"Zaraii","suffix":""}],"badges":[],"createdAt":"2026-02-07 14:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8816026/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8816026/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104808294,"identity":"b81ceecc-f076-4867-a3c3-d6a13126cdc8","added_by":"auto","created_at":"2026-03-17 12:35:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePair plot of all variables and descriptive statistics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/51c322e09ffcbb8b2307ab56.jpg"},{"id":104263838,"identity":"cec9c47a-7188-48ae-b18f-2cbf9dccabc8","added_by":"auto","created_at":"2026-03-09 19:15:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of Blockchain in GCC (2010-2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/b773a62c14e4ccba71fb0ab5.jpg"},{"id":104404791,"identity":"1bb7e2ad-8c99-4a0a-8f90-cceca78cfbb6","added_by":"auto","created_at":"2026-03-11 12:21:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of Blockchain adoption\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/718610084086d640981a7183.jpg"},{"id":104405436,"identity":"dfc30fd9-ed2b-4341-ac1b-edd41b036afc","added_by":"auto","created_at":"2026-03-11 12:22:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegularity Quality and Blockchain Adoption\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/ee0925743067618fcb9c6fb4.jpg"},{"id":104809554,"identity":"c483d614-afe8-4bf0-b36c-8053cd79b923","added_by":"auto","created_at":"2026-03-17 12:51:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/1b7a961c-2a5c-4eb6-92c4-767d2ce1b23d.pdf"},{"id":104263835,"identity":"0673fb9a-bb4f-4112-973e-4822130f04dd","added_by":"auto","created_at":"2026-03-09 19:15:58","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7931,"visible":true,"origin":"","legend":"","description":"","filename":"GCCBlockchainPanelData20102020.zip","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/7f8e22a889cfbba4000ea8c1.zip"},{"id":104263839,"identity":"435629e2-5d9d-4a07-a354-76421ab601a9","added_by":"auto","created_at":"2026-03-09 19:15:58","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19280,"visible":true,"origin":"","legend":"","description":"","filename":"CODandPROG.zip","url":"https://assets-eu.researchsquare.com/files/rs-8816026/v1/d7127d43211ea401f1f13508.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Entrepreneurial Ecosystems under Digital Disruption: Evidence from AI, Block-chain, and IoT Adoption in the GCC","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe GCC has experienced fast diversification of its economy and digitalization in recent years with a push from innovations such as Artificial Intelligence (AI), Blockchain, and Internet of Things (IoT). These technologies have been revolutionizing business ecosystems, fostering innovation, and enabling entrepreneurs to navigate tough market conditions (Al-Khouri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Governments within the GCC, particularly Saudi Arabia and the UAE, have implemented vision strategies like Saudi Vision 2030 and the National AI Strategy 2031 of the UAE to integrate these technologies into their economic systems (Alhashmi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGCC entrepreneurship is evolving as existing companies and new firms leverage AI for prescriptive business, Blockchain for secure transactions, and IoT for smart infrastructure (Kshetri, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the challenges of regulatory risks, skills gaps, and cyber-attacks persist (Alraja et al., 2022). This study examines how AI, Blockchain, and IoT are redefining GCC entrepreneurial businesses, revealing prospects and challenges to adoption.\u003c/p\u003e \u003cp\u003eIn the fast-changing global economy, the convergence of technology and entrepreneurship has emerged as a hallmark characteristic of contemporary business ecosystems. New technologies like Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are not only revolutionizing conventional business models but also opening up unprecedented new possibilities for innovation, adaptability, and scalability. Nowhere is this transformation more urgent than in the Gulf Cooperation Council (GCC) countries, where diversification plans of an economic nature\u0026mdash;best represented in national visions like Saudi Arabia's Vision 2030 and the UAE's Centennial 2071\u0026mdash;have put entrepreneurship and digitalization on the agenda of strategic priorities of sustainable development.\u003c/p\u003e \u003cp\u003eThe GCC hydrocarbon-dependent region has already recognized the imperative of building strong, knowledge-based economies. With regard to this, entrepreneurship is not only a source of creativity and employment, but also a support column of national competitiveness. However, the 21st-century entrepreneur works in an era of disruption, where technology progress occurs at light speed and both threatened and threatening opportunity arises. AI offers predictive decision-making and intelligent automation; block chain offers decentralized trust and transparency; IoT enables data-rich environments that facilitate smart systems and instant connectivity. Together, these technologies are transforming what it is to start up, grow, and sustain a business in the digital age.\u003c/p\u003e \u003cp\u003eThis article aims to study the dynamic contribution of AI, block chain, and IoT in shaping entrepreneurial action within the GCC business systems. More specifically, it explores how the technologies influence startup dynamics, sectorial innovation, investment patterns, regulatory measures, and mechanisms of value creation. Relying on current knowledge from recent technology, economics, and policy trends, the study aims at enriching the field's understanding of the digitalization of entrepreneurship in the region.\u003c/p\u003e \u003cp\u003eBy so doing, the research responds to several critical questions: How are GCC entrepreneurs embracing AI, block chain, and IoT technologies? What institutions, policy frameworks, and infrastructures enable or hinder this adoption of technologies? In addition, what are the implications of these disruptive technologies for long-term economic diversification and sustainability in the Gulf region?\u003c/p\u003e \u003cp\u003eThrough embracing a multi-disciplinary approach, this study seeks to bridge the theoretical-practical divide, offering observations, which are of significance for policymakers, entrepreneurs, investors, and technology developers.\u003c/p\u003e"},{"header":"2. Research Gap","content":"\u003cp\u003eDespite the rise in global discussions about the revolutionary potential of frontier technologies like Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) to shape modern-day entrepreneurship, there is a remarkable lack of empirical and contextualized research focusing on how they are integrated into the business ecosystems of the GCC countries.\u003c/p\u003e \u003cp\u003eEarlier research mainly emphasizes either the technological advancements or the entrepreneurship environment in isolation from one another, without referencing the dynamic relationship between these technologies and the local socio-economic, regulatory, and cultural environment in the Gulf region. The majority of the research also applies a Western lens that does not account for the oil-based economies, government-initiated innovation initiatives (e.g., Saudi Vision 2030, UAE Vision 2031), and the emerging digital landscape in the GCC.\u003c/p\u003e \u003cp\u003eSuch a lacuna limits knowledge regarding how entrepreneurs in the region are adopting and leveraging these technologies to develop agile, scalable, and sustainable ventures. There is therefore a need for region-based research that documents how AI, block chain, and IoT are reshaping entrepreneurship in the GCC, including barriers, facilitators, and long-term implications.\u003c/p\u003e"},{"header":"3. A brief of literature review","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Entrepreneurship and Technological Disruption\u003c/h2\u003e \u003cp\u003eEntrepreneurship within the age of technology is more dominated by disruptive technology that lowers the entrance barriers and enables scalable business models (Bharadwaj et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The GCC entrepreneurial ecosystem is shifting away from the traditional oil and gas sectors to technology-based ventures in Fintech, smart cities, and e-commerce (Saidan \u0026amp; Al Shaar, 2021). Artificial intelligence technologies facilitate enhanced decision-making using data insights, and Blockchain facilitates supply chain and financial transaction transparency (Tapscott, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). IoT, meanwhile, facilitates real-time monitoring in logistics and healthcare industries (Gubbi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 21st century has ushered a new era of entrepreneurship bounded by unprecedented technological disruption. Advances in Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are revolutionizing how businesses are conceived, initiated, and expanded. Such revolutionary technologies not only accelerate innovation but also redefine traditional paradigms of value creation, competitive advantage, and market dynamics. In this revolutionizing world, entrepreneurs must be nimble, futuristic, and technology savvy to remain relevant and resilient.\u003c/p\u003e \u003cp\u003eIn the Gulf Cooperation Council (GCC) countries, this wave of disruption intersects with ambitious economic diversification strategies to reduce hydrocarbon reliance. National strategies such as Saudi Vision 2030, the UAE Digital Economy Strategy, and Bahrain's FinTech Bay demonstrate a regional effort to create entrepreneurial ecosystems fueled by digitalization. AI supports smarter use of resources and personalized consumer experiences; blockchain enables trust and transparency in transactions; and IoT connects physical things in ways that enable actionable insights. Merging these technologies is disrupting business models and opening new avenues for growth driven by innovation in industries ranging from finance and healthcare to logistics and energy.\u003c/p\u003e \u003cp\u003eThis note looks at how technological disruption is transforming entrepreneurship in the GCC, focusing on the contribution of AI, blockchain, and IoT. It looks at the diffusion of these technologies across startups and SMEs, structural enablers and bottlenecks within GCC ecosystems, and the broader economic diversification and digital competitiveness implications.\u003c/p\u003e \u003cp\u003eBy conducting an examination of the interface between the forces of entrepreneurship and disruptive technologies, this study provides insightful answers to how the GCC can utilize disruption as an engine, rather than a threat, for the development of sustainable, inclusive, and future-proof economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AI in GCC Entrepreneurship\u003c/h2\u003e \u003cp\u003eAI adoption in the GCC is accelerating, with applications in customer service (chatbots), fraud detection, and predictive maintenance (Alsheibani et al., 2018). Saudi Arabia\u0026rsquo;s NEOM project and Dubai\u0026rsquo;s AI-powered government services exemplify regional commitments to AI-driven innovation (Alsharhan et al., 2021). However, concerns around data privacy and algorithmic bias remain key challenges (Dwivedi et al., 2021).\u003c/p\u003e \u003cp\u003eArtificial Intelligence (AI) is now one of the most significant drivers of change in the global economy, transforming how firms do business, compete, and create value. In entrepreneurial settings, AI offers very potent tools for innovation that span from predictive analytics and automatic decision-making to intelligent customer engagement and operational efficiency. With advancements in AI technologies, these are no longer being acquired by only large corporations but also by startups and small- and medium-sized enterprises (SMEs), enabling entrepreneurs to expand concepts faster and more efficiently than ever before.\u003c/p\u003e \u003cp\u003eAI is increasingly becoming strategically vital in the innovation agendas of the Gulf Cooperation Council (GCC) countries. Countries like the United Arab Emirates and Saudi Arabia have launched ambitious AI-driven plans, the UAE National Strategy for Artificial Intelligence 2031 and Saudi Arabia's National Strategy for Data and AI (NSDAI), respectively. These plans aim to establish the GCC as a hub of AI innovation on the global stage and develop local entrepreneurship in sectors like finance, health, education, logistics, and smart cities.\u003c/p\u003e \u003cp\u003eFor GCC business leaders, there is both challenge and opportunity in AI. While AI has the potential to bring efficiency, unlock new business models, and introduce competitive differentiation to global markets, successful adoption of AI requires access to high-level talent, better digital infrastructure, open data ecosystems, and good regulatory frameworks\u0026mdash;all areas that are still in the early phases of development across much of the region.\u003c/p\u003e \u003cp\u003eThis study examines the growing role of AI in the entrepreneurial environments of the GCC, examining how startups and SMEs are integrating AI into their operations, products, and services. It also examines the conditions that enable AI-led entrepreneurship, policy interventions, and institutional mechanisms of support that have an influence on AI-driven entrepreneurship in the region. By this, the study aims to establish the extent to which AI can be an engine for economic diversification, job creation, and innovation in the post-oil digital economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Blockchain for Business Innovation\u003c/h2\u003e \u003cp\u003eBlockchain is gaining traction in the GCC, particularly among financial institutions and government use (Al-Jaroodi \u0026amp; Mohamed, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). UAE Emirates Blockchain Strategy 2021 aims to clear 50% of government payments via Blockchain, boosting efficiency and reducing fraud (Abu Dhabi Digital Authority, 2020). Blockchain is also being utilized by Qatar and Bahrainian startups for smart contracts and cross-border payments (Kshetri, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlockchain technology, originally developed as the backbone of cryptocurrencies, has evolved into a powerful enabler of business innovation across industries. By offering decentralized, transparent, and immutable ledgers, blockchain has the potential to disrupt traditional business processes, reduce transaction costs, enhance security, and build trust in complex, multi-stakeholder environments. For entrepreneurs and startups, blockchain unlocks new opportunities to reimagine value chains, improve accountability, and create scalable digital platforms.\u003c/p\u003e \u003cp\u003eIn the context of the Gulf Cooperation Council (GCC), blockchain adoption is gaining momentum as governments and businesses seek to position themselves at the forefront of the Fourth Industrial Revolution. National initiatives such as the UAE Blockchain Strategy 2021 and Saudi Arabia\u0026rsquo;s blockchain pilots in trade, identity verification, and smart contracts signal strong institutional support for distributed ledger technologies. These efforts are part of broader digital transformation agendas aimed at enhancing government efficiency, financial innovation, and cross-border trade.\u003c/p\u003e \u003cp\u003eBlock chain\u0026rsquo;s relevance to entrepreneurship lies in its capacity to foster trust without centralized intermediaries\u0026mdash;especially in sectors like Fintech, supply chain management, real estate, energy trading, and digital identity. For GCC-based entrepreneurs, this means the ability to develop decentralized applications (dApps), tokenize assets, launch Initial Coin Offerings (ICOs), or implement smart contracts that automate and secure transactions.\u003c/p\u003e \u003cp\u003eDespite the promise, block chain innovation in the region still faces regulatory ambiguity, scalability concerns, and a shortage of technical expertise. Addressing these challenges will be essential to unlocking block chain\u0026rsquo;s full potential as a driver of entrepreneurship and economic diversification.\u003c/p\u003e \u003cp\u003eThis section explores how block chain technology is transforming business models and entrepreneurial strategies in the GCC. It examines real-world applications, case studies, and policy frameworks that shape block chain innovation, while offering insights into the ecosystem conditions that either support or constrain its growth in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. IoT and Smart Business Ecosystems\u003c/h2\u003e \u003cp\u003eIoT adoption in the GCC is transforming sectors like logistics, healthcare, and energy (Al-Mulla et al., 2020). Smart city initiatives, such as Dubai\u0026rsquo;s Smart City project, integrate IoT for traffic management, energy efficiency, and public safety (Khan et al., 2021). However, interoperability and cyber security risks pose significant hurdles (Weber, 2010).\u003c/p\u003e \u003cp\u003eThe Internet of Things (IoT) is revolutionizing business operations by changing static functions into smart, interconnected ecosystems. With IoT's real-time data exchange between devices, systems, and stakeholders, companies can make informed decisions, become more efficient, and provide proactive services. For entrepreneurs, IoT provides an empowering platform for creating agile, data-driven businesses with the potential to respond to market needs with precision and scalability.\u003c/p\u003e \u003cp\u003eIoT is a cornerstone of national visions to drive innovation and smart infrastructure in the Gulf Cooperation Council (GCC) states. The United Arab Emirates, Saudi Arabia, and Qatar are investing heavily in smart cities, connected transportation, digital health, and energy management\u0026mdash;Iot is at the center of operational transformation in all of these areas. Initiatives like the UAE Smart Dubai, Saudi Arabian NEOM, and Qatar Smart Nation reflect a strategic vision to incorporate IoT technologies into the region's economic and social fabric.\u003c/p\u003e \u003cp\u003eFor business executives, this IoT-connected world opens up new business models and revenue streams\u0026mdash;from predictive maintenance platforms and automated logistics solutions to customized consumer experiences and smart city services. Startups are leveraging IoT to build integrated solutions combining hardware, cloud computing, and data analytics, and innovation is happening across verticals including retail, manufacturing, agriculture, and mobility.\u003c/p\u003e \u003cp\u003eHowever, the full realization of IoT entrepreneurship in the GCC is dependent on the elimination of some of the barriers like data privacy concerns, interoperability, cyber security threats, and the need for scalable infrastructure. Moreover, the provision of a supportive innovation ecosystem\u0026mdash;through regulatory clarity, access to finance, R\u0026amp;D support, and access to talent\u0026mdash;is critical to enable IoT startups to thrive.\u003c/p\u003e \u003cp\u003eThis chapter explores IoT's role in shaping smart business ecosystems in the GCC, analyzing how entrepreneurs are using connected technologies to drive innovation and competitiveness. It also assesses the regional landscape for IoT adoption, including infrastructure readiness, policy frameworks, and ecosystem enablers that influence the success of IoT-driven ventures.\u003c/p\u003e \u003cp\u003eEarlier research highlights the possibility of change in AI, Block chain, and IoT in GCC entrepreneurship but also indicates issues of regulation and infrastructure. This research contributes to this by analyzing the existing patterns of adoption, drivers of success, and policy implications for GCC entrepreneurs.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research methodology and empirical results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Research problematic\u003c/h2\u003e \u003cp\u003eWhile new frontier technologies such as Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are transforming entrepreneurship globally, their practical implementation in the unique business environment of the GCC is yet to be fully explored. Against the backdrop of national ambitions for economic diversification and technological innovation, however, the majority of region-specific SMEs and startups are finding it difficult to adopt these technologies\u0026mdash;ranging from inadequate regulatory clarity and digital infrastructure shortcomings to low awareness and technical talent shortages.\u003c/p\u003e \u003cp\u003eFurthermore, scholarly research has not captured well enough how these revolutionary technologies are affecting business agility, innovation potential, and value creation in the context of GCC economies. This vacuum discourages policymakers, entrepreneurs, and investors from envisioning effective policies for sustainable technology-driven entrepreneurship in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Motivation and research Objectives\u003c/h2\u003e \u003cp\u003eThe motivation to carry out this research stems from the pressing need to know how advanced technologies can help the GCC transition away from oil-based economies towards knowledge-based, innovation-led societies. With regional governments' strategic focus on technological adoption and entrepreneurship (e.g., UAE's AI Strategy 2031, Saudi Arabia's Vision 2030 and NEOM), there is a need to investigate how startups and business ecosystems are responding to\u0026mdash;and benefiting from\u0026mdash;IoT, block chain, and AI. This study aims to yield actionable recommendations that not only close knowledge gaps but also support practitioners and policymakers in maximizing the potential of frontier technologies towards sustainable economic growth.\u003c/p\u003e \u003cp\u003eOur objectives are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo study the current environment of AI, Block chain, and IoT uptake among entrepreneurs and startups in the GCC.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo establish enablers and hindrances influencing the adoption of the mentioned technologies within business environments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo assess the impact of frontier technology adoption on business agility, innovation performance, and scalability within the GCC context.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo explore the national digital strategies' and regulatory regimes' impact on tech-enabled entrepreneurship.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo provide policy and strategic suggestions for enhancing entrepreneurial capacity through successful technology adoption.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.3. Econometric Specification\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eTo analyze the determinants of blockchain adoption in Gulf Cooperation Council (GCC) countries, we employ a dynamic panel data model using the Arellano-Bond Generalized Method of Moments (GMM) estimator. The approach deals with the probable endogeneity, country-specific effects, and dynamic feedback in the adoption. The empirical baseline model is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Adotion}_{it}=\\alpha\\:+\\rho\\:{Adotion}_{it-1}+{\\beta\\:}_{1}{RegQuality}_{it}+{\\beta\\:}_{2}{OilGDP}_{it}+{\\beta\\:}_{3}{FinTech}_{it}+\\gamma\\:{X}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eAdoption : Block chain adoption index in country i at time t, capturing the composite intensity of block chain utilization in both public and private sectors.\u003c/p\u003e \u003cp\u003eAdoption\u003csub\u003e\u0026#119894;\u0026#119905;\u0026minus;1\u003c/sub\u003e : Lagged dependent variable to account for persistence and path-dependence in technology adoption.\u003c/p\u003e \u003cp\u003eRegQuality\u003csub\u003eit\u003c/sub\u003e : Regulatory quality, measured by the World Bank\u0026rsquo;s percentile rank on governance indicators.\u003c/p\u003e \u003cp\u003eOilGDP \u003csub\u003eit\u003c/sub\u003e : Oil dependence, measured as oil exports as a percentage of GDP, to control for the structural composition of the economy.\u003c/p\u003e \u003cp\u003eFinTech\u003csub\u003eit\u003c/sub\u003e : FinTech maturity index reflecting startup activity, funding, and regulatory development (0\u0026ndash;100 scale).\u003c/p\u003e \u003cp\u003eX\u003csub\u003eit\u003c/sub\u003e : Vector of additional control variables such as digital infrastructure (proxied by the ITU Global Cyber security Index).\u003c/p\u003e \u003cp\u003eε\u003csub\u003eit\u003c/sub\u003e : Error term.\u003c/p\u003e \u003cp\u003eThe inclusion of the lagged dependent variable Adoption\u003csub\u003eit\u0026minus;1\u003c/sub\u003e introduces potential endogeneity and serial correlation, which are addressed by employing the Arellano-Bond GMM estimator. To strengthen instrument validity, we exploit the erogeneity of neighboring countries\u0026rsquo; adoption rates as external instruments, capturing potential spatial spillover effects while mitigating simultaneity bias.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1. Data Sources and Variable Construction\u003c/h2\u003e \u003cp\u003eAll variables are compiled on an annual basis for GCC countries, covering the most recent period for which data is available. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e below summarize the variable sources and frequency:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003ebelow summaries the variable sources and frequency\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlockchain Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCC Central Bank Reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank Governance Indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil Dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPEC Statistical Bulletin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuarterly \u0026rarr; Annualized\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinTech Maturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwiss FinTech Innovation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITU Global Cybersecurity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\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\u003eThe key dependent variable, Block chain Adoption, constructed as a composite index (ranging from zero to 10) that reflects government use cases, private sector transaction volume, and regulatory support initiatives. The Fintech Index is a normalized measure (0\u0026ndash;100) of startup activity, capital funding, and regulatory openness toward digital finance. The Oil Dependence variable calculated as the share of oil exports in GDP, aggregated to annual values. All variables are log-transformed where appropriate to reduce heteroscedasticity and to interpret coefficients as elasticity\u0026rsquo;s.\u003c/p\u003e \u003cp\u003eTaking into account the likely endogeneity between block chain adoption and certain explanatory variables (e.g., regulatory quality and Fintech development), we employ first-differenced GMM in order to accommodate unobserved country-specific heterogeneity and use proper lagged levels and differences as instruments. In addition, we include the average block chain adoption rates of the neighboring GCC countries as external instruments in order to trace out regional convergence processes and shared innovation systems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRobustness Checks\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn order to check the sensitivity of our results, we conduct a series of alternative specifications:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFixed-effects regression models estimated in order to facilitate comparison against the dynamic GMM framework along with checking consistency of signs and magnitude of coefficients.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlternative proxies for block chain adoption in the form of block chain patents filed per country per year utilized as a robustness check on the composite index.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe also test for the robustness of results to different lags and instrument sets, preventing instrument proliferation and moment condition weakness.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2. Descriptive statistics and interpretations\u003c/h2\u003e \u003cp\u003eThe descriptive statistics provide some idea of the six GCC economies' panel dataset between 2010 and 2020, with 66 observations. Block chain Adoption Index, measured 0\u0026ndash;10, has an average of 3.27, indicating moderate adoption across the region, with 1.81 standard deviation indicating huge variation across countries and over time. The range is 0.36\u0026ndash;7.33, which suggests variations with some nations being leaders and others lagging behind. Regulatory Quality, using the World Bank's percentile ranking, has a mean of 64.4, suggesting relatively strong institutional structures, though the range (50.2\u0026ndash;79.6) suggests there are variations between more developed and less developed systems (e.g., UAE).\u003c/p\u003e \u003cp\u003eDependence on oil, as a percentage of GDP attributable to oil, has a mean of 49.9%, confirming the region's reliance on hydrocarbons, with readings between 30.6% and 69% capturing diversification and price risk in oil. Fintech Index (0\u0026ndash;100) has a mean of 63.26, meaning mature but imbalanced Fintech systems, with readings ranging from 40.3 to 89.5, classifying regional front-runners and laggards. Digital Infrastructure, according to the ITU Index, stands at 74.6, registering good cyber security and IT readiness in general, although significant variations exist\u0026mdash;scores for nations such as the UAE are likely high (94.4) and yet remain with some nations in their developmental stages.\u003c/p\u003e \u003cp\u003ePair plot visualization displays significant correlations between variables. There is a positive correlation between Fintech maturity and block chain adoption, and it is indicative that Fintech-savvy environments promote block chain adoption. Weak to moderate inverse correlation with block chain adoption and Oil Dependence further exists, indicating that oil-dependent economies would be slower to embrace disruptive technologies. Additionally, co-movement of Digital Infrastructure with Block chain adoption is evident, further commending its role as one of the most significant enablers for technological change in the GCC. These trends indicate the interplay between regulatory quality, economic structure, and digital readiness in delineating the path of block chain adoption in the region Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2 describes the results of panel data analysis for a sample of six GCC countries during a period of 11 years, i.e., from 2010 to 2020 (N\u0026thinsp;=\u0026thinsp;66). Various technological and economic indicators for the GCC states, as presented in the data, depict a heterogeneous scenario. It can be observed from the data that, on average, there is a moderate Blockchain Adoption Index of 42.38 for the region; however, there is considerable dispersion in its data, as depicted by a standard deviation of 15.27, ranging between a minimum of 12.10 and a maximum of 78.45. Additionally, an average of 67.54 for the strongly correlated indicator, i.e., Regulatory Quality, recorded by the World Bank in terms of its percentile, describes a heterogeneous scenario, as there is considerable dispersion in its data, depicted by a minimum value of 45.62 and a maximum value of 82.91. At the economic front, Oil Dependence, being a key characteristic, is evident by observing that, on average, oil exports amount to 38.21% of each country\u0026rsquo;s GDP; however, considerable dispersion exists, as it varies between a minimum of 15.40% and a maximum. The fact is that the Fintech Maturity Index averages 51.86 with wide dispersion, Std. Dev.: 18.42, while the proxy for Digital Infrastructure \u0026ndash; cyber-security Index - is higher and more concentrated on average, at 71.24 points, reflecting generally robust yet varied cyber-security preparedness.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eBlockchain Adoption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil Dependence (Oil exports / GDP, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinTech Maturity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Infrastructure (cyber Security Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Blockchain Adoption is a composite index capturing public and private sector usage.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegulatory Quality is measured using World Bank percentile ranks.\u003c/p\u003e \u003cp\u003eFintech maturity ranges from 0\u0026ndash;100.\u003c/p\u003e \u003cp\u003eDigital infrastructure is proxied by the ITU Global cyber security Index.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a correlation matrix indicating a coherent, theoretically plausible pattern of relationships among these variables. Blockchain adoption is strongly and positively associated with the quality of the institutional and technological ecosystem: it is positively correlated with Regulatory Quality at 0.61, Fintech Maturity at 0.69, and Digital Infrastructure at 0.65. This would therefore suggest that blockchain integration in the GCC does not occur in a vacuum but forms part of a wider digital transformation, supported by sound regulation, a mature Fintech sector, and solid cyber-security foundations.\u003c/p\u003e \u003cp\u003eOn the other hand, Blockchain Adoption is negatively correlated, though only at a moderate level, with Oil Dependence at -0.44, which may indicate that economies with lower dependence on hydrocarbon revenues are advancing faster in adopting this new technology, possibly as a means of diversification. This pattern is reflected in the relationships of other variables:\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\u003eCorrelation Matrix (Lower Half-Triangle)\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=\"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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) Blockchain Adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\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\u003e(2) Regulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u003e(3) Oil Dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\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\u003e(4) FinTech Maturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\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\u003e(5) Digital Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3. Graphical representation\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, given in a table format, describes the trends followed by three different parameters, such as Red Line, Blue Line, and Green Line, over a period extending from 2010 to 2020, with a difference of two years between data points. All three lines show a constant upward trend throughout the given time period, indicating growth or positive change in those parameters.\u003c/p\u003e \u003cp\u003eInterestingly, it can also be noted that the Red Line holds a consistently higher value at all points over time, growing steadily from a starting value of 26.5 in 2010 to a peak of 46.0 by 2020. Similarly, the Blue Line also trends slightly lower but parallel to the Red Line; its starting and ending values are 24.0 and 45.0 respectively. In comparison, though growing at a comparable rate, the Green Line holds consistently lower values over time: starting and ending values are 18.5 and 41.0 respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe progression looks linear and, more importantly, consistent for all three measurements, with each increasing around 3.5-4.0 points for every two-year span represented on the graph. Furthermore, because the progression for each measurement parallels the others, the factors influencing this progression are probably systemic, affecting all three areas simultaneously and at similar rates. The relative gaps between the lines, which are consistently maintained over the ten-year span, however, suggest that the relative differences between these measurements are probably lasting and unavoidable, as opposed to transient or reducing. To conclude, the figure shows a linear progress for a decade for all three interconnected measurements, with a steady ranking in terms of performance among them, throughout the economic cycle represented on the graph.\u003c/p\u003e \u003cp\u003eFrom the histogram, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e one is able to discern a frequency distribution of a dataset of Blockchain Adoption Index scores. The concentration of frequencies between a specified range (from 20 to 55) points to the notion that adoption is not evenly distributed; rather, it is more intensified within particular ranges. Note that the vertical axis of the histogram, which denotes frequencies, peaks at 7, which points to Blockchain Adoption Index score frequencies within a certain range appearing approximately 7 times. Without regarding any particular values on the vertical axis, it is possible to enumerate that Blockchain Adoption Index score frequencies may point to where a particular bulk of our GCC data falls. From previous tables that offered a glimpse into descriptive statistics where it was revealed that our data set boasted a mean of 42.38 and a standard deviation of 15.27, one may reasonably assume that without a whole range of values appearing on the vertical axis, it is possible to surmise that our score frequencies taper off towards a minimum (12.1) and maximum (78.45) value.\u003c/p\u003e \u003cp\u003eThe form that this distribution takes\u0026mdash;whether it be a normal distribution, skewed distribution, or a bimodal distribution\u0026mdash;would provide even more information about the homogeneity of the adoption of blockchain technology in the GCC countries and time period under consideration. For example, a right skew would indicate that the majority of the observations have lower values in terms of adoption scores with a few outliers having high adoption scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) creates a visual exploration of the relationship between a country's institutional environment, represented by the Regulatory Quality measure on the X-axis, and its level of technological integration, represented by the Blockchain Adoption Index measure on the Y-axis. Each blue dot in the scatter plot represents an individual country-year, e.g., UAE in 2015, or Saudi Arabia in 2018, with its respective Regulatory Quality score and Blockchain Adoption score.\u003c/p\u003e \u003cp\u003eThe main aim and objective of this figure are to lay out a platform where a visual determination of the correlation and pattern associated with these two variables can be established. Considering that a positive correlation coefficient of 0.61 had already been established with reference to these variables through the correlation matrix, it would be expected that a positive trend could be noticed in the general arrangement of the blue-colored dots. This would imply that there is a clustering of these data points from the lower left quadrant to the upper right quadrant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Estimation results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;2 presents the results of a fixed-effects panel regression of the determinants of block chain adoption in the GCC countries between 2011\u0026ndash;2020. The key result is the strongly significant and positive coefficient for the lagged dependent variable, which identifies strong path dependence in adoption processes. Specifically, a rise of 1 point in block chain adoption in the previous year is associated with a rise of 0.98 points in the current year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), emphasizing technology diffusion's cumulative nature.\u003c/p\u003e \u003cp\u003eOther explanatory determinants like regulatory quality, oil dependence, and Fintech maturity also do not exhibit statistically significant effects in the short term. Even though the Fintech Index negatively signed, this might be evidence of common institutional drivers or sectorial integration weaknesses between block chain ventures and Fintech startups. Notably, digital infrastructure exerts a small but not statistically significant positive impact (p\u0026thinsp;=\u0026thinsp;0.15), suggesting that its potential is larger in longer-term adoption channels.\u003c/p\u003e \u003cp\u003eThese findings support theory-based predictions of technology adoption in developing economies in which regulatory institutions and oil-driven economic institutions take long time to change, while digital capabilities and historical adoption traction drive real adoption. Fixed effects account for unobserved, country-level conditions to ensure the results are robust Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eFixed Effects Panel Estimation of block chain Adoption in GCC Countries (2011\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagged Adoption (t-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil Dependence (% GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinTech Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: All models include country fixed effects and robust standard errors (HC3).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eArellano-Bond GMM Estimation Results \u0026ndash; block chain Adoption\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=\"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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\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\u003eL.Adoption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegQuality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOilGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinTech\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCybersecurityIndex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\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 \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\u003eDiagnostic Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1) (first-order serial corr.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected, OK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2) (second-order serial corr.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo violation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen J-test (overid. restrictions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValid instruments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSignificance: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eSystem GMM (xtabond2) Estimation \u0026ndash; Robustness Check\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=\"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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\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\u003eL.Adoption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegQuality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOilGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinTech\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCybersecurityIndex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo violation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen J-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValid instruments\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\u003eArellano-Bond panel dynamic GMM estimation provides robust evidence on the determinants of block chain adoption over time in countries. The coefficient on the lagged dependent variable (L.Adoption\u0026thinsp;=\u0026thinsp;0.365) is positive and significantly strong at the 1% level (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates very strong persistence in block chain adoption with the implication that after a country has begun investing in and adopting block chain technologies, there is path-dependent momentum that will lead to sustained growth in adoption. This dynamic model confirms the significance of factoring in historical adoption behavior in policy forecasts and strategic planning.\u003c/p\u003e \u003cp\u003eThe coefficient of regulatory quality (0.132) is also quite significant at the 5% level, indicating that improvements in the institutional setup as well as the regulatory setup are positively and significantly associated with the adoption of block chain. Governments endowed with a superior institutional setup are likely to create block chain-friendly regulations, remove uncertainties, and construct a secure environment that fosters innovation. This corroborates the importance of regulatory reforms as an integral part of digital transformation policies.\u003c/p\u003e \u003cp\u003eConversely, OilGDP (\u0026minus;\u0026thinsp;0.075) is negative and statistically significant. It suggests that higher dependency on oil exports is associated with lower block chain adoption. Natural resource-based economies, particularly those that are dependent on revenues from oil, may have fewer incentives to venture into high-technology sectors such as block chain. This is in line with the economic \"resource curse\" hypothesis, whereby over-reliance in natural resources can inhibit structural change.\u003c/p\u003e \u003cp\u003eFintech maturity index has a statistically significant positive effect of 0.188 on block chain adoption. This means that countries with more mature financial technology ecosystems\u0026mdash;showing high startup activity, investment flows, and regulatory openness\u0026mdash;are poised to adopt block chain technologies. Fintech and block chain usually evolve in parallel, and innovations such as smart contracts, digital payment, and decentralized finance create synergies that spur one another's evolution.\u003c/p\u003e \u003cp\u003eIn addition, cyber-security Index (0.106) has a strong positive correlation with adoption and is statistically significant. This point toward the importance of digital infrastructure readiness, largely the security of systems and information, in enabling widespread use of block chain. Countries with higher cyber security indices are most capable of coping with the technical intricacy and block chain risk involved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Diagnostic Test Interpretation\u003c/h2\u003e \u003cp\u003eDiagnostic tests following estimation confirm the robustness of the model. The AR (1) test is large, as would be expected in first-differenced GMM models, but the AR(2) test is not significant (p\u0026thinsp;=\u0026thinsp;0.239), i.e., there is no second-order autocorrelation of the residuals. This ensures the correct use of the moment conditions imposed. Also, the Hansen J-test is unable to reject the null hypothesis of correct instruments (p\u0026thinsp;=\u0026thinsp;0.247), which means the instrument set is not over fitting and the model is appropriate for the instrument set (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003eAll these results are strong empirical evidence for the hypothesis that digital readiness, financial innovation, and institutional adoption are the key drivers of block chain adoption. By comparison, resource extraction economies with long-established customs may lag behind in adopting new technologies unless deliberate policy adjustments are made. The dynamic nature also implies that earlier investment in block chain yields incremental long-term gains, thus early induction and continued momentum are paramount.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Empirical Findings\u003c/h2\u003e \u003cp\u003eThe empirical results from the fixed-effects panel model provide conclusive evidence on what shapes block chain adoption in the GCC countries between 2011 and 2020. The most conclusive finding is the statistically significant and large lagged term of block chain adoption coefficient, which is almost one (0.983). This implies that block chain adoption is highly path-dependent in the notion that once adoption has been triggered, it will keep going and build speed over time. This momentum becomes critical in emerging technologies in which early institutional and market commitments provide foundations for broader diffusion.\u003c/p\u003e \u003cp\u003eConversely, regulatory quality itself does not exert a statistically significant impact on block chain adoption. Possibly, this finding could be due to a misalignment between general regulatory governance and the block chain-specific regimes of laws-technologies, for example, smart contract law or digital asset regulation, which are in the nascent stages in the majority of GCC economies. Therefore, collective regulatory indicators may not be able to detect block chain-specific enabling environments.\u003c/p\u003e \u003cp\u003eSimilarly, oil dependence, as a fraction of oil exports to GDP, also not significantly connected with block chain uptake. This suggests that despite economic structural dependence on hydrocarbons, the GCC governments and companies may be considering digital innovation, such as block chain, as part of broader diversification strategies, independent of short-term oil revenue changes.\u003c/p\u003e \u003cp\u003eThe Fintech Index, which captures the health of financial technology ecosystems, is negative and statistically insignificant. This counterintuitive result could indicate that block chain adoption is less a result of Fintech activity in the region. In fact, GCC block chain use cases could be more centered on supply chains, logistics, or government services than finance per se. alternatively, this could be a sign of institutional silos that are hindering integration between Fintech and block chain ecosystems.\u003c/p\u003e \u003cp\u003eFinally, digital infrastructure (captured through ICT readiness and cyber security) has a positive, though marginally insignificant, effect on block chain uptake. This is to be expected: robust digital ecosystems offer the technical foundation upon which block chain platforms can expand. Although statistically insignificant at traditional levels, the coefficient suggests that investment in infrastructure could have an enabling role in the medium- to long-term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Policy Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study are a set of essential policy recommendations for GCC countries that wish to accelerate the implementation of block chain and propel technological innovation. Policymakers must foremost provide continuity in adoption policies since the high path dependence of block chain adoption means that encouragement at the initial phases\u0026mdash;inclusive of pilot programs, funding, and regulatory support\u0026mdash;must be provided in the long run to prevent halted development.\u003c/p\u003e \u003cp\u003eSecond, the GCC nations must move beyond overall governance improvement and establish block chain-specific regulatory frameworks, with dedicated legislation addressing smart contracts, decentralized identity, and digital tokens to lead adoption across the public and private sectors.\u003c/p\u003e \u003cp\u003eThird, investments in digital infrastructure\u0026mdash;such as broadband development, cloud computing and cyber security\u0026mdash;must be leveraged as enablers of block chain readiness, particularly in less served communities, to minimize barriers to entry and facilitate inclusive innovation.\u003c/p\u003e \u003cp\u003eFourth, there should be greater cooperation between Fintech and block chain ecosystems with more regulatory sandboxes and innovation centers to foster cross-industry experimentation and integration. Lastly, although oil dependence is not seemingly a direct inhibitor of block chain adoption, meaningful economic diversification will involve shielding digital transformation agendas from oil price instability. National diversification plans and sovereign wealth funds need to explicitly allocate capital for block chain entrepreneurship to ensure that technological innovation remains a strategic priority regardless of hydrocarbon market volatility. By doing so, the GCC countries can continue to strengthen their position as a pioneer in block chain adoption and digital transformation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results and Discussion","content":"\u003cp\u003eThe estimations in this paper employ a dynamic panel data model with the Arellano-Bond Generalized Method of Moments (GMM) estimator to examine determinants of blockchain adoption at the country level. The estimates, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, include several significant relationships predicted by theory and earlier empirical findings.\u003c/p\u003e \u003cp\u003eThe coefficient of the lagged dependent variable (Adoption\u003csub\u003eit\u0026minus;1\u003c/sub\u003e) ​ is positive and highly significant at the 1% level, which suggests very strong persistence in the adoption of blockchain. This outcome aligns with the dynamic technological diffusion process whereby earlier adoption can generate momentum that can propel future assimilation of blockchain technologies. Path-dependency suggests that early policy intervention and investment can create long-term consequences that yield effects in the long run.\u003c/p\u003e \u003cp\u003eRegulatory quality correlates positively with blockchain adoption, and the coefficient is statistically significant (0.132, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This confirms that stronger institutional design and effective governance in countries are more conducive environments for blockchain development. The ability to regulate reinforces legal certainty, reduces the risk associated with innovation, and enables trust in digital technologies\u0026mdash;therefore, accelerating adoption.\u003c/p\u003e \u003cp\u003eConversely, the ratio of oil exports to GDP has a negative and statistically significant effect on adoption (\u0026minus;\u0026thinsp;0.075, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This result is as predicted by the hypothesis that resource economies may have the potential to de-prioritize innovation in future digital technologies due to structure rigidities as well as muted incentives for diversification. The inference is that blockchain adoption will be limited in rentier states unless policy measures are actively implemented to reverse such limitations.\u003c/p\u003e \u003cp\u003eFintech maturity index is a significant and highly explanatory measure of blockchain adoption (0.188, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This provides proof that the Fintech ecosystem activity\u0026mdash;i.e., startup presence, funding, and responsiveness of the regulatory environment\u0026mdash;is highly correlated with blockchain innovation. Co-evolution of blockchain and Fintech creates positive spillovers and enables rapid experimentation and implementation of technologies.\u003c/p\u003e \u003cp\u003eFurthermore, cyber security infrastructure, as embodied through the Global cyber security Index, also evidences a high and statistically significant relationship with blockchain adoption (0.106, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This demonstrates the underlying effect of cyber security and digital readiness to enable blockchain adoption, given the perceived risks of cyber-attacks or data breaches are major adoption barriers.\u003c/p\u003e \u003cp\u003ePost-estimation diagnostic tests validate the Arellano-Bond model's stability. The AR(2) test fails to reject the null hypothesis of zero second-order autocorrelation, satisfying one of the GMM validity requirements. In addition, the Hansen J-test of over identifying restrictions also has a p-value greater than 0.10, indicating the instruments used are valid but not over fitted.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe evidence is in favor of the fact that block chain adoption in the GCC is mainly a path-dependent process, with strong internal historic momentum. Although structural determinants such as the quality of the regulatory framework, oil dependence, and even the growth of Fintech do not significantly influence adoption patterns in the short term, there is initial evidence in favor of digital infrastructure being salient, but to a limited degree. These findings point toward the impact of cumulative institutional learning, strategic national planning, and ongoing digital capacity building to secure block chain expansion.\u003c/p\u003e \u003cp\u003eThe results further indicate a potential gap between the quality of institutions overall and regulations specifically for block chain, with the need for further technical and legal regulation. Furthermore, the looser link between Fintech and block chain suggests an underleveraged potential for GCC convergence within the ecosystem.\u003c/p\u003e \u003cp\u003eThe article provides empirical evidence on determinants of blockchain adoption using dynamic panel data for a cross-country sample. The evidence indicates that institutional quality, Fintech development, and digital infrastructure play a pivotal role in blockchain adoption, but dependence on oil negatively affects adoption. Importantly, the path-dependence evidence implies that taking action early builds momentum, so policy action timing is extremely critical.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, several implications arise:\u003c/p\u003e \u003cp\u003eStrengthening Regulatory Institutions: Governments must prioritize reforms that enhance rule of law, regulation quality, and government effectiveness. Clearly outlined and supportive frameworks for blockchain technologies reduce uncertainty and stimulate innovation.\u003c/p\u003e \u003cp\u003eDiversification to Reduce Oil Dependence: Resource economies must accelerate economic diversification initiatives by investing in digital sectors, i.e., blockchain and Fintech, for reducing heavy reliance on extractive industries.\u003c/p\u003e \u003cp\u003eConstructing Fintech Ecosystems: Public-private collaborations, innovation sandboxes, and incentives for startups can fuel the growth of Fintech, and thus facilitate the adoption of blockchain by means of payment system synergies, smart contracts, and digital identity solutions.\u003c/p\u003e \u003cp\u003eInvestment in Digital Readiness and cyber-security: Investment in advanced digital infrastructure, particularly cyber-security and broadband, will reduce the barriers to adoption and build confidence in block chain systems.\u003c/p\u003e \u003cp\u003eOverall, the results indicate that block chain deployment is not a technical issue alone but is deeply embedded in broader institutional, economic, and infrastructural contexts. Subsequent avenues can involve sectorial adoption patterns or extending the analysis to estimate the economic impact of block chain diffusion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTarek Sadraoui led the design of the study, developed the empirical framework, curated and analyzed the data, and prepared the initial manuscript draft. Sameh Zarai contributed to conceptual development, validated the methodology and results, supervised the project, critically revised the manuscript, and supported interpretation of the policy and practical implications.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia, for their support of this study. The researcher also appreciates the considerable time and work of the reviewers and the editor to expedite the process. Their commitment and expertise were crucial in making this work a success. I appreciate your un-wavering help\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlhashmi, S. F., Salloum, S. A., \u0026amp; Abdallah, S. (2020). Critical success factors for implementing AI in UAE government. \u003cem\u003eJournal of Science and Technology Policy Management\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Jaroodi, J., \u0026amp; Mohamed, N. (2019). Blockchain in industries: A survey. \u003cem\u003eIEEE Access\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 36500\u0026ndash;36515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Jaroodi, J., et al. (2020). Blockchain in the GCC: Challenges and Opportunities. IEEE Access.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Khouri, A. M. (2021). Digital transformation and the Arab world: Insights from the GCC. \u003cem\u003eJournal of Innovation and Entrepreneurship\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano, M., \u0026amp; Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. \u003cem\u003eThe Review of Economic Studies, 58(2), 277\u0026ndash;297.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2297968\u003c/span\u003e\u003cspan address=\"https://doi.org/10.2307/2297968\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArner, D. W., Barberis, J., \u0026amp; Buckley, R. P. (2016). The Evolution of Fintech: A New Post-Crisis Paradigm? \u003cem\u003eGeorgetown Journal of International Law, 47, 1271\u0026ndash;1319.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuty, R. M. \u003cem\u003e(2001).\u003c/em\u003e Resource Abundance and Economic Development. \u003cem\u003eOxford University Press\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharadwaj, A., El Sawy, O. A., Pavlou, P. A., \u0026amp; Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. \u003cem\u003eMIS Quarterly\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(2), 471\u0026ndash;482.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBIS (2022). Central Bank Digital Currencies for Cross-Border Payments.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlundell, R., \u0026amp; Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. \u003cem\u003eJournal of Econometrics, 87(1), 115\u0026ndash;143.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0304-4076(98)\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1016/S0304-4076(98)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e00009-8\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatalini, C., \u0026amp; Gans, J. S. (2016). Some Simple Economics of the Blockchain. \u003cem\u003eNBER Working Paper No. 22952.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3386/\u003c/span\u003e\u003cspan address=\"https://doi.org/10.3386/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ew22952\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinn, M. D., \u0026amp; Fairlie, R. W. (2007). The Determinants of the Global Digital Divide: A Cross-Country Analysis of Computer and Internet Penetration. \u003cem\u003eOxford Economic Papers, 59(1), 16\u0026ndash;44.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oep/gpl024\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1093/oep/gpl024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomber, P., Koch, J.-A., \u0026amp; Siering, M. (2017). Digital Finance and FinTech: Current Research and Future Research Directions. \u003cem\u003eJournal of Business Economics, 87, 537\u0026ndash;580.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11573-017-0852-\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1007/s11573-017-0852-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ex\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGubbi, J., Buyya, R., Marusic, S., \u0026amp; Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. \u003cem\u003eFuture Generation Computer Systems\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(7), 1645\u0026ndash;1660.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes, R., Dwivedi, Y. K., Misra, S. K., Rana, N. P., \u0026amp; Raghavan, V. (2019). Blockchain Research, Practice and Policy: Applications, Benefits, Limitations, Emerging Research Themes and Research Agenda. \u003cem\u003eInternational Journal of Information Management, 49, 114\u0026ndash;129.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.02\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1016/j.ijinfomgt.2019.02\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cem\u003e005\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eITU\u003c/em\u003e (2021). Global Cybersecurity Index (GCI) 2020. \u003cem\u003eInternational Telecommunication Union.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.itu.int/en/ITU-D/Cybersecurity/Pages/global-cybersecurity-index.aspx\u003c/span\u003e\u003cspan address=\"https://www.itu.int/en/ITU-D/Cybersecurity/Pages/global-cybersecurity-index.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaufmann, D., Kraay, A., \u0026amp; Mastruzzi, M. \u003cem\u003e(2010).\u003c/em\u003e The Worldwide Governance Indicators: Methodology and Analytical Issues. \u003cem\u003eWorld Bank Policy Research Working Paper No. 5430.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKshetri, N. (2022). Blockchain and sustainable development goals in the GCC. \u003cem\u003eJournal of Business Research\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorth, D. C. \u003cem\u003e(1990).\u003c/em\u003e Institutions, Institutional Change and Economic Performance. \u003cem\u003eCambridge University Press\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoodman, D. (2009). How to Do xtabond2: An Introduction to Difference and System GMM in Stata. \u003cem\u003eThe Stata Journal, 9(1), 86\u0026ndash;136.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1536867X0900900106\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1177/1536867X0900900106\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTapscott, D. (2016). \u003cem\u003eBlock chain revolution: How the technology behind Bitcoin is changing money, business, and the world\u003c/em\u003e. Penguin.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003evan der\u003c/em\u003e Ploeg, F., \u0026amp; Poelhekke, S. \u003cem\u003e(2010).\u003c/em\u003e The Pungent Smell of \u0026ldquo;Red Herrings\u0026rdquo;: Subsoil Assets, Rents, Volatility and the Resource Curse. \u003cem\u003eJournal of Environmental Economics and Management, 60(1), 44\u0026ndash;55.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jeem.2010.04.002\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1016/j.jeem.2010.04.002\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank (2023). Digital Governance Indicators.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYermack, D. (2017). Corporate Governance and Blockchains. \u003cem\u003eReview of Finance, 21(1), 7\u0026ndash;31.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/rof/rfw074\u003c/span\u003e\u003cspan address=\"https://doi.org/10.1093/rof/rfw074\" targettype=\"URL\" 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":"Blockchain, Financial Innovation, Regulatory Sandbox, Oil Diversification","lastPublishedDoi":"10.21203/rs.3.rs-8816026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8816026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study, therefore, aims to explore the major factors driving the adoption and implications of blockchain technology in the GCC economic block, highlighting the huge potential scope for the successful digitalization of the finance sector as well as the broader public sector.\u003c/p\u003e\n\u003cp\u003eUsing a panel data on the considered time range from 2015 to 2023, the analysis accounts for the extent to which factors such as the regularity aspects, dependency on oil, and the maturity level of Fintech ecosystems could influence the adoption of blockchain technology in the GCC countries, using the dynamic generalized method of moments approach, highlighting that higher-quality regularity is a major determinant driving the adoption of blockchain technology, while a greater dependence on oil negatively influences its adoption, offering the broader expectations for the GCC block emerging as a global innovation hub for Islamic finance, smart cities, offering valuable lessons for emerging economies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classifications: O38 ; \u003c/strong\u003eG28\u003cstrong\u003e ; \u003c/strong\u003eE42\u003cstrong\u003e ; \u003c/strong\u003eQ55\u003c/p\u003e","manuscriptTitle":"Entrepreneurial Ecosystems under Digital Disruption: Evidence from AI, Block-chain, and IoT Adoption in the GCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 19:15:42","doi":"10.21203/rs.3.rs-8816026/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":"4af6584e-62b0-4d00-9281-79fad77a9d19","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T19:15:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 19:15:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8816026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8816026","identity":"rs-8816026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}