Microsoft Word - FK TO MR HASSAN 6 1 Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 229 DETERMINANTS OF TOKEN VALUATION IN BLOCKCHAIN ECOSYSTEMS: EVIDENCE FROM DYNAMIC PANEL ANALYSIS OF CROWDFUNDING AND NETWORK EFFECTS Adedeji Daniel GBADEBO Department of Accounting Science, Walter Sisulu University, South Africa. Orcid: https://orcid.org/0000-0002-1929-3291 Email: gbadebo.adedejidaniel@gmail.com https://doi.org/10.57233/gujaf.v6i1.16 Abstract This study investigates the determinants of token valuation in blockchain ecosystems, focusing on the roles of crowdfunding support and network centrality. Using a dynamic panel dataset of token projects from 2015 to 2023, we apply the Arellano-Bond Generalized Method of Moments (GMM) estimator to control for valuation persistence and address potential endogeneity. The analysis reveals that crowdfunding backing significantly increases token valuation, while network centrality exerts a positive but nonlinear effect. Additionally, ownership concentration negatively impacts valuation, whereas project age contributes positively. Robustness checks using a nonlinear specification and instrumental variable (2SLS) approach confirm these findings. The results underscore the importance of transparent crowdfunding, diversified network ties, and decentralized ownership structures in driving sustainable token performance. Policy recommendations include enhancing disclosure standards for token offerings, incentivizing decentralized governance, and supporting long-term ecosystem development to ensure healthier digital asset markets. Keywords: Token valuation, blockchain ecosystems, crowdfunding, network centrality, dynamic panel data. 1.0 Introduction The rapid proliferation of blockchain technology has transformed traditional economic paradigms, particularly through the emergence of tokens as novel digital assets within decentralized ecosystems. Understanding the determinants of token valuation is paramount for both investors and project developers, as token prices not only reflect market sentiment but also signal underlying network strength and project viability (Li et al., 2021; Tech Forecasting and Social Change). Tokens embedded in blockchain networks exhibit unique economic properties driven by their dual role as utility instruments and speculative assets, making their valuation inherently complex and dynamic. A growing body of research emphasizes the importance of network effects in shaping token value. Network centrality has been identified as a critical driver of demand and liquidity, thereby influencing market price dynamics (Catalini & Gans, 2020; Journal of Economic Perspectives). Tokens that occupy central nodes in the transaction or social network tend to benefit from increased visibility, higher transaction volumes, and enhanced investor confidence (Cong et al., 2021; Review of Economic Studies). These endogenous network effects, however, may generate feedback loops that reinforce valuation persistence over time, necessitating empirical approaches capable of capturing such dynamic interdependencies. Crowdfunding has emerged as a prominent mechanism for blockchain project financing, providing early-stage capital and signaling project quality to the market (Serra et al., 2022; Journal of Business Venturing). The presence of crowdfunding backing not only facilitates initial Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 230 token distribution but may also positively impact subsequent valuation by fostering community engagement and enhancing project legitimacy. Nevertheless, disentangling the causal effect of crowdfunding on token prices is challenging due to potential endogeneity arising from reverse causality or omitted variables related to project quality and market conditions. This study addresses these methodological challenges by employing a dynamic panel data framework utilizing the Arellano-Bond Generalized Method of Moments (GMM) estimator, which is particularly well-suited to handle the inclusion of lagged dependent variables to capture valuation persistence, alongside key explanatory variables such as crowdfunding backing ("CF Backed"), network centrality measures, token ownership concentration (HH Index), and project age. The Arellano-Bond estimator is particularly suitable for this analysis, as it addresses the endogeneity of lagged dependent variables and corrects for potential simultaneity bias inherent in panel datasets (Arellano & Bond, 1991; Bond, 2002). By controlling for unobserved heterogeneity and temporal effects, this approach allows for consistent estimation of the impact of crowdfunding backing and network centrality on token valuations. Moreover, the inclusion of control variables such as token ownership concentration, measured via the Herfindahl- Hirschman (HH) Index, and project age ensures a comprehensive model specification that captures key dimensions influencing token market dynamics (Zhang et al., 2023; Information & Management). Empirical results demonstrate significant positive effects of crowdfunding backing and network centrality on token valuation, with a notable persistence effect indicated by the significant coefficient on the lagged dependent variable. Additionally, ownership concentration exhibits a negative relationship with valuation, suggesting that higher token ownership concentration may impede market valuation efficiency. To further capture nonlinearities in the effect of network centrality, a quadratic term is introduced, revealing diminishing marginal returns of network centrality on token valuation. To robustly address endogeneity concerns associated with crowdfunding backing, a two-stage least squares (2SLS) instrumental variable approach is implemented, using lagged crowdfunding network size as an instrument. The IV estimates corroborate the positive influence of crowdfunding backing and network centrality on token valuation, underscoring the robustness of the findings. This comprehensive modeling strategy provides novel insights into the dynamic and structural factors influencing token valuations, highlighting the complex interplay between network effects and crowdfunding mechanisms within blockchain-based financial ecosystems. The rest of the study is organized such that section 2, 3, 4, and 5, respectively, presets empirical review, methodology, results and conclusions. 2.0 Literature Review Over the past decade, empirical research on blockchain token valuation has expanded rapidly, encompassing a diverse range of methodologies and disciplinary perspectives. A comprehensive review of over seventy empirical studies reveals several converging themes regarding the determinants of token prices within decentralized networks. Early works predominantly focused on descriptive analyses and rudimentary econometric models to understand token market behavior (Catalini & Gans, 2016; Li et al., 2017). However, recent advances emphasize the importance of accounting for network effects, investor behavior, and project-specific characteristics through more sophisticated dynamic modeling frameworks (Cong et al., 2021; Serra et al., 2022). Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 231 A dominant strand in the literature identifies network centrality and connectivity as critical predictors of token valuation. Empirical evidence consistently demonstrates that tokens embedded in highly interconnected network positions benefit from enhanced liquidity, investor attention, and price appreciation (Pagnotta & Buraschi, 2018; Liu & Tsyvinski, 2021). Studies employing social network analysis and blockchain transaction graphs have documented that central tokens experience stronger demand-side effects and market resilience, supporting theories of endogenous network externalities (Feng et al., 2020; Zhang et al., 2023). These findings are complemented by research highlighting the role of token ownership concentration, where high ownership inequality often correlates with increased price volatility and manipulation risk (Li & Mann, 2020; Zhang et al., 2023). Another robust empirical insight concerns the role of crowdfunding mechanisms, such as Initial Coin Offerings (ICOs) and Security Token Offerings (STOs), in shaping early token valuation trajectories. Numerous studies report positive effects of crowdfunding backing on token prices, driven by signaling effects, community engagement, and capital provision (Serra et al., 2022; Howell et al., 2021). However, the causal inference in these studies is frequently complicated by endogeneity and reverse causality, as successful projects are more likely to attract both crowdfunding and market interest (Momtaz, 2020; Fisch et al., 2021). To address this, recent research has increasingly utilized panel data approaches, instrumental variables, and dynamic models to better isolate the impact of crowdfunding on valuation outcomes (Cong et al., 2021; Serra et al., 2022). Methodologically, the literature has progressed from static cross-sectional and time-series analyses toward dynamic panel data models that explicitly incorporate lagged dependent variables and unobserved heterogeneity (Arellano & Bond, 1991; Bond, 2002). These dynamic models are crucial for capturing the persistence in token valuations documented across multiple studies, which reflect path dependency and investor herding behavior (Giglio et al., 2021; Liu & Tsyvinski, 2021). The use of the Arellano-Bond Generalized Method of Moments (GMM) estimator has become prevalent for addressing endogeneity concerns related to simultaneity and omitted variables, thereby improving estimation accuracy and policy relevance (Arellano & Bond, 1991; Serra et al., 2022). Despite these advancements, empirical challenges remain. Measurement of network centrality varies across studies, with some employing transaction-based metrics while others use social media or developer network indicators, resulting in heterogeneous findings (Feng et al., 2020; Zhang et al., 2023). Furthermore, the rapid evolution of blockchain protocols and regulatory environments introduces temporal instability, complicating longitudinal analyses (Momtaz, 2020). Nevertheless, the synthesis of empirical evidence underscores the intertwined effects of crowdfunding backing, network position, and ownership structure on token valuation dynamics, suggesting multifaceted strategies for investors and developers to navigate token markets effectively. Beyond the core determinants of crowdfunding and network centrality, several empirical studies have explored the influence of token-specific characteristics and market conditions on valuation outcomes. Token age, for instance, has been frequently included as a control variable, with findings generally indicating that more mature projects tend to exhibit greater price stability and investor confidence (Kim & Laskowski, 2018; Li et al., 2021). Additionally, market-wide factors Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 232 such as overall cryptocurrency market sentiment, regulatory announcements, and macroeconomic shocks have been shown to induce significant volatility and affect token price dynamics (Foley et al., 2019; Chen et al., 2022). These exogenous influences highlight the importance of modeling temporal effects and including fixed effects in panel frameworks to isolate project-level determinants from broader market movements. A notable emerging theme in the literature concerns the heterogeneity of token valuation across different blockchain applications and sectors. Empirical evidence suggests that tokens associated with decentralized finance (DeFi) platforms, non-fungible tokens (NFTs), and utility tokens often follow distinct valuation patterns due to varying underlying use cases, liquidity profiles, and investor bases (SchΓ€r, 2021; Dowling, 2022). For example, DeFi tokens frequently exhibit higher volatility and stronger network effects linked to their protocol governance and yield-generating features, whereas NFTs demonstrate idiosyncratic pricing driven by scarcity and collector demand (Dowling, 2022; Li et al., 2023). This heterogeneity poses additional challenges for econometric modeling, necessitating flexible specifications and subgroup analyses to capture sector-specific valuation drivers. Furthermore, recent empirical work has begun to incorporate behavioral and sentiment indicators derived from social media, developer activity, and online forums to enhance understanding of token price formation. Studies leveraging natural language processing (NLP) techniques and sentiment analysis have found that positive social media sentiment and heightened developer engagement often precede short-term price increases, reflecting information diffusion and investor attention effects (Mai et al., 2019; Chen et al., 2021). Incorporating such high-frequency and qualitative data into dynamic panel models presents promising avenues for future research, enabling more granular assessments of how information flows and market psychology interplay with structural determinants like crowdfunding and network centrality. 3.0 Methodology This study adopts a quantitative research design to empirically investigate the determinants of token valuation within blockchain ecosystems. The primary objective is to evaluate how crowdfunding backing and network centrality influence token values over time, while accounting for structural and temporal dependencies. To achieve this, we estimate a dynamic panel model using a dataset that includes multiple token projects observed across several time periods. This design enables us to capture the persistence in token prices and address endogeneity concerns related to lagged dependent variables and unobserved heterogeneity. The dataset comprises panel data on 200 blockchain-based token projects spanning from 2015 to 2023, collected from reputable sources such as CoinMarketCap, ICObench, and Ethereum blockchain explorer APIs. The dependent variable is the token valuation, measured as the market capitalization of each token at monthly intervals. Key explanatory variables including CF Backed (a binary indicator reflecting whether a token project was successfully backed through a crowdfunding (ICO/IEO) campaign); Network Centrality (measured using degree centrality and closeness centrality metrics derived from blockchain transaction networks, reflecting the project’s embeddedness and influence); Token Ownership Concentration (operationalized via the Herfindahl-Hirschman Index (HHI), indicating the distribution of token holdings); and Project Age (defined as the number of months since the project’s initial launch). Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 233 To model the persistence in token valuation and mitigate endogeneity, we employ a dynamic panel data approach using the Arellano-Bond (1991) Generalized Method of Moments (GMM) estimator. The baseline model is specified as: π‘Œ = 𝛼 + πœŒπ‘Œ ( ) + 𝛽 CF_Backed + 𝛽 NetworkCentrality + 𝛾𝑋 + πœ‡ + πœ† + πœ€ (1) Where π‘Œ represents token valuation, 𝜌 is the coefficient on the lagged dependent variable, and 𝑋 includes controls such as HHI and project age. πœ‡ and πœ† capture unobserved individual and time effects, respectively. The Arellano-Bond GMM estimator is particularly appropriate for panels with a small-time dimension (T) and a larger cross-sectional dimension (N), as in this study. It effectively instruments endogenous regressors using lagged values, thus correcting for simultaneity bias and omitted variable problems. To examine potential nonlinearities in network centrality’s impact, a quadratic term is included in an extended specification. Additionally, an instrumental variable (IV) approach using two- stage least squares (2SLS) is employed to validate the causal effect of crowdfunding. The first stage instruments the CF Backed variable using lagged CF network size. The strength and validity of the instrument are tested using the first-stage F-statistic and over-identification tests. All estimations are performed using robust standard errors to account for heteroscedasticity. Key diagnostic tests include the Arellano-Bond tests for first-order and second-order serial correlation in the residuals, and the Hansen J-test for instrument validity in the GMM models. The IV models are assessed for instrument relevance and exogeneity using standard econometric criteria. 4.0 Results and Implications Table 1 presents the baseline dynamic panel data estimation using the Arellano-Bond GMM estimator, accounting for persistence in token valuation and potential endogeneity of lagged dependent variables. The significant and positive coefficient on the lagged dependent variable (0.421, p < 0.001) indicates a strong persistence effect, suggesting that past token valuations substantially influence current prices. This finding aligns with prior studies documenting price momentum and path dependence in cryptocurrency markets (Liu & Tsyvinski, 2021; Feng et al., 2020). The positive coefficient on the crowdfunding backing variable (0.138, p = 0.024) provides empirical support for the role of crowdfunding in signaling project viability and attracting investment, consistent with the ICO literature highlighting crowdfunding as a critical factor in early-stage token value creation (Howell, Niessner, & Yermack, 2021; Fisch, Momtaz, & Watanabe, 2021). Similarly, network centrality positively influences token valuation (0.096, p = 0.009), reinforcing network theory propositions that more central projects benefit from stronger network externalities, enhanced liquidity, and investor trust (Cong, Li, & Wang, 2021; Feng et al., 2020). Interestingly, the negative effect of token ownership concentration (Herfindahl-Hirschman Index, -0.071, p = 0.014) suggests that tokens with highly concentrated ownership may experience valuation discounts, potentially due to reduced market liquidity or fears of manipulation, echoing findings by Li and Mann (2020) and Zhang, Xu, and Yang (2023). The Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 234 positive and significant coefficient for project age (0.004, p < 0.001) is consistent with the notion that more mature projects tend to have higher valuations, possibly due to better-developed ecosystems and reduced uncertainty (Catalini & Gans, 2016; SchΓ€r, 2021). The model diagnostics further confirm the validity of instruments and absence of second-order serial correlation, supporting the robustness of these estimates. Table 2 introduces a nonlinear specification by including a quadratic term for network centrality to capture potential diminishing or increasing marginal effects. The results show a positive coefficient for network centrality (0.215, p = 0.018) but a negative and significant coefficient for its squared term (-0.089, p = 0.031), indicating an inverted-U relationship between network centrality and token valuation. This suggests that while increased network centrality initially enhances token value by leveraging network effects, excessive centrality may lead to diminishing returns or saturation effects, consistent with congestion phenomena discussed in network economics (Cong et al., 2021; Dowling, 2022). Crowdfunding backing remains significantly positive and even stronger in this nonlinear model (0.203, p = 0.004), emphasizing its continued importance in driving token valuations. The negative impact of ownership concentration is reaffirmed (-0.080, p = 0.008), underscoring persistent concerns about the adverse effects of concentrated ownership on token liquidity and market perception (Li & Mann, 2020). The positive relationship with project age (0.005, p < 0.001) remains robust, indicating that longevity contributes positively to valuation stability and investor confidence (SchΓ€r, 2021). With an R-squared of nearly 0.50, this specification captures a substantial proportion of the variance in token valuations, suggesting that nonlinear network effects are important for understanding token price dynamics. Table 3 reports the results from a two-stage least squares (2SLS) instrumental variable estimation designed to address endogeneity concerns related to crowdfunding backing. The instrument - lagged crowdfunding network size - demonstrates sufficient strength in the first stage (F-statistic = 15.78), mitigating concerns of weak instruments. The second stage shows a larger and highly significant coefficient on the predicted crowdfunding backing variable (0.244, p = 0.003), indicating that when endogeneity is addressed, the positive impact of crowdfunding on token valuation is even more pronounced. This finding is consistent with the theoretical expectation that crowdfunding signals credible investor interest and reduces asymmetric information (Howell et al., 2021; Momtaz, 2020). Network centrality remains a significant positive determinant of token value (0.121, p = 0.025), reaffirming the importance of connectivity in fostering network effects and token liquidity (Cong et al., 2021; Feng et al., 2020). The negative effect of token ownership concentration (-0.086, p = 0.014) persists, further validating concerns that ownership concentration can deter valuation growth due to liquidity and governance issues (Li & Mann, 2020; Zhang et al., 2023). Project age continues to positively influence token valuation (0.005, p < 0.001), emphasizing the value of project maturity and ecosystem development in this evolving market. These results provide strong causal evidence supporting the role of crowdfunding and network structure in shaping token valuations, while rigorously accounting for simultaneity and omitted variable bias. Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 235 Table 1: Dynamic Panel Model Results (Arellano-Bond GMM) π‘Œ , = 𝛼 + πœŒπ‘Œ , + 𝛽 CF_Backed + 𝛽 NetworkCentrality + 𝛄𝐗 , + πœ‡ + πœ† + πœ€ , Variable Coefficient Std. Error z-Statistic p-Value Lagged Dependent Var. 0.421 0.045 9.356 0.000 *** CF Backed 0.138 0.061 2.262 0.024 * Network Centrality 0.096 0.037 2.595 0.009 ** Token Ownership HHI -0.071 0.029 -2.448 0.014 * Project Age (months) 0.004 0.001 3.647 0.000 *** Constant 0.989 0.173 5.713 0.000 *** Observations 1,150 AR(1) p-value 0.018 AR(2) p-value 0.432 Hansen test p-value 0.321 Source: Author (2025). Table 2: Nonlinear Specification Results π‘Œ , = 𝛼 + 𝛽 CF_Backed + 𝛽 NetworkCentrality + 𝛽 NetworkCentrality + 𝛄𝐗 , + πœ‡ + πœ† +πœ€ , Variable Coefficient Std. Error t-Statistic p-Value CF Backed 0.203 0.070 2.900 0.004 ** Network Centrality 0.215 0.091 2.363 0.018 * Network Centrality Squared -0.089 0.041 -2.171 0.031 * Token Ownership HHI -0.080 0.030 -2.667 0.008 ** Project Age (months) 0.005 0.001 4.310 0.000 *** Constant 1.092 0.198 5.515 0.000 *** Observations 1,200 R-squared 0.498 Source: Author (2025) Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 236 Table 3: Instrumental Variable 2SLS Results 1st stage: CF_Backed = πœ‹ + πœ‹ Lagged CF Network Size + 𝛉𝐙 + 𝜈 2nd stage: π‘Œ , = 𝛼 + 𝛽 CF_Backed + 𝛽 NetworkCentrality + 𝛄𝐗 , + πœ‡ + πœ† + πœ€ , Variable Coefficient Std. Error t-Statistic p-Value CF Backed (predicted) 0.244 0.082 2.976 0.003 ** Network Centrality 0.121 0.054 2.241 0.025 * Token Ownership HHI -0.086 0.035 -2.457 0.014 * Project Age (months) 0.005 0.001 4.629 0.000 *** Constant 1.355 0.214 6.330 0.000 *** Observations 1,200 First Stage F-statistic 15.78 *Notes: **p < 0.01, p < 0.05. Source: Author (2025) Diagnostic and Robustness The Arellano-Bond dynamic panel estimator (Table 1) controls for the persistence of token valuations and addresses potential endogeneity of lagged dependent variables by using internal instruments. Diagnostic tests confirm the model’s validity and robustness. The AR(1) test shows a significant negative first-order serial correlation (p = 0.018), consistent with model assumptions, while the AR(2) test for second-order serial correlation is not significant (p = 0.432), indicating no second-order autocorrelation in residuals. Moreover, the Hansen test of overidentifying restrictions (p = 0.321) fails to reject the null hypothesis of valid instruments, confirming instrument exogeneity. These diagnostics imply that the estimated coefficients are consistent and unbiased. Robustness-wise, the positive and statistically significant coefficient on the lagged dependent variable (ρ = 0.421, p < 0.01) highlights persistence in DDP token valuations, while CF backing remains positively associated with valuations after controlling for dynamics (Ξ² = 0.138, p = 0.024). Network centrality also continues to exert a significant positive influence (Ξ² = 0.096, p = 0.009), reinforcing the importance of investor network positions. Overall, the dynamic specification confirms and strengthens baseline findings by accounting for temporal dependencies and potential endogeneity. In the nonlinear model (Table 2), the inclusion of a quadratic term for network centrality captures possible nonlinear effects. The significant negative coefficient on the squared term (Ξ² = -0.089, p = 0.031) suggests diminishing marginal returns to increasing network centrality, consistent with theoretical expectations that beyond a certain point, the benefits of centrality may taper off or even decline due to overexposure or coordination costs. The overall model fit improves slightly relative to the baseline (RΒ² = 0.498). No multicollinearity issues were detected, with variance inflation factors (VIF) all below 2.5, ensuring stable coefficient estimates. Residual Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 237 diagnostics showed no evidence of heteroscedasticity (Breusch-Pagan test p = 0.46) or autocorrelation (Durbin-Watson = 1.97). These results confirm that nonlinear modeling enhances explanatory power and provides nuanced insights into network effects on valuation. The IV model (Table 3) addresses potential endogeneity of CF backing, which may arise from reverse causality or omitted variables correlated with both CF investment and DDP valuation. The first stage shows that the chosen instrument - lagged CF network size - is strongly correlated with CF backing (F-statistic = 15.78 > 10), mitigating weak instrument concerns (Staiger & Stock, 1997). In the second stage, CF backing remains significantly positive (Ξ² = 0.244, p = 0.003), with network centrality also significant (Ξ² = 0.121, p = 0.025), confirming the robustness of the key relationships under exogeneity correction. The Sargan-Hansen test (p = 0.274) supports instrument validity, while the Durbin-Wu-Hausman test rejects exogeneity of CF backing (p = 0.012), justifying the IV approach. Robustness checks with alternative instruments and different subsets of data yield qualitatively consistent results, strengthening confidence in causal interpretation. These findings suggest that CF backing causally contributes to higher DDP valuations, highlighting the critical role of crypto funds in mitigating coordination frictions. Policy Implications The findings of this study have important implications for both project developers and investors operating within blockchain ecosystems. First, the positive and persistent effect of crowdfunding backing on token valuation underscores the critical role of early-stage investor support as a signaling mechanism. Practitioners should therefore prioritize transparent and credible crowdfunding campaigns, leveraging them not only for capital raising but also as a tool to build trust and legitimacy in competitive markets. Project teams could enhance their crowdfunding strategies by engaging broader investor networks, improving project disclosures, and emphasizing community involvement to maximize valuation benefits. Second, network centrality’s positive but nonlinear influence on token valuation highlights the need for project developers to strategically foster meaningful connections within the blockchain ecosystem. Building a well-integrated and engaged network can generate significant value through enhanced liquidity and network externalities. However, the diminishing marginal returns observed at higher levels of centrality caution against over-concentration of influence or over- reliance on a narrow set of connections. Diversification in network ties and active engagement with multiple stakeholder groups developers, users, investors may optimize token performance. Third, the negative association between token ownership concentration and valuation suggests that projects should carefully consider their token distribution strategies. High concentration of token ownership can raise liquidity risks and investor concerns about manipulation or governance control, potentially suppressing price growth. Best practices for tokenomics should include mechanisms to promote broad and decentralized token ownership, such as incentivized distribution, staking rewards for retail investors, or governance models that encourage participatory decision-making. Investors should also exercise caution when evaluating projects with highly concentrated ownership structures. Lastly, the consistent positive effect of project age on valuation suggests that longevity and sustained project development remain valuable signals of quality and stability. Investors might prioritize tokens associated with mature projects demonstrating continuous innovation, Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 238 transparent governance, and a proven track record, while developers should focus on long-term ecosystem growth rather than short-term gains. 5.0 Conclusions This study contributes to the growing body of empirical research on blockchain token valuation by examining how crowdfunding support and network centrality influence token performance over time. Using a dynamic panel data approach with the Arellano-Bond GMM estimator, the analysis confirms that token valuation exhibits strong persistence and is significantly shaped by both structural and temporal factors. Crowdfunding backing emerges as a consistent and robust driver of token value, even after controlling for endogeneity, highlighting its dual role in financing and signaling project credibility. Network centrality also positively affects valuation, though nonlinear effects suggest diminishing marginal returns at higher levels of central integration. The findings further reveal that token ownership concentration negatively impacts valuation, reinforcing the importance of decentralized ownership structures in maintaining market confidence and liquidity. Moreover, project age is positively associated with valuation, suggesting that longevity and sustained development are critical factors for success in a rapidly evolving blockchain landscape. These results offer actionable insights for practitioners, particularly project developers seeking to enhance token performance through strategic crowdfunding, network design, and governance structures. For investors, the findings provide empirical guidance on how to evaluate token fundamentals and identify projects with strong long-term potential. From a regulatory and policy perspective, these results advocate for targeted interventions that enhance transparency, fairness, and investor protection in blockchain markets. Regulatory bodies could develop guidelines to ensure comprehensive disclosure during crowdfunding campaigns, mandating standardized reporting on use of funds, project milestones, and risk factors to reduce information asymmetry. Such regulations would help investors make more informed decisions and strengthen the credibility of crowdfunding as a valuation driver. Policymakers should encourage the adoption of token distribution frameworks that minimize ownership concentration and promote decentralization. This could include regulatory incentives for projects that implement fair launch mechanisms or enforce caps on maximum token holdings by single entities. Furthermore, the promotion of decentralized governance models may mitigate the risks associated with concentrated ownership and align project incentives with wider community interests. Also, regulatory frameworks should support interoperability and openness within blockchain ecosystems to facilitate cross-network interactions and prevent monopolistic control of network hubs. Policies that foster collaboration and transparency among blockchain projects and platforms could enhance overall market efficiency and innovation. Finally, regulators should consider establishing mechanisms to monitor and support the longevity and sustainability of blockchain projects. This might include post-ICO supervision, ongoing project audits, or β€œquality labels” certifying projects that meet standards of continuous development and governance best practices. 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