Effect of Leadership Commitment (Normative Commitment, Continuance Commitment, and Affective Commitment) on ECE Policy Implementation in Somaliland Public Preschools

Effect of Leadership Commitment (Normative Commitment, Continuance Commitment, and Affective Commitment) on ECE Policy Implementation in Somaliland Public Preschools | 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 Effect of Leadership Commitment (Normative Commitment, Continuance Commitment, and Affective Commitment) on ECE Policy Implementation in Somaliland Public Preschools Jibril Abdulkadir Ali, Befekadu Zeleke, Bahar Adam, Dawit Negassa Golga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7001557/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study offers a quantitative analysis of the effect of leadership commitment on the implementation of Somaliland's 2020 Early Childhood Education (ECE) policy. While policy adoption is a critical first step for fragile states, the translation of policy into practice is contingent upon the will of those tasked with its execution. The successful implementation of Early Childhood Education (ECE) policy in fragile, post-conflict contexts is critically dependent on the dedication of its leaders. This study quantitatively investigates the hierarchical influence of leadership commitment on ECE policy implementation in Somaliland. Utilizing a census-based survey of 129 stakeholders from all 35 public preschools, this research deconstructs leadership commitment into Normative (duty-based), Affective (passion-based), and Continuance (cost-based) dimensions. A machine learning-informed model comparison, necessitated by violations of classical regression assumptions, identified Quantile Regression as the most robust analytical framework (MAPE = 14.19%). The final model (Pseudo R² = 0.731) revealed a definitive hierarchy of influence. Normative Commitment emerged as the paramount predictor (β = 2.22), its impact more than double that of Affective (β = 1.07) and Continuance (β = 1.08) commitment. This finding challenges leadership models that prioritize charismatic or emotional leadership, suggesting that in resource-scarce environments, a leader’s internalized sense of professional duty is the most critical driver of policy success. The study provides a granular, evidence-based roadmap for leadership selection and development, crucial for translating policy into tangible educational outcomes. Leadership Commitment Policy Implementation Early Childhood Education (ECE) Quantile Regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION The effective implementation of public policy remains a universal and formidable challenge, representing the crucial juncture where abstract governmental goals are translated into tangible societal outcomes (Sharma, 2023). Global experiences consistently demonstrate that even meticulously designed policies can falter during execution, often due to a complex interplay of institutional constraints, resource deficits, and stakeholder dynamics (Bush and Ng 2019; Casali, Aydin, and Comes 2022). This gap between policy rhetoric and on-the-ground reality underscores a fundamental need to understand the specific factors that determine implementation success or failure in diverse contexts (Pont 2020 ). In Sub-Saharan Africa, these implementation hurdles are particularly acute in the education sector. Despite the widespread adoption of Early Childhood Education (ECE) as a national priority, its effective rollout is frequently undermined by systemic weaknesses (Lee and Kim 2018). Research from the region consistently highlights challenges such as chronic underfunding, fragmented governance, and a lack of strategic attention, which severely constrain the potential of ECE policies to deliver on their promises (Haile and Mohammed 2020; Ninsiima et al. 2019 ). This points to a significant research gap in identifying the most effective levers for change within these complex and resource-scarce environments (Abboah-Offei et al. 2022 ). Focusing on the Horn of Africa, Somaliland provides a unique and critical case study. The introduction of its first national ECE policy in 2020 was a landmark achievement for a post-conflict state engaged in institution-building (Abdi-Soojeede 2024 ). This policy was launched into a fragile context defined by nascent public systems and significant resource constraints (Melesse and Obsiye 2022 ). While institutional capacity is a known factor, the role of leadership in driving such reforms is paramount yet poorly understood. This leaves a critical knowledge void regarding the specific leadership attributes required to navigate these challenges and ensure the policy's success (Issa Farah 2023 ). The central problem this study addresses is the significant and persistent disjuncture between the laudable aspirations of Somaliland's 2020 ECE policy and the stark realities of its implementation. The policy provides a strategic roadmap for a quality-driven ECE system, but its translation into sustainable improvements is severely impeded by deep-seated systemic issues (Asuquo-Ekpo 2024 ). These challenges are evidenced by a workforce in which 24.03% of educators have no formal training and 54.26% hold degrees in fields unrelated to education, creating a profound knowledge deficit that committed leaders must navigate. This situation creates a scenario where policy exists on paper but fails to materialize in practice, a phenomenon known as "decoupling" in institutional theory (Pauceanu et al. 2021 ; Yulius 2022 ). While the literature underscores the general importance of leadership, a critical gap remains in understanding the specific nature of the leadership commitment required to overcome these barriers. The core problem is a lack of empirical, granular understanding of which specific dimensions of leadership commitment—be it a sense of duty, emotional passion, or pragmatic calculation—most significantly impact ECE policy implementation in this unique context. To address this issue, the present study conceptualizes Leadership Commitment as a complex psychological construct, transcending simplistic assessments of leadership style. Utilizing foundational theories in organizational behavior, the framework dissects commitment into three distinct dimensions (Gollagari, Beyene, and Mishra 2021). The first dimension, Normative Commitment, pertains to a leader’s professional duty and moral obligation to implement policy, driven by the belief that it is the "right thing to do." This dimension is informed by Ethical Leadership Theory, which underscores the significance of moral character and fairness in cultivating trust and enhancing performance (Haque, Fernando, and Caputi 2020 ; Shareef and Atan 2019). The second dimension, Affective Commitment, involves the leader’s emotional attachment, personal passion, and genuine belief in the policy’s mission. This is closely associated with Transformational Leadership Theory, which highlights the role of inspiring leaders in articulating a compelling vision that fosters deep emotional engagement from followers (Al-Husseini, El Beltagi, and Moizer 2019; Chen and Yang 2024; Peng, Liao, and Sun 2019). The third dimension, Continuance Commitment, represents a more calculative attachment based on the perceived costs of failure, where leaders persist to safeguard their investments of time, reputation, and resources (Koomson 2022 ). This study addresses a significant theoretical gap by testing an integrated, multi-dimensional model of leadership commitment. Much of the extant literature treats leadership as a monolithic style (e.g., "transformational") or fails to differentiate between the various psychological drivers of commitment (Gómez-Leal et al. 2021). This research offers a more nuanced framework to examine the hierarchical and synergistic effects of these specific commitment dimensions on policy implementation, providing a more precise understanding of the "will" in the "will and the way" of policy success. This study is justified by two critical gaps in the existing literature on educational policy. The first is an empirical gap in the measurement of leadership. Research often relies on broad leadership styles as proxies for commitment, neglecting to deconstruct the concept into its distinct psychological components (Mwesigwa, Tusiime, and Ssekiziyivu 2020). There is a paucity of quantitative studies that empirically test the relative importance of normative, affective, and continuance commitment in predicting policy outcomes, particularly in fragile states (Wilson et al. 2021 ). This study addresses this void by operationalizing and hierarchizing these specific dimensions. The second gap pertains to analytical techniques. Policy implementation involves complex, non-linear, and interactive relationships, yet educational policy research has been slow to adopt advanced analytical methods suited for this complexity (Sukarmin and Sin 2022). There is a heavy reliance on traditional linear models that cannot adequately capture the nuanced dynamics of human commitment (Alzoraiki et al. 2023 ). This study addresses this methodological lag by employing a machine learning-informed model selection process to identify a robust analytical framework, thereby responding to the call for more sophisticated quantitative analysis in the field (Akour et al. 2021 ; Rathnayaka et al. 2024 ). The main objective of this study is to determine the effects of the different dimensions of leadership commitment on the implementation of ECE Policy in the public preschools of Somaliland. The specific objectives are: To measure the levels of Normative, Affective, and Continuance Commitment among ECE leaders in Somaliland. To employ a data-driven, machine learning-based approach to select the most accurate and robust regression model for analyzing the relationship between leadership commitment and policy implementation. To evaluate the dominant influence and determine the hierarchical impact of Normative, Affective, and Continuance Commitment on ECE policy implementation. To provide evidence-based policy recommendations for leadership selection and development to enhance ECE policy implementation in Somaliland and similar contexts. METHODOLOGY This study employed a quantitative, non-experimental, cross-sectional research design to examine the factors influencing Early Childhood Education (ECE) policy implementation. This methodology was deemed most suitable for an initial, large-scale investigation within the significantly under-researched context of Somaliland (Mourad, 2022; Ibrahim, Rizwan, Afzal, & Malik, 2022). The cross-sectional design facilitated the efficient collection of data from a diverse array of stakeholders at a single point in time, thereby offering a critical national overview of the variables under investigation (Creswell & Creswell, 2022). The study population comprised all 129 key stakeholders directly responsible for ECE implementation, including all teachers and directors in the 35 public preschools, as well as relevant ministry officials. A census approach was utilized, targeting the entire population to eliminate sampling error and enhance the internal validity of the findings (Babbie, 2022). The primary data collection instrument was a structured, self-constructed questionnaire. The independent variable, Leadership Commitment, was operationalized using the validated three-component model, with specific multi-item subscales for Normative, Affective, and Continuance Commitment. The dependent variable, ECE Policy Implementation, was measured as a composite score derived from five key dimensions: Objective Alignment, Implementation Plan, Staff Training and Resource Allocation, Stakeholder Involvement, and Monitoring and Evaluation. All constructs were assessed on a 5-point Likert scale. The instrument underwent a rigorous validation process, including content validation by a panel of experts and a pilot study to confirm construct validity and internal consistency, with all scales achieving a Cronbach’s alpha above the .85 threshold (Thien, Lim, & Adams, 2021; Kırkıç & Balcı, 2021). The data analysis strategy was a sophisticated, multi-stage process designed to ensure methodological rigor. Following the computation of descriptive statistics, a comprehensive series of regression diagnostics were conducted on a standard linear model. These tests, including the Variance Inflation Factor (VIF) for multicollinearity and White's test for homoscedasticity, revealed significant violations of Ordinary Least Squares (OLS) assumptions, most notably complex, non-linear heteroscedasticity (Field, 2022). This finding rendered standard linear regression inappropriate for this dataset. Consequently, a formal, data-driven model comparison was initiated, adhering to best practices in machine learning (von Krogh, Roberson, & Gruber, 2023). Ten distinct regression models were trained and evaluated against a suite of seven error metrics (e.g., MAE, RMSE, MAPE). This robust, comparative evaluation led to the selection of Quantile Regression as the empirically superior and most methodologically sound model for the final analysis (Jibril Abdikadir Ali et al., 2025; Masih, 2019). RESULTS This chapter presents the empirical findings derived from the quantitative analysis. The presentation begins with descriptive statistics, providing a demographic profile of the respondents and a summary of the core study variables. The chapter then proceeds to the inferential analysis, detailing the machine learning-based model selection process that identified the optimal regression model. Finally, it presents the results of the selected Quantile Regression model, which explains the hierarchical effect of the three dimensions of leadership commitment on ECE policy implementation. Descriptive Statistics Demographics The demographic profile reveals a significant gender imbalance in the Early Childhood Education (ECE) workforce, with females comprising 70% of respondents. This reflects global ECE trends due to societal perceptions and traditional gender roles. Most respondents are teachers rather than headteachers, suggesting insights primarily from teaching staff's experiences. The regional distribution shows concentration in urbanized regions, with Maroodijeex representing over half the sample. Peripheral regions like Sool and Togdheer are underrepresented, indicating concerns about equitable policy reach. Twenty-four percent of respondents lack formal ECE training, highlighting a critical professional development gap. More than half hold non-education degrees, suggesting varying pedagogical expertise, which could affect ECE delivery consistency. While the sector is staffed by higher-qualified individuals, the lack of specialized ECE training remains challenging. The experience distribution indicates a relatively new workforce, potentially limiting institutional memory. These factors emphasize the need for continuous professional development and equitable resource distribution for effective ECE policy implementation in Somaliland. Variable Description The descriptive statistics indicate that all three dimensions of Leadership Commitment—Normative, Continuance, and Affective—exhibited moderate and closely aligned mean scores, ranging from 3.29 to 3.30 at outline in Table 1 . This consistency suggests that leaders generally feel a balanced sense of duty, emotional attachment, and perceived cost of leaving their positions. The low variation in standard deviations, between 1.03 and 1.11, further shows that responses were relatively uniform across the leadership cohort. Table 1 Descriptive Statistics of Core Study Variables Variable Mean (M) Standard Deviation (SD) Normative Commitment 3.29 1.10 Continuance Commitment 3.30 1.03 Affective Commitment 3.30 1.11 ECE Policy Implementation 3.30 1.13 Note. Variables measured on a 5-point Likert scale. N = 129. The mean score for ECE Policy Implementation was also 3.30, reflecting a moderate level of policy execution across the sample. This alignment suggests that the degree of leadership commitment may be directly linked to the effectiveness of policy implementation. When leadership support is neither low nor exceptionally high, policy performance tends to reflect a similar middle ground. These results highlight a steady but not exceptional system, where improving leadership engagement could substantially raise policy success. The close relationship among these variables underscores the importance of enhancing leadership development efforts to achieve stronger, more consistent ECE policy outcomes. Pearson Correlation – Leadership Commitment and ECE Policy Implementation A Pearson product-moment correlation analysis was conducted for the study as summarized in Table 2 . The test was designed to evaluate the relationship between leadership commitment and ECE policy implementation. Leadership commitment was operationalized through three distinct dimensions. These were Normative Commitment (NC), Continuance Commitment (CC), and Affective Commitment (AF). The results of this correlational analysis are presented in Table 10. These findings provide a quantitative basis for understanding the associations between these variables. Table 2 Correlation between Leadership Commitment and Policy Implementation Study Variables NC CC AF ECE PI Normative Commitment (NC) 1.000 Continuance Commitment (CC) 0.846 1.000 Affective Commitment (AF) 0.653 0.718 1.000 ECE Policy Implementation (ECE PI) 0.685 0.682 0.741 1.000 The analysis revealed strong positive correlations between leadership commitment and ECE policy implementation. Affective Commitment showed the strongest relationship (r = .741), followed by Normative Commitment (r = .685) and Continuance Commitment (r = .682). All relationships were statistically significant. Strong positive correlations existed among the three commitment dimensions, with Normative-Continuance showing the strongest correlation (r = .846), followed by Continuance-Affective (r = .718) and Normative-Affective (r = .653). Leadership commitment emerged as a powerful predictor of successful ECE policy implementation, supported by robust correlations across all commitment types. Higher leadership commitment corresponded to more effective policy execution, confirming leaders' critical role in policy implementation. Affective Commitment, based on emotional attachment and shared values, proved most influential in predicting implementation success, surpassing normative and continuance commitment. The strong inter-correlations among commitment types indicate they form a unified construct, suggesting that strengthening one aspect may positively affect others. These findings validate transformational leadership models and emphasize emotional engagement. For practitioners, fostering affective commitment through positive organizational culture and clear communication of policy initiatives should be prioritized. The evidence confirms leadership commitment directly impacts ECE policy implementation. The major finding is that while all forms of commitment matter, affective commitment is the most critical driver. The practical application of this result is to focus leadership development on building genuine emotional and value-based connections to the organization’s goals. Ultimately, leaders who are emotionally invested are best positioned to navigate the complexities of policy implementation and ensure its success. Model Comparison and Selection through Machine Learning A formal model selection process was methodologically crucial. The preceding diagnostic analysis revealed violations of classical regression assumptions. The data exhibited non-linear heteroscedasticity and influential outliers. Relying on a single, standard model like multiple linear regression would have ignored these data imperfections, leading to biased coefficients and unreliable conclusions. Therefore, a comparative evaluation was essential to identify a model robust enough to handle the data's characteristics. This ensures the final analysis is valid and credible. The selection of diverse error metrics was deliberate. No single metric can fully capture model performance. Metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) assess prediction errors in the original data units. RMSE, by squaring errors, is particularly sensitive to large prediction mistakes. Mean Absolute Percentage Error (MAPE) provides a scale-independent measure of relative accuracy, making model performance easily interpretable. This combination ensures the chosen model is accurate and robust against significant deviations. The model selection process proved its value. The empirical evidence in Table 17 directly addresses the concerns from initial diagnostics. Quantile regression's superior performance, shown by leading scores across metrics like MAPE (18.06) and MAE (17.05), stems from its inherent robustness to heteroscedasticity and outliers present in this dataset. This convergence of diagnostic need and empirical performance justifies its selection. The process filtered out weaker models, ensuring the final analysis rests on sound methodology. Model Comparison – Leadership Commitment on ECE Policy Implementation A comparative analysis was conducted to select the optimal regression model across ten analytical approaches as described in Table 3 . The goal was identifying the most accurate model to examine leadership commitment's effect on ECE policy implementation. Seven error metrics provided a comprehensive performance assessment. The results are detailed in Table 19. The empirical evidence showed quantile regression as the superior model, achieving the best scores across all metrics. It recorded the lowest MAPE of 14.19, lowest MAE at 15.38, and best RMSPE of 21.59 among all contenders. Table 3 Model Comparison and Selection – Leadership Commitment Regression Models MAPE RMSPE RMSLE RRSE RMSE MAE MSPE Quantile 14.19 21.59 0.20 58.61 23.14 15.38 535.56 Support Vector 16.11 25.14 0.21 58.31 23.02 16.20 530.09 Robust 15.92 22.88 0.21 58.70 23.18 16.34 537.27 Multiple Linear 17.38 24.45 0.22 59.20 23.38 17.19 546.43 Ridge 17.41 24.49 0.22 59.23 23.39 17.21 546.98 Elastic Net 17.45 24.55 0.22 59.24 23.39 17.23 547.15 LASSO 17.49 24.60 0.22 59.25 23.40 17.25 547.33 Polynomial 16.53 23.17 0.21 59.27 23.40 17.44 547.68 Partial Least Square 18.31 25.59 0.23 60.52 23.90 17.94 571.00 Principal Component 45.92 81.61 0.50 196.23 49.14 37.60 2414.32 The Principal Component model proved entirely unsuitable for the analysis, producing high error values with a MAPE of 45.92 and RMSE of 49.14, indicating a clear failure. Support Vector and Robust regression performed well, ranking second and third. The Robust regression achieved a MAPE of 15.92, while Support Vector had a MAPE of 16.11. Though strong, their error metrics were higher than Quantile regression. Multiple Linear Regression, Ridge, and LASSO models showed similar performance, with MAPE values around 17.4 and MAE scores near 17.2. For this dataset, regularization offered no significant advantage over standard linear approaches. The analysis revealed Quantile regression's clear superiority across all metrics. This establishes that modeling leadership commitment requires an approach beyond mean-based regression. This selection is supported by earlier diagnostic checks that revealed non-linear heteroscedasticity and influential outliers. Quantile regression's focus on modeling different conditional quantiles makes it less sensitive to these issues. The Principal Component model's failure provides insight - its dimensionality reduction discarded crucial information about leadership commitment variables, resulting in an oversimplified model that couldn't capture true relationships. The visual evidence in Fig. 7 provides compelling confirmation. The bar chart translates numerical data into an intuitive graphical narrative. The consistently low bars for the Quantile model create a clear signature of success, contrasting with the high error bars of the Principal Component model. This visual disparity makes the performance gap immediately apparent. The model selection process was critical and successful. Evidence from seven error metrics points to an unequivocal choice: Quantile regression is the most accurate and reliable model for this analysis. Its selection ensures conclusions about leadership commitment's influence on ECE policy implementation will be based on robust statistical foundations. Quantile Regression for Leadership Commitment on ECE Policy Implementation This section provides a detailed analysis of the Quantile Regression results. The model was specified to determine the influence of leadership commitment on ECE policy implementation. Table 4 presents the statistical findings for the three predictor variables. It includes the regression coefficients, p-values, and 95% confidence intervals. The predictors are Normative, Continuance, and Affective Commitment. The analysis is based on a sample of 129 observations. The numerical results reveal a clear and significant hierarchy of influence. Normative Commitment emerged as the most powerful predictor. It recorded a large positive coefficient (b = 2.22, p < .001). Continuance Commitment and Affective Commitment also had strong, positive, and significant effects. Their coefficients were nearly identical at b = 1.08 (p < .001) and b = 1.07 (p < .001), respectively. The confidence intervals for all three predictors were positive, confirming the reliability of their effects. Table 4 Quantile Regression: Leadership Commitment vs ECE Policy Implementation ECE Policy Implementation Coef. p-value [95% Conf Interval] Normative Commitment 2.2196 0.000 1.29195 2.80339 Continuance Commitment 1.0756 0.000 0.69098 2.05312 Affective Commitment 1.0733 0.000 0.57679 1.62090 Constant 6.6481 0.000 0.56518 16.44179 Pseudo R 2 0.7307634 Number of Obs 129 Mean dependent var 122.217 SD dependent var 37.785 The overall model statistics provide essential context. The constant term was 6.65 and highly statistically significant (p < .001), representing the baseline level of ECE policy implementation. The model's Pseudo R-squared value was .731, indicating the proportion of variance explained. The dependent variable had a mean of 122.22 and standard deviation of 37.79. The findings establish that leadership commitment is a profoundly influential driver of ECE policy implementation, supported by highly significant p-values for all commitment types. The results show that committed leadership directly associates with effective policy execution, highlighting leaders' role in translating policy to practice. A critical finding is that Normative Commitment is the paramount driver, with its coefficient more than double that of other commitment types. This indicates a leader's sense of duty and professional responsibility is the most powerful motivator for policy success, more impactful than emotion or personal cost. Continuance and Affective Commitment are also significant, showing nearly equal strong positive impact. They represent practical and emotional dimensions of leadership dedication, though secondary to normative obligation. The model's constant term suggests that even with zero leadership commitment, a baseline implementation level exists, reflecting established systems and procedures. The Pseudo R-squared value of .731 is noteworthy, indicating the three leadership commitment dimensions explain 73% of implementation variability. This high explanatory power confirms leadership commitment as the dominant force shaping implementation success. The key finding reveals normative commitment as most influential, suggesting efforts to enhance ECE policy implementation should focus on cultivating professional duty among leaders. While fostering emotional attachment is beneficial, instilling a deep-seated commitment to professional obligation appears to be the most effective strategy for driving change. Figure 8 presents the feature importance plot from the Quantile Regression model, offering a visual representation of each leadership commitment variable's influence. The plot reveals a clear ranking among the three predictors, with Normative Commitment shown as the most important feature by a substantial margin. The relative lengths of the bars in Fig. 16 directly mirror the coefficients in Table 20, reinforcing the study's primary conclusion. It graphically demonstrates that a leader's sense of professional duty is the most critical factor driving policy implementation. The plot reveals a distinct two-tiered structure of importance. Normative Commitment occupies the top tier alone, highlighting its exceptional impact. In the lower tier, Continuance and Affective Commitment are grouped together with nearly identical lengths, showing their similar, secondary influence. This suggests that practical and emotional commitments function as supportive elements rather than primary drivers. The feature importance plot distills the regression analysis into an actionable visual statement, demonstrating the dominance of normative commitment. To improve ECE policy implementation, resources should focus on cultivating professional obligation and ethical responsibility in leaders, while other forms of commitment, though valuable, have less power to effect change. DISCUSSION The central finding of this study—the unequivocal dominance of Normative Commitment in predicting ECE policy implementation—offers a profound and contextually specific insight into the nature of effective leadership in fragile states. This result challenges a significant body of leadership literature that often prioritizes charismatic, visionary, or emotionally resonant (affective) leadership styles (Gómez-Leal et al., 2021b). While our findings confirm that affective commitment is indeed a significant positive factor, its impact is overshadowed by the power of a leader’s internalized sense of professional duty and ethical obligation. This preeminence of normative commitment aligns strongly with theories of ethical and servant leadership, which posit that a leader's moral compass and commitment to service are foundational to building trust and achieving organizational goals (Haque, Fernando, & Caputi, 2021a; Mcquade, Harrison, & Tarbert, 2020). In a post-conflict environment like Somaliland, where formal systems are weak and resources are scarce, a leader's passion may wane, and pragmatic calculations may lead to despair. However, a resilient sense of professional duty appears to be the most stable and powerful anchor for sustained action. The strong, secondary roles of Affective and Continuance Commitment suggest they function as a crucial support system for the primary driver of normative duty. A leader’s passion (affective) provides the energy for innovation, while their pragmatic desire to protect past investments (continuance) ensures persistence (Peng, Liao, & Sun, 2019). Without the guiding principle of normative commitment, these other forms of dedication may lack direction or resilience. This suggests a more complex, composite model of leadership psychology is at play (Semedo, Coelho, & Ribeiro, 2019). This finding has significant implications for how leadership is understood in developing contexts. It suggests that the most effective leaders are not necessarily the most charismatic, but the most ethically grounded and professionally responsible. This provides a critical refinement to the application of Western leadership theories in non-Western, fragile contexts, supporting scholars who call for more contextually sensitive models (Melesse & Obsiye, 2022 ; Tarekegne & Megersa, 2019). The high explanatory power of the model (R² = 0.731) is itself a major finding. It confirms that leadership commitment is not just one factor among many but is the dominant force shaping policy outcomes in this system. This underscores the immense importance of human agency in environments with low institutionalization (Yizengaw & Tessega, 2020). The success or failure of the ECE policy appears to rest squarely on the shoulders of its leaders. Methodologically, this study substantiates the application of advanced quantitative techniques to elucidate nuanced relationships. The selection of Quantile Regression, predicated on its empirical efficacy, facilitated a more robust estimation of effects, thereby circumventing the biases inherent in standard linear models when their assumptions are violated (Almulla & Al-Rahmi, 2023). This rigorous approach engenders high confidence in the final hierarchical ranking of the commitment dimensions. The descriptive data, which reveal significant training gaps, provide the context within which this leadership must function. The fact that committed leaders are achieving any level of implementation in a system characterized by such profound human capital deficits attests to the power of their resolve (Rashid, 2019b). It underscores that leadership transcends the mere management of a functional system, extending to the compensation for a dysfunctional one. The strong inter-correlations among the three commitment types (Table 10 in the dissertation) suggest that they are not entirely distinct constructs but rather facets of a singular, resilient psychological state. A leader with a strong sense of duty is also more likely to be emotionally invested and pragmatically persistent (Hameduddin & Engbers, 2021). This supports a holistic approach to leadership development, wherein fostering one aspect of commitment may yield positive spillover effects on the others (Fonsén & Ukkonen-Mikkola, 2019). In conclusion, the discussion of these results elucidates a clear and compelling principle: within the challenging context of Somaliland's ECE sector, policy success is predominantly driven by the normative commitment of its leaders. This duty-bound, ethically-grounded leadership constitutes the most critical asset for translating policy rhetoric into classroom reality. Policy Implications Based on these findings, three targeted policy implications are proposed: Revise Leadership Selection Criteria: The Ministry of Education and Civil Service Commission should reform leadership position criteria, shifting focus from qualifications to demonstrated normative commitment. This can be assessed through competency-based interviews with ethical scenarios and professional conduct review, prioritizing candidates with strong public service ethos. Reorient Leadership Development: ECE leadership training must be redesigned to cultivate normative commitment. Programs should use local case studies to explore ethical decision-making and professional responsibility, building the most impactful leadership attribute identified. Establish "Code of Professional Conduct": The Ministry should implement a formal code of conduct articulating ethical and professional obligations of ECE leaders, providing performance benchmarks and accountability. This would transform individual duty into a systemic expectation. Conclusion This study sought to provide a quantitative understanding of how leadership commitment influences ECE policy implementation in Somaliland. By deconstructing leadership into psychological components and employing a robust analytical approach, the research has yielded a clear conclusion. The success of educational policy in this fragile context is systematically driven by the normative commitment of its leaders. The finding that a leader's sense of professional duty and ethical obligation is a more potent driver than emotional passion or pragmatic calculation is significant. It refines our understanding of effective leadership in post-conflict settings and challenges leadership models that prioritize charisma over character. The study's methodological rigor, using a machine learning-informed model selection process, provides confidence in this central finding. This approach allowed the analysis to transcend traditional models and produce accurate results, offering a template for future quantitative policy research in complex environments. This research provides an evidence-based roadmap for the Ministry of Education and partners. To bridge the gap between policy and practice, the most effective strategy is to invest in cultivating leaders defined by a sense of public service. Fostering this duty-bound commitment is critical for building a sustainable ECE system for Somaliland's children. Study Limitations This study, while methodologically robust, is subject to two primary limitations. First, the cross-sectional design, while appropriate for an initial investigation, captures only a single point in time and cannot establish definitive causality. The relationship between commitment and implementation is likely reciprocal, a dynamic that this design cannot fully model. Second, the reliance on survey data, even with validated scales, introduces the possibility of self-report and social desirability biases, which may influence respondents' answers. Study Recommendations Specific Recommendations for Policy and Practice Revise Leadership Selection Criteria: Based on findings that Normative Commitment best predicts policy success, the Ministry of Education and Civil Service Commission should reform ECE leadership recruitment. New criteria should prioritize candidates demonstrating professional duty and public service ethics through competency-based interviews with ethical scenarios and professional conduct review. Reorient Leadership Development: ECE leadership programs should be redesigned to build Normative Commitment by incorporating applied ethics, moral imperatives, and professional responsibilities. Using Somaliland-specific case studies would help leaders connect decisions to ethical obligations, strengthening this key leadership attribute. Establish Professional Conduct Code: The Ministry should develop and implement a "Code of Professional Conduct for ECE Leaders" that articulates ethical standards and responsibilities. This code would provide performance benchmarks, ensure accountability, and transform individual duty into an enforceable professional standard. Recommendations for Future Research Conduct Longitudinal Study on Commitment: This design provides a snapshot but cannot show causality. Future research should track leadership commitment and implementation outcomes over years, analyzing commitment evolution and normative commitment's impact. Qualitative Study of "Positive Deviant" Leaders: Data shows normative commitment's primacy. Research should examine leaders with high levels of this attribute through interviews to uncover experiences shaping their sense of duty. Investigate Relationship with Institutional Capacity: Future research should model the relationship between leadership commitment and institutional capacity, testing how committed leaders build capacity and capacity sustains commitment. Comparative Analysis: To enhance generalizability, examine leadership commitment dimensions in another Horn of Africa state to distinguish Somaliland-specific findings from universal principles of leadership in institution-building. Declarations Ethics, Consent to Participate, and Consent to Publish Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. This study used secondary publicly available data, and no direct involvement of human participants was required. Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution J.A.A. conceptualized the study, developed the methodology, conducted the formal analysis, and wrote the main manuscript text. B.Z., B.A., and D.N.G. provided supervision, validated the findings, and critically reviewed the manuscript. All authors reviewed and approved the final manuscript. Data Availability The data used in this study, while not publicly available, can be obtained and will be available from the corresponding author upon reasonable request. 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A Literature Review of School Leadership Policy Reforms. Eur J Educ. 2020;55(2):154–68. 10.1111/ejed.12398 . Rathnayaka M, Karunasinghe D, Gunasekara C, Wijesundara K, Lokuge W, and David W. Law. Machine Learning Approaches to Predict Compressive Strength of Fly Ash-Based Geopolymer Concrete: A Comprehensive Review. Constr Build Mater. 2024;419:135519. 10.1016/j.conbuildmat.2024.135519 . Shareef RA, and Tarik Atan. The Influence of Ethical Leadership on Academic Employees’ Organizatio Citizenship Behavior and Turnover Intention. Manag Decis. 2019;57(3):583–605. 10.1108/md-08-2017-0721 . Sukarmin S, and Ishak Sin, INFLUENCE OF PRINCIPAL INSTRUCTIONAL LEADERSHIP BEHAVIOUR ON THE ORGAN COMMITMENT OF JUNIOR HIGH SCHOOL TEACHERS IN SURAKARTA.. Malaysian J Learn Instruction. 2022;19. 10.32890/mjli2022.19.2.3 . Wilson JC, Marisa P, Kandege AJR, Edjoukou, Mussie TT. Unpacking Smart Education’s Soft Smartness Variables: Leadership and H Resources Capacities as Key Participatory Actors. Educ Inform Technol. 2021;26(5):6267–98. 10.1007/s10639-021-10599-9 . Yulius Y. The Effect of Islamic Visionary Leadership on Organisational Commitmen and Its Impact on Employee Performance. HTS Teologiese Stud / Theological Stud. 2022;78(1). 10.4102/hts.v78i1.7722 . Additional Declarations No competing interests reported. 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legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7001557/v1/596b22c27ff7e046aee3fe0c.png"},{"id":88209081,"identity":"1ea527ef-2f8b-4104-a503-c72c6152ef3c","added_by":"auto","created_at":"2025-08-04 04:14:11","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135447,"visible":true,"origin":"","legend":"\u003cp\u003eModel Selection Metrics: Leadership Commitment\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7001557/v1/97838c957830abc119f6dc90.jpeg"},{"id":88209091,"identity":"18100433-9b73-4a3a-b8db-f5251fd7b715","added_by":"auto","created_at":"2025-08-04 04:14:11","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":65581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFeature Importance – Leadership Commitment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7001557/v1/7481d8c3ebb46046353adc20.jpeg"},{"id":88209600,"identity":"a21b4432-a861-46c5-bdf9-af9fad320847","added_by":"auto","created_at":"2025-08-04 04:38:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1273013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7001557/v1/d31e1c21-64e3-4c79-b6f7-da32ab847446.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of Leadership Commitment (Normative Commitment, Continuance Commitment, and Affective Commitment) on ECE Policy Implementation in Somaliland Public Preschools","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe effective implementation of public policy remains a universal and formidable challenge, representing the crucial juncture where abstract governmental goals are translated into tangible societal outcomes (Sharma, 2023). Global experiences consistently demonstrate that even meticulously designed policies can falter during execution, often due to a complex interplay of institutional constraints, resource deficits, and stakeholder dynamics (Bush and Ng 2019; Casali, Aydin, and Comes 2022). This gap between policy rhetoric and on-the-ground reality underscores a fundamental need to understand the specific factors that determine implementation success or failure in diverse contexts (Pont \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Sub-Saharan Africa, these implementation hurdles are particularly acute in the education sector. Despite the widespread adoption of Early Childhood Education (ECE) as a national priority, its effective rollout is frequently undermined by systemic weaknesses (Lee and Kim 2018). Research from the region consistently highlights challenges such as chronic underfunding, fragmented governance, and a lack of strategic attention, which severely constrain the potential of ECE policies to deliver on their promises (Haile and Mohammed 2020; Ninsiima et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This points to a significant research gap in identifying the most effective levers for change within these complex and resource-scarce environments (Abboah-Offei et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Focusing on the Horn of Africa, Somaliland provides a unique and critical case study.\u003c/p\u003e\u003cp\u003eThe introduction of its first national ECE policy in 2020 was a landmark achievement for a post-conflict state engaged in institution-building (Abdi-Soojeede \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This policy was launched into a fragile context defined by nascent public systems and significant resource constraints (Melesse and Obsiye \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While institutional capacity is a known factor, the role of leadership in driving such reforms is paramount yet poorly understood. This leaves a critical knowledge void regarding the specific leadership attributes required to navigate these challenges and ensure the policy's success (Issa Farah \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The central problem this study addresses is the significant and persistent disjuncture between the laudable aspirations of Somaliland's 2020 ECE policy and the stark realities of its implementation. The policy provides a strategic roadmap for a quality-driven ECE system, but its translation into sustainable improvements is severely impeded by deep-seated systemic issues (Asuquo-Ekpo \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These challenges are evidenced by a workforce in which 24.03% of educators have no formal training and 54.26% hold degrees in fields unrelated to education, creating a profound knowledge deficit that committed leaders must navigate. This situation creates a scenario where policy exists on paper but fails to materialize in practice, a phenomenon known as \"decoupling\" in institutional theory (Pauceanu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yulius \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While the literature underscores the general importance of leadership, a critical gap remains in understanding the specific nature of the leadership commitment required to overcome these barriers. The core problem is a lack of empirical, granular understanding of which specific dimensions of leadership commitment—be it a sense of duty, emotional passion, or pragmatic calculation—most significantly impact ECE policy implementation in this unique context.\u003c/p\u003e\u003cp\u003eTo address this issue, the present study conceptualizes Leadership Commitment as a complex psychological construct, transcending simplistic assessments of leadership style. Utilizing foundational theories in organizational behavior, the framework dissects commitment into three distinct dimensions (Gollagari, Beyene, and Mishra 2021). The first dimension, Normative Commitment, pertains to a leader’s professional duty and moral obligation to implement policy, driven by the belief that it is the \"right thing to do.\" This dimension is informed by Ethical Leadership Theory, which underscores the significance of moral character and fairness in cultivating trust and enhancing performance (Haque, Fernando, and Caputi \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shareef and Atan 2019). The second dimension, Affective Commitment, involves the leader’s emotional attachment, personal passion, and genuine belief in the policy’s mission. This is closely associated with Transformational Leadership Theory, which highlights the role of inspiring leaders in articulating a compelling vision that fosters deep emotional engagement from followers (Al-Husseini, El Beltagi, and Moizer 2019; Chen and Yang 2024; Peng, Liao, and Sun 2019). The third dimension, Continuance Commitment, represents a more calculative attachment based on the perceived costs of failure, where leaders persist to safeguard their investments of time, reputation, and resources (Koomson \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study addresses a significant theoretical gap by testing an integrated, multi-dimensional model of leadership commitment. Much of the extant literature treats leadership as a monolithic style (e.g., \"transformational\") or fails to differentiate between the various psychological drivers of commitment (Gómez-Leal et al. 2021). This research offers a more nuanced framework to examine the hierarchical and synergistic effects of these specific commitment dimensions on policy implementation, providing a more precise understanding of the \"will\" in the \"will and the way\" of policy success. This study is justified by two critical gaps in the existing literature on educational policy. The first is an empirical gap in the measurement of leadership. Research often relies on broad leadership styles as proxies for commitment, neglecting to deconstruct the concept into its distinct psychological components (Mwesigwa, Tusiime, and Ssekiziyivu 2020). There is a paucity of quantitative studies that empirically test the relative importance of normative, affective, and continuance commitment in predicting policy outcomes, particularly in fragile states (Wilson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study addresses this void by operationalizing and hierarchizing these specific dimensions. The second gap pertains to analytical techniques. Policy implementation involves complex, non-linear, and interactive relationships, yet educational policy research has been slow to adopt advanced analytical methods suited for this complexity (Sukarmin and Sin 2022). There is a heavy reliance on traditional linear models that cannot adequately capture the nuanced dynamics of human commitment (Alzoraiki et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study addresses this methodological lag by employing a machine learning-informed model selection process to identify a robust analytical framework, thereby responding to the call for more sophisticated quantitative analysis in the field (Akour et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rathnayaka et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe main objective of this study is to determine the effects of the different dimensions of leadership commitment on the implementation of ECE Policy in the public preschools of Somaliland.\u003c/p\u003e\u003cp\u003eThe specific objectives are:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTo measure the levels of Normative, Affective, and Continuance Commitment among ECE leaders in Somaliland.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo employ a data-driven, machine learning-based approach to select the most accurate and robust regression model for analyzing the relationship between leadership commitment and policy implementation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo evaluate the dominant influence and determine the hierarchical impact of Normative, Affective, and Continuance Commitment on ECE policy implementation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo provide evidence-based policy recommendations for leadership selection and development to enhance ECE policy implementation in Somaliland and similar contexts.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eThis study employed a quantitative, non-experimental, cross-sectional research design to examine the factors influencing Early Childhood Education (ECE) policy implementation. This methodology was deemed most suitable for an initial, large-scale investigation within the significantly under-researched context of Somaliland (Mourad, 2022; Ibrahim, Rizwan, Afzal, \u0026amp; Malik, 2022). The cross-sectional design facilitated the efficient collection of data from a diverse array of stakeholders at a single point in time, thereby offering a critical national overview of the variables under investigation (Creswell \u0026amp; Creswell, 2022). The study population comprised all 129 key stakeholders directly responsible for ECE implementation, including all teachers and directors in the 35 public preschools, as well as relevant ministry officials. A census approach was utilized, targeting the entire population to eliminate sampling error and enhance the internal validity of the findings (Babbie, 2022). The primary data collection instrument was a structured, self-constructed questionnaire. The independent variable, Leadership Commitment, was operationalized using the validated three-component model, with specific multi-item subscales for Normative, Affective, and Continuance Commitment.\u003c/p\u003e\u003cp\u003eThe dependent variable, ECE Policy Implementation, was measured as a composite score derived from five key dimensions: Objective Alignment, Implementation Plan, Staff Training and Resource Allocation, Stakeholder Involvement, and Monitoring and Evaluation. All constructs were assessed on a 5-point Likert scale. The instrument underwent a rigorous validation process, including content validation by a panel of experts and a pilot study to confirm construct validity and internal consistency, with all scales achieving a Cronbach’s alpha above the .85 threshold (Thien, Lim, \u0026amp; Adams, 2021; Kırkıç \u0026amp; Balcı, 2021). The data analysis strategy was a sophisticated, multi-stage process designed to ensure methodological rigor. Following the computation of descriptive statistics, a comprehensive series of regression diagnostics were conducted on a standard linear model. These tests, including the Variance Inflation Factor (VIF) for multicollinearity and White's test for homoscedasticity, revealed significant violations of Ordinary Least Squares (OLS) assumptions, most notably complex, non-linear heteroscedasticity (Field, 2022). This finding rendered standard linear regression inappropriate for this dataset. Consequently, a formal, data-driven model comparison was initiated, adhering to best practices in machine learning (von Krogh, Roberson, \u0026amp; Gruber, 2023). Ten distinct regression models were trained and evaluated against a suite of seven error metrics (e.g., MAE, RMSE, MAPE). This robust, comparative evaluation led to the selection of Quantile Regression as the empirically superior and most methodologically sound model for the final analysis (Jibril Abdikadir Ali et al., 2025; Masih, 2019).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis chapter presents the empirical findings derived from the quantitative analysis. The presentation begins with descriptive statistics, providing a demographic profile of the respondents and a summary of the core study variables. The chapter then proceeds to the inferential analysis, detailing the machine learning-based model selection process that identified the optimal regression model. Finally, it presents the results of the selected Quantile Regression model, which explains the hierarchical effect of the three dimensions of leadership commitment on ECE policy implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic profile reveals a significant gender imbalance in the Early Childhood Education (ECE) workforce, with females comprising 70% of respondents. This reflects global ECE trends due to societal perceptions and traditional gender roles. Most respondents are teachers rather than headteachers, suggesting insights primarily from teaching staff\u0026apos;s experiences. The regional distribution shows concentration in urbanized regions, with Maroodijeex representing over half the sample. Peripheral regions like Sool and Togdheer are underrepresented, indicating concerns about equitable policy reach. Twenty-four percent of respondents lack formal ECE training, highlighting a critical professional development gap. More than half hold non-education degrees, suggesting varying pedagogical expertise, which could affect ECE delivery consistency.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhile the sector is staffed by higher-qualified individuals, the lack of specialized ECE training remains challenging. The experience distribution indicates a relatively new workforce, potentially limiting institutional memory. These factors emphasize the need for continuous professional development and equitable resource distribution for effective ECE policy implementation in Somaliland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics indicate that all three dimensions of Leadership Commitment\u0026mdash;Normative, Continuance, and Affective\u0026mdash;exhibited moderate and closely aligned mean scores, ranging from 3.29 to 3.30 at outline in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. This consistency suggests that leaders generally feel a balanced sense of duty, emotional attachment, and perceived cost of leaving their positions. The low variation in standard deviations, between 1.03 and 1.11, further shows that responses were relatively uniform across the leadership cohort.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of Core Study Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormative Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuance Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECE Policy Implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e Variables measured on a 5-point Likert scale. N\u0026thinsp;=\u0026thinsp;129.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe mean score for ECE Policy Implementation was also 3.30, reflecting a moderate level of policy execution across the sample. This alignment suggests that the degree of leadership commitment may be directly linked to the effectiveness of policy implementation. When leadership support is neither low nor exceptionally high, policy performance tends to reflect a similar middle ground. These results highlight a steady but not exceptional system, where improving leadership engagement could substantially raise policy success. The close relationship among these variables underscores the importance of enhancing leadership development efforts to achieve stronger, more consistent ECE policy outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePearson Correlation \u0026ndash; Leadership Commitment and ECE Policy Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Pearson product-moment correlation analysis was conducted for the study as summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The test was designed to evaluate the relationship between leadership commitment and ECE policy implementation. Leadership commitment was operationalized through three distinct dimensions. These were Normative Commitment (NC), Continuance Commitment (CC), and Affective Commitment (AF). The results of this correlational analysis are presented in Table 10. These findings provide a quantitative basis for understanding the associations between these variables.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between Leadership Commitment and Policy Implementation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eECE PI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormative Commitment (NC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuance Commitment (CC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffective Commitment (AF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECE Policy Implementation (ECE PI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis revealed strong positive correlations between leadership commitment and ECE policy implementation. Affective Commitment showed the strongest relationship (r\u0026thinsp;=\u0026thinsp;.741), followed by Normative Commitment (r\u0026thinsp;=\u0026thinsp;.685) and Continuance Commitment (r\u0026thinsp;=\u0026thinsp;.682). All relationships were statistically significant. Strong positive correlations existed among the three commitment dimensions, with Normative-Continuance showing the strongest correlation (r\u0026thinsp;=\u0026thinsp;.846), followed by Continuance-Affective (r\u0026thinsp;=\u0026thinsp;.718) and Normative-Affective (r\u0026thinsp;=\u0026thinsp;.653). Leadership commitment emerged as a powerful predictor of successful ECE policy implementation, supported by robust correlations across all commitment types. Higher leadership commitment corresponded to more effective policy execution, confirming leaders\u0026apos; critical role in policy implementation. Affective Commitment, based on emotional attachment and shared values, proved most influential in predicting implementation success, surpassing normative and continuance commitment. The strong inter-correlations among commitment types indicate they form a unified construct, suggesting that strengthening one aspect may positively affect others.\u003c/p\u003e\n\u003cp\u003eThese findings validate transformational leadership models and emphasize emotional engagement. For practitioners, fostering affective commitment through positive organizational culture and clear communication of policy initiatives should be prioritized. The evidence confirms leadership commitment directly impacts ECE policy implementation. The major finding is that while all forms of commitment matter, affective commitment is the most critical driver. The practical application of this result is to focus leadership development on building genuine emotional and value-based connections to the organization\u0026rsquo;s goals. Ultimately, leaders who are emotionally invested are best positioned to navigate the complexities of policy implementation and ensure its success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Comparison and Selection through Machine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA formal model selection process was methodologically crucial. The preceding diagnostic analysis revealed violations of classical regression assumptions. The data exhibited non-linear heteroscedasticity and influential outliers. Relying on a single, standard model like multiple linear regression would have ignored these data imperfections, leading to biased coefficients and unreliable conclusions. Therefore, a comparative evaluation was essential to identify a model robust enough to handle the data\u0026apos;s characteristics. This ensures the final analysis is valid and credible. The selection of diverse error metrics was deliberate. No single metric can fully capture model performance. Metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) assess prediction errors in the original data units. RMSE, by squaring errors, is particularly sensitive to large prediction mistakes. Mean Absolute Percentage Error (MAPE) provides a scale-independent measure of relative accuracy, making model performance easily interpretable. This combination ensures the chosen model is accurate and robust against significant deviations. The model selection process proved its value. The empirical evidence in Table\u0026nbsp;17 directly addresses the concerns from initial diagnostics. Quantile regression\u0026apos;s superior performance, shown by leading scores across metrics like MAPE (18.06) and MAE (17.05), stems from its inherent robustness to heteroscedasticity and outliers present in this dataset. This convergence of diagnostic need and empirical performance justifies its selection. The process filtered out weaker models, ensuring the final analysis rests on sound methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Comparison \u0026ndash; Leadership Commitment on ECE Policy Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparative analysis was conducted to select the optimal regression model across ten analytical approaches as described in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The goal was identifying the most accurate model to examine leadership commitment\u0026apos;s effect on ECE policy implementation. Seven error metrics provided a comprehensive performance assessment. The results are detailed in Table 19. The empirical evidence showed quantile regression as the superior model, achieving the best scores across all metrics. It recorded the lowest MAPE of 14.19, lowest MAE at 15.38, and best RMSPE of 21.59 among all contenders.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Comparison and Selection \u0026ndash; Leadership Commitment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegression Models\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSLE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRRSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSPE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuantile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport Vector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e530.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple Linear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRidge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElastic Net\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial Least Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e571.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2414.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Principal Component model proved entirely unsuitable for the analysis, producing high error values with a MAPE of 45.92 and RMSE of 49.14, indicating a clear failure. Support Vector and Robust regression performed well, ranking second and third. The Robust regression achieved a MAPE of 15.92, while Support Vector had a MAPE of 16.11. Though strong, their error metrics were higher than Quantile regression. Multiple Linear Regression, Ridge, and LASSO models showed similar performance, with MAPE values around 17.4 and MAE scores near 17.2. For this dataset, regularization offered no significant advantage over standard linear approaches. The analysis revealed Quantile regression\u0026apos;s clear superiority across all metrics. This establishes that modeling leadership commitment requires an approach beyond mean-based regression. This selection is supported by earlier diagnostic checks that revealed non-linear heteroscedasticity and influential outliers. Quantile regression\u0026apos;s focus on modeling different conditional quantiles makes it less sensitive to these issues. The Principal Component model\u0026apos;s failure provides insight - its dimensionality reduction discarded crucial information about leadership commitment variables, resulting in an oversimplified model that couldn\u0026apos;t capture true relationships.\u003c/p\u003e\n\u003cp\u003eThe visual evidence in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provides compelling confirmation. The bar chart translates numerical data into an intuitive graphical narrative. The consistently low bars for the Quantile model create a clear signature of success, contrasting with the high error bars of the Principal Component model. This visual disparity makes the performance gap immediately apparent. The model selection process was critical and successful. Evidence from seven error metrics points to an unequivocal choice: Quantile regression is the most accurate and reliable model for this analysis. Its selection ensures conclusions about leadership commitment\u0026apos;s influence on ECE policy implementation will be based on robust statistical foundations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantile Regression for Leadership Commitment on ECE Policy Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section provides a detailed analysis of the Quantile Regression results. The model was specified to determine the influence of leadership commitment on ECE policy implementation. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the statistical findings for the three predictor variables. It includes the regression coefficients, p-values, and 95% confidence intervals. The predictors are Normative, Continuance, and Affective Commitment. The analysis is based on a sample of 129 observations. The numerical results reveal a clear and significant hierarchy of influence. Normative Commitment emerged as the most powerful predictor. It recorded a large positive coefficient (b\u0026thinsp;=\u0026thinsp;2.22, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Continuance Commitment and Affective Commitment also had strong, positive, and significant effects. Their coefficients were nearly identical at b\u0026thinsp;=\u0026thinsp;1.08 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and b\u0026thinsp;=\u0026thinsp;1.07 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), respectively. The confidence intervals for all three predictors were positive, confirming the reliability of their effects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantile Regression: Leadership Commitment vs ECE Policy Implementation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eECE Policy Implementation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e[95% Conf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterval]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormative Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.29195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eContinuance Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.69098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAffective Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.57679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.56518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.44179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.7307634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNumber of Obs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e122.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSD dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe overall model statistics provide essential context. The constant term was 6.65 and highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), representing the baseline level of ECE policy implementation. The model\u0026apos;s Pseudo R-squared value was .731, indicating the proportion of variance explained. The dependent variable had a mean of 122.22 and standard deviation of 37.79. The findings establish that leadership commitment is a profoundly influential driver of ECE policy implementation, supported by highly significant p-values for all commitment types. The results show that committed leadership directly associates with effective policy execution, highlighting leaders\u0026apos; role in translating policy to practice. A critical finding is that Normative Commitment is the paramount driver, with its coefficient more than double that of other commitment types. This indicates a leader\u0026apos;s sense of duty and professional responsibility is the most powerful motivator for policy success, more impactful than emotion or personal cost. Continuance and Affective Commitment are also significant, showing nearly equal strong positive impact. They represent practical and emotional dimensions of leadership dedication, though secondary to normative obligation. The model\u0026apos;s constant term suggests that even with zero leadership commitment, a baseline implementation level exists, reflecting established systems and procedures. The Pseudo R-squared value of .731 is noteworthy, indicating the three leadership commitment dimensions explain 73% of implementation variability. This high explanatory power confirms leadership commitment as the dominant force shaping implementation success. The key finding reveals normative commitment as most influential, suggesting efforts to enhance ECE policy implementation should focus on cultivating professional duty among leaders. While fostering emotional attachment is beneficial, instilling a deep-seated commitment to professional obligation appears to be the most effective strategy for driving change.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents the feature importance plot from the Quantile Regression model, offering a visual representation of each leadership commitment variable\u0026apos;s influence. The plot reveals a clear ranking among the three predictors, with Normative Commitment shown as the most important feature by a substantial margin. The relative lengths of the bars in Fig.\u0026nbsp;16 directly mirror the coefficients in Table\u0026nbsp;20, reinforcing the study\u0026apos;s primary conclusion. It graphically demonstrates that a leader\u0026apos;s sense of professional duty is the most critical factor driving policy implementation.\u003c/p\u003e\n\u003cp\u003eThe plot reveals a distinct two-tiered structure of importance. Normative Commitment occupies the top tier alone, highlighting its exceptional impact. In the lower tier, Continuance and Affective Commitment are grouped together with nearly identical lengths, showing their similar, secondary influence. This suggests that practical and emotional commitments function as supportive elements rather than primary drivers. The feature importance plot distills the regression analysis into an actionable visual statement, demonstrating the dominance of normative commitment. To improve ECE policy implementation, resources should focus on cultivating professional obligation and ethical responsibility in leaders, while other forms of commitment, though valuable, have less power to effect change.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe central finding of this study\u0026mdash;the unequivocal dominance of Normative Commitment in predicting ECE policy implementation\u0026mdash;offers a profound and contextually specific insight into the nature of effective leadership in fragile states. This result challenges a significant body of leadership literature that often prioritizes charismatic, visionary, or emotionally resonant (affective) leadership styles (G\u0026oacute;mez-Leal et al., 2021b). While our findings confirm that affective commitment is indeed a significant positive factor, its impact is overshadowed by the power of a leader\u0026rsquo;s internalized sense of professional duty and ethical obligation. This preeminence of normative commitment aligns strongly with theories of ethical and servant leadership, which posit that a leader's moral compass and commitment to service are foundational to building trust and achieving organizational goals (Haque, Fernando, \u0026amp; Caputi, 2021a; Mcquade, Harrison, \u0026amp; Tarbert, 2020). In a post-conflict environment like Somaliland, where formal systems are weak and resources are scarce, a leader's passion may wane, and pragmatic calculations may lead to despair.\u003c/p\u003e\u003cp\u003eHowever, a resilient sense of professional duty appears to be the most stable and powerful anchor for sustained action. The strong, secondary roles of Affective and Continuance Commitment suggest they function as a crucial support system for the primary driver of normative duty. A leader\u0026rsquo;s passion (affective) provides the energy for innovation, while their pragmatic desire to protect past investments (continuance) ensures persistence (Peng, Liao, \u0026amp; Sun, 2019). Without the guiding principle of normative commitment, these other forms of dedication may lack direction or resilience. This suggests a more complex, composite model of leadership psychology is at play (Semedo, Coelho, \u0026amp; Ribeiro, 2019). This finding has significant implications for how leadership is understood in developing contexts. It suggests that the most effective leaders are not necessarily the most charismatic, but the most ethically grounded and professionally responsible. This provides a critical refinement to the application of Western leadership theories in non-Western, fragile contexts, supporting scholars who call for more contextually sensitive models (Melesse \u0026amp; Obsiye, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tarekegne \u0026amp; Megersa, 2019). The high explanatory power of the model (R\u0026sup2; = 0.731) is itself a major finding. It confirms that leadership commitment is not just one factor among many but is the dominant force shaping policy outcomes in this system. This underscores the immense importance of human agency in environments with low institutionalization (Yizengaw \u0026amp; Tessega, 2020). The success or failure of the ECE policy appears to rest squarely on the shoulders of its leaders.\u003c/p\u003e\u003cp\u003eMethodologically, this study substantiates the application of advanced quantitative techniques to elucidate nuanced relationships. The selection of Quantile Regression, predicated on its empirical efficacy, facilitated a more robust estimation of effects, thereby circumventing the biases inherent in standard linear models when their assumptions are violated (Almulla \u0026amp; Al-Rahmi, 2023). This rigorous approach engenders high confidence in the final hierarchical ranking of the commitment dimensions. The descriptive data, which reveal significant training gaps, provide the context within which this leadership must function. The fact that committed leaders are achieving any level of implementation in a system characterized by such profound human capital deficits attests to the power of their resolve (Rashid, 2019b). It underscores that leadership transcends the mere management of a functional system, extending to the compensation for a dysfunctional one.\u003c/p\u003e\u003cp\u003eThe strong inter-correlations among the three commitment types (Table\u0026nbsp;10 in the dissertation) suggest that they are not entirely distinct constructs but rather facets of a singular, resilient psychological state. A leader with a strong sense of duty is also more likely to be emotionally invested and pragmatically persistent (Hameduddin \u0026amp; Engbers, 2021). This supports a holistic approach to leadership development, wherein fostering one aspect of commitment may yield positive spillover effects on the others (Fons\u0026eacute;n \u0026amp; Ukkonen-Mikkola, 2019). In conclusion, the discussion of these results elucidates a clear and compelling principle: within the challenging context of Somaliland's ECE sector, policy success is predominantly driven by the normative commitment of its leaders. This duty-bound, ethically-grounded leadership constitutes the most critical asset for translating policy rhetoric into classroom reality.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on these findings, three targeted policy implications are proposed: Revise Leadership Selection Criteria: The Ministry of Education and Civil Service Commission should reform leadership position criteria, shifting focus from qualifications to demonstrated normative commitment. This can be assessed through competency-based interviews with ethical scenarios and professional conduct review, prioritizing candidates with strong public service ethos. Reorient Leadership Development: ECE leadership training must be redesigned to cultivate normative commitment. Programs should use local case studies to explore ethical decision-making and professional responsibility, building the most impactful leadership attribute identified. Establish \"Code of Professional Conduct\": The Ministry should implement a formal code of conduct articulating ethical and professional obligations of ECE leaders, providing performance benchmarks and accountability. This would transform individual duty into a systemic expectation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study sought to provide a quantitative understanding of how leadership commitment influences ECE policy implementation in Somaliland. By deconstructing leadership into psychological components and employing a robust analytical approach, the research has yielded a clear conclusion. The success of educational policy in this fragile context is systematically driven by the normative commitment of its leaders. The finding that a leader's sense of professional duty and ethical obligation is a more potent driver than emotional passion or pragmatic calculation is significant. It refines our understanding of effective leadership in post-conflict settings and challenges leadership models that prioritize charisma over character. The study's methodological rigor, using a machine learning-informed model selection process, provides confidence in this central finding. This approach allowed the analysis to transcend traditional models and produce accurate results, offering a template for future quantitative policy research in complex environments. This research provides an evidence-based roadmap for the Ministry of Education and partners. To bridge the gap between policy and practice, the most effective strategy is to invest in cultivating leaders defined by a sense of public service. Fostering this duty-bound commitment is critical for building a sustainable ECE system for Somaliland's children.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study, while methodologically robust, is subject to two primary limitations. First, the cross-sectional design, while appropriate for an initial investigation, captures only a single point in time and cannot establish definitive causality. The relationship between commitment and implementation is likely reciprocal, a dynamic that this design cannot fully model. Second, the reliance on survey data, even with validated scales, introduces the possibility of self-report and social desirability biases, which may influence respondents' answers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Recommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific Recommendations for Policy and Practice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRevise Leadership Selection Criteria: Based on findings that Normative Commitment best predicts policy success, the Ministry of Education and Civil Service Commission should reform ECE leadership recruitment. New criteria should prioritize candidates demonstrating professional duty and public service ethics through competency-based interviews with ethical scenarios and professional conduct review. Reorient Leadership Development: ECE leadership programs should be redesigned to build Normative Commitment by incorporating applied ethics, moral imperatives, and professional responsibilities. Using Somaliland-specific case studies would help leaders connect decisions to ethical obligations, strengthening this key leadership attribute. Establish Professional Conduct Code: The Ministry should develop and implement a \"Code of Professional Conduct for ECE Leaders\" that articulates ethical standards and responsibilities. This code would provide performance benchmarks, ensure accountability, and transform individual duty into an enforceable professional standard.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations for Future Research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConduct Longitudinal Study on Commitment: This design provides a snapshot but cannot show causality. Future research should track leadership commitment and implementation outcomes over years, analyzing commitment evolution and normative commitment's impact. Qualitative Study of \"Positive Deviant\" Leaders: Data shows normative commitment's primacy. Research should examine leaders with high levels of this attribute through interviews to uncover experiences shaping their sense of duty. Investigate Relationship with Institutional Capacity: Future research should model the relationship between leadership commitment and institutional capacity, testing how committed leaders build capacity and capacity sustains commitment. Comparative Analysis: To enhance generalizability, examine leadership commitment dimensions in another Horn of Africa state to distinguish Somaliland-specific findings from universal principles of leadership in institution-building.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics, Consent to Participate, and Consent to Publish\u003c/h2\u003e\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable. This study used secondary publicly available data, and no direct involvement of human participants was required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.A.A. conceptualized the study, developed the methodology, conducted the formal analysis, and wrote the main manuscript text. B.Z., B.A., and D.N.G. provided supervision, validated the findings, and critically reviewed the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study, while not publicly available, can be obtained and will be available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbboah-Offei M, Amboka P, Nampijja M, Owino GE, Okelo K, Kitsao-Wekulo P, Chumo I, Muendo R, Oloo L, Wanjau M, Mwaniki E, Mutisya M, Haycraft E, Hughes R, Griffiths P, and Helen Elsey. Improving Early Childhood Development in the Context of the Nurturing Care Framework in Kenya: A Policy Review and Qualitative Exploration O Emerging Issues with Policy Makers. 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HTS Teologiese Stud / Theological Stud. 2022;78(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4102/hts.v78i1.7722\u003c/span\u003e\u003cspan address=\"10.4102/hts.v78i1.7722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Leadership Commitment, Policy Implementation, Early Childhood Education (ECE), Quantile Regression","lastPublishedDoi":"10.21203/rs.3.rs-7001557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7001557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study offers a quantitative analysis of the effect of leadership commitment on the implementation of Somaliland's 2020 Early Childhood Education (ECE) policy. While policy adoption is a critical first step for fragile states, the translation of policy into practice is contingent upon the will of those tasked with its execution. The successful implementation of Early Childhood Education (ECE) policy in fragile, post-conflict contexts is critically dependent on the dedication of its leaders. This study quantitatively investigates the hierarchical influence of leadership commitment on ECE policy implementation in Somaliland. Utilizing a census-based survey of 129 stakeholders from all 35 public preschools, this research deconstructs leadership commitment into Normative (duty-based), Affective (passion-based), and Continuance (cost-based) dimensions. A machine learning-informed model comparison, necessitated by violations of classical regression assumptions, identified Quantile Regression as the most robust analytical framework (MAPE\u0026thinsp;=\u0026thinsp;14.19%). The final model (Pseudo R\u0026sup2; = 0.731) revealed a definitive hierarchy of influence. Normative Commitment emerged as the paramount predictor (β\u0026thinsp;=\u0026thinsp;2.22), its impact more than double that of Affective (β\u0026thinsp;=\u0026thinsp;1.07) and Continuance (β\u0026thinsp;=\u0026thinsp;1.08) commitment. This finding challenges leadership models that prioritize charismatic or emotional leadership, suggesting that in resource-scarce environments, a leader\u0026rsquo;s internalized sense of professional duty is the most critical driver of policy success. The study provides a granular, evidence-based roadmap for leadership selection and development, crucial for translating policy into tangible educational outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Effect of Leadership Commitment (Normative Commitment, Continuance Commitment, and Affective Commitment) on ECE Policy Implementation in Somaliland Public Preschools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 04:14:06","doi":"10.21203/rs.3.rs-7001557/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T18:36:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T22:35:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324502404817198600620736422418064376041","date":"2025-09-28T22:11:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T17:43:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220868349890802554032204369819966165876","date":"2025-08-25T10:19:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263964541717499387046156818965280104229","date":"2025-08-24T21:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217085807045035629133566307952016437085","date":"2025-07-29T10:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T08:10:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-12T08:44:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-12T08:44:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-06-29T08:24:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6ed6110-0f75-4f40-a55e-35c92728ef9d","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T12:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 04:14:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7001557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7001557","identity":"rs-7001557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}