An empirical study on factors shaping effective police leadership for crime prevention in Bishoftu city

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An explanatory sequential mixed-method design was employed, integrating quantitative survey data with qualitative interviews. Quantitative data from 100 officers were analyzed using multiple linear regression, probit regression, and Spearman correlation to identify determinants of leadership effectiveness. The results revealed that poor communication and budget shortages negatively affected leadership effectiveness, whereas educational status, experience, leadership style, and follower commitment had positive and significant influences on leadership performance, explaining over 70% of the variance in effectiveness. Qualitative findings supported these results, highlighting the need for improved communication systems, adequate resource allocation, and leadership development programs. The study contributes empirical evidence to the limited literature on police leadership in Ethiopia and offers practical recommendations for enhancing crime prevention outcomes. Other Public Policy Police leadership Crime prevention Organizational effectiveness Public safety Figures Figure 1 INTRODUCTION Leadership is a central element in shaping organizational performance, influencing how institutions respond to challenges and achieve their goals. Within policing, leadership plays a particularly critical role because it directly affects both internal effectiveness and public safety. Police leaders are expected to guide, motivate, and support their officers while addressing complex challenges such as crime, limited resources, and community trust. When leadership is effective, police organizations are better positioned to prevent crime, ensure justice, and build confidence among the communities they serve (Yigzaw, Y., Mekuriaw, A., & Amsalu, T. ( 2023 ). Globally, scholars agree that leadership effectiveness is influenced by a range of factors, including communication, organizational resources, leadership style, job commitment, and emotional intelligence (Benmira & Agboola, 2021 ; Martin, 2020 ; Avolio, 2022 ; Mansaray, 2019 ). Other studies emphasize the importance of professional skills, incentives, awareness, commitment, and even the negative influence of corruption (Bashir, 2020 ). In the Ethiopian context, research highlights factors such as top management commitment, reward systems, teamwork, training, culture, supervision, planning, and infrastructure as central to leadership effectiveness (Tariku et al., 2020). Despite being one of the most widely studied subjects worldwide, leadership remains complex and is often misunderstood, particularly within policing institutions where organizational outcomes directly affect community safety and social order (Alehegn, D., Karunakara, R., & Engeda, B. 2024 ). Although there has been progress in leadership research in Ethiopia, most studies have focused on schools, companies, and other public institutions. For example, Hailu ( 2017 ) identified empowerment, transparency, and vision sharing as important factors influencing leadership in private organizations, while Hunde and Desalegn ( 2019 ) found that school leaders lacked adequate preparation in mobilizing financial and material resources. Similarly, Mesfin (2017) reported that leadership effectiveness in Ethiopian technical and vocational colleges was influenced by empowerment, motivation, communication, and vision sharing. However, research specifically examining leadership effectiveness in the police sector remains scarce (Alehegn, D., Ramasay, K., & Engeda, B. 2024 ). The case of Bishoftu City Police Department illustrates this gap clearly. According to its human resources data, employee turnover has been rising, particularly in the criminal investigation department. Leadership is likely among the factors contributing to this problem. Yet, no study has systematically examined how leadership influences effectiveness in crime prevention within this police administration. Despite existing research on leadership in other sectors, there is a lack of empirical evidence on how leadership effectiveness shapes crime prevention outcomes in Ethiopian police institutions. Previous studies in Addis Ababa emphasized community participation and crime pattern analysis (Alehegn et al., 2024 ; Yigzaw et al., 2023 ), yet little attention has been given to how police leadership practices themselves influence crime prevention outcomes. This study addresses that gap by examining internal leadership factors affecting police effectiveness in Bishoftu City. This study fills that gap by systematically examining the key factors influencing police leadership effectiveness in Bishoftu City. It explores how communication, education, resource allocation, leadership style, and follower commitment affect leadership performance and, in turn, the success of crime prevention efforts. RESEARCH METHODS Research design This study employed an explanatory sequential mixed-method design to investigate the factors influencing leadership effectiveness in crime prevention within the Bishoftu City Police Administration. The quantitative phase focused on identifying relationships among key variables such as communication, resource allocation, education, experience, leadership style, and follower commitment. The qualitative phase was then conducted to explain and enrich the quantitative findings by exploring officers’ perceptions, experiences, and interpretations in more depth. This design was selected because it allows for both statistical precision and contextual understanding quantitative data reveal general trends, while qualitative insights provide deeper explanations for observed patterns. The design aligns with Creswell (2012), who emphasizes that sequential explanatory approaches strengthen the validity and interpretive depth of mixed-method studies. Research approach A sequential explanatory mixed approach was used, involving two phases. First, quantitative data were gathered through structured surveys and analyzed using descriptive and inferential statistics to identify patterns and relationships. Second, qualitative data were collected through in-depth interviews and open-ended questions to explain and enrich the quantitative results. This design provided deeper insights into the “why” and “how” behind the statistical findings. Consistent with Creswell’s (2012) recommendation, the integration of both methods strengthened the validity of the study and offered a comprehensive understanding of leadership effectiveness in the police department (Mideksa, 2024). The combination of quantitative and qualitative approaches enhanced the study’s reliability and depth. Quantitative analysis provided objective measures of relationships, while qualitative insights offered interpretive understanding of the underlying causes. This integration is particularly valuable in policing studies, where leadership effectiveness is shaped not only by measurable organizational variables but also by human factors such as perception, motivation, and interpersonal communication. Population of the Study, Sample size and sampling technique The study population included all members of the Bishoftu City Police Administration, covering staff from crime prevention, crime investigation, leadership, management, and other units. This diverse group represents the entire workforce engaged in law enforcement and crime prevention activities, making it appropriate for examining leadership effectiveness. This study combined probability and non-probability sampling techniques. A stratified census approach was applied to the entire population of 134 police officers. The workforce was divided into meaningful categories such as crime prevention, investigation, leadership, and management to ensure balanced representation. Since the population size was manageable, including all members reduced sampling error and captured the full range of perspectives. In addition, a purposive sampling method was used to select key informants for in-depth interviews. These included senior officers, crime prevention experts, and community policing leaders. They were deliberately chosen because of their experience and specialized knowledge, which provided richer insights beyond what the survey data could reveal. Instrument of Data Collection For this study, the researcher employed both questionnaires and interviews to investigate factors affecting leadership effectiveness in crime prevention at the Bishoftu City Police Department. The questionnaire developed by the researcher in English and translated into Afan Oromo, included two sections: personal information and closed-ended items on key themes, using a 5-point Likert scale. This design ensured clarity for respondents and alignment with the study’s objectives (Parvizi et al., 2018). To obtain deeper insights and validate the survey data, the researcher conducted face-to-face interviews with senior police officials in Afan Oromo. This qualitative approach was justified as it captures opinions, attitudes, experiences, and emotions that cannot be measured quantitatively. Responses were coded and translated into English for reliable analysis, with each interview lasting less than an hour. Methods of Quantitative Data Analysis Quantitative data were analyzed using descriptive and inferential statistics. Percentages, frequencies, mean scores, and standard deviations summarized survey responses. Two parametric regression methods multiple linear regression and probit regression were conducted using SPSS (version 27) to model the relationship between leadership effectiveness and explanatory variables (poor communication, budget shortage, educational status, experience, department automation, leadership style, and follower commitment). Additionally, Spearman’s rank correlation, a non-parametric method, was used to assess associations between these variables and leadership effectiveness, providing a robust check for monotonic relationships without assuming normality. Diagnostic tests including multicollinearity, (Variance Inflation Factor, Tolerance Test), normality, and heteroscedasticity (Breusch-Pagan Test), ensured model validity. Econometric Model specification The researcher used a probit model to examine factors affecting leadership effectiveness in the Bishoftu City Police Department. Leadership effectiveness is the dependent variable, while poor communication, lack of teamwork, budget shortage, educational status, experience, leadership style, followers’ commitment, and department automation are explanatory variables. The model assumes normally distributed errors, independent observations, and no perfect multicollinearity. It estimates the impact of each factor on the probability of achieving effective leadership. SLR = β0 + β1 * PCM + β2 * BST + β3 * EDS + β4 * EX + β5 * DA + β6 * LST + β7 * FLC + Ui Data Reliability and Validity (quality assurance) The researcher ensured data quality through a pilot test and statistical checks. A pilot test was conducted with 28 respondents to assess the clarity and relevance of the questionnaire items. Reliability was measured using Cronbach’s Alpha, with all constructs exceeding the acceptable threshold (α ≥ 0.7) and an overall reliability of 0.823, indicating good internal consistency (Mugenda, 2003). Validity was confirmed through factor analysis using KMO and Bartlett’s tests, ensuring that the instrument accurately measured the intended constructs (C. Zaja & Blair, 2005). Table1: validity test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy .919 Bartlett's Test of Sphericity Approx. Chi-Square 256.309 Df 21 Sig. 0.000 The KMO value (0.919) indicates excellent sampling adequacy, and Bartlett’s Test (χ² = 256.309, p = 0.000) confirms significant correlations among variables, showing that the data is suitable for factor analysis. RESULTS AND FINDING The study found that leadership effectiveness at the Bishoftu City Police Department is moderately influenced by several factors, with a cumulative mean of 3.53. Poor communication, with a mean of 3.57 and a standard deviation of 1.18, and budget shortages, with a mean of 3.75 and a standard deviation of 1.23, were identified as the most significant constraints, limiting managerial oversight, law enforcement, and emergency response. A senior officer noted that limited budgets hinder effective staff monitoring and resource allocation (Interview #1, May 21, 2025). Educational status, with a mean of 3.61 and a standard deviation of 2.35, and experience, with a mean of 3.60 and a standard deviation of 1.07, showed variable effects across officers. Department automation, with a mean of 3.55 and a standard deviation of 1.00, and leadership style, with a mean of 3.50 and a standard deviation of 1.08, moderately impacted effectiveness, while followers’ commitment, with a mean of 3.12 and a standard deviation of 1.11, had the least influence. Overall, these findings suggest that effective police leadership depends on clear communication, adequate resources, education, experience, leadership style, automation, and follower engagement, consistent with Sogunro (2016), who emphasizes that leadership effectiveness, is shaped by both leader abilities and team characteristics. These results show that communication and budget limitations reduce leadership effectiveness, while better education, experience, and supportive leadership styles enhance it. Inferential Data Analysis Results and Findings The study employed multiple linear regressions and probit regression, both parametric methods, to model the impact of explanatory variables on leadership effectiveness. Additionally, Spearman’s rank correlation, a non-parametric method, was used to test monotonic associations between leadership effectiveness and explanatory variables (poor communication, budget shortage, educational status, experience, department automation, leadership style, and follower commitment), complementing the regression analyses by not assuming normality or linearity. The model is expressed as: Table 2: Model Summary Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .862 a .743 .725 .347 Predictors: (Constant), Poor Communication, Budget Shortage, Educational Status, Experience, Department Automation, Leadership Style, Followers Commitment Dependent Variable: Leadership effectiveness. Source: Compiled from survey questionnaires, (2025) The model summary shows a strong linear relationship between the seven predictors and leadership effectiveness, with a correlation coefficient (R) of 0.862. The R-squared value of 0.743 indicates that 74.3% of the variation in leadership effectiveness is explained by the predictors, while the remaining 25.7% is due to factors not included in the model. The adjusted R-squared of 0.725 confirms that approximately 72.5% of the variation is reliably captured by the model, demonstrating a good fit. Test of Model Adequacy (ANOVA) Analysis of variance (ANOVA) is a method of splitting the total variation into meaningful components that measure different sources of variation. Table 3: ANOVA test result ANOVA a Model Sum of Squares Df Mean Square F Sig. 1 Regression 32.263 7 4.609 38.09 .000 b Residual 11.158 92 .121 Total 43.421 99 a. Dependent Variable: Leadership effectiveness b. Predictors: (Constant),Poor Communication, Budget Shortage, Educational Status, Experience, Department Automation, Leadership Style, Followers Commitment Source: Compiled from survey questionnaires using SPSS V 27, (2025) The ANOVA results indicate that the regression model is statistically significant (F = 38.09, p < 0.001). The sum of squares for the regression is 32.263, and the residual sum of squares is 11.158. The degrees of freedom for the regression and residuals are 7 and 92, respectively, with mean squares of 4.609 and 0.121. The significant F-statistic confirms that the model provides a good fit and that the predictors collectively have a meaningful impact on leadership effectiveness. Evaluating significance of individual regression Coefficients Regression coefficients estimate the relationship between each predictor and the dependent variable, leadership effectiveness. Table 4: test of coefficients Model Un standardized Coefficients Standardized Coefficients T Sig. Beta Std. Error Beta 1 (Constant) .733 .250 4.898 0.000 Poor Communication .168 .064 .587 6.956 0.000 Budget Shortage .153 .056 .441 6.437 0.000 Leadership Style .160 .045 .537 6.724 0.000 Experience .155 .045 .504 5.149 0.000 Department Automation .354 .021 .395 5.463 0.201 Educational Status .234 .021 .552 4.674 0.400 Followers Commitment .101 .110 .488 6.854 0.000 Source: Compiled from survey questionnaires using SPSS V 27, (2025) As indicated in table 4 above on the SPSS out put on regression coefficients, the regression equation Poor Communic ation Hypothesis 1 (H1): Poor communication has a significant positive effect on leadership effectiveness. The p-value for poor communication is 0.000, less than 0.05, leading to rejection of the null hypothesis (H0). The unstandardized coefficient (B) is 0.168, indicating that a one-unit increase in poor communication increases leadership effectiveness by 16.8%, holding other variables constant. Budget Shortage Hypothesis 2 (H2): Budget shortage negatively affects leadership effectiveness. The p-value is 0.000 (<0.05), and B = −0.153, implying that a one-unit increase in budget shortage corresponds to a 15.3% decrease in leadership effectiveness, holding other variables constant. Leadership Style Hypothesis 3 (H3): Leadership style significantly influences leadership effectiveness. With a p-value of 0.000 and B = 0.160, a unit increase in leadership style leads to a 16% increase in leadership effectiveness. Experience Hypothesis 4 (H4): Experience positively affects leadership effectiveness. The coefficient (B = 0.155) suggests that greater experience increases leadership effectiveness by 15.5%, holding other variables constant. Followers’ Commitment Hypothesis 5 (H5): Followers’ commitment positively influences leadership effectiveness. The p-value is 0.000, and B = 0.101, indicating a 10% increase in leadership effectiveness with higher follower commitment. Multi collinearity Multicollinearity tests are used to identify strong correlations among explanatory variables and to prevent duplication in a regression model (Gujarati, 2003). The Variance Inflation Factor (VIF) and tolerance values are commonly applied measures. Table5: Multicollinearity result Variables Tolerance VIF Poor Communication .258 3.87 Budget Shortage .219 4.55 Leadership Style .694 1.44 Experience .383 2.61 Department Automation .752 1.33 Educational Status .568 1.76 Followers Commitment .429 2.33 Source: Compiled from survey questionnaires using SPSS V 27, (2025) VIF indicates how much the variance of a regression coefficient is inflated due to multicollinearity, with values below 10 suggesting no serious multicollinearity. Tolerance measures the proportion of variability in an independent variable not explained by the other predictors. The above table 5 presents the multicollinearity results. All VIF values are below 10, and tolerance values are greater than 0.1, indicating that multicollinearity is not a concern among the independent variables. Therefore, the regression coefficients can be considered reliable, and the null hypothesis of multicollinearity is rejected. Auto-correlation test : The Durbin-Watson statistic was 1.6251, indicating a low likelihood of autocorrelation in the regression residuals. This confirms that the regression results are reliable. Heteroscedasticity: The Breusch-Pagan test was used to check for heteroscedasticity in the regression model. The F-statistic p-value of 0.4096 exceeds 0.05, indicating no evidence of heteroscedasticity. Thus, the residuals have constant variance, confirming the reliability of the model. Table 6: Breusch-Pagan-Godfrey Result Diagnostic Test Description Statistic p-value F-test for model specification and heteroscedasticity 1.068897 0.4096 Breusch-Pagan LM test for heteroscedasticity 10.75869 0.3766 Scaled explained sum of squares for model fit 5.390081 0.8636 Source: Compiled from survey questionnaires using SPSS V 27, (2025) Normality Test Normality of the regression residuals was assessed using the Shapiro-Wilk test, along with skewness and kurtosis values. The Shapiro-Wilk p-value was 0.15, greater than 0.05, leading to acceptance of the null hypothesis that the residuals are normally distributed. Skewness (-0.626) and kurtosis (3.648) further confirm that the data follow a normal distribution, supporting the validity of the regression analysis. Correlation between Leadership Effectiveness and Its Determinant Factors Hypotheses were tested using both regression and correlation results. The multiple linear regression shows significant effects for poor communication (p = 0.000), budget shortage (p = 0.000), educational status (p = 0.04), experience (p = 0.000), leadership style (p = 0.000), and follower commitment (p = 0.000), rejecting the null hypotheses of no relationship. The probit regression (Table 7) confirms significant effects for educational status, budget shortage, poor communication, and follower commitment. Spearman’s correlation (below) further supports these findings with significant associations (p < 0.01) for most variables, except department automation. Table7: Spearman correlations Leadership Effectiveness Variables N Spearman Correlation Sig.(2-tailed) Poor communication 100 .702** .000 Budget shortage 100 .177 .008 Educational status 100 .192 .045 Dep. automation 100 -.572 .310 Experience 100 .903** .000 Leadership style 100 .862** .000 Followers‟ commitment 100 .903** .000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Source: Compiled from survey questionnaires using SPSS V 27, (2025) Table 7 shows significant correlations (p < 0.01) for poor communication, budget shortage, educational status, experience, leadership style, and follower commitment, but not for department automation (p = 0.31). Logistics Regression Result For probit models with a qualitative dependent variable, conventional R-squared is not appropriate, so likelihood ratio-based measures are used to assess goodness of fit. This measure compares the log-likelihood of the model with predictors to the log-likelihood of a model with only a constant, and ranges from 0, indicating no explanatory power, to 1, indicating perfect prediction. In this study, the likelihood ratio for the probit model is 0.6856, indicating that the model explains approximately 68.56% of the variation in the dependent variable. Probit Regression Analysis By running logistic regression analysis above the relationship between leadership effectiveness and the rest explanatory variables was analyzed in the following table 8 shows the probit regression analysis model summary. Table 8: regression result Variables Coefficient Std. Err. Z. value P. value EDS 2.5222 0.08031380 2.37 .005 EX 6.2770 0.01106400 3.88 .175 BST -0.0032 1.59881700 -0.49 .005 DA 4.2279 0.80295180 1.54 .999 LST 0.8960 1.29921100 0.28 .510 PCM 2.8890 0.79107030 2.13 .010 FLC 1.99900 0.8345122 2.31 .001 __cons -2.4180 2.81610400 -2.68 -0.20 Observation = 100 LR chi2(12) = 0.7856 Prob > chi2 = 0.0000 Pseudo R2 = 0.72 Source: Compiled from survey questionnaires using SPSS V 27, (2025) The probit regression (Table 8) identified educational status (p = 0.005), budget shortage (p = 0.005), poor communication (p = 0.010), and follower commitment (p = 0.001) as significant predictors, with a pseudo R² of 0.7263, explaining 72% of the variation. According to Sogunro (2016), studied on leadership effectiveness in group situations, conclude that leadership effectiveness is affected by personality characteristics of members of the group being led. This was conducted with the variables of personality and training of the leader, the characteristics of the group being led, the situation in which the group operates, and the goals being sought. Post Estimation The table below shows marginal effect of significant variables of factors influencing leadership effectiveness in criminal justice sectors of police institutions: the case of Bishoftu city police administration. Table 9: Marginal effect of significant variables Variables dy/dx Delta-method Std. Err. Z P>z EDS 0.112863 0.0056583 2.3400 0.015 EX 0.100004 0.0007287 2.8330 0.550 BST -0.59367 0.1043849 -0.480 0.030 DA 0.840211 0.0436023 0.1300 0.622 LST 0.483639 0.0638016 0.8100 0.533 PCM 0.353675 0.0000415 1.870 0.012 FLC .02117854 0.0243111 0.941 0.022 *, ** & *** are at 10, 5% and 1% level of significance respectively. Source: Compiled from survey questionnaires using SPSS V 27, (2025) Table 9 reports the marginal effects from the probit regression, showing that a one-unit increase in educational status (11%, p = 0.015), poor communication (35%, p = 0.012), and follower commitment (21%, p = 0.022) increases the probability of effective leadership, while budget shortage (-59%, p = 0.030) reduces it. Consistent with theoretical expectations, budget shortage has a negative marginal effect, meaning that as financial constraints increase, the probability of achieving effective leadership decreases. CONCLUSION AND RECOMMENDATIONS Conclusion This study concludes that effective police leadership plays a decisive role in promoting successful crime prevention within the Bishoftu City Police Administration. The findings show that communication barriers and limited budgets negatively influence leadership effectiveness, while educational level, experience, leadership style, and follower commitment positively enhance it. In addition, experience and leadership style were also found to contribute meaningfully to leadership effectiveness, particularly in fostering coordination and motivation among officers. The statistical results from multiple linear regression (R² = 0.743), probit regression (pseudo R² = 0.7263; likelihood ratio = 0.6856), and Spearman’s correlation consistently confirm these relationships. Complementary qualitative evidence from interviews further highlighted the importance of improving internal communication, ensuring adequate funding, and expanding leadership training programs to strengthen institutional capacity. This study concludes that leadership effectiveness in the Bishoftu City Police Administration is strongly shaped by both institutional and individual factors. Communication barriers and budget shortages weaken leadership performance, whereas education, experience, supportive leadership styles, and committed followers strengthen it. Therefore, the Oromia Police Commission and training institutions should prioritize continuous leadership education, enhance budget efficiency, and develop communication systems that promote transparency and collaboration. Recommendations Addressing financial and communication constraints is essential because resource shortages and weak communication channels significantly reduce leadership effectiveness. Encourage continuous education for police leaders through scholarships or academic partnerships, given its consistent significance across analyses. Introduce recognition programs and team-building activities to boost officer engagement, enhancing leadership effectiveness. Prioritize experienced officers in leadership roles and establish mentoring programs to transfer expertise, as experience was significant in linear regression and correlation. Develop leadership training programs focusing on transformational and situational leadership, given its significance in linear regression and correlation. Investigate additional factors, such as community engagement or technological integration, to further enhance leadership effectiveness in crime prevention. Suggestion for future researchers Since this research is only limited to Bishoftu city and researchers can use it as a benchmark for the study of another similar research. The scope of this study was cross-sectional, whereas the researchers were advised to follow longitudinal to ensure that the findings were more comprehensive and the research result contribution was maximized. Further research should also be conducted using another variable that have an effect on leadership effectiveness, such as empowerment, transparency, relationship building, vision sharing, and working on leadership traits. Conclusion This study concludes that effective police leadership plays a decisive role in promoting successful crime prevention within the Bishoftu City Police Administration. The findings show that communication barriers and limited budgets negatively influence leadership effectiveness, while educational level, experience, leadership style, and follower commitment positively enhance it. In addition, experience and leadership style were also found to contribute meaningfully to leadership effectiveness, particularly in fostering coordination and motivation among officers. The statistical results from multiple linear regression (R² = 0.743), probit regression (pseudo R² = 0.7263; likelihood ratio = 0.6856), and Spearman’s correlation consistently confirm these relationships. Complementary qualitative evidence from interviews further highlighted the importance of improving internal communication, ensuring adequate funding, and expanding leadership training programs to strengthen institutional capacity. This study concludes that leadership effectiveness in the Bishoftu City Police Administration is strongly shaped by both institutional and individual factors. Communication barriers and budget shortages weaken leadership performance, whereas education, experience, supportive leadership styles, and committed followers strengthen it. Therefore, the Oromia Police Commission and training institutions should prioritize continuous leadership education, enhance budget efficiency, and develop communication systems that promote transparency and collaboration. Recommendations Addressing financial and communication constraints is essential because resource shortages and weak communication channels significantly reduce leadership effectiveness. Encourage continuous education for police leaders through scholarships or academic partnerships, given its consistent significance across analyses. Introduce recognition programs and team-building activities to boost officer engagement, enhancing leadership effectiveness. Prioritize experienced officers in leadership roles and establish mentoring programs to transfer expertise, as experience was significant in linear regression and correlation. Develop leadership training programs focusing on transformational and situational leadership, given its significance in linear regression and correlation. Investigate additional factors, such as community engagement or technological integration, to further enhance leadership effectiveness in crime prevention. Suggestion for future researchers Since this research is only limited to Bishoftu city and researchers can use it as a benchmark for the study of another similar research. The scope of this study was cross-sectional, whereas the researchers were advised to follow longitudinal to ensure that the findings were more comprehensive and the research result contribution was maximized. Further research should also be conducted using another variable that have an effect on leadership effectiveness, such as empowerment, transparency, relationship building, vision sharing, and working on leadership traits. 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1","display":"","copyAsset":false,"role":"figure","size":47617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 1.1. Conceptual Framework. Source: Researcher’s own design, 2025.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7934736/v1/7d759b1a0dbd71c213776b54.jpg"},{"id":94728329,"identity":"7e120d5d-f439-4daa-9640-81618ec5e52e","added_by":"auto","created_at":"2025-10-30 07:03:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1477323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7934736/v1/9c1430db-37d1-4558-b1f1-85dce5af872e.pdf"},{"id":94672192,"identity":"d00f3384-2570-46f1-9a8f-bc855d1aceb4","added_by":"auto","created_at":"2025-10-29 13:39:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":828841,"visible":true,"origin":"","legend":"","description":"","filename":"PoliceLeadershiparticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7934736/v1/9e1d9803c75405780131b523.pdf"},{"id":94656875,"identity":"092d5632-824f-4833-bc40-ce7eda37506e","added_by":"auto","created_at":"2025-10-29 10:52:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":136373,"visible":true,"origin":"","legend":"","description":"","filename":"PoliceLeadershiparticle.docx","url":"https://assets-eu.researchsquare.com/files/rs-7934736/v1/28a43a05a2d4ac764207d8af.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAn empirical study on factors shaping effective police leadership for crime prevention in Bishoftu city\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLeadership is a central element in shaping organizational performance, influencing how institutions respond to challenges and achieve their goals. Within policing, leadership plays a particularly critical role because it directly affects both internal effectiveness and public safety. Police leaders are expected to guide, motivate, and support their officers while addressing complex challenges such as crime, limited resources, and community trust. When leadership is effective, police organizations are better positioned to prevent crime, ensure justice, and build confidence among the communities they serve (Yigzaw, Y., Mekuriaw, A., \u0026amp; Amsalu, T. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, scholars agree that leadership effectiveness is influenced by a range of factors, including communication, organizational resources, leadership style, job commitment, and emotional intelligence (Benmira \u0026amp; Agboola, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Avolio, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mansaray, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other studies emphasize the importance of professional skills, incentives, awareness, commitment, and even the negative influence of corruption (Bashir, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the Ethiopian context, research highlights factors such as top management commitment, reward systems, teamwork, training, culture, supervision, planning, and infrastructure as central to leadership effectiveness (Tariku et al., 2020). Despite being one of the most widely studied subjects worldwide, leadership remains complex and is often misunderstood, particularly within policing institutions where organizational outcomes directly affect community safety and social order (Alehegn, D., Karunakara, R., \u0026amp; Engeda, B. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough there has been progress in leadership research in Ethiopia, most studies have focused on schools, companies, and other public institutions. For example, Hailu (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified empowerment, transparency, and vision sharing as important factors influencing leadership in private organizations, while Hunde and Desalegn (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that school leaders lacked adequate preparation in mobilizing financial and material resources. Similarly, Mesfin (2017) reported that leadership effectiveness in Ethiopian technical and vocational colleges was influenced by empowerment, motivation, communication, and vision sharing. However, research specifically examining leadership effectiveness in the police sector remains scarce (Alehegn, D., Ramasay, K., \u0026amp; Engeda, B. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe case of Bishoftu City Police Department illustrates this gap clearly. According to its human resources data, employee turnover has been rising, particularly in the criminal investigation department. Leadership is likely among the factors contributing to this problem. Yet, no study has systematically examined how leadership influences effectiveness in crime prevention within this police administration.\u003c/p\u003e\u003cp\u003eDespite existing research on leadership in other sectors, there is a lack of empirical evidence on how leadership effectiveness shapes crime prevention outcomes in Ethiopian police institutions. Previous studies in Addis Ababa emphasized community participation and crime pattern analysis (Alehegn et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yigzaw et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), yet little attention has been given to how police leadership practices themselves influence crime prevention outcomes. This study addresses that gap by examining internal leadership factors affecting police effectiveness in Bishoftu City. This study fills that gap by systematically examining the key factors influencing police leadership effectiveness in Bishoftu City. It explores how communication, education, resource allocation, leadership style, and follower commitment affect leadership performance and, in turn, the success of crime prevention efforts.\u003c/p\u003e"},{"header":"RESEARCH METHODS","content":"\u003ch3\u003eResearch design\u003c/h3\u003e\n\u003cp\u003eThis study employed an explanatory sequential mixed-method design to investigate the factors influencing leadership effectiveness in crime prevention within the Bishoftu City Police Administration. The quantitative phase focused on identifying relationships among key variables such as communication, resource allocation, education, experience, leadership style, and follower commitment. The qualitative phase was then conducted to explain and enrich the quantitative findings by exploring officers’ perceptions, experiences, and interpretations in more depth.\u003c/p\u003e\n\u003cp\u003eThis design was selected because it allows for both statistical precision and contextual understanding quantitative data reveal general trends, while qualitative insights provide deeper explanations for observed patterns. The design aligns with Creswell (2012), who emphasizes that sequential explanatory approaches strengthen the validity and interpretive depth of mixed-method studies.\u0026nbsp;\u003c/p\u003e\n\u003ch3 id=\"_Toc165893835\"\u003eResearch approach\u003c/h3\u003e\n\u003cp\u003eA sequential explanatory mixed approach was used, involving two phases. First, quantitative data were gathered through structured surveys and analyzed using descriptive and inferential statistics to identify patterns and relationships. Second, qualitative data were collected through in-depth interviews and open-ended questions to explain and enrich the quantitative results. This design provided deeper insights into the “why” and “how” behind the statistical findings. Consistent with Creswell’s (2012) recommendation, the integration of both methods strengthened the validity of the study and offered a comprehensive understanding of leadership effectiveness in the police department (Mideksa, 2024). The combination of quantitative and qualitative approaches enhanced the study’s reliability and depth. Quantitative analysis provided objective measures of relationships, while qualitative insights offered interpretive understanding of the underlying causes. This integration is particularly valuable in policing studies, where leadership effectiveness is shaped not only by measurable organizational variables but also by human factors such as perception, motivation, and interpersonal communication.\u003c/p\u003e\n\u003ch2 id=\"_Toc165893836\"\u003ePopulation of the\u0026nbsp;Study, Sample size and sampling technique\u003c/h2\u003e\n\u003cp id=\"_Toc151573962\"\u003eThe study population included all members of the Bishoftu City Police Administration, covering staff from crime prevention, crime investigation, leadership, management, and other units. This diverse group represents the entire workforce engaged in law enforcement and crime prevention activities, making it appropriate for examining leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eThis study combined probability and non-probability sampling techniques. A stratified census approach was applied to the entire population of 134 police officers. The workforce was divided into meaningful categories such as crime prevention, investigation, leadership, and management to ensure balanced representation. Since the population size was manageable, including all members reduced sampling error and captured the full range of perspectives.\u003c/p\u003e\n\u003cp\u003eIn addition, a purposive sampling method was used to select key informants for in-depth interviews. These included senior officers, crime prevention experts, and community policing leaders. They were deliberately chosen because of their experience and specialized knowledge, which provided richer insights beyond what the survey data could reveal.\u003c/p\u003e\n\u003ch3\u003eInstrument of Data Collection\u003c/h3\u003e\n\u003cp\u003eFor this study, the researcher employed both questionnaires and interviews to investigate factors affecting leadership effectiveness in crime prevention at the Bishoftu City Police Department. The questionnaire developed by the researcher in English and translated into Afan Oromo, included two sections: personal information and closed-ended items on key themes, using a 5-point Likert scale. This design ensured clarity for respondents and alignment with the study’s objectives (Parvizi et al., 2018).\u003c/p\u003e\n\u003cp\u003eTo obtain deeper insights and validate the survey data, the researcher conducted face-to-face interviews with senior police officials in Afan Oromo. This qualitative approach was justified as it captures opinions, attitudes, experiences, and emotions that cannot be measured quantitatively. Responses were coded and translated into English for reliable analysis, with each interview lasting less than an hour.\u003c/p\u003e\n\u003ch2 id=\"_Toc165893844\"\u003eMethods of\u0026nbsp;Quantitative Data Analysis\u003c/h2\u003e\n\u003cp\u003eQuantitative data were analyzed using descriptive and inferential statistics. Percentages, frequencies, mean scores, and standard deviations summarized survey responses. Two parametric regression methods multiple linear regression and probit regression were conducted using SPSS (version 27) to model the relationship between leadership effectiveness and explanatory variables (poor communication, budget shortage, educational status, experience, department automation, leadership style, and follower commitment). Additionally, Spearman’s rank correlation, a non-parametric method, was used to assess associations between these variables and leadership effectiveness, providing a robust check for monotonic relationships without assuming normality. Diagnostic tests including multicollinearity, (Variance Inflation Factor, Tolerance Test), normality, and heteroscedasticity (Breusch-Pagan Test), ensured model validity.\u003c/p\u003e\n\u003ch2\u003eEconometric Model specification\u003c/h2\u003e\n\u003cp\u003eThe researcher used a probit model to examine factors affecting leadership effectiveness in the Bishoftu City Police Department. Leadership effectiveness is the dependent variable, while poor communication, lack of teamwork, budget shortage, educational status, experience, leadership style, followers’ commitment, and department automation are explanatory variables. The model assumes normally distributed errors, independent observations, and no perfect multicollinearity. It estimates the impact of each factor on the probability of achieving effective leadership.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSLR = β0 + β1 * PCM + β2 * BST + β3 * EDS + β4 * EX + β5 * DA + β6 * LST + β7 * FLC + Ui\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eData Reliability and Validity (quality assurance)\u003c/h2\u003e\n\u003cp id=\"_Toc151906706\"\u003eThe researcher ensured data quality through a pilot test and statistical checks. A pilot test was conducted with 28 respondents to assess the clarity and relevance of the questionnaire items. Reliability was measured using Cronbach’s Alpha, with all constructs exceeding the acceptable threshold (α ≥ 0.7) and an overall reliability of 0.823, indicating good internal consistency (Mugenda, 2003). Validity was confirmed through factor analysis using KMO and Bartlett’s tests, ensuring that the instrument accurately measured the intended constructs (C. Zaja \u0026amp; Blair, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1: validity test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;KMO and Bartlett's Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.919\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBartlett's Test of Sphericity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eApprox. Chi-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e256.309\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe KMO value (0.919) indicates excellent sampling adequacy, and Bartlett’s Test (χ² = 256.309, p = 0.000) confirms significant correlations among variables, showing that the data is suitable for factor analysis.\u003c/p\u003e"},{"header":"RESULTS AND FINDING","content":"\u003cp\u003eThe study found that leadership effectiveness at the Bishoftu City Police Department is moderately influenced by several factors, with a cumulative mean of 3.53. Poor communication, with a mean of 3.57 and a standard deviation of 1.18, and budget shortages, with a mean of 3.75 and a standard deviation of 1.23, were identified as the most significant constraints, limiting managerial oversight, law enforcement, and emergency response. A senior officer noted that limited budgets hinder effective staff monitoring and resource allocation (Interview #1, May 21, 2025).\u003c/p\u003e\n\u003cp\u003eEducational status, with a mean of 3.61 and a standard deviation of 2.35, and experience, with a mean of 3.60 and a standard deviation of 1.07, showed variable effects across officers. Department automation, with a mean of 3.55 and a standard deviation of 1.00, and leadership style, with a mean of 3.50 and a standard deviation of 1.08, moderately impacted effectiveness, while followers\u0026rsquo; commitment, with a mean of 3.12 and a standard deviation of 1.11, had the least influence.\u003c/p\u003e\n\u003cp\u003eOverall, these findings suggest that effective police leadership depends on clear communication, adequate resources, education, experience, leadership style, automation, and follower engagement, consistent with Sogunro (2016), who emphasizes that leadership effectiveness, is shaped by both leader abilities and team characteristics. These results show that communication and budget limitations reduce leadership effectiveness, while better education, experience, and supportive leadership styles enhance it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInferential Data Analysis Results and Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study employed multiple linear regressions and probit regression, both parametric methods, to model the impact of explanatory variables on leadership effectiveness. Additionally, Spearman\u0026rsquo;s rank correlation, a non-parametric method, was used to test monotonic associations between leadership effectiveness and explanatory variables (poor communication, budget shortage, educational status, experience, department automation, leadership style, and follower commitment), complementing the regression analyses by not assuming normality or linearity. The model is expressed as:\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Model Summary\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 639px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error of the Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.862\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.743\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.725\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.347\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 639px;\"\u003e\n \u003col\u003e\n \u003cli\u003ePredictors: (Constant), Poor Communication, Budget Shortage, Educational Status, Experience, Department Automation, Leadership Style, Followers Commitment\u003c/li\u003e\n \u003cli\u003eDependent Variable: Leadership effectiveness.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Compiled from survey questionnaires, (2025)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe model summary shows a strong linear relationship between the seven predictors and leadership effectiveness, with a correlation coefficient (R) of 0.862. The R-squared value of 0.743 indicates that 74.3% of the variation in leadership effectiveness is explained by the predictors, while the remaining 25.7% is due to factors not included in the model. The adjusted R-squared of 0.725 confirms that approximately 72.5% of the variation is reliably captured by the model, demonstrating a good fit.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e\u0026nbsp;Test of Model Adequacy (ANOVA)\u003c/em\u003e\u003c/h3\u003e\n\u003cp id=\"_Toc151906722\"\u003eAnalysis of variance (ANOVA) is a method of splitting the total variation into meaningful components that measure different sources of variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: ANOVA test result\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 649px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ANOVA\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e32.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e38.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e11.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e43.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 649px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Leadership effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 649px;\"\u003e\n \u003cp\u003eb. Predictors: (Constant),Poor Communication, Budget Shortage, Educational Status, Experience, Department Automation, Leadership Style, Followers Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ANOVA results indicate that the regression model is statistically significant (F = 38.09, p \u0026lt; 0.001). The sum of squares for the regression is 32.263, and the residual sum of squares is 11.158. The degrees of freedom for the regression and residuals are 7 and 92, respectively, with mean squares of 4.609 and 0.121. The significant F-statistic confirms that the model provides a good fit and that the predictors collectively have a meaningful impact on leadership effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluating significance of individual regression Coefficients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression coefficients estimate the relationship between each predictor and the dependent variable, leadership effectiveness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: test of coefficients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUn standardized\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003ePoor Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eBudget Shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eLeadership Style\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eDepartment Automation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eEducational Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eFollowers Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Source: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc151573997\"\u003eAs indicated in table 4 above on the SPSS out put on regression coefficients, the regression equation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePoor Communic\u003c/strong\u003eation\u003c/p\u003e\n\u003cp\u003eHypothesis 1 (H1): Poor communication has a significant positive effect on leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eThe p-value for poor communication is 0.000, less than 0.05, leading to rejection of the null hypothesis (H0). The unstandardized coefficient (B) is 0.168, indicating that a one-unit increase in poor communication increases leadership effectiveness by 16.8%, holding other variables constant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBudget Shortage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 2 (H2): Budget shortage negatively affects leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eThe p-value is 0.000 (\u0026lt;0.05), and B = \u0026minus;0.153, implying that a one-unit increase in budget shortage corresponds to a 15.3% decrease in leadership effectiveness, holding other variables constant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeadership Style\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 3 (H3): Leadership style significantly influences leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eWith a p-value of 0.000 and B = 0.160, a unit increase in leadership style leads to a 16% increase in leadership effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 4 (H4): Experience positively affects leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eThe coefficient (B = 0.155) suggests that greater experience increases leadership effectiveness by 15.5%, holding other variables constant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollowers\u0026rsquo; Commitment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 5 (H5): Followers\u0026rsquo; commitment positively influences leadership effectiveness.\u003c/p\u003e\n\u003cp\u003eThe p-value is 0.000, and B = 0.101, indicating a 10% increase in leadership effectiveness with higher follower commitment.\u003c/p\u003e\n\u003cp id=\"_Toc165893867\"\u003e\u003cstrong\u003e\u003cem\u003eMulti collinearity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMulticollinearity tests are used to identify strong correlations among explanatory variables and to prevent duplication in a regression model (Gujarati, 2003). The Variance Inflation Factor (VIF) and tolerance values are commonly applied measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Table5: Multicollinearity result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"536\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTolerance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePoor Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eBudget Shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eLeadership Style\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDepartment Automation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eEducational Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFollowers Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eSource: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVIF indicates how much the variance of a regression coefficient is inflated due to multicollinearity, with values below 10 suggesting no serious multicollinearity. Tolerance measures the proportion of variability in an independent variable not explained by the other predictors.\u003c/p\u003e\n\u003cp\u003eThe above table 5 presents the multicollinearity results. All VIF values are below 10, and tolerance values are greater than 0.1, indicating that multicollinearity is not a concern among the independent variables. Therefore, the regression coefficients can be considered reliable, and the null hypothesis of multicollinearity is rejected.\u003c/p\u003e\n\u003cp id=\"_Toc165893868\"\u003e\u003cstrong\u003eAuto-correlation test\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe Durbin-Watson statistic was 1.6251, indicating a low likelihood of autocorrelation in the regression residuals. This confirms that the regression results are reliable.\u003c/p\u003e\n\u003ch3 id=\"_Toc165893869\"\u003eHeteroscedasticity:\u0026nbsp;The Breusch-Pagan test was used to check for heteroscedasticity in the regression model. The F-statistic p-value of 0.4096 exceeds 0.05, indicating no evidence of heteroscedasticity. Thus, the residuals have constant variance, confirming the reliability of the model.\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Breusch-Pagan-Godfrey Result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"645\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Test Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-test for model specification and heteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.068897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBreusch-Pagan LM test for heteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.75869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScaled explained sum of squares for model fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.390081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Source: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNormality Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormality of the regression residuals was assessed using the Shapiro-Wilk test, along with skewness and kurtosis values. The Shapiro-Wilk p-value was 0.15, greater than 0.05, leading to acceptance of the null hypothesis that the residuals are normally distributed. Skewness (-0.626) and kurtosis (3.648) further confirm that the data follow a normal distribution, supporting the validity of the regression analysis.\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc165893871\"\u003eCorrelation between Leadership Effectiveness and Its Determinant Factors\u0026nbsp;\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eHypotheses were tested using both regression and correlation results. The multiple linear regression shows significant effects for poor communication (p = 0.000), budget shortage (p = 0.000), educational status (p = 0.04), experience (p = 0.000), leadership style (p = 0.000), and follower commitment (p = 0.000), rejecting the null hypotheses of no relationship. The probit regression (Table 7) confirms significant effects for educational status, budget shortage, poor communication, and follower commitment. Spearman\u0026rsquo;s correlation (below) further supports these findings with significant associations (p \u0026lt; 0.01) for most variables, except department automation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Table7: Spearman correlations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLeadership Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSpearman Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSig.(2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePoor communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.702**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBudget shortage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eEducational status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDep. automation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.903**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLeadership style\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.862**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFollowers‟ commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e.903**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 589px;\"\u003e\n \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 589px;\"\u003e\n \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/p\u003e\n\u003cp\u003eTable 7 shows significant correlations (p \u0026lt; 0.01) for poor communication, budget shortage, educational status, experience, leadership style, and follower commitment, but not for department automation (p = 0.31).\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc165893872\"\u003eLogistics Regression Result\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eFor probit models with a qualitative dependent variable, conventional R-squared is not appropriate, so likelihood ratio-based measures are used to assess goodness of fit. This measure compares the log-likelihood of the model with predictors to the log-likelihood of a model with only a constant, and ranges from 0, indicating no explanatory power, to 1, indicating perfect prediction. In this study, the likelihood ratio for the probit model is 0.6856, indicating that the model explains approximately 68.56% of the variation in the dependent variable.\u003c/p\u003e\n\u003ch3\u003e\u003cspan id=\"_Toc165893874\"\u003eProbit Regression Analysis\u003c/span\u003e\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;By running logistic regression analysis above the relationship between leadership effectiveness and the rest explanatory variables was analyzed in the following table 8 shows the probit regression analysis model summary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Table 8: regression result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Err.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ. value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP. value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eEDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.5222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.08031380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.01106400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.59881700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.2279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.80295180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.8960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.29921100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.8890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.79107030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.99900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.8345122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e__cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-2.4180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.81610400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eObservation = 100\u003c/p\u003e\n \u003cp\u003eLR chi2(12) = 0.7856\u003c/p\u003e\n \u003cp\u003eProb \u0026gt; chi2 =\u0026nbsp;0.0000\u003c/p\u003e\n \u003cp\u003ePseudo R2 = 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSource: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe probit regression (Table 8) identified educational status (p = 0.005), budget shortage (p = 0.005), poor communication (p = 0.010), and follower commitment (p = 0.001) as significant predictors, with a pseudo R\u0026sup2; of 0.7263, explaining 72% of the variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Sogunro (2016), studied on leadership effectiveness in group situations, conclude that leadership effectiveness is affected by personality characteristics of members of the group being led. This was conducted with the variables of personality and training of the leader, the characteristics of the group being led, the situation in which the group operates, and the goals being sought.\u003c/p\u003e\n\u003ch3\u003e\u003cspan id=\"_Toc165893875\"\u003e\u0026nbsp;Post Estimation\u0026nbsp;\u003c/span\u003e\u003c/h3\u003e\n\u003cp\u003eThe table below shows marginal effect of significant variables of factors influencing leadership effectiveness in criminal justice sectors of police institutions: the case of Bishoftu city police administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9: Marginal effect of significant variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003edy/dx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eDelta-method Std. Err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eP\u0026gt;z\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eEDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.112863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0056583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.3400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.100004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0007287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.8330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eBST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.59367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.1043849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.840211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0436023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eLST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.483639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0638016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.8100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003ePCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.353675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0000415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e.02117854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.0243111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 565px;\"\u003e\n \u003cp\u003e*, ** \u0026amp; *** are at 10, 5% and 1% level of significance respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Compiled from survey questionnaires using SPSS V 27, (2025)\u003c/p\u003e\n\u003cp\u003eTable 9 reports the marginal effects from the probit regression, showing that a one-unit increase in educational status (11%, p = 0.015), poor communication (35%, p = 0.012), and follower commitment (21%, p = 0.022) increases the probability of effective leadership, while budget shortage (-59%, p = 0.030) reduces it. Consistent with theoretical expectations, budget shortage has a negative marginal effect, meaning that as financial constraints increase, the probability of achieving effective leadership decreases.\u003c/p\u003e"},{"header":"CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study concludes that effective police leadership plays a decisive role in promoting successful crime prevention within the Bishoftu City Police Administration. The findings show that communication barriers and limited budgets negatively influence leadership effectiveness, while educational level, experience, leadership style, and follower commitment positively enhance it. In addition, experience and leadership style were also found to contribute meaningfully to leadership effectiveness, particularly in fostering coordination and motivation among officers.\u003c/p\u003e\n\u003cp\u003eThe statistical results from multiple linear regression (R² = 0.743), probit regression (pseudo R² = 0.7263; likelihood ratio = 0.6856), and Spearman’s correlation consistently confirm these relationships. Complementary qualitative evidence from interviews further highlighted the importance of improving internal communication, ensuring adequate funding, and expanding leadership training programs to strengthen institutional capacity. This study concludes that leadership effectiveness in the Bishoftu City Police Administration is strongly shaped by both institutional and individual factors. Communication barriers and budget shortages weaken leadership performance, whereas education, experience, supportive leadership styles, and committed followers strengthen it.\u003c/p\u003e\n\u003cp\u003eTherefore, the Oromia Police Commission and training institutions should prioritize continuous leadership education, enhance budget efficiency, and develop communication systems that promote transparency and collaboration.\u003c/p\u003e\n\u003ch2\u003eRecommendations\u003c/h2\u003e\n\u003col\u003e\n \u003cli\u003eAddressing financial and communication constraints is essential because resource shortages and weak communication channels significantly reduce leadership effectiveness.\u003c/li\u003e\n \u003cli\u003eEncourage continuous education for police leaders through scholarships or academic partnerships, given its consistent significance across analyses.\u003c/li\u003e\n \u003cli\u003eIntroduce recognition programs and team-building activities to boost officer engagement, enhancing leadership effectiveness.\u003c/li\u003e\n \u003cli\u003ePrioritize experienced officers in leadership roles and establish mentoring programs to transfer expertise, as experience was significant in linear regression and correlation.\u003c/li\u003e\n \u003cli\u003eDevelop leadership training programs focusing on transformational and situational leadership, given its significance in linear regression and correlation.\u003c/li\u003e\n \u003cli\u003eInvestigate additional factors, such as community engagement or technological integration, to further enhance leadership effectiveness in crime prevention.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eSuggestion for future researchers\u003c/h2\u003e\n\u003cp\u003eSince this research is only limited to Bishoftu city and researchers can use it as a benchmark for the study of another similar research. The scope of this study was cross-sectional, whereas the researchers were advised to follow longitudinal to ensure that the findings were more comprehensive and the research result contribution was maximized. \u0026nbsp;Further research should also be conducted using another variable that have an effect on leadership effectiveness, such as empowerment, transparency, relationship building, vision sharing, and working on leadership traits.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study concludes that effective police leadership plays a decisive role in promoting successful crime prevention within the Bishoftu City Police Administration. The findings show that communication barriers and limited budgets negatively influence leadership effectiveness, while educational level, experience, leadership style, and follower commitment positively enhance it. In addition, experience and leadership style were also found to contribute meaningfully to leadership effectiveness, particularly in fostering coordination and motivation among officers.\u003c/p\u003e\u003cp\u003eThe statistical results from multiple linear regression (R\u0026sup2; = 0.743), probit regression (pseudo R\u0026sup2; = 0.7263; likelihood ratio\u0026thinsp;=\u0026thinsp;0.6856), and Spearman\u0026rsquo;s correlation consistently confirm these relationships. Complementary qualitative evidence from interviews further highlighted the importance of improving internal communication, ensuring adequate funding, and expanding leadership training programs to strengthen institutional capacity. This study concludes that leadership effectiveness in the Bishoftu City Police Administration is strongly shaped by both institutional and individual factors. Communication barriers and budget shortages weaken leadership performance, whereas education, experience, supportive leadership styles, and committed followers strengthen it.\u003c/p\u003e\u003cp\u003eTherefore, the Oromia Police Commission and training institutions should prioritize continuous leadership education, enhance budget efficiency, and develop communication systems that promote transparency and collaboration.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRecommendations\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAddressing financial and communication constraints is essential because resource shortages and weak communication channels significantly reduce leadership effectiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEncourage continuous education for police leaders through scholarships or academic partnerships, given its consistent significance across analyses.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntroduce recognition programs and team-building activities to boost officer engagement, enhancing leadership effectiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePrioritize experienced officers in leadership roles and establish mentoring programs to transfer expertise, as experience was significant in linear regression and correlation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDevelop leadership training programs focusing on transformational and situational leadership, given its significance in linear regression and correlation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInvestigate additional factors, such as community engagement or technological integration, to further enhance leadership effectiveness in crime prevention.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eSuggestion for future researchers\u003c/h2\u003e\u003cp\u003eSince this research is only limited to Bishoftu city and researchers can use it as a benchmark for the study of another similar research. The scope of this study was cross-sectional, whereas the researchers were advised to follow longitudinal to ensure that the findings were more comprehensive and the research result contribution was maximized. Further research should also be conducted using another variable that have an effect on leadership effectiveness, such as empowerment, transparency, relationship building, vision sharing, and working on leadership traits.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlehegn D, Karunakara R, Engeda B (2024) Community advancement in community policing within the Addis Ababa City Organization. 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Heliyon 9(2):e13282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e13282\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e13282\" 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":true,"hideJournal":true,"highlight":"","institution":"Oromia police college","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Police leadership, Crime prevention, Organizational effectiveness, Public safety","lastPublishedDoi":"10.21203/rs.3.rs-7934736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7934736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined the factors influencing police leadership effectiveness in crime prevention within the Bishoftu City Police Administration, Ethiopia. An explanatory sequential mixed-method design was employed, integrating quantitative survey data with qualitative interviews. Quantitative data from 100 officers were analyzed using multiple linear regression, probit regression, and Spearman correlation to identify determinants of leadership effectiveness. The results revealed that poor communication and budget shortages negatively affected leadership effectiveness, whereas educational status, experience, leadership style, and follower commitment had positive and significant influences on leadership performance, explaining over 70% of the variance in effectiveness. Qualitative findings supported these results, highlighting the need for improved communication systems, adequate resource allocation, and leadership development programs. The study contributes empirical evidence to the limited literature on police leadership in Ethiopia and offers practical recommendations for enhancing crime prevention outcomes.\u003c/p\u003e","manuscriptTitle":"An empirical study on factors shaping effective police leadership for crime prevention in Bishoftu city","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 10:52:46","doi":"10.21203/rs.3.rs-7934736/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a066402e-e7f5-478b-916e-2488927859ea","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56795317,"name":"Other Public Policy"}],"tags":[],"updatedAt":"2025-10-29T10:52:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 10:52:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7934736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7934736","identity":"rs-7934736","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}