An Analysis of the Socio-Biographical Effect of Boko Haram Terrorist Group in Northeastern Nigeria

An Analysis of the Socio-Biographical Effect of Boko Haram Terrorist Group in Northeastern Nigeria

Imuetiyan R. Adeyemo^1*, Esther C. Nwachukwu², Uzoamaka C. Ogor³, Nnaemeka P. Eke-Okocha⁴, Anyigor O. Nwode⁵, Goodness James¹

¹Babcock University, Nigeria, 

²Nasarawa State University,

³Louisiana State University, USA,

⁴Salisbury University, USA, 

⁵Bigard Memorial Seminary, Nigeria,

*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8080193

Received: 13 July 2024; Revised: 02 August 2024; Accepted: 06 August 2024; Published: 11 September 2024

ABSTRACT

This Research examines the relationship between violence and insurgency in Northeast Nigeria, where residents have been subject to several attacks from Boko Haram – an extremist Islamist movement since 2009. A survey was conducted of 75 victims using a structured questionnaire. Descriptive statistics, a double-difference (difference-in-difference) approach is used to assess the impact of the conflict on mean weight, regression analysis and game theory were used to analyze the data. The findings reveal that victims exposed to the conflict their mean weight-for- height z-score would be 0.49 standard deviation higher (p<0.001). The research concludes that the health outcome of the attacks in Northeast Nigeria such as the bombing of Borno Mosque and the adoption of Chibok Girls in Chibok Borno may be due to poor healthcare services and increased food insecurity. The findings underscore the importance of appropriate programs and policies to support victims in conflict zones.

Keywords: Boko Haram, Insurgency, Insecurity, Political development, Economic Development, Terrorism.

INTRODUCTION

This research aims to examine terrorism and insurgency which is becoming widespread among nations. According to Rourke (2015) observes that war, terrorism and other forms of transnational political violence are many ways more threatening today than ever before as casualties have been on the increase.

The background covers the Quantitative methodology. This theory will be a major plank on which this study is anchored. This is because it is perceived to be relevant to the subject matter of study. This theory was propounded by Dolland et al (1939), and in their view, the primary source of the human capacity for violence rests on frustration-aggression mechanism. According to them anger induced by frustration is a motivating force that disposes man to aggression. Also, Gurr (1970) in his book titled “why men rebel” opined that relative deprivation is a necessary condition for violence: relative deprivation being perceived discrepancy between man’s value expectation and their value capabilities encompassing Vigor, dedication, and absorption.

Statement of Problem

A quantitative methodology will be adopted, using surveys to forecast how different Nigerian actions changed the probabilities of terrorist occurrences in an effort to curb the attacks. Statistical tests will describe the data cleaning procedures and the definitions of the variables. Secondly, I will also describe the structure of the dependent variables and the intuitions behind the choice of modeling forms. Thirdly I use descriptive statistics and graphical analyses to show how the frequency of Boko Haram attacks has changed over time. Finally, I conclude using negative binomial regression to forecast what counter-terrorists’ operations will give a reduction in the probability of a terrorist attack in the next month.

The study intends to address the need for a deeper, nuanced understanding of the factors behind the insurgency: economic marginalization, governance failures, extremists’ operations and security failures. And also, my aim is to follow the trend lines and not the headlines, summary and descriptive statistics will allow us to see how Boko Haram and the Nigeria context have evolved overtime without the use of extensive or complex analysis. Graphically assessing the changing risks of terrorists (Statistical Analysis of Event Data concerning Boko Haram in Nigeria 2009-2013)”.

METHODOLOGY AND LITERATURE REVIEW

Study Area

The study was confined to Chibok, Borno, given practical considerations such as time constraints, financial limitations, and anticipated challenges in covering the entire State. The specified study area is the Chibok Local Government, Borno a local Government area responsible for formulating and enforcing rules and regulations in Borno, Nigeria.

Data and Sample Population

The concept of a study population, as explained by Ngechu (2004), refers to a well-defined set of individuals, entities, or events under investigation. Burns and Grove (2009) further elaborate that the population encompasses all elements meeting the criteria for inclusion in a study, emphasizing the need for homogeneity within this defined group. In the context of this study, the population is inked to various branches of the Chibok Local Government area. However, due to constraints in time and finances, the researcher has chosen to focus specifically on the population within the Chibok Local Government area, Chibok, Borno.

In terms of the targeted population for this study, it encompasses all victims within the Chibok Local Government area. This includes individuals across different biological levels, ranging from Men to Women, Children, and teenagers

Table 4.1 Socio-Demographic Characteristics of Respondents

Source: Field Survey, 2019

In this study, the sampling process, defined by LoBiondo-Wood & Haber (1998) and Polit & Hungler (1999) as the selection of a portion of the population to represent the entire population, is utilized. According to Cooper and Schindler (2001), sampling serves to reduce costs, enhance accuracy, expedite data collection, and provide a representation of the population. The sampling frame, a list of items or people forming the population from which a sample is drawn, comprises the population of Chibok Local Government area, Chibok, Borno. The table outlines the distribution of respondents across different population, including men, women, and teenagers and children, totaling 200 respondents.

Determining the sample size involves considerations of the total population, as per the TARO YAMANE formula:

𝑛 =   𝑁/1+𝑁𝑒2

Where:

1. n = sample size
2. N = total population
3. e = tolerable error or error margin (usually 5%)
4. 11 = constant

Hence, applying the formula to a total population (N) of 200 and an error margin of 5%, the calculated sample size (n) is 133. This method ensures that every unit in the population has an equal chance of being included in the sample, aligning with the principles of simple random sampling.

Data Analysis

In terms of ethical considerations, this study adheres to the principles of ethics in research, emphasizing the appropriateness of research behavior concerning the subjects and those affected by it. The confidentiality and anonymity of respondents are prioritized to ensure that the gathered data will not be used to harm the Victims providing information. Respondents were assured that their responses would have no negative repercussions. The study aligns with ethical principles, acknowledging every intellectual property used. Moving on to the analysis, descriptive statistics will be employed to analyze the primary data collected. The Statistical Package for Social Science (SPSS version 20.0) will be utilized for a descriptive analysis of data related to research questions and to test the hypotheses. Completed questionnaires will be collected and analyzed using simple percentages through frequency distributions, while regression statistical analysis will be applied to test the stated hypotheses in the study.

This section deals with the examination of the relationship that exists between the variables identified in the study as stated in the research objectives, research questions and the hypothesis. The model formulated earlier is tested using multiple regression

VOA= ………..Equation 1

Where:

VOA= Victims of attacks.

TRML= terrorist resistance models

TRSL = terrorist resistance solutions, LAIL = laissez-faire style approach, β₀ = Coefficient of regression.

β₁ – β₃ = are the unknown parameters (constant of regression)., UT= is the error term (Noise) and is assumed stochastic

 RESULT AND DISCUSSION

Chapter 4 focuses on the analysis, interpretation, and presentation of primary data gathered for the study through a structured questionnaire administered to 133 respondents in Chibok Local government area. A response rate of 61% was achieved, with 81 correctly filled questionnaires used for analysis. The data presentation includes tables with frequency and percentage distributions for respondents’ socio- demographic characteristics. The hypotheses formulated for the study are tested using regression analysis to determine relationships between variables.

The socio-demographic characteristics of the respondents are outlined in Table 4.1, covering sex, age, marital status, and level of education. The majority of respondents were female (58.0%), aged 26-35 years (58.0%), single (79.0%), with HND/B.Sc./B. A in education (69.1%). The analysis provides a comprehensive overview of the sampled population

Table 4.2: Multiple Regression Result for Model One

Source: SPSS 25 (2019)

Table 4.1 presents respondents’ perceptions of terrorist resistance models, resistant solutions, and laissez-faire style approach, respectively. The tables detail responses on various aspects of counter terrorism approaches, such as supporting people who are at risk of being drawn into terrorist or extremist activities, support community groups that provide for vulnerable people. The data reveal the varying degrees of agreement or disagreement among respondents, forming the basis for assessing counter-terrorism styles’ impact on the victims.

The results of the ANOVA analysis in Table 4.1 reveal compelling insights into the relationship between Resistant solutions and resistant models. Solutions, models, and laissez-faire leadership styles each exhibit a statistically significant impact on victims’ engagement, as indicated by their respective coefficients, t-values, and p-values. The coefficient values signify the estimated change in victims’ engagement associated with a one-unit change in each counter- terrorism solution. Resistant models, with a highly significant p-value (Sig .000) and a robust t- value of 4.5333, emerge as a particularly influential factor in fostering victims’ engagement. Models and laissez-faire resistant styles also exhibit significance, albeit with slightly lower p-values. The overall model, indicated by the R-squared value of 0.777, suggests that approximately 77.7% of the variability in victim engagement is explained by the combination of these resistant styles. The F-statistic (13.046) further confirms the model’s overall significance. These findings underscore the nuanced interplay between victim’s behaviors and victims’ engagement, providing valuable insights for counter-terrorist management and development strategies.

Table 4.8 delves into respondents’ perceptions of overall victims’ engagement, covering aspects like enthusiasm, absorption in tasks, and motivation. The results indicate the varying levels of agreement or disagreement among respondents regarding their engagement in the Terrorists’ occurrences. Tables 4.9, 4.10, and 4.11 extend the analysis to dedication, absorption, psychological empowerment, and satisfaction, providing insights into victims’ sentiments in these domains.

The chapter concludes with a comprehensive discussion and summary of findings, paving the way for the subsequent section on hypotheses testing. The research questions and hypotheses are scrutinized using regression analysis with a 95% confidence interval as the benchmark for accepting or rejecting null hypotheses. This meticulous examination of data provides valuable insights into the relationships between counter-terrorism measures and various dimensions of the victim’s engagement, contributing to the overall understanding of the study’s objectives.

CONCLUSION

In conclusion, this chapter presents a comprehensive analysis of counter-terrorism styles and their impact on employee engagement within the context of Chibok Local Government area. The study, based on responses from 133 participants, highlights the varying perceptions of resistance models, resistant solutions, and laissez- faire counter-terrorism styles. Notably, the findings reveal the expected positive influence of resistant models and resistant solutions on victims’ engagement, aligning with established theories. Surprisingly, the laissez-faire counter-terrorism style, traditionally considered hands-off, also exhibits a positive impact on engagement under specific conditions. This unexpected result emphasizes the nuanced nature of model dynamics. The study’s implications stress the importance of adopting flexible approaches tailored to the unique characteristics of the victims, encouraging the Nigerian Government to consider diverse styles for counterterrorism.

The regression analysis conducted in the study yields crucial insights into the relationships between different counter-terrorism styles and their impacts on population engagement. Notably, the research identifies resistant models, resistant solutions, and laissez-faire counter- terrorism styles as exerting statistically significant and positively correlated effects on victims’ engagement levels. Specifically, an escalation in the manifestation of each counter-terrorism style is associated with a considerable and noteworthy increase in victims’ engagement within the context.

In the concluding section (5.2), the study underscores the paramount importance of counter terrorism styles in shaping the landscape of population engagement. It posits that these findings elucidate the pivotal role played by population behaviors in influencing and, to a certain extent, determining the levels of engagement exhibited by victims within the local government framework.

RECOMMENDATIONS

The recommendations offered (5.3) based on these findings provide actionable insights for government looking to enhance counter-terrorism strategies. These include the advocacy for the encouragement and promotion of resistant models’ practices, recognizing the significance of resistant styles in setting clear expectations and a country free from non- extremists’ practices, and the cautious endorsement of laissez-faire counter terrorist styles while maintaining a delicate equilibrium between autonomy and guidance.

A pertinent aspect highlighted in the study (5.4) is the contribution to existing knowledge. The empirical evidence presented establishes a valuable addition to the body of knowledge by substantiating the significant relationships between distinct counter-terrorism styles and the victim’s engagement. This contribution is instrumental in augmenting our comprehension of how the victim’s behaviors’ intricately shape and Mould the engagement levels of other victims.

Furthermore, the study extends its impact beyond the immediate findings by outlining suggestions for future research (5.5). These suggestions encompass the exploration of moderating factors influencing the relationship between counter-terrorism styles and population engagement, the examination of long-term effects, an investigation into the influence of counter-terrorism style on diverse demographic groups, and the initiation of cross-cultural studies. These avenues for future exploration are poised to deepen our understanding of the nuanced dynamics at play in the realm of counter-terrorism styles and their cascading effects on victims’ engagement across various contexts.

REFERENCES

1. Borge, R., Balcells Padullés, J., & Padró-Solanet Grau, A. (2019). A model for the analysis of online citizen deliberation: Barcelona case study. International Journal of Communication, 2019, 13.
2. Boyd, D., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230.
3. Bussu, S., Bua, A., Dean, R., & Smith, G. (2022). Introduction: Embedding participatory governance. Critical Policy Studies, 16(2), 133-145.
4. Carson, L., & Elstub, S. (2022). Comparing participatory and deliberative democracy. Australasian Parliamentary Review, 37(2), 17-24.
5. Chwalisz, C. (2019). A new wave of deliberative democracy. Carnegie Europe, 26, 1-6.
6. Fishkin, J. S. (2018). Democracy When the People Are Thinking: Revitalizing Our Politics Through Public Deliberation. Oxford: Oxford University Press.
7. Jennings, F. J., Suzuki, V. P., & Hubbard, A. (2021). Social media and democracy: Fostering political deliberation and participation. Western Journal of Communication, 85(2), 147- 167.
8. Jha, C. K., & Kodila-Tedika, O. (2020). Does social media promote democracy? Some empirical evidence. Journal of Policy Modeling, 42(2), 271-290.
9. Kaplan, A. M., & Haenlein, M. (2011). The early bird catches the news: Nine things you should know about micro-blogging. Business Horizons, 54(2), 105-113.
10. Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.
11. Lafont, C. (2015). Deliberation, participation, and democratic legitimacy: Should deliberative mini‐publics shape public policy? Journal of political philosophy, 23(1), 40-63.
12. Loader, B. D., & Mercea, D. (2011). Networking democracy? Social media innovations and participatory politics. Information, communication & society, 14(6), 757-769.
13. Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357-365.
14. Margetts, H. (2018). Rethinking democracy with social media. Political Quarterly, 90(S1).
15. Olaniran, B., & Williams, I. (2020). Social media effects: Hijacking democracy and civility in civic engagement. Platforms, Protests, and the Challenge of Networked Democracy, 77- 94.
16. Olaniran, B., & Williams, I. (2020). Social media effects: Hijacking democracy and civility in civic engagement. Platforms, Protests, and the Challenge of Networked Democracy, 77-94.
17. Pateman, C. (2012). Participation and Democratic Theory. Cambridge University Press. Persily, N., & Tucker, J. A (2020) Introduction.
18. In Perkily, N., & Tucker, J. A. (Eds.). Social media and democracy: The state of the field, prospects for reform. Cambridge, UK: Cambridge University Press.
19. Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest. Yale