SOFT POWER AS A COUNTER MEASURE AGAINST GLOBAL TERRORISM: SOCIAL MEDIA WITHIN EMERGING MIDDLE EASTERN DEMOCRACIES

Edmond R Gray

Research Associate
The Wish Center

Abstract: Through quantitative method, this research demonstrated that by complimenting hard power with soft power to fight terrorism, the war on terror will not only end quickly, it will be less costly than using only military power.  According to Joseph Nye (2006), “soft power is used by states, non-state actors, non-governmental agencies, and other international political players.” The exclusive use of military power, economic and enhanced interrogation techniques to counter terrorism has been quite successful.  However, failure to compliment hard power with soft power has prolonged the war on terror and made it much expensive to sustain. Consequently, through the use of television, Facebook and Twitter, social media play a big role in the diffusion of power from all states to non-state actors (Nye, 2004). Moreover, in an information age, not just whose army wins, but whose story is also heard. This study illustrated the role social media plays in ending the war on terror. To further illustrate the assertion that behavior can be influenced with a set of variables, a multiple linear regression test was done. The plot conducted for that analysis indicates that in the multiple linear regression analysis, there is no tendency in the error terms. When that happens, the graph appears as a staircase.

Introduction

This research used quantitative methodology to evaluate the possibility of blending smart power and soft power to end the war on terror.  According to Joseph Nye (2006), “not only is soft power used by states, but by non-state actors, non-governmental agencies, and other international political players.” The exclusive use of military power, economic and enhanced interrogation techniques to counter terrorism has produced mixed results so far.  However, failure to compliment hard power with soft power has prolonged the war on terror and made it quite expensive to sustain. Consequently, through the use of television, Facebook and Twitter, social media play a big role in the diffusion of power from all states to non-state actors (Nye, 2004). Moreover, in an information age, not just whose army wins, but whose story is also heard. This study fully demonstrated the role of social media in ending the war on terror. The problem of this research is that using only hard power, which is military might, economic and enhanced interrogation methods will costly and prolong the war on terror if it is not complimented with soft power.

The purpose of this research is to evaluate the likelihood of ending the war on terror if the counterterrorist strategy includes soft power. This study demonstrated that using more persuasive methods (soft power) will quickly end terrorism, than hard power alone. Studies have shown that the exclusive use of hard power by the United States and its allies is influencing great deal of anti-western sentiments, including grievances, anger, revenge, and strains among terrorists and their sympathizers (Raman, 2007; Kochan, 2008; Agnew, 2010). This study looked at how soft power defuses these sentiments that lead to terrorism.

The question to be addressed in this research is: Does complimenting hard power with soft power diffuse terrorism sentiments? Hypothetically: Does influencing the behavior of someone change his or her attitude?

The answers to these questions are supported by a set of non-experimental designs (relational designs), which tested the variables or similar variables said to influence terrorism. Mostly, relational designs or correlational studies measure a range of variables, which are connected to a particular event (Adèr, Mellenbergh, & Hand, 2008). In addition, each dependent variable (i.e., anger, grievance, revenge and strains), which is said to be influenced by hard power, is tested on a set of multiple linear regression. In addition, the central tendency and dispersion of each variable is further evaluated in terms of its relationship to terrorism (independent variable), its frequency of occurrence through graphs and charts.  For this analysis, the theory of planned behavior will be tested against a set of independent and dependent variables to determine their correlation to terrorism. Accordingly, theory of planned behavior is a theory about the correlation between attitude and behavior (Sniehotta, 2009).

Problem

Hard power is the use of military might, economic and enhanced interrogation methods. Failure on the part of the United States to compliment hard power with soft power is why the war against terrorism has not only prolonged, but proven quite costly to maintain.

Purpose

This study will demonstrate that using more persuasive methods (soft power) will quickly end terrorism, than using only hard power.

Studies have shown that the exclusive use of hard power by the United States and its allies is influencing great deal of anti-western sentiments, including grievances, anger, revenge, and strains among terrorists and their sympathizers (Raman, 2007; Kochan, 2008; Agnew, 2010). This study looked at how soft power defuses these sentiments that lead to terrorism.

Research Questions

The question to be addressed in this research is: Does complimenting hard power with soft power diffuse terrorism sentiments? Hypothetically: Does influencing the behavior of someone change their attitude?

Research Theory

For this research, the theory of planned behavior will be tested against a set of independent and dependent variables to determine their correlation to terrorism. Accordingly, theory of planned behavior is a theory about the correlation between attitude and behavior (Sniehotta, 2009).

Research Audience

The audiences for this study are those policy makers who are involved with the war on terror, students of social science, non-state actors, such as international organizations that are seeking peaceful means as alternative measures to end the war on terror, treatment facilities, and civic society. The collection of data is mostly through observation of inmates, interviews, gathering of document and other public records such as past cases that have been decided in federal courts (Creswell, 2009). In order to evolve concrete findings, this study utilized a triangulation design. The aim is to be able to combine the stronger aspects of the non-experimental designs. This will also provide the opportunity to compare and contrast various findings. Whenever the outcomes or findings of a research correlate, they tend to internally or externally solidify or validate the study (Onwuegbuzie & Leech, 2005).  

Summary of Literature

Since the war on terror was officially declared by the Bush administration in 2001, the Department of Homeland Security has done exceptionally well in marshaling the resources of the United States government in preventing further terrorist attacks against America, interests and allies.  Moreover, these efforts of the Department of Homeland Security have yielded optimistic results (Chertoff, 2008).  Thus, assessing these results fairly, the United States is much securely safer today. However, it would be erroneous to profess that the threat presented by terrorism no longer holds today.  For instance, according to a National Intelligence Estimate published in July, 2007, the threat of terrorism over the United States and its allies, by al Qaeda and like-minded organizations is relentlessly evolving.  Accordingly, the adherents to al Qaeda’s violent and extremist ideology continue to pose threats to civilization. Furthermore, Joseph Nye (2004) stated that “the primary goal of extremists is to gain full access to modern technology and far greater destructive capabilities for future globally use.”

Moreover, studies agreed that the use of military action and other smart power options like economics and enhanced interrogation methods by the United States and its allies to fight terrorism have so far proven successful. Those successes included, but not limited to the removal of the Taliban from power in Afghanistan, killing Osama Bin Laden and a number of his loyalists, significantly weakening al Qaeda, deposing of Saddam Hussein). In addition, key terrorism enablers like communications, finance, and travel have practically been paralyzed or frustrated (Krieger & Meierrieks, 2010; Garfinkle, 2004; Friedman, 2004).  As a result, these gains have prevented terrorists from entering the United States, and sometimes capturing, stopping or killing them before traveling.

Consequently, to prevent further growth of terrorism and loss of recent gains, the United States should find means of wining nations and people who are sympathetic to the goals of terrorism by complementing the use of hard or smart power with soft power.  While it is necessary to use smart power to combat terrorism, not complimenting it with soft power shortens the road to permanent victory in the war against terror (Nye, 2008; Chertoff, 2008). Accordingly, the fight against terrorism should not only focus on preventing terrorists from attacking the United States and its allies, but must prevent people from being recruited by terrorist organizations. Doing so entails addressing the variables that are mostly considered key in the growth of terrorism: existence of extremism within politically failed Third World economies.

The idea of complimenting smart power with soft power is one, which has been fully supported by some studies. For instance, according to Martha Crenshaw (2007), “understanding the motivations and steps to radicalization are two factors, which democratic government can adapt to fight terrorism.” Moreover, understanding that the threats of terrorism cannot be entirely defeated is a factor to acknowledge while strategizing against terrorism.  Similarly, David Omand (2005) contended that, “the lack of a theoretical based framework is an obstacle to understanding the precise strategy to end terrorism.” Furthermore, simply eradicating the ideological base of terrorism will not end terrorism, without completely changing the fundamental ideology.  Accordingly, changing the ideology base of causes of terrorism requires soft power.

However, in order to improve on the meaning of terrorism based on ideology, Chava Frankfort-Nachmias and David Nachmias (2009) recommended a method of measuring dependent variable (terrorism) at various levels in terms of its scope, intensity, and frequency. Consequently, this is based on the assumption that certain independent variables influence low and high levels terrorism (e.g., revenge, anger, grievance, etc.). According to J. I. Ross (1996), each form of terrorism has different pattern of causation.  Measuring cause and effect is an important aspect of any study. For this analysis, in order to validate the assertion that ideology and other psychological factors sustain terrorism, there is the need to measure for accuracy.

Measurement entails the assigning of numbers or other empirical symbols properties (variables) based on a set of rules (Frankfort-Nachmias & Nachmias, 2009 p.139). Incompleteness of data affects how it is analyzed in a number of ways. The classic example is when we draw a probable with the aim of using inferential statistics to answer questions about a population (USGAO, 1992). Hence, to prove the accuracy of a data, not only should the variables be effectively measured, instrument used to measure must be validated also.

To test and re-test this research, it is suggested to administer the measuring instrument to the same group or population at least twice. In addition, compute the correlation between the two sets of observations or scores (Frankfort-Nachmias & Nachmias, 2009). Moreover, since measuring instruments are rarely completely or equally valid, evidence of validity may be almost lacking. As such, most researchers estimate reliability by one or more of the following methods: test – retest, parallel-forms, and split half.

This research takes into account that knowing the level of measurement is very important, in that it helps with the process of interpreting the data from the variables (Trochim and Donnelly, 2007). In addition, collection of data in the social sciences entails measurement of perceptions, cognitions, opinions, and other latent constructs that can’t be measured directly (Garger, 2010). There are four levels of measurements considered in this analysis. Nominal is the lowest of the measurements, which will be the level of measurement for this analysis.

Since this is a quantitative analysis, using nominal level of measurement is not uncommon. Usually, variables measured on a nominal scale are often referred to as categorical or qualitative variables (Lane, 2010). The variables in the analysis are terrorism, religious preference, age of respondent, and others, a Likert Scale would have been used if this were a real study. The reason being, it is effective in obtaining consistent surveys responses, and also allows respondents to provide feedbacks, which can be slightly more expansive than simple close-ended questions. Moreover, it is much easier to quantify than a completely open-ended response. A Likert Scale lists a set of statements (not questions) and provides a 5-6-point scale for which the participant can state his or her opinions (Parnaby, 2007).  At this level, numbers of other symbols are used to classify objects or events into categories that are names, classes of qualitative characteristics.

For this analysis, multiple regression tables are used. Ordinal relation requires the equivalence level to hold for all the cases in the same rank, whereas, the > relation holds between any pair of ranks. Interval level is the exact distant between the observation. For this reason, at the interval level of measurement, the differences between observations are isomorphic to the structure of the arithmetic used with the associated values. Therefore, ratio levels are variables that have absolute and fixed natural zero points (such as the frequency of worship) can be measured. Additionally, variables that can be measured at the ratio level can also be measured at interval, ordinal and nominal levels. As a rule, properties that can be measured at a higher level can also be measured at a lower level, but not vice versa. As such, empirical research in the social sciences requires both accurate and reliable measures (Frankfort-Nachmias and Nachmias, 2009).

 The population or respondents of this analysis will come from Facebook, Twitter, and other social media entities. This analysis used religion and religious believers. In order to validate its content, this analysis must cover all the attributes of the concept of terrorism and all possible variables that lead to terrorism. The measuring instrument must be empirically valid, and the measuring instrument and the measured outcomes must be in sync. And lastly, the research has to relate to the theoretical framework and other variables being covered, so as to determine its logical conclusion.  Moreover, caution must be taken to avoid errors, or not to influence the validity of the conclusions drawn from testing the hypotheses (Creswell, 2009). Hence, experimental designs, individuals or other units or analysis are randomly assigned to the experimental and controlled groups, and the independent variable is introduced only to the experimental group.

For this analysis, a standard regression test will be done using data from SPSS. Krieger & Meierrieks (2010) argued that, “standard regression models require that the dependent variable is random and continuous.  However, since this analysis is not dealing with an actual population sample, it will only analyze the correlation of a set of descriptive variables. For instance, in the following multiple regressions, the outcome or dependent variable (terrorism) is single, with multiple predictors. Here the dependent variable regresses on all of the predictor variables in the data set.

This example is based on a 2006 FBI’s crime statistics. The rational for using this example is to show the relationship between the respondent’s faith, age and patter or frequency of worship. The idea is to determine whether age and level of belief are correlated to belief (ideology).  The hypothesis of this analysis is high religious affiliation creates room for terrorism affiliation.  The effect of those who frequently attend religious worship is measured by age and religious preference.  According to S. Green and N. Salkind (2011), the first step to a multiple linear regression test is to determine whether there is a linear relationship between the independent variables and the dependent variable. Based on the scatter plots, the indication is that there is a good linear relationship between frequency of worship, worshiper’s age, and religious affiliation.

The descriptive table below tells us the different variables.

Descriptive Statistics
	Mean	Std. Deviation	N
RS RELIGIOUS PREFERENCE	2.28	2.290	1486
HOW OFTEN R ATTENDS RELIGIOUS SERVICES	3.85	2.722	1486
AGE OF RESPONDENT	46.17	16.707	1486

The second table shows the multiple linear regression model summary and overall fit statistics. In this test, the adjusted R² of our model is 0.029 with the R² = 0.049 that means that the linear regression explains 4.9% of the variance in the data. The Durbin-Watson d = 1.959, which is between the two critical values of 1.5 < d < 2.5 and therefore we can assume that there is no first order linear auto-correlation in the multiple linear regression data.

Model Summaryc
Model		R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
dimension0	1	.172a	.030	.029	2.257
	2	.225b	.051	.049	2.233	1.959
a. Predictors: (Constant), AGE OF RESPONDENT
b. Predictors: (Constant), AGE OF RESPONDENT, HOW OFTEN R ATTENDS RELIGIOUS SERVICES
c. Dependent Variable: RS RELIGIOUS PREFERENCE

The next table below is the t-test, the linear regression’s t-test has the null hypothesis that there is no linear relationship between the variables (in other words R²=0). Testing for variables relating to ideology like religious belief, the t-test is highly significant, thus we can assume that there is a linear relationship between the variables in our model.

ANOVAc
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	230.362	1	230.362	45.216	.000a
	Residual	7560.515	1484	5.095
	Total	7790.878	1485
2	Regression	395.586	2	197.793	39.664	.000b
	Residual	7395.291	1483	4.987
	Total	7790.878	1485
a. Predictors: (Constant), AGE OF RESPONDENT
b. Predictors: (Constant), AGE OF RESPONDENT, HOW OFTEN R ATTENDS RELIGIOUS SERVICES
c. Dependent Variable: RS RELIGIOUS PREFERENCE

The next table below shows the multiple linear regression estimates including the intercept and the significance levels.In our stepwise multiple linear regression analysis it was discovered that a non-significant intercept but highly significant level of religious attendance coefficient, which can be interpreted as the higher the respondent’s the more they are likely to increase their rate of worship.

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	3.364	.172		19.546	.000
	AGE OF RESPONDENT	-.024	.004	-.172	-6.724	.000	1.000	1.000
2	(Constant)	3.719	.181		20.536	.000
	AGE OF RESPONDENT	-.021	.003	-.153	-5.990	.000	.983	1.017
	HOW OFTEN R ATTENDS RELIGIOUS SERVICES	-.124	.021	-.147	-5.756	.000	.983	1.017
a. Dependent Variable: RS RELIGIOUS PREFERENCE

Through sampling, units required to conduct tests are selected. In the above example, the unit or population was drawn from those who three criteria, age, religious preference, and frequency of worship. To select randomly, the presumed population must have equal probabilities of choice (Trochim & Donnelly, 2007). However, a distorted population size skews, which can impact the outcome or credibility of the research. For instance, a classic example is drawing a probability sample and hoping to use inferential statistics to answer to a question such as the one asked in this analysis: Does influencing the behavior of someone change their attitude?

The sampling strategy used for this analysis is probability sampling known for its representativeness and its ability to minimize errors, which can be controlled. The strength of the sampling strategy is that every sampling unit of the population has an equal and known probability of being included. However, the weakness is an exclusive reliance on non-probability techniques (convenience, purposive and quota sample techniques), which is also known as particularly purposeful sampling (Pearson Education, 2010).

A probability sample is distinguished by the ability to specify the probability at which each sampling unit of the population will be included in the sample. In the simplest case, all units of a population have the same probability of being included in the sample (Frankfort-Nachmias & Nachmias, 2009). Accordingly, another factor to consider is the cost of the research and how practical the study would be. Therefore, if cost and other practical limitations do not enter into the decision about the sample size, there is no difficulty in determining the desired size. Studies have indicated that “generalization has to do with the external validity of the study (Cook & Campbell, 1979; Shadish, Cook & Campbell, 2002, p. 34).

For instance, in a randomized controlled trial involving 409 women recruited from various family planning clinics in Northern California, which evaluated the efficacy of skill training design to increase the use of condom among women, chose the experimental design method over others (Choi, Hoff, Gregorich, Grinstead, Gomez & Hussey, 2008). The relevance of the randomized controlled trial of 409 women to this analysis is that they are both behavioral studies that looked at variables such as race, attitude, and race. 

Additionally, Frankfort-Nachmias and Nachmias (2009, p. 104) agreed with the idea that in experimental designs, individuals or other units of analysis are randomly assigned to the experimental and control groups, and the independent variable is introduced only to the experimental group. Such designs allow for comparison, control, manipulation, and, usually, generalizability.  The outcome of this study may be generalized only to the variables covered in the test. Secondly, there is always the possibility of having an oversized sample for studies, which openly invite study participants (Choi et al., 2008). Therefore, a better approach to selecting study participants is securitizing them. Not only is this key to social science, it brings credibility to the study.

The ethical concerns raised in the 1971 Stanford Prison Experiment serve as synergy to this study. Firstly, participation in this study would be fully consensual—no one will participate through survey and other means, if he/she has not consented after full or partial disclosures have been made. To achieve this goal, this study has been designed to incorporate the basic moral values surrounding the various legal and ethical research implications (Creswell, 2009; Onwuegbuzie & Leech, 2005).

Accordingly, using force is not only discouraged, all financial and other potential benefits to participants will be fully disclosed before hand to research participants—in other words, every participant must provide informed consent only after fully understanding the potential risks, mentally and physically, that may come up in the study. Besides, the stress of participants’ confidentiality is not only an ethical concern that must be upheld in this study, it is a legal obligation as well.

Consequently, confidential concerns mostly apply to those participants, such as rehabilitated terrorists, or those serving prison times for acts of terrorism, which are imminently in danger of hurting themselves and others.  The ensure that these and other ethical standards are protected, there is an Institutional Review Board (IRB) that will established by Walden to assist in reviewing this study against all potential ethical violations against participants.  This study has also adopted codes to help protect subjects and further guide the direction of the research—autonomous participation, non-malfeasance (ensuring that participants are not intentionally hurt by the study), but beneficence (rewarding to participants), treat all human subjects in a just manner regardless of race, tribe, color and creed, fidelity (i.e., ensuring that promises to participants are honestly honored), genuineness (being truthful). Moreover, in addition to the ethical expectations set by the IRB, Creswell (2009) has listed a number of ethical expectations established by the research profession which this study will be utilizing. 

Conclusion

The plot from the multiple linear regression test conducted for this analysis indicates that in this multiple linear regression analysis has no tendency in the error terms. When that happens, the graph appears as a staircase. This analysis presented an overview of existing literature on complimenting hard power with soft power from a theory-based framework on the development of counterterrorism strategies. Consistent with the multiplicity of literature, which provided an array of counterterrorism strategies, the emphasis is mostly on the use of hard power.  However, the gap in counterterrorism strategies exists in the absence of a coherent strategy that blends hard and soft powers, yet to be formulated.  The inclination from the literature, suggests the need for the development of multicultural strategies that suggest a shift from smart power to diplomacy.

This analysis also conducted a multivariate linear test to be able to determine whether it is possible to determine the assertion that the causes of terrorism are psychologically connected to a set of testable behaviors, which can been influenced positively through communication, economic and social variables.  Furthermore, in the conflict of ideas, words count. However, though critical, good deeds by themselves are not enough. They have to be accompanied by a consistent level of ideological messages that covert the minds of terrorists and those sympathetic to their causes.

This research will serve as a viable alternative to the smart and hard traditional methods used to counter terrorism. In addition, it will serve as a guide for public administrators, diplomats, and nonprofit organizations looking for ways to positively impact likely terrorists and their sympathizers. Moreover, the research will articulate the various means of spreading cultural and political influences among emerging democracies through social networking and other traditional methods (i.e., radio and TV).

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