Hypotheses and Theories of Cybercrime

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Cybercrime has multiple similarities and differences from traditional crime. Firstly, there are generally the same motivations for committing crimes and performing violations. People choose to get involved in criminal activity due to the desire for financial and material goods, forbidden sexual interests and perversions, or even harassment. Although despite all the resemblances, cybercrimes differ from the real ones in a way that sometimes is harder to detect, and it gives more freedom for people to commit them. For instance, on the Internet, it is much easier to bully and harass people with impunity due to the distance that separates the offender and victim (Lowry et al., 2016). It is crucial to add that there is a possibility of staying anonymous and not revealing the true identity so that it is possible to avoid direct exposure. Thus, this paper aims to compare and contrast different theories connected with cybercrimes and identify the hypotheses with the main reasons for committing an offense.

The first theory is built around the subcultures, the groups with their specific features and norms. While it may be harmless and serve to unite people with similar interests or life perceptions, subcultures can also unite those with unacceptable preferences in general society (Holt et al., 2017). Since the Internet provides some sort of anonymity, people can find each other and have conversations about their subjects of interest in a safe environment without being afraid of judgment.

It is connected with the social learning theory because the members of a certain subgroup are affected by the common molars and outlooks characterized specifically for this circle. Often, it brings together individuals with deviant sexual preferences and desires, so they can exchange their thoughts and even materials to find satisfaction in the webspace (Holt et al., 2017). Thus, the subcultures and the social learning theory have the same behavioral patterns based on observing other people’s actions online.

There is Agnew’s General Strain Theory, which also has some foundations for cybercrimes. The theory implies that when a person fails to achieve financial success or avoids losses and adequate risks, they are more likely to be engaged in criminal behavior (Holt et al., 2017). The frustration the individual feels when they fail to accomplish something can stimulate them to find new ways of achieving it and stimulates them to take illegal actions.

In addition, Deterrence theory has several main principles about criminology and why people commit crimes. The hypothesis comes from the classical school of criminology that, in the first place, views the individual as a rational creature with free will and the ability to make decisions (Holt et al., 2017). Since the person consciously decided to commit a crime knowing that it will have negative consequences for someone and for themselves in the first place if they are caught. Moreover, when no one pushes them to perform a violation, it demonstrates that the individual chose the easiest way.

Therefore, without considering certain exceptions and mental illnesses, those who committed the crime were perfectly aware of its illegibility and the possible outcomes. It aligns with the example of digital pirates who know that they will take no responsibility for their actions, and they are more stimulated to pirate games, films, series, and many more (Holt et al., 2017). Deterrence theory has several similarities with Agnew’s General Strain Theory, and in some way, they complement each other. Since the person has committed a crime, even if they may feel like it was necessary and their only chance to get what they want, they acted according to their free will and purposely made a choice.

Techniques of neutralization aim to identify how personal morals and perceptions influence the decision of whether to commit a crime or not. For example, the student may have conformal beliefs and respect the law but download pirated music and books without considering it a criminal act. Moreover, many people seem to be perceived cybercrime differently and find reasons why it has nothing to do with criminality (Holt et al., 2017). It resembles Agnew’s General Strain Theory because people have justifications for their actions in both cases. In the first case, individuals might not see anything wrong with it. In the other case, they might claim that they barely had any other choice.

However, neutralization and Deterrence theory techniques seem to be the main reasons offenders commit crimes on the Internet. The crucial fact is that there is a lack of physical interactions since it is happening online, which gives the feeling of action insignificance. That is why some people can genuinely believe that pirating materials is not an actual crime or that watching and sharing child pornography is improper because it can be done safely without external judgment.

In conclusion, different cybercrimes can be easier to perform due to the possibility of staying anonymous and the distance separating the victims and the offenders. Therefore, the individuals may bully people, spread viruses, and exchange illegal materials with a minimized feeling of danger and impunity. The subculture and social learning theory imply that online behavior can be influenced by watching other people from the same community or group, which can be a foundation for criminal actions. Deterrence and the techniques of neutralization seem to contain the most explanation of why people commit cybercrime. Agnew’s General Strain Theory compliments them in a way that offenders act out of their free will and choice to get something the easier way and gain more pleasure out of it.

References

Holt, T. J., Bossler, A. M., & Seigfried-Spellar, K. C. (2017). Cybercrime and digital forensics: An introduction. Routledge.

Lowry, P. B., Zhang, J., Wang, C., & Siponen, M. (2016). Why do adults engage in cyberbullying on social media? Integration of online disinhibition and deindividuation effects with the social structure and social learning model. Information Systems Research, 27(4), 962-986.

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