# Understanding Internet Fraud: Denial of Risk Theory Perspective

Martin Offei, Francis Kofi Andoh-Baidoo, Emmanuel Ayaburi, David Asamoah

► To cite this version:

Martin Offei, Francis Kofi Andoh-Baidoo, Emmanuel Ayaburi, David Asamoah. Understanding In-
ternet Fraud: Denial of Risk Theory Perspective. International Working Conference on Transfer
and Diffusion of IT (TDIT), Jun 2019, Accra, Ghana. pp.415-424, 10.1007/978-3-030-20671-0_28.
hal-02294692

HAL Id: hal-02294692

https://inria.hal.science/hal-02294692

Submitted on 23 Sep 2019

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# Understanding Internet Fraud: Denial of Risk Theory Perspective

Martin Offei<fnref n="1" />, Francis Kofi Andoh-Baidoo<fnref n="2" />, Emmanuel Ayaburi<fnref n="2" />, David Asamoah<fnref n="3" />

**Abstract.** Internet fraud has become a global problem attracting the attention of researchers, practitioners and policy makers. Existing empirical theoretical studies on internet crimes have mostly used neutralization and deterrence theories. Despite the insights from these theories, we are still observing an increase in the number of internet crimes. We argue that Denial of Risk theory may provide new insights on internet crimes such as internet fraud. We examined how each of the three dimensions of Denial of Risk theory (scapegoating, self-confidence and comparing of risk) serve as antecedent of the intention to commit internet fraud. Using responses from 350 individuals from internet fraud hotspots, we showed that scapegoating, self-confidence and comparing of risk are positively related to intention to commit internet fraud. The study offers theoretical and practical contributions to research in the spectrum of internet fraud and the theoretical application of denial of risk in cybercrime research.

**Keywords:** Internet Fraud, Denial of Risk Theory, Scapegoating, Self-Confidence, Comparing of Risk.

## 1 Introduction

Cybercrime has become a global issue as it affects individuals, businesses and governments everywhere including developing economies (Chatterjee et al. 2018; Li, & Cheng, 2013, Jayabalan et al., 2014; Shareef et al. 2018). Examples of cybercrimes include identity theft, piracy, hacking and financial fraud committed over the internet. We focus on internet fraud in this research because such crimes affect a wide audience and perpetrators are usually anonymous. Internet fraud is broadly described as crime perpetuated on the internet components such as web sites, chat rooms, and e-mail, to offer non-existent goods or services to consumers, communicating false or fraudulent representations about the schemes to consumers, or transmitting victims' funds, access devices, or other items of value to the control of the scheme's perpetrators (Kubic, 2001). Internet fraud appears in diverse forms such as “skimming, Card-Not-Present (CNP), stolen credentials buyers, professional hackers/crackers, identity theft and among others (Boyle & Walker, 2016, Jegede et al., 2016).

<footnote n="1">¹ Koforidua Technical University, P.O. Box KF 981, Koforidua, Ghana</footnote>

<footnote n="2">² University of Texas Rio Grande Valley, 1201 W University Drive, Edinburg, TX 78539, USA
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana</footnote>

<footnote n="3">³martin.offei@ktu.edu.gh</footnote>
Most prior research have used theories such as neutralization, deterrence and motivation to extend our understanding of internet crime phenomenon (Lazarus, 2018). In general, these theories assume the individual appreciates the enormity of their crime or severity of the punishment. We argue that the increasing number of internet crimes despite the recommendations from prior research is because those individuals involve do not envision any risk in their quest to defraud unsuspecting victims. This is because most individuals involved in internet crime believe they cannot be located due to the anonymity and vast size of the internet. Employing a risk perspective, this study seeks to understand the internet fraud phenomenon. Specifically, we seek to answer the following research question: What risk perception factors influence individuals who intend to commit internet fraud?

This research seeks to make theoretical and practical contributions to research in the spectrum of internet fraud by using denial of risk theory as its basis. Denial of risk theory refers to the cognitive way individuals deal with risky behaviors by rejecting the presence or effect of risk (Peretti-Watel, 2003). We developed a research model to understand how risk perception influence individuals' intention to commit internet fraud using components of denial of risk theory. We collected data from 350 individuals located in internet crime hotspots on key constructs, denial of risk and intention to commit internet fraud in our model. The results of the analysis of response data offer both theoretical and practical implications. For theory, we have shown the efficacy of Denial of Risk theory in understanding burgeoning Internet fraud discourse. For practice, our study provides insights for law enforcement agencies to understand the factors that act as drivers of internet fraud.

## 2 Theoretical Background – Denial of Risk Theory

Denial of risk refers to cognitive ways to develop adaption to risky behaviors by rejecting the possibility of suffering any loss (Peretti-Watel 2003). The theory posits that an individual may deflect the level of risk by comparing the crime to an acceptable action they consider a crime. The theory of denial of risk has three main constructs: scapegoating, self-confidence and comparing of risk. Scapegoating is when an individual stereotype actions of others they consider harmful (them) relative to their behavior. For example, young drivers label (scapegoat) older drivers as overly cautious as a justification for their over speeding habit. Self-confidence is when an individual reject risk by distinguishing themselves from anonymous group. For instance, some drivers who overspeed justify their action by suggesting that they possess better driving abilities than an average driver on the road. Comparison-between-risk arises when an individual compares their actions with that of a group whose action are already accepted. For example, individuals who text-while-driving may deflect risk by suggesting that driving-while-using-wireless communication is equally risky as individuals in both situations are affected by the information been communicated. Denial of risk theory has been used in the criminology literature to understand why individuals commit crime such as the use of cannabis (Apostolidis et al., 2006). We argue that each of these dimensions of denial of risk theory may influence an individual's intention to commit internet fraud because of the anonymity provided by the internet.
Internet fraud involves the use of internet and related activities that violate the eth-
ical conducts of the internet (Kubic, 2001). Internet fraud is of concern globally for
governments and law enforcement agencies (Grabosky, 2015). Internet fraud is per-
petuated by individuals or groups of individuals with different backgrounds such as
needs, education and cultural idiosyncrasies (Morris & Higgins, 2009). The tech-
niques used by perpetrators of internet fraud make them believe the consequences of
their actions are less harmful and sometimes beneficial to the victims (Brooks, 2016).
We employ the three dimensions of denial of risk to develop a conceptual model that
explains intention to commit internet fraud.

**3 Conceptual Model Development**

Scapegoating

The scapegoat construct of the denial of risk theory deals with the propensity to la-
bel an identifiable group as taking actions that are deemed riskier (Lazarus, 2018).
Employing scapegoating technique results in feelings of prejudice or stigmatization
toward the person or group that one has accused of committing crimes that are rela-
tively harmful. It provides the basis for the perpetrator of the crime to deflect any risk
arising from their actions, thus resulting in positive self-image (Harris & Dumas,
2009, Lazarus, 2018). For instance, individuals who are engaged in internet fraud feel
they are not causing harm to their victims’ relative to harm caused by hackers who
alter or prevent victims from continuing their normal lives. For instance, some cyber-
criminals in developing nations have believe that criminals in western countries cause
more harm to their victims than they do (Hutchings, 2013). Such rationalization of the
risk of committing internet fraud transforms risk into blame (Sugiura, 2018). The
criminals use scapegoating to absolve themselves from their criminal behavior. Thus,
we posit that:

**H1:** *Scapegoating as a technique of denial of risk positively affects the intention to commit internet fraud*

Self-confidence

Self-confidence is the cognizance that makes one believe in their abilities to per-
form certain tasks without much difficulty. The self-belief in a confident person is
very high and rises with more complex task such as creating situations that makes it
easy to defraud another person. Self-confidence helps individuals to participate in the
crimes they commit (Sigala, 2017, Jegede et al., 2016). Although individuals know
that they are committing a crime that is punishable by law, they deny this risk by
showing confidence they will not be punished. Sometimes perpetrators of internet
crimes draw solidarity among themselves to increase their confidence level (Jegede et
al., 2016, Koay, 2018). Therefore, it is posited that:

**H2:** *Self-confidence as a technique of denial of risk positively affect the intention to commit internet fraud*

**Comparison between Risks.** Comparing of risk looks at the lesser evil perceived by the perpetrator of internet fraud. Individuals who commit internet fraud compare their activities to other crimes such as hackers or identity theft and perceive that the
activities they are involved in are better evil (Choo, 2011). By comparing the risk of their crimes, criminals perceive some crimes as much riskier than others.

It is reported that some individuals who are engaged in internet fraud compare the crime to other crimes such as stealing and cheating and perceive that such crimes are not less harmful when compared to what they do (Mekonnen Padayachee, & Meshesha, 2015). Individuals who commit internet fraud are shielded in the comfort of anonymity. Such individuals proclaim and perceive they are far away from the victims they defraud and believe that they cannot be caught or punished (McMullan, & Rege, 2010). Therefore, it is posited that:

**H3:** *Comparing of risk as a technique of denial of risk positively affects the intention to commit internet fraud*

<figure><img src="image_2.png" /><figcaption>**Figure 1. Research Model**</figcaption></figure>

# 4 Methodology

## Measures and Sample

This study was conducted using a survey research design (Choudrie & Dwivedi, 2005). The target population for this study are individuals located in alleged hotspots (internet cafes) where internet fraud originates (Odou & Bonnin, 2014). Unique high unemployment in some of these regions encourages internet fraud (Smith, Torstensson, & Johansson, 2001, Boateng et al., 2011). Due to the heterogeneous and illegality of the phenomenon understudy, the total population of the study is not known. Our sample included 350 respondents. The sampling techniques used in this study are cluster sampling, convenient sampling, and snowball sampling approach. Cluster sampling are the regional distribution of internet fraudsters within Ghana. The clusters represented internet fraud hot spots within the selected regions (Greater Accra, Eastern, Volta, Ashanti, Central and Western), convenient sampling was used to select a section from each cluster in the internet fraudsters' cafes. Furthermore, snowball sampling technique was used to identify other individuals suspected to be involved with internet fraud and online relationship scams.
We adopted 12 items from Peretti-Watel, (2003) to measure scapegoating, self-confidence and comparing of risk. These items were used in prior study to juvenile intent to commit an illegal act -weed smokers- in France.

Our dependent variable, Internet fraud intention was operationalized as a second order construct consisting of two first order constructs (deception and fraud). The 8 items used to measure our dependent variable were adopted from (Park and Sung, 2015; Siponen, & Vance, 2010). Details of the measures used in the study along with their loadings are presented in the appendix.

## 5 Results and Analysis

The Structural Equation Modeling (SEM) was used to investigate the causal paths hypothesized in this study. A two-step approach was used to analyze the data. In the first step, the covariance-based technique was used to assess the appropriateness of the measurement model. The covariance based technique was used as it minimizes the differences between the covariance of the collected sample and that of the ones predicted by the model (Haenlein and Kaplan, 2004) and reproduces the covariance matrix of the observable variable (Chin and Newsted, 1999). For testing the structural model, variance based partial least square (PLS) SEM was used as it maximizes the variance of the dependent variable which is explained by the independent variables (Hair et al., 2014).

We used the Cronbach Alpha, Composite Reliability and Average Variance Extracted (AVE) to test for reliability and validity of the constructs. Discriminant validity tests were performed to test for accuracy of the measurement items by using Förnell-Lecker Criterion and Heterotrait-Monotrait (HTMT). A HTMT is a more resilient test for discriminant validity than cross loadings. As shown in Table 1, constructs reliability are confirmed as composite reliability (CR) values for all factors were above the recommended 0.7 value threshold, indicating item consistency (Hair et al. 2012). The variance explained (AVE) is above the satisfactory threshold of 0.5, confirming convergent validity (Hair et al. 2012).

<table><caption>**Table 1 Construct Validity**</caption><thead><tr><th></th><th>CR</th><th>AVE</th><th>CA</th><th>DorCr</th><th>DorSc</th><th>DorSg</th><th>IcifDp</th><th>IcifId</th></tr></thead><tbody><tr><td>DorCr</td><td>0.833</td><td>0.625</td><td>0.842</td><td><strong>0.79</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>DorSc</td><td>0.878</td><td>0.705</td><td>0.724</td><td>0.214</td><td><strong>0.84</strong></td><td></td><td></td><td></td></tr><tr><td>DorSg</td><td>0.874</td><td>0.699</td><td>0.790</td><td>0.222</td><td>0.971</td><td><strong>0.836</strong></td><td></td><td></td></tr><tr><td>IcifDp</td><td>0.882</td><td>0.652</td><td>0.775</td><td>0.179</td><td>0.417</td><td>0.428</td><td><strong>0.807</strong></td><td></td></tr><tr><td>IcifId</td><td>0.866</td><td>0.684</td><td>0.689</td><td>0.265</td><td>0.116</td><td>0.143</td><td>0.225</td><td><strong>0.827</strong></td></tr></tbody></table>

DorCr- Denial of risk-Comparing of risk, DorSc- Denial of risk-Self-confidence, DorSg- Denial of risk - Scapegoating, IcifDp-Intention to commit internet fraud-Deception, IcifId- Intention to commit internet fraud-Defraud

We conducted model robustness checks for multicollinearity. VIF values for scapegoating (2.04), self-confidence (1.937), and comparing of risk (1.5) are much less than the threshold of (VIF < 10), indicating absence of multicollinearity problem.
In assessing the explanatory power, our model accounted for 58% of variance (R² = 0.58) in explaining intention to commit internet fraud.

The Adjusted R² (0.56) further strengthens the explanatory power as it takes into account our sample size and number of variables in our model (Khang et al. 2013; Turner and Turner 2013) (Hair et al. 2010). Summary of the hypotheses testing are shown in Table 2 and Figure 2.

<table><caption>**Table 2.** Summary of Results</caption><thead><tr><th>Hypothesis</th><th>Support?</th></tr></thead><tbody><tr><td>H1 Scapegoating positively affect the intention to commit internet fraud</td><td>Supported</td></tr><tr><td>H2 Self-confidence positively affect the intention to commit internet fraud</td><td>Supported</td></tr><tr><td>H3 Comparing of risk positively affect the intention to commit internet fraud</td><td>Supported</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Model Results</figcaption></figure>

As hypothesized, internet fraudsters use scapegoating to commit internet fraud (p < 0.05; t= 2.44), and hypothesis 2 supports a positive relationship between self-confidence and intention to commit internet fraud (p < 0.05; t=5.272). Hypothesis 3 is also supported (p < 0.05; t= 2.405); comparing of risk positively influence the intention to commit internet fraud.

**Discussion and Future Research Direction**

Denial of risk theory refers to a mental way to deal with risk associated with deviant behaviors. Individuals who employ denial of risk attempt to reject the risk from the actions by comparing their actions to others who they deem more harmful. We
4. Biegler, S. (2003). Beyond our control? Confronting the limits of our legal system in the age of cyberspace. MIT press.

5. Boateng, R. Longe, O. Isabalija, R. S. Budu, J. (2011), Sakawa - Cybercrime and Criminality in Ghana. Journal of Information Technology Impact. Vol. 11, No. 2, pp. 85-100.

6. Boyle, K. M., & Walker, L. S. (2016). The neutralization and denial of sexual violence in college party subcultures. Deviant Behavior, 37(12), 1392-1410.

7. Brooks, G. (2016). Explaining Corruption: Drifting In and Out of Corruption and Techniques of Neutralization. In Criminology of Corruption (pp. 107-125). Palgrave Macmillan, London.

8. Bullock, K., & Condry, R. (2013). Responding to denial, minimization and blame in correctional settings: The 'real world' implications of offender neutralizations. European Journal of Criminology, 10(5), 572-590.

9. Chatterjee, S., Kar, A. K., Dwivedi, Y. K., & Kizgin, H. (2018). Prevention of cybercrimes in smart cities of India: from a citizen's perspective. Information Technology & People. DoI: https://doi.org/10.1108/ITP-05-2018-0251

10. Choo, K. K. R. (2011). Cyber threat landscape faced by financial and insurance industry. Trends and issues in crime and criminal justice, (408), 1.

11. Choudrie, J., & Dwivedi, Y. K. (2005). Investigating the research approaches for examining technology adoption issues. Journal of Research Practice, 1(1), 1. Available at: http://jrp.icaap.org/index.php/jrp/article/viewFile/4/7

12. D'Arcy, J., Hovav, A., & Galletta, D. (2009). User awareness of security countermeasures and its impact on information systems misuse: A deterrence approach. Information Systems Research, 20(1), 79-98.

13. Das, S., & Nayak, T. (2013). Impact of cybercrime: Issues and challenges. International journal of engineering sciences & Emerging technologies, 6(2), 142-153.

14. Esala, J. J. (2013). Communities of denial: The co-construction of gendered adolescent violence. Deviant behavior, 34(2), 97-114.

15. Gottschalk, P., & Smith, R. (2011). Criminal entrepreneurship, white-collar criminality, and neutralization theory. Journal of Enterprising Communities: People and Places in the Global Economy, 5(4), 300-308.

16. Grabosky, P. (2015). Organized cybercrime and national security. In Cybercrime Risks and Responses (pp. 67-80). Palgrave Macmillan, London.

17. Harris, L. C., & Dumas, A. (2009). Online consumer misbehavior: an application of neutralization theory. Marketing Theory, 9(4), 379-402.

18. Hutchings, A. (2013). Hacking and fraud: Qualitative analysis of online offending and victimization. Global criminology: Crime and victimization in the globalized era, 93-114.

19. Ingram, J. R., & Hinduja, S. (2008). Neutralizing music piracy: An empirical examination. Deviant Behavior, 29(4), 334-366.

20. Jegede A. E., Olowookere I. E. (2014). Cyber Risks and Fraud in the Nigeria's Business Environment: A Postmortem of Youth Crime. Journal of Social and Development Sciences, Vol. 5, No. 4, pp. 258-265

21. Jegede A. E., Olowookere I. E., Elegbeleye, A. O. (2016). Youth Identity, Peer Influence and Internet Crime Participation in Nigeria: A Reflection. Ife Psycholog, 24(1), 37-47

22. Jegede A. E., Oyesomi, K., Olorunyomi, B. R., (2016). Youth crime and the organized attributes of cyber fraud in the modern technological age: a thematic review. International Journal of Social Sciences and Humanities Reviews Vol.6 No.1, p.153 – 164.

23. Kim, W., Jeong, O. R., Kim, C., & So, J. (2011). The dark side of the Internet: Attacks, costs and responses. Information systems, 36(3), 675-705.
24. Koay, K. Y. (2018). Understanding consumers' purchase intention towards counterfeit luxury goods: An integrated model of neutralization techniques and perceived risk theory. Asia Pacific Journal of Marketing and Logistics, 30(2), 495-516.

25. Kubic T. T. 2001. Internet Fraud Complaint Center, https://archives.fbi.gov/archives/news/testimony/internet-fraud-crime-problems

26. Lazarus, S. (2018). Birds of a Feather Flock Together: The Nigerian Cyber Fraudsters (Yahoo Boys) and Hip Hop Artists. Criminology, Criminal Justice, Law & Society. 19, (2). p. 63–80.

27. LI Chi-mei, J. (2008). Neutralization techniques, crime decision-making and juvenile thieves. International Journal of Adolescence and Youth, 14(3), 251-265.

28. Li, W., & Cheng, L. (2013). Effects of Neutralization Techniques and Rational Choice Theory on Internet Abuse in the Workplace. In PACIS (p. 169).

29. McMullan, J. L., & Rege, A. (2010). Online crime and internet gambling. Journal of Gambling Issues, (24), 54-85.

30. Meesters, M. E., & Behagel, J. H. (2017). The Social Licence to Operate: Ambiguities and the neutralization of harm in Mongolia. Resources Policy, 53, 274-282.

31. Mekonnen, S., Padayachee, K., & Meshesha, M. (2015). A privacy preserving context-aware insider threat prediction and prevention model predicated on the components of the fraud diamond. In 2015 Annual Global Online Conference on Information and Computer Technology (GOCICT) (pp. 60-65). IEEE.

32. Morris, R. G., & Higgins, G. E. (2009). Neutralizing potential and self-reported digital piracy: A Multitheoretical exploration among college undergraduates. Criminal JusticeReview, 34(2), 173-195.

31. Odou, P., & Bonnin, G. (2014). Consumers’ neutralization strategies to counter normative pressure: The case of illegal downloading. Recherche et Applications en Marketing (English Edition), 29(1), 103-121.

32. Park J. and Sung C. (2015), The effect of online piracy deterrence on self-control and piracy intention, PACIS, Jooyeon Park, School of Business, Yonsei University, Seoul, Korea

33. Peretti-Watel, P. (2000). Sociologie du risque. A. Colin.

34. Peretti- Watel, P. (2003), Neutralization theory and the denial of risk: some evidence from cannabis use among French adolescents*. The British Journal of Sociology, 54: 21-42

35. Prasad J, Roslina I, Azizah A M (2014), Understanding Cybercrime in Malaysia: An Overview, Advanced Informatics School, University Teknologi Malaysia (UTM, Sains Humanika 2:2 (2014), 109-115

36. Schuchter, A., & Levi, M. (2016). The fraud triangle revisited. Security Journal, 29(2), 107-121.

37. Scott, K., & Straus, M. (2007). Denial, minimization, partner blaming, and intimate aggression in dating partners. Journal of Interpersonal Violence, 22(7), 851-871.

38. Shareef, M. A., Dwivedi, Y. K., Kumar, V., Davies, G., Rana, N., & Baabdullah, A. (2018). Purchase intention in an electronic commerce environment: a trade-off between controlling measures and operational performance. Information Technology & People. DoI: https://doi.org/10.1108/ITP-05-2018-0241

39. Sigala, M. (2017). How “bad” are you? Justification and normalization of online deviant customer behavior. In Information and Communication Technologies in Tourism 2017 (pp. 607-622). Springer, Cham.

40. Silic, M., Barlow, J. B., & Back, A. (2017). A new perspective on neutralization and deterrence: Predicting shadow IT usage. Information & management, 54(8), 1023-1037.

41. Siponen, M., & Vance, A. (2010). Neutralization: new insights into the problem of employee information systems security policy violations. MIS quarterly, 487-502.
42. Smith, W. R., Torstensson, M., & Johansson, K. (2001). Perceived risk and fear of crime: Gender differences in contextual sensitivity. International Review of Victimology, 8(2), 159-181.

43. Spurrier, M., & Blaszczyński, A. (2014). Risk perception in gambling: A systematic review. Journal of Gambling Studies, 30(2), 253-276.

44. Sugiura, L. (2018). Challenging the Risks in Online Medicine Purchasing: Respectable Deviance. In Respectable Deviance and Purchasing Medicine Online Palgrave Macmillan, Cham. (pp. 101-138).

45. Vasquez, A., & Vieraitis, L. M. (2016). "It's Just Paint": Street Taggers' Use of Neutralization Techniques. Deviant Behavior, 37(10), 1179-1195.

46. Vida, I., Kos Koklič, M., Kukar-Kinney, M., & Penz, E. (2012). Predicting consumer digital piracy behavior: The role of rationalization and perceived consequences. Journal of Research in Interactive Marketing, 6(4), 298-313.

47. Whyte, D. (2016). It's common sense, stupid! Corporate crime and techniques of neutralization in the automobile industry. Crime, Law and Social Change, 66(2), 165-181.

48. Wiebe, D. J., & Korbel, C. (2003). Defensive denial, affect, and the self-regulation of health threats. The self-regulation of health and illness behavior, 184-20
# Appendix

<table><caption>Table 3. Survey Instrument</caption><thead><tr><th></th><th>Item</th><th>loading</th></tr></thead><tbody><tr><td colspan="3"><strong>Perceived Scapegoating</strong></td></tr><tr><td>Dorsg1</td><td>Defrauding “clients” is not as bad as armed robbery.</td><td>-</td></tr><tr><td>Dorsg2</td><td>Defrauding “clients” is not as bad as ‘blood money’.</td><td>0.829</td></tr><tr><td>Dorsg3</td><td>Defrauding “clients” is not as bad as ‘Sakawa’.</td><td>0.875</td></tr><tr><td>Dorsg4</td><td>Defrauding “clients” is not as bad as corruption.</td><td>0.735</td></tr><tr><td colspan="3"><strong>Perceived Self-Confidence</strong></td></tr><tr><td>Dorsc1</td><td>I feel confident in my ability to defraud “clients”.</td><td></td></tr><tr><td>Dorsc2</td><td>I know how to get “clients” believe in me than the average person.</td><td>0.836</td></tr><tr><td>Dorsc3</td><td>It is not dangerous to maintain relationship with “clients” after defrauding them.</td><td>0.858</td></tr><tr><td>Dorsc4</td><td>I have confidence in determining “clients” who are less willing to be defrauded.</td><td>0.749</td></tr><tr><td colspan="3"><strong>Perceived Comparison-between-risk</strong></td></tr><tr><td>Dorcr1</td><td>Defrauding “clients” who is in relationship is no different from taking money from a spouse.</td><td>-</td></tr><tr><td>Dorcr2</td><td>Defrauding “clients” is no different from begging for livelihood.</td><td>0.813</td></tr><tr><td>Dorcr3</td><td>Defrauding “clients” is no different from paid workers who receive tips before they provide service.</td><td>0.792</td></tr><tr><td>Dorcr4</td><td>Defrauding “clients” is not no different from taking money from a woman you are not sure you will marry.</td><td>0.758</td></tr><tr><td colspan="3"><strong>Intention to Commit Internet Fraud</strong></td></tr><tr><td>Icifid1</td><td>What is the chance that you will defraud “clients”?</td><td>0.865</td></tr><tr><td>Icifid2</td><td>I am certain that I will defraud “clients”</td><td>0.859</td></tr><tr><td>Icifid3</td><td>I am likely to defraud “clients”</td><td>0.752</td></tr><tr><td>Icifidp1</td><td>I tell all my “clients” lies.</td><td>0.785</td></tr><tr><td>Icifidp2</td><td>My “clients” believe the lies I tell them.</td><td>0.863</td></tr><tr><td>Icifidp3</td><td>My “clients” don’t know I am deceiving them.</td><td>0.830</td></tr><tr><td>Icifidp4</td><td>I am crafty with my lies.</td><td>0.746</td></tr><tr><td>Icifidp5</td><td>“Clients” needs to believe your story before you get the money.</td><td>-</td></tr></tbody></table>