TYPE Original Research

PUBLISHED 23 December 2022

DOI 10.3389/fpsyg.2022.1030670

The role of social-psychological

OPEN ACCESS factors of victimity on
EDITED BY
Paolo Roma,
Department of Human Neurosciences,
victimization of online fraud in
Faculty of Medicine and Dentistry,
Sapienza University of Rome, Italy China
REVIEWED BY
Fulvio Carabellese,
University of Siena, Italy Zhi Zhang1,2* and Zhi Ye2,3
Gareth Norris, 1
Department of Investigation, Zhejiang Police College, Hangzhou, China, 2 New Crime Research
Aberystwyth University,
Center, Zhejiang Police College, Hangzhou, China, 3 School of Marxism, Zhejiang Police College,
United Kingdom
Hangzhou, China
Katalin Parti,
Virginia Tech, United States
*CORRESPONDENCE Introduction: The high incidence of online fraud in China poses a great threat
Zhi Zhang
to the social stability and property security of society. Victimity is the state or
zhangzhi@zjjcxy.cn
mindset of victims, referring to the various conditions related to being victims
SPECIALTY SECTION
This article was submitted to of crimes. Examining the association between the inherent and extrinsic
Forensic and Legal Psychology, factors of victimity and victimization of online fraud is of great importance
a section of the journal
Frontiers in Psychology
for revealing the psychological mechanism of victimization and drawing up
preventative measures.
RECEIVED 29 August 2022
ACCEPTED 05 December 2022
Methods: Through the questionnaire survey of 504 online fraud victims and
PUBLISHED 23 December 2022
523 non-victims, univariate analysis and logistic regression analysis were used
CITATION
Zhang Z and Ye Z (2022) The role to investigate the risk factors correlated with victimization.
of social-psychological factors
of victimity on victimization of online Results: Age, education, and social support are positively correlated with fraud
fraud in China. victimization, while impulsiveness, trust tendency, smartphone usage, and
Front. Psychol. 13:1030670.
doi: 10.3389/fpsyg.2022.1030670 negative life experiences are negatively correlated with fraud victimization.
COPYRIGHT
Subsequent regression analyses showed that all the factors above, except for
© 2022 Zhang and Ye. This is an smartphone usage, are significant predictors for online fraud victimization.
open-access article distributed under
the terms of the Creative Commons Discussion: Our conceptual model and empirical results demonstrate the
Attribution License (CC BY). The use,
important role of victimity in making an individual become a victim and help
distribution or reproduction in other
forums is permitted, provided the clarify the mixed findings of previous studies on the risk factors for online
original author(s) and the copyright fraud.
owner(s) are credited and that the
original publication in this journal is
cited, in accordance with accepted KEYWORDS
academic practice. No use, distribution
or reproduction is permitted which online fraud, victimity, victimization, China, psychology
does not comply with these terms.

1 Introduction
Recently, internet technology has developed and spread rapidly in China. As of
December 2021, the number of internet users in China reached 1.032 billion, and the
internet penetration rate reached 73.0% (China Internet Network Information Center,
2022). Types of social media shed light on different victimization methods (Lee, 2021a).
Traditional crimes are transferring rapidly to non-contact crimes through the medium
of telecommunications and the internet, and new crimes such as online fraud, gambling,
and violence are increasing. Among them, online fraud has become the main type

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of crime that endangers the safety of people’s lives and property large population and high incidence of online fraud crime, its
and disrupts the network order (Fan and Yu, 2021). The Chinese sample study on the risk factors of online fraud victimization is
government attaches great importance to cracking down on representative.
new illegal and criminal activities in telecommunications and Psychological characteristics are another risk factor
the internet (General Office of Central Committee of the contributing to the victimity of fraud victims, such as self-
Communist Party of China [CCCPC], 2022). How to effectively control, impulsiveness, trust tendency, and risk-taking. It is
control online fraud has become a real problem that law consistently suggested that low self-control, high impulsiveness,
enforcement departments at all levels must think deeply about and high risk-taking are positively related to victimization
and focus on solving. However, there are just a few empirical (Bossler and Holt, 2010; Ngo and Paternoster, 2011; Pattinson
studies on the social-psychological factors contributing to online et al., 2011; Alseadoon et al., 2013; Williams et al., 2017; Norris
fraud victimization in the Chinese population (Fan and Yu, et al., 2019; Mikkola et al., 2021; Parti, 2022). Individuals with
2021; Lee, 2021a,b), which makes it difficult to design effective low self-control are more likely to engage in online activities
preventive policy and legal programs. Therefore, the current and may expose more personal information (Mesch and
study explores the key factors and psychological mechanisms Dodel, 2018). Impulsivity is a sign of low self-control and
of online fraud in China to help the government develop impulsive individuals enjoy immediate benefits regardless of
intervention programs to reduce property damage. the long-term harmful consequences of their actions and are
Because of the interaction between the criminal and insensitive to the intentions of others (Pratt et al., 2014). For
victim in the process of online fraud, the victimity of the trust tendency it is commonly believed that more trusting
victim should be considered a risk factor in the crime. The people would be more likely to be fraud victims, while the
concept of victimity was first put forward by Mendelssohn,
results of trust tendency are surprisingly more mixed. For
who defined it as the state, quality, or fact of being a
example, Ross et al. (2014) suggested that increased trust
victim (Mendelsohn, 1963). Victimity is determined by the
was one of the key areas where older people were more
victim’s inherent factors and extrinsic factors (Schneider, 1982).
likely to be disproportionately exploited by fraudsters. Other
The former refers to the victim’s demographic characteristics,
studies consider that trust has a negative or no effect on fraud
psychological characteristics, lifestyle, and other characteristics
victimization (Carter and Weber, 2010; Judges et al., 2017).
inherent in individuals. The latter includes the victim’s bad
Therefore, the role of psychological characteristics such as
family environment, unhealthy cultural environment of the
impulsivity and trust tendency on the online fraud victimization
community, social factors that promote repeat victimization
needs to be examined.
and other objective factors that easily lead to victimization
Regarding lifestyle, studies have found that lifestyles such
(Li, 2010). The current study attempts to examine the role of
as high internet usage, high online consumer behavior, habitual
inherent factors and extrinsic factors of victimity on online
email response, and having provided money to scammers
fraud victimization through the comparison of victims and non-
before predict fraud victimization (Holtfreter et al., 2008; Reisig
victims of online fraud to profile people with high victimity and
and Holtfreter, 2013; Vishwanath, 2015; Balleisen, 2018; Parti,
thus a high likelihood of victimization.
2022). According to routine activities theory, some routine
From the perspective of the inherent features of victimity,
demographic characteristics are one of the risk factors for online online activities are positive predictors of cyber victimization by
fraud victimization and have been examined by many studies exposing potential victims to cybercriminals (Van Wilsem, 2013;
with mixed and inconclusive findings (Ross et al., 2014; Judges Mesch and Dodel, 2018). The integrated lifestyles and routine
et al., 2017; Yin, 2017; Kadoya et al., 2021). For example, Fan activities theory (L-RAT) further suggests that the differences in
and Yu (2021) found that older age, lower education, and both daily activities and risk taking behavior make some people
being a migrant were associated with a higher risk of fraud suitable targets for victimization (Tapp, 2018). However, Parti
victimization. While in Whitty’s (2020) study, age was not a (2022) recently found that the frequency of social media use
predictive variable for cyberscam victimization and education was a predictor of online fraud victimization for younger people
was a positive predictor, which was also found in Parti’s (2022) instead of older ones and time spent online did not predict
study that higher education was not a protective but rather online victimization. According to the 49th statistical report
a predictive factor for online fraud in younger respondents. on internet development in China (China Internet Network
Besides, the findings of Reyns and Randa (2019) showed a Information Center, 2022), as of December 2021, the average
negative association between age and fraud victimization. For Chinese netizen spent 28.5 h online per week. The proportion
gender, some studies have suggested that victims of financial of Chinese netizens using mobile phones to access the internet
fraud are mostly males (Button et al., 2014; Deliema et al., 2019; reached 99.7 percent, and mobile phones are the most important
Whitty, 2020), while others have found no significant effects device for accessing the internet in China (China Internet
for gender (Yang, 2017; Fan and Yu, 2021). The inconsistency Network Information Center, 2022). As a result, smartphone
of the existing conclusions may be related to the differences usage may be an indicator of online lifestyle to examine its
in the study subjects and types of fraud. As a country with a predictive effect on online fraud victimization.

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With respect to victims’ extrinsic features, the effects scammer. The non-victim sample came from those who went
of negative life experiences and social support are usually to the police station to handle personal matters or report
examined. Negative life events can affect individual’s cognitive crimes other than fraud. The victims and non-victims were
judgment, information processing, and decision-making ability. asked to fill out an online questionnaire. All the returned
Fraudsters rely on cognitive biases or errors brought by negative questionnaires were completed anonymously and voluntarily
life events to their victims to execute attacks and produce by the participants after their approval. We requested that
automatic emotional responses (Emami et al., 2019). Although participants under 18 years old fill out the questionnaires
a few studies have suggested a positive prediction effect of under the written approval and supervision of a guardian. In
negative life experiences (Anderson, 2019; Emami et al., 2019), total, 1,027 valid questionnaires were collected. The participants
Sur et al. (2021) found that experiencing negative life events was included 512 males, accounting for 49.9%, and 515 females,
not associated with the risk of self-reported fraud victimization, accounting for 50.1%. The youngest was 12 years old, and
which makes the role of the factor ambiguous. Social support the oldest was 80, for an average age of 35.74 (SD = 11.83).
is the support and help that individuals can get through social Among them, 504 people have experienced online fraud,
interaction to reduce psychological stress response and improve accounting for 49.1%, and 523 people have not experienced
social adaptability. The lack of social support will reduce the online fraud victimization, accounting for 50.9%. The study
individual’s access to information resources and knowledge procedures were approved by the Institutional Review Board of
reserves, thus increasing the possibility of being deceived Zhejiang Police College.
(Zhang et al., 2017). There are also inconsistent results about
the effect of social support varying from negative, insignificant,
to positive relationships (Beach et al., 2018; Fan and Yu, 2.2 Measures
2021). For example, James et al. (2014) argued for a negative
association between susceptibility to scams and social support, 2.2.1 Basic information
while in Parti’s (2022) study living alone did not predict scam Basic information consisted of demographic variables of the
victimization and asking for help associated with a higher risk participants, which included age, gender and education, and
of being victims, which might suggest the null or even negative whether the participants had victimization experience of online
effect of social support. Sur et al. (2021) also revealed that higher fraud (“No” = 0, “Yes” = 1).
consistent social support increases the average probability of
fraud victimization. 2.2.2 Impulsiveness
Taken together, the findings above suggest that the The degree of impulsiveness of participants was measured
victim’s inherent factors, such as demographic characteristics, by the Barratt Impulse Scale (BIS-11) revised by Zhou et al.
psychological characteristics and lifestyle, as well as extrinsic (2006). From the original scale, we selected nine items that
factors, such as negative life experiences and social support, will are highly related to the degree of impulsiveness in acting and
play an important role in victimity and further predict fraud planning, such as “I act on impulse.” The scale adopts a 4-
victimization. However, the findings of existing research were point scoring method from 1 (never) to 4 (always). The sum
inconsistent has been mentioned above. To this end, the current score ranges from 9 to 36. The higher the score is, the higher
study aimed to clarify the role of the above victimity features the impulsiveness of acting and planning. The fitting result
on victimization of online fraud in China. Based on the existing of confirmatory factor analysis of this scale is χ2 /df = 6.32,
research, we hypothesized that there are significant differences root-mean-square error of approximation (RMSEA) = 0.07,
between victims and non-victims in terms of demographic comparative fit index (CFI) = 0.94, and tucker-lewis index
characteristics, psychological characteristics, lifestyle, negative (TLI) = 0.91. In this study, the Cronbach’s alpha of the scale was
life experiences and social support, and these factors can 0.64.
significantly predict online fraud victimization.
2.2.3 Trust tendency
Using the questionnaire of the deception tendency of the
2 Materials and methods elderly compiled by Zhang et al. (2017), this study measured the
trust tendency of the participants. Because of the wide age range
2.1 Participants of the research sample in this study, the items in the original
questionnaire were deleted based on the previous interviews.
The sample consisted of victims and non-victims of The final questionnaire consists of 10 items, such as “How likely
online fraud using a convenient sampling method in Zhejiang are you to ask the telecommunications bureau to help you when
Province. The victim sample came from victims who reported it calls to remind you of your arrears and is willing to help you to
the case to the police and the case needed to meet the following pay?” The questionnaire uses the 4-point scoring method, with 1
two criteria: (1) the victim received the fraud information meaning very unlikely and 4 meaning very likely. The sum score
online, and (2) the victim had transferred money to the ranges from 10 to 40. The higher the score is, the higher the

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trust tendency. The fitting result of confirmatory factor analysis usage, negative life experiences, and social support. Second,
of this scale is χ2 /df = 4.81, RMSEA = 0.06, CFI = 0.96, and the chi-square test and independent sample t-test were
TLI = 0.93. In this study, the Cronbach’s alpha of the scale performed to examine the difference in demographic
was 0.77. characteristics between victims and non-victims. Third,
an independent sample t-test was conducted to compare
2.2.4 Smartphone usage the differences in social-psychological variables between
The smartphone dependence scale was used to measure the the two groups. Finally, binary logistic regression analyses
mobile phone usage of the participants (Kwon et al., 2013). were performed to investigate the predictive effects of
Three items of the scale, such as “I frequently check my mobile all the variables on online fraud victimization. The data
phone to avoid missing conversations with others on social were statistically analyzed using statistical product service
software (such as QQ or WeChat),” were selected based on the solutions (SPSS) 24.0. The level of significant difference was
previous interviews. The scale adopts a 5-point scoring method, p < 0.05.
where 1 means totally disagree and 5 means totally agree. The
sum score ranges from 3 to 15. The higher the score, the greater
the smartphone usage. The fitting result of confirmatory factor 3 Results
analysis of this scale is χ2 /df = 0.00, RMSEA = 0.00, CFI = 1.00,
and TLI = 1.00. In this study, the Cronbach’s alpha of the scale 3.1 Preliminary analysis
was 0.67.

2.2.5 Negative life experiences Table 1 shows the demographic characteristics of the
The life events scale was used to measure the effects sample. All participants were divided into victims and non-
of total important life events in the past 6 months (Li victims according to their victimization experience of online
et al., 2009). The scale includes 12 items, such as “I was fraud. For non-victims, the average age was 39.46 (SD = 11.73),
hospitalized due to an accidental injury.” (See Supplementary while for victims, it was 31.89 (SD = 10.65). The proportion
Appendix A) the scale adopts a 6-point scoring method, with of males and females was similar for both groups (50.7% men
0 representing no occurrence, 1 representing occurrence but no for non-victims and 49.0% men for victims). A total of 16.3
significant distress, 2 representing mild distress, 3 representing and 54.9% of non-victims had junior high school education
moderate distress, 4 representing somewhat severe distress, and or less and junior college education or more, respectively,
5 representing severe distress. The sum score ranges from 0 to while for victims, the proportions were 31.9 and 42.3%,
60. The higher the scale score, the more negative life events respectively.
experienced in the past 6 months, and the higher the degree The independent sample t-test was conducted for age, and
of distress. The fitting result of confirmatory factor analysis of the chi-square test was conducted for gender and education to
this scale is χ2 /df = 5.12, RMSEA = 0.06, CFI = 0.94, and compare non-victims and victims. As shown in Table 1, the
TLI = 0.91. In this study, the Cronbach’s alpha of the scale
TABLE 1 The demographic characteristics of non-victims and victims
was 0.82. (N = 1,027).

2.2.6 Social support Personal basic Victimization t or χ2

The Social Support Revalued Scale revised by Xiao (1994) characteristics
was used to measure the degree of social support (Xiao, 1994). Non-victims Victims
Considering that some of the participants were unemployed, M (SD) or M (SD) or
two items involving colleagues and organizations were deleted, n (%) n (%)
so there were eight items on the scale, including three Age 39.46 (11.73) 31.89 (10.65) 10.83***
dimensions named subjective support, objective support, and
Gender −0.53
support utilization. (See Supplementary Appendix B) the total
Male 265 (50.7) 247 (49.0)
score of social support is the sum of three dimensions. The sum
score ranges from 10 to 58. The higher the score, the higher the Female 258 (49.3) 257 (51.0)

level of social support. The fitting result of confirmatory factor Education −6.31***
analysis of this scale is χ2 /df = 5.90, RMSEA = 0.07, CFI = 0.94, Junior high school or less 85 (16.3) 161 (31.9)
and TLI = 0.92. In this study, the Cronbach’s alpha of the scale
Senior high school/ 151 (28.8) 130 (25.8)
was 0.81. vocational high school/
technical secondary school

2.3 Statistical analysis Junior college/ 259 (49.5) 208 (41.3)

Undergraduate

First, descriptive statistics were reported on demographic Graduate or more 28 (5.4) 5 (1.0)

characteristics, impulsiveness, trust tendency, smartphone ***p < 0.001.

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two groups had significant differences in age and education. TABLE 3 Factors predicting online fraud victimization in the first
binary logistic regression analysis (N = 1,027).
Specifically, the victims were younger and less educated than the
non-victims, supporting our hypothesis. However, there was no Factors B Exp (B) (95% CI)
significant difference between the two groups based on gender,
Age −0.07*** 0.93 (0.92–0.95)
which is inconsistent with the hypothesis.
Gender 0.13 1.14 (0.85–1.52)

Education −0.42*** 0.66 (0.60–0.73)

3.2 Differences between victims and Impulsiveness 0.06* 1.06 (1.01–1.11)

non-victims in social-psychological Trust tendency 0.05** 1.05 (1.02–1.09)
variables Smartphone usage 0.00 1.00 (0.96–1.05)

Negative life experiences 0.05*** 1.05 (1.03–1.07)

Table 2 shows the mean scores and standard deviations
Social support −0.06*** 0.94 (0.93–0.96)
of the social-psychological variables, as well as the difference
between victims and non-victims conducted by independent *p < 0.05, **p < 0.01, ***p < 0.001.

sample t-test. The results showed significant differences

between the two groups in impulsiveness, trust tendency, fraud victimization, while age, education, and social support
smartphone usage, negative life experiences, and social support. have significant negative predictive effects on victimization.
Victims had significantly greater impulsiveness, trust tendency, Gender and smartphone usage as predictors yielded no
and smartphone usage frequency than non-victims, as well significant effects; therefore, a second regression analysis was
as experiencing significantly more negative life events and performed with victimization as the dependent variable; age,
having less social support, which is consistent with our education, impulsiveness, trust tendency, negative life events
hypothesis. experience, and social support as the independent variables; and
non-victims as the reference category. The results are shown
in Table 4. Specifically, younger and less educated people had
3.3 Logistic regression analysis of the a significantly higher likelihood of being victims of online
measured variables fraud. Impulsiveness, trust tendency and negative life events
experience are significantly more likely to lead to an individual
To analyze the predictive effects of demographic and social- being victimized by online fraud. Social support is negatively
psychological characteristics on online fraud victimization, correlated with victimization. The results shown in Figure 1
binary logistic regression was conducted with victimization as indicate that the younger the age is, the lower the education
the dependent variable; age, gender, education, impulsiveness, level, the stronger the impulsiveness, the higher the level of trust
trust tendency, smartphone usage, negative life events tendency, the greater the influence of negative life events, the
experience, and social support as the independent variables; lower the level of social support, and the more likely one is to
and non-victims as the reference category. As shown in become a victim of online fraud.
Table 3, impulsiveness, trust tendency, and negative life events
experience have significant positive predictive effects on online
4 Discussion
TABLE 2 Differences between victims and non-victims in
social-psychological variables (N = 1,027). Online fraud is currently the main form of crime against
property in China. The effectiveness of victimization prevention
Variable M (SD) t p
Impulsiveness Non-victims 19.34 (3.86) −8.12 <0.001
TABLE 4 Factors predicting online fraud victimization in the second
Victims 21.17 (3.35) binary logistic regression analysis (N = 1,027).

Trust tendency Non-victims 18.01 (4.39) −6.93 <0.001

Factors B Exp (B) (95% CI)
Victims 20.02 (4.88)
Age −0.07*** 0.93 (0.92–0.95)
Smartphone usage Non-victims 9.82 (3.47) −2.02 0.044
Education −0.42*** 0.66 (0.59–0.73)
Victims 10.27 (3.66)
Impulsiveness 0.06** 1.06 (1.02–1.11)
Negative life experiences Non-victims 6.13 (6.43) −5.51 <0.001
Trust tendency 0.05** 1.05 (1.02–1.09)
Victims 8.73 (8.48)
Negative life experiences 0.05*** 1.05 (1.03–1.07)
Social support Non-victims 35.71 (7.52) 10.79 <0.001
Social support −0.06*** 0.94 (0.93–0.96)
Victims 30.54 (7.83)
**p < 0.01, ***p < 0.001.

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FIGURE 1
The predictive effects of the measured variables on online fraud victimization. OR, odds ratio.

largely depends on the investigation of factors contributing to is important to make sure that people have the knowledge
the victimity of the victims. The present study investigated to identify scams and handle large amounts of money. Our
the association of demographic and social-psychological factors results do not support the hypothesis that gender is a predictor
with online fraud victimization to examine the role of these of victimization. Although the present study shows no gender
inherent and extrinsic factors in the victimity of fraud victims. difference between victims and non-victims, the effect may be
The univariate analyses found that there was a significant displayed when discussed by the type of fraud. For example,
difference in age, education, impulsiveness, trust tendency, Whitty (2020) found that women were much more likely to be
smartphone usage, negative life experiences, and social support victims of consumer scams, while men were more likely to be
between victims and non-victims. This was mostly consistent victims of investment scams. This may be due to the different
with our hypotheses, except that gender exhibited a null social roles and needs of men and women that leads to the
effect. With the logistic regression analysis, age, education, gender gap in the victimity of the specific type of online fraud.
impulsiveness, trust tendency, negative life experiences, and Future research may explore the demographic characteristics
social support were found to be significant predictive factors associated with different types of online fraud in China to
of victimization. more deeply understand victimity and more precisely prevent
the crime.
For psychological characteristics associated with fraud
4.1 Inherent factors of victimity victimization, our results show a positive predictive role of
associated with victimization impulsiveness and trust tendency, supporting our hypothesis
that greater impulsiveness and trust tendency are associated
The independent sample t-test shows that victims are with a higher risk of fraud victimization. Impulsiveness is often
significantly younger and less educated than non-victims. The regarded as a tendency to respond quickly to unplanned internal
logistic regression also suggests a negative predictive role of and external stimuli, which often causes adverse effects due to a
the two demographic characteristics. The results support our lack of consideration of behavioral consequences (An and Jiang,
hypothesis that younger age and less education are associated 2020). When individuals are impulsive, their decision-making
with a higher risk of fraud victimization. According to the is more susceptible to the influence of others rather than their
48th statistical report on internet development in China (China own rational thinking, and they are more likely to trust the
Internet Network Information Center, 2021), Chinese netizens defrauders under elaborate scams. However, individuals with
aged 30–39 accounted for 20.3%, the highest proportion among low levels of impulsiveness have a better ability to identify
all ages, followed by those aged 40–49 (18.7%) and 20–29 potential fraudulent information (Norris et al., 2019). Some
(17.4%). The younger age of the victims may correlate with studies have examined the relationship between internet or
the greater usage of the internet in young people, which makes mobile phone dependence and impulsiveness (Cao et al., 2007;
them more easily exposed to fraud messages. Education may be Billieux et al., 2010; An and Jiang, 2020). The results showed a
related to knowledge of the internet and internet scams. Wright positive correlation, which may explain why impulsive people
and Marett (2010) suggested that people with higher levels of are more likely to be victims of online fraud. Trust tendency, also
computer self-efficacy, web experience, and security knowledge known as the general level of trust, refers to the inherent trust
were less susceptible to phishing attempts. Considering that level of an individual without any known information about
overconfidence of high education people in not being defrauded others (Dinesen, 2012). Our results support a previous study
may be related to high risk of victimization (Parti, 2022), it showing that victims of online fraud often have a higher level

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of trust in others (Zhao et al., 2020). The study of network 4.2 Extrinsic factors of victimity
communication situations also showed that the higher the level associated with victimization
of trust tendency, the easier it is trust other individuals and other
groups (Teo and Liu, 2007). Compared to the participants in Negative life events are considered to be a risk factor for
the study of Carter and Weber (2010), who found that high fraud victimization in some studies (Deliema, 2015; Anderson,
trusters were significantly better than low trusters at detecting 2019; Emami et al., 2019). The current study finds that negative
lies through watching videos of job interviews, the victims life events positively predict victimization, supporting our
of communication and internet fraud have fewer clues for hypothesis. There are several explanations for this result. First,
judgment, thus making people with a high trust tendency more negative life events may increase fraud vulnerability by reducing
likely to be deceived. social support and the number of social activities out of the
o investigate the association between lifestyle and home so that people may spend more time on the internet (Sur
victimization, the current study examines the role of et al., 2021). Second, people who experience negative life events
smartphone usage on fraud victimization. The results show such as divorce or unemployment are more likely to have a
that although smartphone usage is not a significant predictor need to make friends, get a job or get a loan, increasing their
of online fraud, it is positively correlated with victimization. exposure to scams. Emami et al. (2019) found that victims of
According to lifestyle exposure theory, people will have online fraud were more likely than non-victims to experience
different chances of encountering crime risks based on different the breakdown of marriage or other intimate relationships. The
lifestyles. If some lifestyles have more contact opportunities third explanation is that according to the Elaboration Likelihood
with potential crimes, or they are often in a situation where Model (ELM; Petty and Cacioppo, 1986), individual differences
crimes occur, their risk of being victimized will be higher (Li, such as mood at the time of receiving a message are heavily
2010). Further, once people provide money to scammers, they correlated with the depth of processing that a person engages
may be in a higher risk of repeat victimization (Balleisen, 2018; in when encountering a potential scam message (Norris and
Parti, 2022). Individuals’ dependence on smartphones actually Brookes, 2020). The negative state relief model (NSR; Cialdini
reflects their lifestyle of frequent occupational activities and et al., 1973) implies that individuals in a negative mood state are
entertainment activities through the internet, which leads to more likely to respond to positively framed scam messages to
an increased risk of exposure to online fraud; thus, they are relieve their negative mood (Norris and Brookes, 2020). The role
of negative life events in our study may be realized through the
more likely to become the target of defrauders. In addition,
effect of negative mood caused by the events on scam message
Holtfreter et al. (2008) found that the differences in personal
processing. Considering the view that the influence of emotional
basic characteristics such as age, gender, and socioeconomic
factors can occur through different primary mechanisms and in
status may actually reflect the differences in daily activities such
different contexts (Norris and Brookes, 2020), future research
as consumption behavior, thus affecting their victimization.
should investigate the role of different negative life events in
On the other hand, online consumption behavior increases the
different types of online fraud.
risk of being targeted for fraud (Holtfreter et al., 2008). In the
The present study shows a significant correlation between
present study, the null prediction of smartphone usage may be
social support and fraud victimization. Specifically, people with
due to the lack of typicality in the question setting. Parti (2022)
less social support have a higher risk of fraud victimization.
measured the frequency of social media use by the variable
Social support refers to the spiritual or material support and
named online services and found that using dating services
help received by individuals from society, including the support
and playing online games made younger people vulnerable to provided by relatives and friends, colleagues and neighbors, or
online fraud. Future research can use more specific questions public welfare organizations and enterprise associations (He,
to investigate the role of lifestyle especially online activities in 2019). If victims of online fraud receive less support from
fraud victimization. the real society, they are more likely to blindly pursue the
The inherent factors of victimity associated with scope of social activities online, thus connecting with strangers
victimization indicates that online fraud victimization is online. The possibility of being exposed to the crime situation
largely related to the victim’s own stable internal characteristics. increases accordingly. In addition, less social support will leave
In some cases, it is not because the victim’s initiative leads victims without effective supervisors. According to routine
to the victimization, but their characteristics make them activity theory, a crime occurs in the convergence of a motivated
more vulnerable to the crime, which may also partly offender, a suitable target, and absent or ineffective prevention
explain the repeat victimization. The results suggest that efforts (Cohen and Felson, 1979; Cohen et al., 1981). If a victim
relevant departments should pay special attention to the talks about the incident with someone else before or during
population with the above characteristics, and carry out the victimization, that person may act as a “protector” who
targeted education to prevent them from being deceived as effectively prevents the occurrence and development of fraud.
much as possible. For individuals who lack social support, this is a key deficiency,

Frontiers in Psychology 07 frontiersin.org

Zhang and Ye 10.3389/fpsyg.2022.1030670

thus promoting the successful realization of the crime. For perspective of victimity. The results show that younger age, less
the result in the study of Sur et al. (2021) that perceived that education, greater impulsiveness, greater trust tendency, more
social support was positively associated with victimization of negative life experiences, and less social support are associated
old people, it may be that social support increased the odds of with a higher risk of online fraud victimization. The present
remembering and reporting fraud instead of the risk of being findings provide a further understanding of the victimity of
victimized. Since reporting or asking for help does little to online fraud victims and demonstrate a social-psychological
prevent scam victimization (Parti, 2022), the specific forms of profile of the victims. In terms of practice, some preventative
social support need to be further explored. measures can be put forward based on these findings. People
The role of extrinsic factors of victimity on victimization who meet the profile of online fraud victims should be screened
suggest that social connection plays an important role in and educated for fraud prevention. Since social support plays
preventing online fraud. Enhancing social connection can an important role in both the causes and the consequences
improve the individuals’ social support level, and reduce the of criminal behavior (Cullen, 1994), a social support system
online social demands and information processing deviation could be established to provide effective support for online fraud
caused by negative emotions when individuals experience victims and potential victims to reduce the risk of victimization
negative life events, so as to effectively prevent victimization. and repeat victimization of online fraud.

5 Limitations Data availability statement

This study investigated the correlation between inherent and The raw data supporting the conclusions of this article will
extrinsic factors of victimity, including age, gender, education, be made available by the authors, without undue reservation.
impulsiveness, trust tendency, lifestyle, negative life events and
social support, and online fraud. The results show significant
relations for all of the factors except for gender and lifestyle.
Ethics statement
As mentioned above, the null effect may be due to the lack of
The studies involving human participants were reviewed
research on the influence of these factors on different types of
and approved by Institutional Review Board of Zhejiang
fraud. Since there are many different methods of committing
Police College. Written informed consent to participate in this
fraud in China and different people have different needs and
study was provided by the participants’ legal guardian/next
characteristics, it may be that different types of people fall for
of kin. Written informed consent was obtained from the
different types of scams (Whitty, 2020). The analysis could have
individual(s), and minor(s)’ legal guardian/next of kin, for
benefited from investigating the risk factors for victimization
the publication of any potentially identifiable images or data
according to different types of fraud. The lack of sample
included in this article.
representativity and generalizability caused by the sampling
method in this study is also one of the limitations. Future
research need to adopt more appropriate sampling methods and
Author contributions
further expand the sample size. In addition, the current study is
also limited by the self-report measure, which may be affected
ZZ designed the study. ZZ and ZY collected and analyzed
by the participants’ interpretation of questions, the perception
the data and wrote the manuscript. Both authors contributed to
of their own behaviors, social expectations, and so on (De Leeuw
the article and approved the submitted version.
et al., 1999; Deevy and Beals, 2013; Judges et al., 2017; Williams
et al., 2017). Besides, there may be a small part of non-victims
who do not know that they have been deceived, so they are Funding
misclassified in the questionnaire which may affect the results.
Future research could consider experimental or behavioral This work was supported by the Public Security Theory and
methods to investigate the risk factors for fraud victimization Soft Science Program of Ministry of Public Security of China
and their effect mechanisms to obtain more objective data. (Grant No. 2020LLYJZJST069) and National Social Science
Fund Project of China (Grant No. 21BSH029).

6 Conclusion and implications

Acknowledgments
Considering the current severe situation and prevention
bottlenecks of online fraud in China, this study investigated We would like to thank Department of Public Security
the risk factors correlated with fraud victimization from the Zhejiang Province for their support in providing data.

Frontiers in Psychology 08 frontiersin.org

Zhang and Ye 10.3389/fpsyg.2022.1030670

Conflict of interest organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
The authors declare that the research was conducted in the claim that may be made by its manufacturer, is not guaranteed
absence of any commercial or financial relationships that could or endorsed by the publisher.
be construed as a potential conflict of interest.

Supplementary material
Publisher’s note
The Supplementary Material for this article can be
All claims expressed in this article are solely those of the found online at: https://www.frontiersin.org/articles/10.3389/
authors and do not necessarily represent those of their affiliated fpsyg.2022.1030670/full#supplementary-material

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