Page 1

ISSN: 1795-6889

https://ht.csr-pub.eu

Volume 19(2), September 2023, 178–206

178

DO IT USERS BEHAVE RESPONSIBLY IN TERMS OF

CYBERCRIME PROTECTION?

Abstract: This study aims to analyze the behaviour of IT users regarding their personal

protection against potential cybercrimes. The research data set is based on surveys

conducted by the European Commission in 2020-2021 for 35 European countries.

Canonical analysis revealed that 66.67% of cybercrime cases (Phishing, Pharming,

Online identity theft, etc.) determine individuals' choice of personal protection method

(using a security token, social media logins, electronic identification, etc.). Kohonen's self-

organizing maps were used to form 9 clusters of countries depending on the attitude of IT

users to personal cybersecurity. The map results showed that individuals behave less

responsibly using a security token, electronic identification certificate or card, pin code

list or random characters of a password, and other electronic identification procedures.

Users from Denmark, the Netherlands, Iceland, Norway, the UK, Austria, and Finland

were the most responsible Europeans in terms of personal protection, while people from

Bulgaria, Romania, Serbia, Albania, North Macedonia, Bosnia and Herzegovina were the

least conscientious about protection.

Keywords: cybercrime, IT user, Kohonen map, personal cybersecurity, responsible

behaviour, protection.

©2023 Hanna Yarovenko, Serhiy Lyeonov, Krzysztof Andrzej Wojcieszek, & Zoltán

Szira, and the Centre of Sociological Research, Poland

DOI: https://doi.org/10.14254/1795-6889.2023.19-2.3

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Hanna Yarovenko

Madrid University of Carlos III

Spain

Sumy State University

Ukraine

ORCID 0000-0002-8760-6835

Serhiy Lyeonov

University of Social Sciences

Poland

ORCID 0000-0001-5639-3008

Krzysztof Andrzej Wojcieszek

Academy of Justice,

Warsaw, Poland

ORCID 0000-0003-1047-8000

Zoltán Szira

Budapest Metropolitan University,

Budapest, Hungary

ORCID 0000-0002-7299-4695

Do IT users behave responsibly…

179

INTRODUCTION

The consequences of Industry 4.0 and the beginning of its new 5.0 phase have been influencing

the development of social life. Although the Fourth Revolution was focused on automating

many processes and simplifying their execution by a person, the next stage aims to achieve a

balanced interaction between humans and computer technologies based on artificial

intelligence and intricate robotic complexes. On the one hand, these processes positively

impact society, the economy, and the environment and ensure sustainable development. On the

other hand, rapid digitization and automation have given rise to new types of crime.

Cybercrimes are illicit actions conducted by a person or a group of persons aimed at individual

users, companies, governments, and even countries that use information and communication

technologies. They result in theft or leakage of personal data, especially financial data,

malfunctioning of mobile devices, computers, enterprise infrastructure, damage to software,

etc.

Statistics show that the level of cybercrime in the world is constantly increasing. In 2001,

six people per hour became victims of cybercrimes. In 2022, this number increased almost 15

times to 91 people, although the maximum number reached 97 victims per hour in 2021

(Surfshark, 2022). Comparitech research team estimated that approximately 71.1 million

people suffered from cybercrime annually (Bischoff, 2022). Every ten-thousandth resident of

the European Union became a victim of cybercrime in 2020 (Vasilyeva et al., 2022). A 2007

University of Maryland study found that a cyber hacker attack occurred every 39 seconds

(Cukier, 2007). However, according to the Internet Crime Complaint Center of the Federal

Bureau of Investigation, one successful attack occurred every 1.12 seconds in 2020 (Federal

Bureau of Investigation, 2020). This indicates that the frequency and effectiveness of

cybercrime is increasing. If cybercrime financial losses per hour in 2001 amounted to $2,055,

this figure increased approximately 572 times by 2022 and amounted to $1,175,799 (Surfshark,

2022). According to expert estimates, losses from cybercrime in 2018 reached 0.86 trillion U.S.

dollars; by 2022, they increased 9.8 times and equalled 8.44 trillion U.S. dollars. In 2027, they

may increase to 23.82 trillion U.S. dollars (Fleck, 2022).

The given data show that cybercrime has become a serious problem for ensuring the

sustainable development of society. At the same time, it has a peculiar, steady growth trend,

facilitated by a significant decrease in the price of computer technologies and an increase in

their availability for ordinary IT users. For example, the IT market offers cybercrime devices

that start at $1, making it easy for anyone to become a cybercriminal (Rapp & Hackett, 2017).

It is not for nothing that the World Economic Forum (2022) singles out the spread of

cybercrime among the ten most critical risks for the world. Thus, in ranking risks that have

worsened since the beginning of the COVID-19 pandemic, cybersecurity mistakes leading to

the spread of cybercrimes occupy the seventh position. Among the risks that will become the

most serious threat worldwide in the next two years, it also ranks seventh, and among those

that will be critical in the next five years, it ranks eighth. That is why combating cybercrimes

is the prerogative of the cyber defense department. However, 88% of cybersecurity breaches

are caused by human factors, and since anti-virus software is only 50% effective, the IT user’s

behavior influences the success or failure of cyber attacks (MacKay, 2022). According to a

survey conducted by KPMG, 86% of respondents believe that it is IT users who are responsible

for data security. However, 90% identify the responsibility as government and 91% as business

Yarovenko, Lyeonov, Wojcieszek, & Szira

180

(Orson & Stein, 2020). It follows from this that an individual’s behavior in cyberspace is

important in the personal security organization, forming his/her security environment.

What is responsible behaviour, and why is it so important today? It is a key issue surveyed

by the Geneva Dialogue on Responsible Behavior in Cyberspace. This project was launched in

2018 by the Swiss Federal Department of Foreign Affairs (FDFA), the Geneva Internet

Platform (GIP), the United Nations Institute for Disarmament Research (UNIDIR), ETH

Zurich and the University of Lausanne. The main goal was to identify the stakeholders - the

state, business entities and civil society - and their roles in using information and

communication technologies for security in global cyberspace. Within the framework of the

organized webinar “What is responsible behaviour in cyberspace?” it was determined that

responsible behaviour is “behaviour by a given actor in a given set of circumstances that can

be said to conform to the laws, customs and norms generally expected from that actor in those

circumstances” (Gavrilović, 2018). This concept does not fully reveal its essence and needs to

be clarified depending on the roles that various subjects perform in cyberspace. It especially

applies to the determination of the individual IT users’ behaviour, which should be based on

their attitude to protecting personal data and applying basic and particular actions to counter

cyber threats. For example, ignoring emails received in spam or from unfamiliar addresses,

regularly changing passwords, not disclosing personal, especially financial, information in

social networks, etc.

The Geneva Dialogue has proposed and implemented many initiatives related to the

responsible behaviour of states in cyberspace. 11 central voluntary UN norms define what

states can and cannot do as part of their responsible behaviour in cyberspace (Hogeveen, 2022).

However, to a greater extent, this activity is directed at the macro and global level of

cyberspace. As for ordinary IT users, a mechanism for increasing digital literacy on personal

security issues is proposed. Since it is very difficult to regulate the cyber protection of every

citizen at the state or global level, the spread of educational activities should deepen

individuals’ awareness in this area and motivate them to apply more advanced security

measures.

Based on the above, should society combat cyber threats? Sure, because the losses from

cybercrimes can be quite tangible for their victims. Who should be responsible for personal

safety? In most cases, each IT user should be responsible for personal cyber protection, but this

should also not abdicate the responsibility of companies, governments and international

organizations. How should an IT user behave in such cases? His/her behaviour must be

responsible, including applying a set of actions aimed at protecting personal data. Is this how

IT users behave today? This study will deal with the answers to this question.

LITERATURE REVIEW

Today, digitization and computerization processes are important factors in achieving the

benefits of the economy and sustainable development of the country for the long term

(Tiutiunyk et al., 2021; Millia et al., 2022). Thanks to them, the processes of convergence of

the cybersecurity system with the economy and business take place (Kuzior et al., 2022). For

example, some Internet users directly affect economic growth, especially in transition

Do IT users behave responsibly…

181

economies (Remeikiene et al., 2021). Mobile technologies contribute to increasing the level of

human well-being (Rosenberg & Taipale, 2022).

The scope of application of modern information and communication technologies is broad.

Their features have been studied and continue to be studied for health care (Alimbaev et al.,

2021), construction (Popov et al., 2021), transport (Okunevičiūtė Neverauskienė et al., 2021),

finance (Mnohoghitnei et al., 2022) and the banking sector (Haddad, 2021), the hospitality and

tourism industry (Lustigova et al., 2021), small and medium-sized businesses (León-Gómez et

al., 2022), e-commerce (Kiba-Janiak et al., 2022), e-government (Kolosok et al., 2022). Despite

the field of activity, global, macro-, micro- or individual level, the key figure is always the

person who plays an important role in the digital transformation processes (Tran et al., 2022).

Therefore, the effectiveness of the information and communication technologies will depend

on its behaviour, which will affect the cybersecurity level, both personal and for enterprises,

the country and the world as a whole.

Where is the individual’s responsible behavior most important to ensure cybersecurity?

The current development of technologies is aimed at their evolution from being used in simple

calculations to playing the key role of a decision-maker in a company (Gladden et al., 2022).

Therefore, today, humanity wants to consolidate the role of the host in its interaction with

artificial intelligence technologies (Tugui et al., 2022). Beňo (2022) proved the existence of a

direct correlation between the number of highly educated and electronic workers, indicating

the intellectualization of work that requires the IT application. On the one hand, it contributes

to developing cybersecurity measures. On the other hand, it can lead to the future degeneration

of many professions since introducing technological innovations always leads to increased

unemployment (Lydeka & Karaliute, 2021). Some individuals have difficulty integrating into

digital technologies, such as the oldest generation and members of the younger generation

(Dečman et al., 2022). That is why they often become victims of cybercrimes because they do

not have enough knowledge to apply basic protection tools. However, the advantages of society

digitalization allow the use of electronic learning tools that help to increase the level of digital

and cyber literacy of users regardless of age, status, and type of activity (Davidovitch &

Eckhaus, 2022).

The global pandemic of COVID-19 contributed to the fact that most companies transferred

employees to remote work. On the one hand, it should have contributed to a significant

reduction in costs, but this factor may not be confirmed in practice (Navickas et al., 2022). On

the other hand, it led to the appearance of vulnerabilities in the cyber defence system due to the

influence of the human factor (Kobis & Karyy, 2021). In such cases, it is reasonable to create

virtual teams, which contribute to the development of human capital, ensure the organizational

sustainability of enterprises and can contribute to increasing the effectiveness of cybersecurity

measures (Capolupo et al., 2022). The experience of using virtual reality in tourism confirms

it (Florek & Lewicki, 2022). The information technology factor is not influential for knowledge

management in enterprises (Gad & Yousif, 2021). However, the exchange of knowledge

between company employees can affect the quality of information systems of companies,

forming the appropriate knowledge banks for countering cybercrimes (Zamir & Kim, 2022).

The vulnerability of IT users’ cybersecurity is noticeable in the electronic services of the

financial sphere. For example, electronic banking is firmly rooted among them and is the most

used by individuals. Their quality in general and security in particular directly impact the

satisfaction and loyalty of electronic customers of banks (Ahmed et al., 2021). Hassan & Lee

Yarovenko, Lyeonov, Wojcieszek, & Szira

182

(2021) prove that a key factor in the perception of e-commerce is consumers' level of trust in

this type of service, which can undoubtedly influence the personal cybersecurity measures they

use in online payments. Fülöp et al. (2022) found a positive impact of Fintech Accounting and

Industry 4.0 technology on increasing trust in digital accounting services. Blockchain

technologies and smart contracts increase the reliability of partners and contribute to the

competitiveness of enterprises, although they can act as a source of information vulnerability

for cybercriminals (Chen et al., 2022).

Speaking about information technologies and their impact on society, business, and

individuals, one should remember the ethics of this issue (Constantinescu & Edu, 2022). It is

because disclosing personal data online can become a vulnerable link and create favourable

conditions for cybercriminals. Therefore, companies and governments should pay attention to

implementing tools that ensure this issue's ethical side.

The possibilities of information and communication technologies are powerful. The

introduction of artificial intelligence, industrial robots, blockchains, smart contracts, smart

cities, etc., makes life easier and contributes to the country's economic development. At the

same time, they create opportunities for cybercriminals. Therefore, the level of cybersecurity

will depend on the company employees’ behaviour, as well as users of relevant services and

technologies. It includes digital literacy, quality of services, intellectualization and

virtualization of work, knowledge exchange, cyber ethics, etc.

METHODS AND DATA

Data

The article used data from surveys conducted by the European Commission in 2020-2021 for

35 European countries. Two sets of data were used in the study. The first set includes eight

indicators characterizing the number of respondents who have encountered cybercrime cases

(Eurostat, 2022c). The second set concerns the interviewees’ answers regarding appropriate

measures of personal cyber protection.

The first data set identifies the relationship between cybercrime incidents and responsible

behaviour. It included an indicator “Fraudulent credit or debit card use” showing what

percentage of respondents had fallen victim to fraudulent credit or debit card use. This type of

cybercrime is social engineering, which assumes that a fraudster misleads a person and

voluntarily gives information related to his/her bank cards. A second indicator “Online identity

theft” is the number of individuals who have had their personal information stolen online,

allowing the criminal to impersonate the victim and gain some benefit. A third indicator

“Phishing” shows the number of respondents who became phishing victims, i.e., received

fraudulent messages from cybercriminals. A fourth indicator “Pharming” is the number of

victims of pharming, i.e., they were redirected to fake websites that asked for personal

information. The fifth indicator “Misuse of personal information available on the Internet” is

the number of respondents who experienced abuse of their personal information online,

resulting in discrimination, harassment or bullying by cybercriminals. The sixth indicator

“Social network or e-mail account being hacked” is the number of victims of social network or

email account hacking, as well as posting or sending content without the knowledge of

Do IT users behave responsibly…

183

individuals. The seventh indicator “Data loss due to a virus” is the number of individuals who

lost personal information (documents, videos, images, etc.) due to a virus attack and infection

of a computer or other device. The final indicator “Identity theft, receiving fraudulent messages

or being redirected to fake websites” is the percentage of respondents who experienced

financial loss as a result of having their personal information stolen, receiving fraudulent

messages, or being redirected to fake websites.

The second data set includes 16 indicators reflecting the percentage of individuals who

demonstrate behaviour in the information environment in the form of their implementation of

specific cyber protection actions. These are the following indexes: “Using simple login with

username and password”, “Using a social media login”, “Using a security token”, “Using an

electronic identification certificate or card with a card reader or an app”, “Using a procedure

involving the mobile phone”, “Using a single-use PIN code list or random characters of a

password”, “Using other electronic identification procedures”, “Using at least 4 identification

procedures”, “Individuals know that cookies can be used to trace the movements of people on

the Internet”, “Changing the settings in the internet browser to prevent or limit cookies on any

devices”, “Using software that limits the ability to track activities on the Internet”, “Reading

privacy policy statements before providing personal data”, “Restricting or refusing access to

the geographical location”, “Limiting access to profiles or content on social networking sites

or shared online storage”, “Rejection of using personal data for advertising purposes”,

“Checking the security of the website where personal data is provided”. The data from the first

to the eighth indicator were taken from the results of the survey conducted by Eurostat (2022a),

and from the ninth to the sixteenth, from Eurostat (2022b).

Methods

Several stages are foreseen in the study. In the first stage, linear dependencies are studied, and

the relationships between two sets of variables are identified. One of them is formed by

variables characterizing the number of respondents who have encountered cybercrime cases.

The second is represented by indicators that determine the responsible behaviour of IT users.

For this stage, canonical analysis is used to assess the influence of one set on another and

substantiate its statistical significance. The canonical analysis will reveal a group of factors and

individual factors of greatest influence. The statistical significance of the correlation between

groups of indicators and the statistical significance of canonical roots will be checked using the

χ2 distribution. The calculated value χ

2

is compared to the critical one. If it is smaller, the value

of the correlation between groups of indicators is statistically significant. At the same time, the

p-value is also estimated, which should not exceed 0.05, confirming statistical significance.

In the second step, data standardization and assessment of the multicollinearity of the data

array is carried out. Standardization will help scale the data to the value of the variance and

cancel out the effect of the averages. For this purpose, Z-score normalization is used (Kreyszig,

1979). For checking the array of data for the presence or absence of multicollinearity, 𝜒

2

is used

which will be calculated based on the data of the entire array. The obtained value is compared

with the critical value if

1

2

𝑚(𝑚 − 1) – freedom degrees and the corresponding level of

significance 𝛼. If χ

2 > χ

𝑐𝑟

2

, there is multicollinearity in the array of variables, otherwise it is

absent.

Yarovenko, Lyeonov, Wojcieszek, & Szira

184

In the third step, the principal component method is used to reduce the dimensionality of

the data and eliminate multicollinearity. This procedure will help to avoid the influence of this

phenomenon when justifying the number of clusters.

In the fourth step of the research, the number of clusters is substantiated using the

Silhouette technique. Most researchers use the Elbow method to perform this task, but it only

calculates the Euclidean distance. The Silhouette method considers various features -

dispersion, asymmetry, the difference between maximum and minimum values, etc. This

method was proposed by Rousseeuw (1987) to check the consistency of data in clusters using

visualization. Its main purpose is to calculate the silhouette coefficient for all observations of

the sample and for the cluster as a whole, taking into account the average distance between the

group and the average distance to the nearest cluster. Silhouette scores are calculated according

to formula (1) (Rousseeuw, 1987):

{

𝑠(𝑖) =

𝑘(𝑖) − 𝑙(𝑖)

𝑚𝑎𝑥{𝑘(𝑖), 𝑙(𝑖)}

𝑠(𝑖) = 0, 𝑖𝑓 |𝑂𝐼| = 1

, 𝑖𝑓 |𝑂𝐼| > 1,

(1)

where 𝑠(𝑖) is a silhouette score for 𝑖-th observation from the data sampling; 𝑘(𝑖) is the

average distance between 𝑖-th observations and other cluster observations; 𝑙(𝑖) is the shortest

distance between 𝑖-th observations in the cluster to other observations of other clusters; |𝑂𝐼| is

a set of observations of one cluster. Accordingly, the average and shortest distances are

calculated using formulas (2) – (3) (Rousseeuw, 1987):

𝑘(𝑖) =

1

|𝑂𝐼| − 1

∑ 𝑑(𝑖,𝑗)

𝑗∈𝑂𝐼,𝑖≠𝑗

,

(2)

𝑙(𝑖) = min

𝐽≠𝐼

1

|𝑂𝐽|

∑ 𝑑(𝑖,𝑗)

𝑗∈𝑂𝐽

,

(3)

where 𝑑(𝑖,𝑗) is the distance between 𝑖-th and 𝑗-th observations.

Since the Silhouette score is used to justify the number of clusters in this methodology, the

corresponding scores will be calculated for their different number. To make a decision, it is

necessary that 𝑠(𝑖) → 1. The estimate close to one indicates that the distribution of

observations on a given number of clusters is the most effective.

In the fifth step, self-organizing Kohonen maps, a technique of unsupervised machine

learning allowing to create a two-dimensional data representation in the form of maps while

preserving the data structure, are built. Maps are formed from neurons or nodes associated with

a vector of weight estimates determining their position in space. The learning process consists

of moving the weight estimates in such a way as to reduce the distance between neurons. First,

the input vector of weights is chosen randomly. Then, there is a transition from neuron to

neuron. This process is accompanied by determining the similarity of the input vector and the

vector of weight estimates corresponding to a node on the map. The neuron with the smallest

Euclidean distance value is assigned the matching unit. Based on this knowledge, the weight

Do IT users behave responsibly…

185

vectors of neurons are updated in combination with the best matching unit (formula (4),

Kohonen & Honkela, 2007):

𝑊𝑣(𝑟 + 1) = 𝑊𝑣(𝑟) + 𝜃(𝑢, 𝑣, 𝑟) ∙ 𝛼(𝑟) ∙ (𝐵(𝑡) − 𝑊𝑣(𝑟)),

(4)

where 𝑊𝑣(𝑟 + 1) is the updated vector of weights of the neuron taking into account 𝑟–

iterations of updating the weights; 𝑊𝑣 is the current vector of neuron weights; 𝜃(𝑢,𝑣,𝑟) is a

neighborhood function that defines the distance between two neurons – 𝑢 and 𝑣 on the step 𝑟;

𝑢 is the index of the best matching neuron score in the map; 𝑣 is the index of the neuron in the

map; 𝛼(𝑟) are limitations of the learning process or a monotonically decreasing learning

coefficient; 𝐵(𝑡) is the vector of input weights.

After updating the weight vector, the iteration step 𝑟 is increased. The procedure is

repeated until it reaches the final limit of the iteration. As a result of applying the unsupervised

machine learning technique, a Kohonen map is built, demonstrating the distribution of the

analyzed elements according to the corresponding clusters. It helps to analyze the elements

grouped according to the similarity of their characteristics.

RESEARCH RESULTS

At the first stage of the proposed methodology, a canonical analysis was performed

between the two data sets using the STATISTICA analytical package. Figure 1 presents the

obtained results.

Yarovenko, Lyeonov, Wojcieszek, & Szira

186

Figure 1. Results of the canonical analysis.

[Source: calculated by the authors]

Figure 1 shows a high value of the canonical correlation (𝑅 = 0.95865), indicating that

there is a strong correlation between the set of selected cybercrime cases (Left Set) and

indicators characterizing the responsible behaviour of IT users (Right Set). The statistical

significance of the correlation coefficient is confirmed by the high value of the Pearson test

(𝜒

2 = 155.70), the significance level of which does not exceed 0.05 (𝑝 = 0.04877). The

redundancy value for the left set is 66.7662%; i.e., the right set variables, which correspond to

IT users’ responsible behaviour indicators, explain 66.7662% of the changes caused by

cybercrime cases. In two out of three cases, the user's vulnerability in the network depends on

incorrectly or insufficiently correctly chosen personal cyber protection tools. In turn, the cases

of cybercrimes explain by 48.9594% the variability of the IT users’ behaviour regarding

Canonical Analysis Summary

Canonical R: .95865

Chi²(128)=155.70 p=.04877

N=35

Left

Set

Right

Set

No. of variables

Variance extracted

Total redundancy

Variables:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

8

16

100.000%

66.2904%

66.7662%

48.9594%

Fraudulent credit or debit card use

Using a simple login with username

and password

Online identity theft

Using a social media login

Phishing

Using a security token

Pharming

Using an electronic identification certificate or card

with a card reader or an app

Misuse of personal information available

on the Internet

Using a procedure involving the mobile phone

Social network or e-mail account being

hacked

Using a single -use PIN code list or random

characters of a password

Data loss due to a virus

Using other electronic identification procedure s

Identity theft, receiving fraudulent

messages or being redirected to fake

websites

Using at least 4 identification procedures

Individuals know that cookies can be used to

trace the movements of people on the Internet

Changing the settings in the internet browser to

prevent or limit cookies on any devices

Using software that limits the ability to track

the activities on the Internet

Reading privacy policy statements before

providing personal data

Restricting or refusing access to the

geographical location

Limiting access to profile s or content on social

networking sites or shared online storage

Rejection of using personal data

for advertising purposes

Checking the security of the website

where personal data is provided

Do IT users behave responsibly…

187

personal cyber protection, i.e., in approximately 50% of cases, the choice of the appropriate

person’s behaviour regarding its protection depends on the type of cybercrime.

For further analysis, it is necessary to select those canonical roots that are statistically

significant. The result of the obtained roots and verification of their statistical significance is

presented in Figure 2.

Figure 2. Evaluation of statistical significance of canonical roots.

[Source: calculated by the authors]

Figure 2 shows that the Chi-square in the first row, corresponding to the analysis without

root removal, is statistically significant because 𝑝 < 0.05, so, at least one canonical root is also

statistically significant. When it was removed, the authors found that the remaining roots were

statistically insignificant. Therefore, the result is one statistically significant root, i.e., it is

appropriate to consider one pair of canonical variables. To confirm these conclusions, the

authors demonstrate the factor structure and redundancy (Appendix A, Figures 1-2).

The largest factor loadings have indicators to the first root, both for the left and right set,

confirming the previous conclusion. Since the factor loadings represent correlations between

the set indicators, the responsible IT users’ behaviour indicators demonstrate average, above-

average and significant correlations. Only the variable related to “Reading privacy policy

statements before providing personal data” shows a very weak relationship, which indicates

the insignificance of this responsible behaviour type for countering cybercrimes (Appendix A,

Figure 1, Row 12). “Using at least 4 identification procedures” and “Using a procedure

involving the mobile phone” is the most correlated (Appendix A, Figure 1, Rows 5 and 8).

Users who apply multiple cyber protection tools are more likely to use a mobile phone to

confirm their actions on the Internet. These measures are the factors reducing the vulnerabilities

of users’ security and affecting the number of cybercrime cases.

The least correlated cybercrime cases are those related to losing personal information due

to a virus attack (Appendix A, Figure 2, Row 7). The highest level of communication is

demonstrated by "Fraudulent credit or debit card use" and "Identity theft, receiving fraudulent

messages or being redirected to fake websites" (Appendix A, Figure 2, Rows 1 and 8). It

suggests that IT users who were victims of fraudulent use of credit or debit cards may also have

experienced identity theft. At the same time, phishing cases have a weak connection with other

cases of cybercrime.

Canonical weights were determined to understand which responsible behavior measures

and cybersecurity cases are the most important (Appendix A, Figures 3-4). The value of

Chi-Square Tests with Successive Roots Removed

Root

Removed

Canonicl

R

Canonicl

R-sqr.

Chi-sqr.

df

p

Lambda

Prime

0

1

2

3

4

5

6

7

0.9586455

0.9190012

155.6953 128

0.048774

0.0007161

0.9013787

0.8124835

101.6590 105

0.574071

0.0088413

0.8012128

0.6419420

65.6704

84

0.930365

0.0471494

0.7208380

0.5196074

43.5886

65

0.980964

0.1316808

0.6607936

0.4366482

27.8258

48

0.991270

0.2741108

0.5692695

0.3240677

15.4880

33

0.995882

0.4865712

0.4537064

0.2058495

7.0673

20

0.996454

0.7198520

0.3058713

0.0935572

2.1119

9

0.989569

0.9064428

Yarovenko, Lyeonov, Wojcieszek, & Szira

188

canonical weights, taken by the module, determines the contribution of each indicator to the

formation of canonical variables. The greatest contribution to the responsible behaviour of IT

users will be made by “Using at least 4 identification procedures” (|0.397156|), “Using a

procedure involving the mobile phone” (|0.360203|), “Restricting or refusing access to the

geographical location” (|0.351491|), “Limiting access to profiles or content on social

networking sites or shared online storage” (|-0.2732531|), “Checking the security of the website

where personal data is provided” (|-0.2598138|), “Individuals know that cookies can be used

to trace the movements of people on the Internet” (|0.2342221|) (Appendix A, Figure 3). “Using

other electronic identification procedures” (|-0.004255596|), “Using a single-use PIN code list

or random characters of a password” (|0.008669309|), “Using software that limits the ability to

track the activities on the Internet” (|-0.01907673|), “Using an electronic identification

certificate or card with a card reader or an app” (|-0.041001|), “Using a simple login with

username and password” (|0.06513116|), “Rejection of using personal data for advertising

purposes” (|0.04041022|). Positive variables indicate that increasing these measures will

effectively increase the cases where IT users become victims of the respective cyber threats

(Appendix A, Figure 3). Negative values indicate a decrease in the effectiveness of applying

these measures under such conditions.

All values are significant when it comes to cybercrime cases. The biggest contribution will

be made (Appendix A, Figure 4) by “Misuse of personal information available on the Internet”

(|0.6336418|), “Online identity theft” (|-0.4745117|), “Fraudulent credit or debit card use”

(|0.374483|), “Data loss due to a virus” (|-0.3130664|), “Identity theft, receiving fraudulent

messages or being redirected to fake websites” (|0.2306303|). These variables will strongly

influence the responsible behaviour of IT users. Positive values indicate that as cybercrime

cases increase, the number of users applying personal security measures will increase. The

negative values show feedback. The growth of such cybercrimes affects the decrease in IT

users who will install appropriate security measures. It occurs because the security measures

in this study that shape the individuals’ responsible behaviour are not effective enough to

counter these cyber threats.

The next part of the study defines clusters of countries where the respondents are more or

less responsible for personal protection. For this purpose, it was determined whether

multicollinearity exists in the array of variables. The calculated value of the determinant of the

correlation matrix is equal to 3.8493Е-09. Its value approaches zero, i.e., there is

multicollinearity between the explanatory variables. Accordingly, the calculated value of the

Chi-sqr criterion is equal to 𝜒

2 = 539.2813 and exceeds its critical level (𝜒2

𝑐𝑟

= 146.5674),

defined for 0.5𝑚(𝑚 − 1) freedom degrees and significance level 𝛼 = 0.05. It confirms the

multicollinearity in the array of variables.

For ist eliminating and reducing the data dimensionality, the principal component method

performed using Python programming was applied. Its results are presented in Figure 3, where

it can be seen that the first component explains 57.6619% of the total variation, the second –

11.0215%, the third – 7.2305%, and the fourth – 5.0867%. The other components explain less

than 5% of the total variation each. Therefore, the authors decide that the first four components

should be chosen for further calculations and justification of clusters, which together explain

81.0006%.

Silhouette Scores were used to determine the potential number of clusters (Figure 4). The

results show that the most similar objects correspond to 11 clusters. Since the studied data set

Do IT users behave responsibly…

189

contains observations for 35 countries, using many groups contributed to obtaining those with

only one country. Therefore, it was decided to form 9 clusters since this value corresponds to

the next highest Silhouette Score. Later, this distribution turned out to be optimal, confirmed

by the analysis of average values for each cluster.

Figure 3. The results of the principal component method.

[Source: calculated by the authors]

Yarovenko, Lyeonov, Wojcieszek, & Szira

190

Figure 4. Results of cluster significance assessment using Silhouette Score.

[Source: calculated by the authors]

The Viscovery SOMine analytical package was used to obtain Kohonen maps. Under this

process, various methods of its construction were experimentally tested. As a result, a map with

1000 nodes and an automatic ratio of 51:21 was trained. A Normal training schedule with a

voltage of 0.5 and temperature of 0.2 was also used. The Ward (hierarchical) method with 9

clusters was chosen as an algorithm for initial clustering. The indicator weights, taken from the

canonical analysis results, were set. Based on the given features, clusters of countries were

obtained, grouped by the level of responsible behaviour of IT users towards personal cyber

protection (Figure 5).

Figure 5. Map of Kohonen.

[Source: calculated by the authors]

The lowest level of responsible behaviour of IT users characterizes the clusters on the right

in Figure 5. Thus, the third cluster (C3) includes six countries: Romania, Bulgaria, Serbia,

Albania, North Macedonia and Bosnia and Herzegovina (Figure 5, yellow cluster). Analyzing

the average values (Appendix B, Table 1), it can be concluded that this group has the lowest

level of responsible behaviour of IT users. This means that the respondents from these countries

Do IT users behave responsibly…

191

pay little attention to personal protection in carrying out activities using computers and mobile

devices. The first largest cluster (C1) includes eight countries - Montenegro, Lithuania, Greece,

Slovenia, Slovakia, Italy, Poland, and Kosovo (Figure 5, turquoise cluster). Their averages

(Appendix B, Table 1) show that the number of respondents who used the analyzed personal

security tools is more significant than for the third group, i.e., IT users are more responsible for

their protection.

The share of respondents from the countries in the left clusters in Figure 5 is the highest,

indicating their high level of responsibility for personal cybersecurity. So, cluster 6 (C6)

includes three countries – Denmark, Iceland, and Netherlands (Figure 5, pink cluster), cluster

8 (C8) – United Kingdom and Norway (Figure 5, blue cluster), cluster 9 (C9) – Finland and

Austria (Figure 5, brown cluster). Their average values (Appendix B, Table 1) for most

indicators are the highest, confirming the conclusion about the high level of responsible

behavior of IT users from these countries.

As for the other clusters, closer to the centre of Figure 5 (green cluster – C4, red cluster –

C2, purple cluster – C5, olive cluster – C7), they are characterized by moderate behaviour of

individuals regarding personal cyber protection. This conclusion is confirmed by the average

values for each indicator under the created group (Appendix B, Table 1).

The authors analyze the Kohonen maps for each of the selected indicators. The map

generated for “Using a simple login with username and password” (Appendix C, Figure 5a)

shows an exemplary distribution by clusters. IT users from Norway, Great Britain, the

Netherlands, Iceland, the Czech Republic and Germany prefer this tool. Respondents from the

third cluster and Montenegro countries use this security measure less actively. Figure 5b in

Appendix C shows the behaviour for “Using a social media login”. According to this indicator,

the distribution is uneven for some clusters. For example, in the first cluster, respondents from

Kosovo preferred this protection method more than respondents from other countries. Users

from Luxembourg and Belgium have diametrically different attitudes towards the use of this

measure for working on the Internet.

The map showing the distribution of clusters according to the indicator “Using a security

token” (Appendix C, Figure 5c) also has certain exceptions for Croatia, Luxembourg,

Montenegro, Malta, etc. This type of cyber protection is most popular among IT users in

Norway. It is the least popular for the third cluster countries. Such discrepancies indicate that

this tool is quite specific, and perhaps not enough people are ready to use it for security

purposes. Figure 5d of Appendix C shows the behaviour regarding using an electronic

identification certificate or card with a card reader or an app. The results of distribution by

clusters are also heterogeneous. It can be clearly observed in Iceland, Sweden, Estonia and

Lithuania. Although this security measure is quite famous for calculation in various Internet

services, for the respondents from most countries, there is no suitable application for personal

protection.

The map created for “Using a procedure involving the mobile phone” (Appendix C, Figure

5e) shows a reasonably weighted distribution by clusters, clearly visible in the example of the

sixth and eighth clusters countries, using this security measure the most. It is the least popular

for residents of the third cluster. Figure 5f of Appendix C shows the behaviour for “Using a

single-use PIN code list or random password characters”. The colour gradation of the map

indicates the uneven application of this measure for most clusters. Residents of Denmark and

Finland give the greatest preference to its use. IT users in the vast majority of countries limit

Yarovenko, Lyeonov, Wojcieszek, & Szira

192

the use of this security measure, possibly due to the difficulty to remember complex random

passwords and the loss of further access to their account.

The map showing the distribution of clusters according to the indicator “Using other

electronic identification procedures” (Appendix C, Figure 5g) shows some exceptions for the

Netherlands, Spain, Denmark and Hungary. In general, a situation shows the unpopularity of

other security measures. In most cases, IT users apply well-known traditional tools; a relatively

low percentage try to use other additional ones. Figure 5h of Appendix C shows the behaviour

of using at least four identification procedures. The colour distribution of the map indicates the

uniformity of this security measure in the middle of individual clusters. At the same time, it is

preferred by Iceland, the Netherlands, Denmark, Norway and Great Britain residents. The

lowest level of responsibility in this case is demonstrated by IT users of countries belonging to

the third cluster.

Figure 6a of Appendix C shows the behaviour of individuals who know that cookies can

be used to trace people's online movements. The colour gradation of the map indicates a good

balance of the distribution of values for most clusters. This type of behaviour is one of the

popular activities among others. It is preferred by respondents from those countries that belong

to high-responsibility clusters. It is the least popular among users of the third cluster. The map

shows A fairly even distribution for the indicator “Changing the settings in the internet browser

to prevent or limit cookies on any devices” (Appendix C, Figure 6b). But some clusters have

intra-group differences, for example, fifth, seventh, sixth and ninth.

The map in Figure 6c of Appendix C shows the cluster distribution according to the “Using

software that limits the ability to track the activities on the Internet” indicator. Generally, it

shows a balanced formation of clusters, although there are exceptions for Belgium, Malta and

Cyprus. At the same time, this tool is the most popular among individuals from Belgium and

the least - from Cyprus. If we compare this measure with others, users give it a much lower

preference than “Using a simple login with username and password”, “Using a procedure

involving the mobile phone,” or “Individuals know that cookies can be used to trace the

movements of people on the Internet”. Figure 6d of Appendix C shows the distribution by

group for “Reading privacy policy statements before providing personal data”. Most

respondents from Finland, Austria, Germany, and Cyprus chose this type of protection. Not

preferred by IT users from Luxembourg, Belgium and Albania.

Figure 6e of Appendix C demonstrates the behaviour of individuals who restrict or refuse

access to the geographical location. The colour gradation of the map shows a fairly even

distribution of values for most clusters. This security measure is preferred by users of countries

grouped into clusters with the highest responsibility. It is not famous for clusters with a low

level of responsible behavior. The map in Figure 6f of Appendix C characterizes the

distribution according to the indicator “Limiting access to profiles or content on social

networking sites or shared online storage”. Some clusters have within-group differences, such

as the third, fourth, and ninth. In general, this security measure is quite popular among IT users

of different countries.

The map in Figure 6g of Appendix C refers to the behaviour of individuals to reject of

using personal data for advertising purposes. One should be noted the balanced distribution of

countries by this type of personal cyber protection, but there are exceptions for Iceland, Kosovo

and Latvia. This measure is famous for the countries of the left-hand cluster (Figure 5) and the

Germany-Cyprus-Croatia sector. The countries of the third cluster do not prefer it. Figure 6g

Do IT users behave responsibly…

193

of Appendix C demonstrates the behaviour of individuals who check the website's security

where personal data is provided. IT users in the Netherlands apply this tool more often than

others. Respondents from Albania prefer him less than individuals from other countries.

DISCUSSION

The issue of cybersecurity behaviour is an understudied area of research to date, as evidenced

by Oliveira & Baldi (2022). But despite the limited information on this problem, Pollini et al.

(2022) believe that human components of sociotechnical systems are its weakest and most

vulnerable component. Therefore, their responsible behaviour in the computer environment

affects the level of personal, corporate or state cybersecurity. This statement is synchronized

with the obtained results of this article. Although there are many best practices today, IT users'

application of the most effective security and privacy methods remains relatively low (Krsek

et al., 2022).

People's awareness of cyber threats strongly influences proactive cybersecurity behaviour,

especially after the global pandemic (Alsmadi et al., 2022). Therefore, increasing the level of

responsible behaviour should occur for different population categories. Witsenboer et al.

(2022) investigated the issue of personal cybersecurity in the school system, and based on the

results, they suggested that cybersecurity education should start in elementary school, as the

lack of relevant knowledge can affect them and the organization to which they belong. Also,

students' awareness of cyber threats and risks has a positive effect in situations where students

become targets of cybercrimes (Mohammed & Bamasoud, 2022). Older people tend to share

their passwords, which creates a favourable environment for cyber criminals (Patel & Doshi,

2022). Therefore, this category of IT users needs unique approaches for forming special

knowledge on personal cyber protection.

As for businesses and government institutions, the formation of responsible user behaviour

should become vital in ensuring cyber protection. Boritz et al. (2022) proved that employees

who are accountable for cybersecurity are less likely to become victims of cyber crimes. At the

same time, bank employees are more susceptible to phishing than employees of professional

services companies. Also, highly motivated public institution employees with a high level of

responsiveness and self-efficacy contribute to ensuring reliable information protection

(Sulaiman et al., 2022).

To increase the level of responsibility, it is vital to promote users' motivational behaviour

to protect themselves and companies from cyber threats (Li et al., 2022). One should also be

aware that a person's activities on the Internet can have a digital footprint that can lead to

distortion of information about him and help cybercriminals (Nicol et al., 2022). Therefore,

cyber hygiene should be observed, i.e. changing passwords, updating software, limiting the

disclosure of personal information on social networks, etc. (Schoenherr & Thomson, 2021).

Since not only individuals but also companies must be responsible for the security of their

customers' data, protection methods must evolve towards using more reliable and advanced

technologies. For example, the individual characteristics of a person typing on a keyboard,

moving a cursor using a mouse, trackball, touchpad, etc., are unique and difficult to steal

(Nnamoko et al., 2022). Therefore, implementing biometric technologies in online and mobile

applications will reduce users' vulnerability to cyber criminals. For easy perception of

Yarovenko, Lyeonov, Wojcieszek, & Szira

194

cybersecurity information, it is possible to use computer games to teach different categories of

the population about personal protection (Hwang & Helser, 2022). Although the level of

cybersecurity is formed under the influence of many factors, the responsible behaviour of IT

users may be the critical aspect that will create favourable or unfavourable conditions for

developing cybercrime.

CONCLUSIONS

The consequences of Industry 4.0 have had a positive impact on the life of society, which is

manifested in the economic growth of the countries, increasing business efficiency, and

improving the quality of individuals' lives. The results of the literature review confirm these

facts. But scientific and technical progress also causes cybercrime, showing in the illegal

appropriation of personal and financial information, violation of the software and technical

support functioning. Therefore, the issue of ensuring cybersecurity is relevant for IT users,

companies, and the government. In this research, it is stated that in the processes of digitization

and informatization, a person is a crucial link, and his behaviour in the computer environment

affects the level of cyber protection. The more responsible an individual is about cyber safety,

especially personal security, the less likely he is to become a cyber victim.

The article, based on a survey of IT users from the European Union, confirmed the

existence of a high correlation between cybercrime cases and indicators characterizing the

responsible behaviour of individuals. At the same time, the impact of cybercrime cases is more

significant on the choice of security measures. The number of users applying personal security

measures will increase with growing cybercrime cases. All variables characterizing cases of

cybercrimes proved essential for the respondents, indicating the expansion of cyber threats and

the increase in the types of cyber victims. The authors found that the most significant

contribution to the responsible behaviour of IT users will be made by such security measures

as using at least four identification procedures and a procedure involving the mobile phone,

restricting or refusing access to the geographical location, limiting access to profile or content

on social networking sites or shared online storage, checking the security of the website where

personal data is provided, understanding that cookies can be used to trace movements of people

on the Internet. The results showed a slight influence of such variables as using other electronic

identification procedures, single-use PIN code lists or random characters of a password, the

software that limits the ability to track the activities on the internet, electronic identification

certificate or card with a card reader or an app, simple login with username and password and

refusing to allow the use of personal data for advertising purposes. The obtained results will

enable us to conclude that the growth of cybercrimes determines the choice of security

measures, and their application will not guarantee the complete freedom of IT users from

various types of cyber threats.

The second part of the study concerned the construction of Kohonnen maps to identify

regions characterized by the responsible or irresponsible behaviour of individuals to ensure

personal cyber protection. As a result, 9 clusters were identified and substantiated using

Silhouette Scores. Romania, Bulgaria, Serbia, Albania, North Macedonia and Bosnia and

Herzegovina are countries whose respondents' behaviour can be characterized as the least

responsible. IT users of Montenegro, Lithuania, Greece, Slovenia, Slovakia, Italy, Poland, and

Do IT users behave responsibly…

195

Kosovo behave more responsibly concerning personal cyber protection than the respondents

of the previous cluster. Still, they can also be classified as those who need to be more attentive

to cybersecurity. It was found that a high level of responsible behaviour is characteristic of IT

users from Denmark, Iceland, the Netherlands, the United Kingdom, Norway, Finland and

Austria. The reliability of the cluster analysis is confirmed by the study of average values and

the distribution of each type of individual cyber activity in the clusters.

The obtained results may be of interest primarily to international organizations engaged in

developing a sustainable development strategy to ensure a safe and reliable cyberspace for the

entire world, as well as for countries, businesses, and individuals. Also, they can become the

basis for improving the population's digital and cyber literacy programs, which are developed

and implemented by educational institutions and relevant centres engaged in professional

development and training.

The main limitation of this study is obtaining survey data from respondents. The article is

localized with the results provided by the European Commission, so it is impossible to conclude

the behaviour of IT users from other countries. Also, the range of cybercrime cases and

personal cyber protection tools that shape the appropriate behaviour of individuals is

represented only by official data. It does not allow the conclusion about the different categories

of respondents and consider various aspects of the IT users' responsible behaviour, which

include psychological, social, economic, and other factors.

It is advisable to focus future research on studying the following questions. Firstly, a

potential direction is the assessment of risks associated with non-compliance or inappropriate

application of personal protection measures and identification of expected consequences for IT

users. Emphasis should be placed on different groups of individuals - ordinary users, company

employees, decision-makers, officials, etc. The obtained results will contribute to the formation

of more precise preventive measures that will reduce the risk of cyber threats in a specific

situation. Secondly, the next direction is the study of psychological and social factors that can

affect the vulnerability of IT users, for example, emotional state, level of education,

communication activity in social networks, level of digital and cyber literacy, trust in outsiders

and their actions, etc. The potential results will identify the most critical factors for IT users

that cybercriminals can use in the process of cybercrime. This knowledge should be taken into

account, for example, by representatives of e-commerce or banks in the process of improving

security measures in online or mobile applications.

FUNDING

This research was performed within the framework of state budget research: No 0121U109559

“National security through the convergence of financial monitoring systems and cybersecurity:

intelligent modeling of financial market regulation mechanisms”, No 0123U101945 “National

security of Ukraine through the prevention of financial fraud and money laundering: war and

post-war challenges”, No 0121U109553 “Convergence of economic and educational

transformations in the digital society: modelling of the impact on regional and national

security”.

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196

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Authors’ Note

All correspondence should be addressed to

Zoltán Szira

Budapest Metropolitan University,

Budapest, Hungary

ORCID 0000-0002-7299-4695

zszira@metropolitan.hu

Human Technology

ISSN 1795-6889

https://ht.csr-pub.eu

Do IT users behave responsibly…

201

Appendix A

Results of factor structures and canonical weights

Figure 1. Factor structure of the right set characterizing the IT users’ responsible behavior.

[Source: calculated by the authors]

Factor Structure, right set

Variable

Root 1 Root 2 Root 3 Root 4 Root 5 Root 6 Root 7 Root 8

Using a simple login

with username and password

Using a social media login

Using a security token

Using an electronic

identification certificate or

card with a card reader or an app

Using a procedure involving

the mobile phone

Using a single-use PIN code list

or random characters of a password

Using other electronic

identification procedures

Using at least 4 identification procedures

Individuals know that cookies

can be used to trace the movements

of people on the Internet

Changing the settings in the internet

browser to prevent or limit cookies

on any devices

Using software that limits

the ability to track the activities

on the Internet

Reading privacy policy statements

before providing personal data

Restricting or refusing access

to the geographical location

Limiting access to profiles or

content on social networking

sites or shared online storage

Rejection of using

personal data for

advertising purposes

Checking the security

of the website where personal

data is provided

0.7275

0.0062

0.2535

0.1502

0.1615 -0.1639

0.1353 -0.0983

0.7418

0.0123 -0.0435

0.0706 -0.0852 -0.1544 -0.3002 -0.0818

0.6852 -0.1391

0.4003

0.0295 -0.1462

0.1254

0.0780 -0.3447

0.6107

0.0216 -0.1726

0.2098

0.0914

0.1279

0.1832

0.2248

0.8404 -0.1283

0.1629

0.1178

0.0791 -0.0733 -0.0379 -0.0975

0.5952

0.2725 -0.2600

0.2596 -0.1724 -0.1978 -0.0605 -0.2053

0.5866 -0.0468 -0.1715 -0.0653 -0.1044 -0.4235 -0.1569 -0.3725

0.9480

0.0064

0.0099

0.0974 -0.1696 -0.0917

0.0058 -0.0201

0.6780

0.0450 -0.0971

0.1870 -0.0811

0.1644

0.2527 -0.3110

0.5163

0.1489

0.1407

0.3938

0.0139 -0.1462

0.2759 -0.1350

0.6442 -0.0549

0.0247

0.1999 -0.0910 -0.1836

0.0086 -0.1967

0.1724

0.4170

0.3114 -0.1211 -0.0349

0.0095

0.2183 -0.4352

0.7421

0.2999

0.0883

0.0016

0.0046

0.1472 -0.0451 -0.0088

0.5173

0.0812

0.1681

0.1675 -0.1764

0.1213 -0.2000 -0.3251

0.5362

0.1853

0.0816

0.2721 -0.1379

0.0887 -0.0739 -0.2948

0.6239 -0.1538

0.1830

0.3393 -0.1617 -0.0302 -0.0634 -0.1773

Yarovenko, Lyeonov, Wojcieszek, & Szira

202

Figure 2. Factor structure of the left set characterizing cases of cybercrime.

[Source: calculated by the authors]

Figure 3. Canonical weights for the right set characterizing the IT users’ responsible behavior

[Source: calculated by the authors]

Factor Structure, left set

Variable

Root 1 Root 2 Root 3 Root 4 Root 5 Root 6 Root 7 Root 8

Fraudulent credit or debit

card use

Online identity theft

Phishing

Pharming

Misuse of personal

information available

on the Internet

Social network or e-mail

account being hacked

Data loss due to a virus

Identity theft, receiving

fraudulent messages or being

redirected to fake websites

0.8398 -0.0549 -0.1020 -0.1618 -0.3889 -0.2090

0.0037 -0.2453

0.6504 -0.0228 0.4616

0.1571 -0.5029 -0.2055 -0.1332 -0.1606

0.7750

0.3117 0.1648

0.2857

0.0522

0.1517 -0.1138 -0.3934

0.7702

0.0133 0.2890 -0.1289 -0.4248

0.2528 -0.0852 -0.2341

0.7648 -0.0175 0.2964 -0.0970 -0.0893 -0.3698

0.3882

0.1486

0.5812 -0.5956 0.3507

0.2777

0.0449 -0.0289 -0.1437 -0.2897

0.1008 -0.2835 0.0179 -0.1569 -0.2469 -0.3174

0.8248 -0.2062

0.8285

0.0016 -0.2763

0.3125 -0.2698

0.2546

0.0258

0.0368

Canonical Weights, right set

Variable

Root 1 Root 2 Root 3 Root 4 Root 5 Root 6 Root 7 Root 8

Using a simple login

with username and password

Using a social media login

Using a security token

Using an electronic

identification certificate or

card with a card reader or an app

Using a procedure involving

the mobile phone

Using a single-use PIN code list

or random characters of a password

Using other electronic identification

procedures

Using at least 4 identification procedures

Individuals know that cookies

can be used to trace the movements

of people on the Internet

Changing the settings in the internet

browser to prevent or limit cookies

on any devices

Using software that limits

the ability to track the activities

on the Internet

Reading privacy policy statements

before providing personal data

Restricting or refusing access

to the geographical location

Limiting access to profiles or

content on social networking

sites or shared online storage

Rejection of using

personal data for

advertising purposes

Checking the security

of the website where personal

data is provided

0.0651

0.0869

0.0363

0.3738

1.0141 -0.7187 -0.0951

0.0064

0.1782

0.3464

0.0815

0.3232

0.5801

0.2880 -0.5661

0.0417

0.1688

0.1573

0.4065

0.6672

1.1148

0.8591 -0.3190 -0.9391

-0.0410

0.5157 -0.5853

1.2713

1.4733

0.5971 -0.2054 -0.5930

0.3602 -0.7199

0.3399 -0.4942

0.8296

0.3046 -0.4228 -0.2041

0.0087

0.4143 -0.8250

1.1622

0.9228

0.2255 -0.2568 -0.7530

-0.0043 -0.1619 -0.5994 -0.4002

0.9391 -0.6570 -0.2051 -0.7557

0.3972

0.1799

0.9708 -1.7964 -4.9879 -1.3866

0.8463

2.3949

0.2342 -1.1448 -1.1725 -0.9201 -0.1718

0.8631

1.2506 -0.4715

-0.0985

0.4340

1.0568

0.2539

0.0320 -0.6453

0.0728

0.3060

-0.0191 -0.1229 -0.1019 -0.0986 -0.3712 -0.3548

0.1609 -0.4599

-0.1552

0.5593

0.2738 -0.2357 -0.4510 -0.1980

0.8739 -0.3032

0.3515

0.2555 -0.3204 -0.7235

1.5855

0.7127

0.0093

0.3431

-0.2733 -0.2203

0.6234 -0.4786 -0.4317

0.0656 -1.6637 -0.9600

0.0404

1.5063

0.2425

1.0828 -1.0620 -0.0239 -0.7246

0.5950

-0.2598 -1.3011 -0.5101

1.1490

0.2125

0.2024

1.2212

0.3140

Do IT users behave responsibly…

203

Figure 4. Canonical weights for the left set characterizing cybercrime cases

[Source: calculated by the authors]

Canonical Weights, left set

Variable

Root 1 Root 2 Root 3 Root 4 Root 5 Root 6 Root 7 Root 8

Fraudulent credit or debit

card use

Online identity theft

Phishing

Pharming

Misuse of personal

information available

on the Internet

Social network or e-mail

account being hacked

Data loss due to a virus

Identity theft, receiving

fraudulent messages or being

redirected to fake websites

0.3745 -0.1452 -1.2003 -0.9257

0.0359 -1.1507 -0.8632 -0.7419

-0.4745

0.3961

0.5340

1.2503 -1.3537 -0.8327 -0.2824

0.0917

0.1466

0.9631

0.0993

0.4957

0.7107 -0.0994

0.3000 -1.1193

0.1599 -0.1874

0.7936 -1.3122 -0.2376

1.6642

0.2402

0.0869

0.6336

0.1453

0.5347 -0.4704

0.8055 -0.2516

0.3073

1.1667

0.1949 -1.1461

0.1512

0.1183

0.5834

0.0643 -0.2493 -0.2005

-0.3131 -0.0834 -0.0183

0.3491 -0.4064

0.1417

1.0377 -0.7364

0.2306 -0.2105 -0.6307

1.0219 -0.5209

0.5359

0.3577

0.7995

Yarovenko, Lyeonov, Wojcieszek, & Szira

204

Appendix B

The calculated average values of indicators

Table 1. Average values of the indicators in accordance with the cluster profile.

Indicator

C1*

C2

C3

C4

C5

C6

C7

C8

C9

Using a simple login with

username and password

65.3700 76.4200 46.2200 70.7000 71.1400 84.3600 71.3100 89.4200 74.0400

Using a social media login

32.7300 37.2100 20.1600 42.6600 28.7000 46.6400 36.8300 49.4700 30.2000

Using a security token

15.3000 13.0600 4.2800 24.1400 15.6500 29.5600 23.2400 58.0000 6.6200

Using an electronic

identification certificate or

card with a card reader or an

app

14.5800 27.9400 2.3300 32.9400 10.7200 65.8900 44.8200 22.8500 12.4000

Using a procedure involving

the mobile phone

31.5000 55.7200 12.0800 52.6000 34.5300 76.8400 42.2100 73.7800 57.1700

Using a single-use PIN code

list or random characters of

a password

13.0100 13.5400 2.7100 20.2800 10.2200 44.6000 12.1500 32.9600 38.5400

Using other electronic

identification procedures

5.4500

8.1800

1.8300 11.5900 3.0900 16.4500 5.4000 13.7100 7.1600

Using at least 4 identification

procedures

15.0400 22.9200 2.9400 33.2000 13.1600 58.0500 25.4900 48.2100 18.6500

Individuals know that

cookies can be used to trace

movements of people on the

Internet

50.9700 62.4000 34.4000 64.1000 74.3000 84.4600 70.1800 78.5000 80.3500

Changing the settings in the

internet browser to prevent

or limit cookies on any

devices

22.2700 27.4500 14.8400 28.0000 27.7100 37.3800 35.6000 30.4200 43.8900

Using software that limits the

ability to track the activities

on the Internet

15.9000 14.3100 8.2500 22.1400 11.4500 27.3600 34.0500 25.9000 17.5100

Reading privacy policy

statements before providing

personal data

37.4500 37.6800 29.0400 40.0400 46.7700 40.0500 22.0800 40.0300 50.1700

Restricting or refusing

access to the geographical

location

31.0000 37.7000 24.4100 52.1800 49.2400 71.1500 35.9000 56.8500 63.6300

Limiting access to profiles or

content on social networking

sites or shared online

storage

25.3700 36.6500 13.1500 47.7300 44.6100 51.2400 28.6900 41.0300 55.1600

Rejection of usingpersonal

data for advertising

purposes

31.4100 41.6600 16.4000 50.2400 57.5500 59.8000 39.4800 51.0400 65.2000

Checking the security of the

website where personal data

is provided

20.0400 40.0000 6.6700 43.5200 31.2000 49.9300 23.3200 43.3200 42.5500

*C means a cluster

[Source: calculated by the authors]

Do IT users behave responsibly…

205

Appendix С

Kohonen maps

a)

b)

c)

d)

e)

f)

g)

h)

Figure 5. Kohonen maps for indicators characterizing the responsible behaviour of IT consumers:

a) Using a simple login with username and password; b) Using a social media login; c) Using a security

token; d) Using an electronic identification certificate or card with a card reader or an app; e) Using a

procedure involving the mobile phone; f) Using a single-use PIN code list or random characters of a

password; g) Using other electronic identification procedures; h) Using at least 4 identification procedures.

[Source: calculated by the authors]

Yarovenko, Lyeonov, Wojcieszek, & Szira

206

a)

b)

c)

d)

e)

f)

g)

h)

Figure 6. Kohonen maps for indicators characterizing the responsible behaviour of IT consumers:

a) Individuals know that cookies can be used to trace the movements of people on the Internet; b)

Changing the settings in the internet browser to prevent or limit cookies on any devices; c) Using software

that limits the ability to track the activities on the Internet; d) Reading privacy policy statements before

providing personal data; e) Restricting or refusing access to the geographical location; f) Limiting access to

profiles or content on social networking sites or shared online storage; g) Rejection of using personal data

for advertising purposes; h) Checking the security of the website where personal data is provided.

[Source: calculated by the authors]