DOI: JEL codes: M51, M54, O3 /

Received 22 September 2022; Revised 5 January 2023; Accepted 11 January 2023.

This is an open access paper under the CC BY license (

Alicja Balcerak, PhD., Assistant Professor at the Wrocław University of Science and Technology, Faculty of Management, Wybrzeże S. Wyspiańskiego 27, 50-370 Wrocław, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Jacek Woźniak, Ph.D. habil., Professor at University of Human and Economics Studies, Warsaw, Faculty of Management, Okopowa 59, 01-043 Warsaw, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Alexandra Zbuchea, Professor, Ph.D. habil., National University of Political Studies and Public Administration, Faculty of Management, 30A Expoziției Blvd., District 1, 012104 Bucharest, Romania, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


PURPOSE: The purpose of this paper is to analyze the factors that determine the response of potential candidates to the screening of private (represented by Facebook) and professional (LinkedIn) social networking sites (SNS) for personnel selection purposes, and in particular to examine how SNS screening in the personnel selection process is perceived by innovative candidates. METHODOLOGY: The empirical data were obtained through an e-questionnaire survey among c. 150 young Polish Internet users in 2021. Multiple linear regression with backward elimination was used to determine the predictors of perceived justice of Facebook and LinkedIn screening in the selection process. FINDINGS: The results confirmed previous scientific findings that the perceived justice of Facebook cybervetting is significantly lower than for LinkedIn and the privacy invasiveness of Facebook screening was rated significantly higher than for LinkedIn. The results of linear regression with backward elimination indicated that among the assumed factors influencing the perceived justice of Facebook and LinkedIn screening in the selection process (i.e., privacy invasiveness, personal innovativeness, self-image management, risk aversion, ability to control a social networking site’s information, above average performance self-assessment, a general concern for internet privacy, and – in the case of LinkedIn – having an account on LinkedIn) the perceived privacy invasiveness is the best predictor of perceived justice of both private (Facebook), and professional (LinkedIn) social networking site screening for personnel selection purposes. Also, the candidate’s self-image management affects the perceived justice of both types of social media used as selection tools, whereas personal innovativeness increases the acceptance of private social media (Facebook) scanning for this purpose. IMPLICATIONS: This study contributes to the body of knowledge regarding the perceived justice of ICT-based selection tools, and of social networking site screening for personnel selection purposes in particular. It expands the knowledge about the applicability of social networking site content analysis of Polish users, especially of innovative candidates. The paper also provides some practical recommendations to help organizations apply social media content analysis in a way that minimizes potential candidates’ perception of privacy invasiveness and increases their fairness perception. ORIGINALITY AND VALUE: It is the first application of a cybervetting scale on a Polish sample that is advantageous in terms of comparability of data from different countries. We found that activities focused on creating one’s online image foster a higher acceptance of cybervetting that can diminish predictive validity of this type of selection practices.

Keywords: social networking sites, ICT-based selection tools, employee selection, fairness assessment, cybervetting


Proper staffing is a key HR task that enables the organization to function. Increasing turbulence in organizations’ environments, as well as the increasing importance of employees’ knowledge and their ability to interact with people from different backgrounds, requires HR not to overlook any rich sources of information that can be useful to predict how a given person will behave in various situations at work. The emergence of new sources from which information can be obtained to enable such predictions, i.e., containing manifestations of behavior of potential candidates, results in attempts by HR to use this information for staffing purposes. Social media – with their ever-wider groups of people and social interactions – are another such source of information that HR cannot bypass.

According to the latest Digital Global Overview report (Kemp, 2022) there were 27.2 million (70% of the total population) social media users in Poland in January 2022. Between 2021 and 2022, this number increased by 5% (1.3 million). Facebook had 17.65 million users, Instagram had 10.70 million, TikTok had 7.70 million, and LinkedIn had 4.60 million users in Poland in early 2022. This widespread use of social media paves the way for using it for HR purposes, especially for gathering information for employee selection processes.

The use of ICT-based techniques has revolutionized not only business, but also HR operations. In response to the new needs of organizations and the environment in which they operate today, which is turbulent not only economically and technologically but also in terms of social values, changes in Human Resources Management (HRM) tools are emerging. A significant proportion of them use ICT-based solutions and new types of data, which ICT help to create. Naturally, questions arise about the utility of the new methods for organizations that operate in this innovation-intensive environment.

One such new HRM method is the analysis of information from social networking sites (SNS) in the recruitment and selection processes of job candidates. Despite its widespread use by organizations, we know little about its actual usefulness and the reaction of candidates to this type of behavior by recruiters, and scientific research has yielded divergent results. Notably, there is no data to assess which candidates react most negatively to the use of social media screening, in particular whether the use of these methods discourages innovative candidates from applying.

The purpose of this article is to identify factors that promote a positive response from potential candidates to the use of social media information analysis as a selection method, which is often referred to in the literature as cybervetting (Cook et al., 2020; Gruzd et al., 2020), and in particular to see if the use of cybervetting for evaluating innovative candidates elicits negative reactions from them. Based on the data obtained by means of an electronic questionnaire from c.150 young Polish Internet users, it was confirmed that the acceptance for SNS screening for professional content (LinkedIn) is higher than for private content (Facebook). Then it was examined whether factors affecting the perceived justice (the proxy for the acceptance, typically used in recruitment studies – see Anderson et al., 2010) of SNS screening include those that indirectly promote organizational entrepreneurship. Based on a literature review, we hypothesize that the perceived justice of SNS screening (cybervetting) can be predicted by privacy invasiveness, personal innovativeness, self-image management, risk aversion, ability to control SNS information, above average performance self-assessment, a general concern for internet privacy, and – in the case of LinkedIn – having an account on LinkedIn.

The results of linear regression with backward elimination demonstrated that a candidate’s perceived privacy invasiveness and self-image management influence the perceived justice for both types of SNSs used as selection tools. However, personal innovativeness increases acceptance for screening private SNSs for this purpose (Facebook). The results of the study expand the scientific knowledge on the applicability of professional-type social media content analysis on innovative candidates, and provide some practical recommendations to help organizations apply social media content analysis without discouraging potential candidates.


Social media and their types

The growth of social media and its widespread use by potential employees has led to interest in the possibility of using the social media content as a source of information in employee selection processes. Social media is usually defined as an IT application that creates a space with user-driven content, and the role of the owner of the application that enables access to this content is only to provide opportunities for users to interact with each other, according to the rules for creating this content. For example, A.M. Kaplan and M. Haenlein define social media as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlein, 2010, p. 61). These applications include social networking sites (SNSs), which aim to create a shared social space where not only interactions occur with the use of extensive capabilities and a variety of expressive tools, but also a sense of social connection is achieved. Their growth in the 21st century has resulted in combining user-constructed profiles with the ability to communicate with others through various types of messages. This enables users to be pseudo-permanently in touch with friends and maintain social ties of various kinds. From the perspective of social media users, they enhance their real-world social relationships, and serve not only as a means to exchange information (Levinson, 2010; Richey et al., 2018; Gonzalez et al., 2019).

However, not all SNSs are private and egocentric. Some are oriented toward creating bonds between people (acquaintances) and raising one’s own self-esteem from place of position and by receiving praise (Levinson, 2010, p. 32). Other SNSs (e.g., LinkedIn) focus on professional matters and knowledge sharing, and thus serve to establish professional relationships and discuss professional problems (Levinson, 2010). However, the line between private and professional SNSs is becoming increasingly blurred today (Richey et al., 2018, p. 426). There is a growing number of SNSs’ users who treat their online activity as a self-promotional tool, not only in the sense of promoting their contribution to the network, but also in terms of representing themselves to potential job markets (Richey et al., 2018; Jacobson & Gruda, 2020).

Social media (SM) can be divided into three main types: Entertainment networks (a cluster of SM that have to do with general entertainment, such as games, sports, cinema, travel, and so on), Profiling Networks (a cluster of SM that offer functions promoting skills, goals, personal journals, etc.) and Social Networks (a cluster of SM with primary utility of connecting and sharing information) (Koukaras et al., 2020). From the perspective of the current study, the most important division separates SNSs into two types: (a) communities of people who share some type of professional interests (e.g., LinkedIn); and (b) “egological” ones, the purpose of which is building relations among groups of peoples (e.g., Facebook). This division is the one most commonly used in research on the use of SNSs in HR practices (Aguado et al., 2016; Roth et al., 2016; Cook et al., 2020; Roulin et al., 2021). Both types of SNSs “allow individuals: (a) to build a public or semi-public profile within a well-defined system, (b) to articulate a list of users with whom they have a connection and, finally, (c) to see and cross their connections list with others made by different individuals belonging to the same system” (Gonzalez et al., 2019, p. 707). It should be clearly emphasized that from the perspective of their users, these two types of SNSs (i.e., professional and private) have different functions and different rules for the disclosure of personal facts. Both types of networks allow users to use the privacy settings to ensure which of their online activities will not be revealed to others, and individuals may maintain public/private self-disclosure. However, the admiration- and entertainment-oriented private-type networks are characterized by a stronger tendency to reveal private information, while the professional-type network users focus on establishing their positions as professionals worthy of cooperation (which also includes employment).

Social media and their recruitment use

Both types of SNSs contain information that can be useful in the employee selection process (Chauhan et al., 2013; Roth et al., 2016; Zacny et al., 2020) and studies – both academic and industry reports – show that recruiters often use SNSs of both types in the selection process. Given the widespread use of Facebook by potential employees, the Huffington Post reported back in 2012 that “37% of current employers are using social media to find information on potential employees. Of that group, 65 percent use Facebook as their primary tool (Curran et al., 2014, p. 444)”. To this day, Facebook is the most widely used SNS. It is not surprising that, according to surveys conducted in the USA and Europe, up to 85% of managers or organizations have used LinkedIn and 78% have used Facebook for selection purposes (Cook et al., 2020, p. 383).

In Poland, the scale of this utilization may be somewhat reduced, as the spread of social networks in Poland was delayed in comparison, and the SNS user base has slightly different demographic characteristics than that of Western countries (Woźniak, 2013). According to 2021 data of Polish industry research, Facebook is used mainly by people over the age of 35 and there are about 18 million Polish accounts. LinkedIn is used by 4.1 million people, also extensively by senior executives, and the main user base is aged between 25–44 years, with 50% of users being in this age range (Social Media in Poland, 2021). In 2010, industry surveys showed that recruiters were declaring the use of SNS screening in the recruitment process (Woźniak, 2013), and limited studies demonstrated a fairly widespread use of such media in specific companies. According to 2018 data from a survey conducted by Lee Hecht Harrison DBM Poland, 97% of Polish recruiters use social media for work-related purposes. Up to 77% of headhunters and 35% of internal recruiters do it every day (Latus, 2018). Similar results are presented in other industry reports, showing that the larger the company, the more often recruiters use social media for recruitment purposes, and that recruiters are more likely to check candidates’ social media before an interview than after – 56% vs. 36% (Błaszczak, 2018).

Many researchers are skeptical about the predictive value of using Facebook as a data source in employee selection processes (Van Iddekinge et al., 2016; Zhang et al., 2020; Roulin et al., 2021). Opinions on data extracted from LinkedIn are sometimes more favorable (Cook et al., 2020; Roulin et al., 2021). For example, Roulin and Levashina (2019) found that hiring recommendations based on LinkedIn assessments were positively associated with several career success indicators. However, there are also studies questioning the value of information obtained by screening LinkedIn accounts (Cubrich et al., 2021).

Negative fairness assessment as an obstacle to the use of ICT-based selection tools

The use of non-traditional, ICT-based selection tools encounters obstacles related not only to their accuracy, and therefore the possibility of collecting information that predicts well the success of the position being filled, but also to the side-consequences that the use of new tools can bring to the organization. Early research on the application of ICT in selection has already shown that applicant reactions to personnel selection methods can be negative if novel technologies are used (e.g., Blacksmith et al., 2016) and, consequentially, some applicants might self-select out of the application process because they experience negative feelings toward technologically advanced selection procedures. It is not surprising that applicant reactions toward specific selection and preselection tools have generated much research over the past decades (Anderson et al., 2010) pointing to the importance of fairness and justice in selection processes (Stone et al., 2013), both in studies of traditional (Anderson et al., 2010) and new selection methods (McCarthy et al., 2017; Woods et al., 2020), also in Poland (Balcerak & Woźniak, 2021; Woźniak, 2019).

Research on SNS screening in the selection process is still scarce (Roth et al., 2016; McCarthy et al., 2017; Woods et al., 2020) and their findings often diverge. Some indicate the negative effects of social media screening on candidate responses (Aguado et al., 2016), while some state that the effects can be positive, particularly in cases of professional SNSs (Cook et al., 2020; Roulin et al., 2021). It is also shown that the opinions of candidates may differ from country to country (Gruzd et al., 2020), or be indirectly and situationally affected. For instance, a company’s image improvements, may favor positive fairness assessment of the selection processes based on SNS screening (Folger et al., 2022).

Quite unanimously, studies show negative reactions from potential candidates to private SNS screening (Bohnert & Ross, 2010; Aguado et al., 2016; Baglione et al., 2020; Roulin et al., 2021), although it is sometimes emphasized that social media competencies, such as the ability to restrict access to profile information, mitigate this negative reaction (Suen, 2018; Baglione et al., 2020). Since the start of this research, differences between private and professional SNSs have also been highlighted, both in terms of the scale of their use by HR (Nikolaou, 2014) and candidates’ reactions to this use. It is generally believed that job applicants have more favorable reactions toward the use of information posted on LinkedIn compared to other social media platforms such as Facebook (Stoughton, 2016; Stoughton et al., 2015; Aguado et al., 2016; Roulin et al., 2021). Some studies have shown that candidates’ attitudes toward social media screening in the selection process can differ geographically. For example, Gruzd et al. (2020, p. 1) found that after researching respondents from India, they are “significantly more comfortable” with this method than those living in the United States. Therefore, it is important not to neglect research on the determinants of responses to SNS screening (both private and professional) in Poland.

Gaps in the literature

There are also a number of additional needs in the field of research into candidates responses to ICT-based selection tools. There is a lack of knowledge about the reasons for the differentiation of candidates’ reactions to SNS screening (Roth et al. 2016; McCarthy et al., 2017; Baglione et al., 2020). Cook et al. (2020, p. 384) note that existing research on attitudes toward cybervetting usually is based on measures “borrowed from attitudes toward traditional selection methods.”

Hence, the purpose of the current study is to analyze the factors that determine the positive response of candidates to private and professional SNS screening, and in particular to examine whether selection based on such data fosters negative reactions of potential candidates characterized by high personal innovativeness and other features indicating their entrepreneurial traits. This partially answers the call for further research, stated in the Journal of Management, on how candidates’ individual traits differentiate their responses to cybervetting (Roth et al., 2016, p. 273, 289).

Even a cursory review of the scattered detailed knowledge identifies a number of variables that should foster a less negative attitude toward cybervetting. If candidates believe they are highly competent professionally, they should expect success when applying, as research shows that successful candidates will positively evaluate the use of SNS screening during the recruitment process (Gardner & Dunkin, 2019).

The ability to secure private information on one’s own social media profile is also important here. Suen’s (2018) study found that potential candidates, who were able to restrict access to information on their profiles, rated SNS screening for selection purposes higher than those who were not. It is to be expected, therefore, that both a potential job candidate’s perception of their professional competence as high, and a good ability to manage own SNS profiles, should foster a more favorable assessment of cybervetting.

In addition, it is worth noting that potential candidates are increasingly aware that SNS content analysis is becoming an employee selection tool. Consequently, for the purpose of creating their image as a professional and increasing their chances of obtaining employment, they no longer use only their profile on the professional SNSs, but also create a separate profile on the private SNSs (e.g., on Facebook), or remove certain information from it (Suen 2018, p. 398), generally taking care to “create positive professional impressions” (Richey et al., 2018, p. 426) to increase their chances of obtaining suitable employment. Hence, it is to be expected that those who put care into creating their professional image on social media will be positive about SNS screening as a selection practice.

Older research indicates that people with high computer skills (i.e., general, not social media related), particularly IT students, are less anxious when interacting with computers (Beckers & Schmidt, 2003; Potosky & Bobko, 1998), react more positively to using ICT tools in employee selections (Wiechmann & Ryan, 2003; Zacny et al., 2019), and have more favorable reactions to the selecting organizations (Bauer et al., 2006). It is also assumed – and confirmed by some studies – that a younger age, which is an indirect indicator of computer competence (or at least a sense of agency in this area), promotes a positive response to cybervetting (Roth et al., 2016). However, there is no shortage of research showing that IT knowledge (e.g., being an IT student – Zacny et al., 2019) does not significantly affect attitudes toward organizations’ use of ICT-based tools during selection (Langer et al., 2018). One may also think that a general IT knowledge makes the opportunities presented by given ICT-based procedures visible, and so it will reinforce psychological inclinations related to attitudes concerning privacy protection (Langer et al., 2018) or entrepreneurship traits.

From the perspective of the purpose of the study, which is to verify how SNS screening in the personnel selection process is perceived by innovative candidates and in what ways organizations can increase the strength of positive responses to these tools in candidates with traits associated with entrepreneurial inclinations, it is important to consider the role of personal innovativeness in building a response to SNS screening. Personal innovativeness is understood as “the willingness of an individual to try out any new information technology” (Agarwal & Prasad, 1998, p. 206). Research shows the influence of personal innovativeness on information technology acceptance (Slade et al., 2015; Ahmad, 2018) and SNS acceptance (Wijesundara & Xixiang, 2018; Kim et al., 2019). This specific type of innovation should also foster a positive attitude toward cybervetting, as it involves the use of new technologies in the personal area (Mochi et al., 2017; Parasuraman & Colby, 2015).

In turn, negative reactions to SNS screening should be fostered by risk aversion and a sense of privacy risk. Previous research has shown that the perception of privacy risks is one of the reasons why SNS screening is treated with reluctance by candidates (Roth et al., 2016, p. 288; McCarthy et al., 2017, p. 1705; Zacny et al., 2019).

As highlighted above, an important factor influencing attitudes toward cybervetting is the use of one’s online profile to create a professional image. This aspect has not been studied directly to date, but was postulated by Gruzd et al. (2020). However, the authors of that study used as an indicator for this factor not the scale associated with managing one’s self-image through social media activities, but the number of accounts one has on such portals, and the results of their study did not support such an operationalized hypothesis.

We hypothesize that predictors of perceived justice of screening Facebook and LinkedIn in the personnel selection process includes privacy invasiveness, personal innovativeness, self-image management, risk aversion, ability to control SNS information, above average performance self-assessment, and a general concern for internet privacy, and – in the case of LinkedIn - having an account on LinkedIn.


Respondents’ characteristics

Data were collected in June 2021 using snowball methodology by one of the authors’ MA students, who agreed to extend the survey used for her thesis (Szczygieł, 2021) and agreed for further use of the data in the authors’ research. The research sample consists of 147 adults, of whom 61.3% were 18–25 years old, 27% were 26–36 years old, and 11.7% were over 36 years old. The participants can be treated as young professionals, or on the way to be, as 49.3% of them were students or had a bachelor’s degree, 38.7% had a master’s degree, and only 12.0% had received at most a secondary education. Only 26.7% of our respondents were not actually working – being fulltime students, while 36.7% had been employed for up to 4 years, 27.3% employed for 5–14 years, and 9.3% had a longer work history.

It should be clearly noted that the scale of Facebook and LinkedIn use among the respondents varies, although it seems to be in line with data that comes from earlier Polish incidental sample surveys (e.g., “almost 80% of respondents have their own profile on social network site... [but] 28% of respondents admitted having their own virtual professional profile”, Zdonek et al., 2015, p. 225) and the trend toward increasing levels of social media use over time. In our sample, 99% of respondents had an account on Facebook and 33% declared that it was mostly public (although the answer “public” was chosen twice as often as “primary public”). Only 38% of respondents had an account on LinkedIn, and 13% intended to set up a LinkedIn account soon. The percentage of those who declared that their profiles were public was 75%, and the answer “public” was selected twice as often as “primary public.” This breakdown of social network use is consistent with the claim about the prevalence of Facebook among Polish Internet users and their significantly lower use of LinkedIn. It also means that it is useful to study the implications of having a LinkedIn account for attitudes toward cybervetting, while it is not possible to study that based on the data available for Facebook account ownership.


Perceived justice and privacy invasiveness were measured on the scales developed by Cook et al (2020). Representative items included: “It is fair for a potential employer to make a hiring decision based on the information they acquired from my Facebook/LinkedIn profile” and “I believe that screening my Facebook/LinkedIn profile is an effective tool for an employer to use in the hiring process” (perceived justice), “I would be concerned if I knew a potential employer might access my Facebook/LinkedIn profile” and “I would feel uncomfortable if I learned that a potential employer had viewed my Facebook/LinkedIn profile without my knowledge” (privacy invasiveness). Participants responded on a seven-point Likert-type scale (from 1– strongly disagree to 7 – strongly agree). Cronbach’s α for perceived justice =0.686; for privacy invasiveness =0.874.

Self-image management was measured by an ad hoc constructed three items scale: 1. Before adding a post or photo on social profiles, I wonder how other people will react to it. 2. Have you ever wondered if your future employer is looking for information about you on the Internet? 3. Do you think you care about your image on the Internet? Participants responded on a seven-point Likert-type scale (from 1 – strongly disagree to 7 – strongly agree). Cronbach’s α=0.663.

Above average performance self-assessment was a dichotomous variable indicating whether the respondent assessed his/her work performance as “rather higher than average”, “higher than average,” or “definitely higher than average.”

Ability to control SNS information. Three items, two of them adopted from Suen (2018), were used to measure this construct: “I know how to use the privacy settings in my social media accounts,” “I know which information on my social media accounts may provoke a negative impression on potential recruiters,” “I have quite a lot of knowledge about the latest solutions related to ensuring privacy on the Internet”. Participants responded on a five-point Likert-type scale (from 1 – strongly disagree to 5 – strongly agree). Cronbach’s α =0.677.

Risk aversion was measured using three items derived from Disatnik and Steinhart (2015): “I would rather be safe than sorry,” “I want to be sure before I try things that are new or unfamiliar to me,” “I avoid risky things.” Participants responded on a five-point Likert-type scale (from 1 – strongly disagree to 5 – strongly agree). Cronbach’s α =0. 858.

A general concern for internet privacy was measured using four items derived from Schumann, von Wangenheim and Groene (2014): “In general, I am concerned about my privacy when using the Internet,” “I am concerned that information I submit on the Internet could be misused,” “I am concerned that a person can find private information about me on the Internet,” “I am concerned about submitting information on the Internet, because they could be used in a way that I cannot foresee.” Participants responded on a five-point Likert-type scale (from 1 – strongly disagree to 5 – strongly agree). Cronbach’s α =0. 914.

Personal innovativeness was measured on the scale developed by Agarwal and Prasad (1998). This scale consists of four items: “I like to experiment with new information technologies,” “Among my peers I am usually the first to try out new information technologies,” “If I heard about a new information technology I would look for ways to experiment with it,” “In general, I am hesitant to try out new information technologies (reversed)”. Cronbach’s α =0. 821.

LinkedIn user – was a dichotomous variable indicating whether the respondent has or is intending to create an account on LinkedIn soon.

IBM SPSS Statistic software (ver. 27) was used to conduct all statistical analyses. The criterion for statistical significance was set at 5%.


Table 1 presents the mean, standard deviations, and correlations among the variables used in this study.

Table 1. Mean, standard deviations, and correlations

















Perceived justice (Facebook)






Privacy invasiveness (Facebook)







Perceived justice (LinkedIn)








Privacy invasiveness (LinkedIn)









Self-image management










Personal innovativeness











Risk aversion












Ability to control SNS information













Above average performance self-assessment














General concern for internet privacy














LinkedIn user













Notes: ** - correlations significant at the 0.01 level, * - correlations significant at the 0.05 level.

Source: data from research.

Firstly, we assessed if there are expected differences in perceived justice and privacy invasiveness ratings between Facebook and LinkedIn screening for personnel selection purposes. The perceived justice of Facebook was rated significantly lower than LinkedIn’s, with Cohen’s d=0.71, t(146) =-8.57, p<0.001 whereas the privacy invasiveness of Facebook was rated significantly higher than LinkedIn’s with Cohen’s d=0.65, t(146)= 7.90, p<0.001.

To identify the predictors of perceived justice of Facebook screening (the first analysis) and LinkedIn (the second analysis) in the personnel selection process, two multiple regressions with backward elimination were conducted.

In both models, residuals are normally distributed. In the Facebook analysis the Shapiro-Wilk statistic = 0.99 (p = 0.384), in the LinkedIn analysis the Shapiro-Wilk statistic = 0.99 (p = 0.402). The homoscedasticity assumption was examined by analysing the scatterplots of standardized predicted values against the standardized residuals (Figure 1). As Figure 1 shows, the residuals are randomly scattered and therefore the assumption of homoscedasticity was met.

Figure 1. Scatterplots of homoscedasticity tests for perceived justice
of a) Facebook, b) LinkedIn

Figure 1. Scatterplots of homoscedasticity tests for perceived justice of a) Facebook, b) LinkedIn (cont.)

The first regression analysis indicates that the perceived justice of Facebook screening in the selection process is influenced mostly by perceived privacy invasiveness (negative coefficient), personal innovativeness and self-image management. The final model also includes risk aversion, although this predictor is not significant at the .05 level (Table 2). The model was statistically significant F(4, 142)=14.094, p<0.001 and accounts for 26.4% of the variance in the perceived justice of Facebook screening in the selection process (R Square =0.284, Adjusted R-Square =0.264).

The squared multiple semi-partial correlation coefficients indicate that privacy invasiveness uniquely accounts for 24.90% of the variation of the perceived justice of Facebook screening, whereas personal innovativeness accounts for 2.46%, and self-image management 2.89%.

As the second regression analysis revealed, privacy invasiveness, self-image management, and risk aversion also influenced the perceived justice of LinkedIn screening in the selection process (Table 2). The last predictor in the model is being a LinkedIn user. The model was statistically significant F(3, 142)=39.513, p<0.001 and accounts for 51.3% of the variance in the perceived justice of LinkedIn screening in the selection process (R Square =0.527, Adjusted R-Square =0.513).

Table 2. The final linear regression models

Dependent variable: Perceived justice of screening



Stand. Beta





Collinearity Statistics

Std. Error















Personal innovativeness











Self-image management











Privacy invasiveness











Risk aversion



















Privacy invasiveness











Self-image management











Risk aversion











LinkedIn user











Source: data from research.

The squared multiple semi-partial correlation coefficients indicate that privacy invasiveness uniquely accounts for 21.07% of the variation of the perceived justice of LinkedIn screening, whereas being a LinkedIn user accounts for 11.29%, and self-image management for 1.99%.

In both analyses, the variance inflation factors (VIF) for all predictors indicates acceptable levels of collinearity (Studenmund, 2001). The Durbin-Watson statistic in the first analysis is 2.146, and in the second is 1.947, which indicates that there is no correlation between residuals. Cook’s distance maximum values are 0.064 in the Facebook analysis and 0.087 in the LinkedIn analysis, indicating no problems with significant outliers.


SNS screening, especially professional ones, is already a well-established method of gathering information for employee selection, and data from industry reports and academic studies show that recruiters often reach for information from both professional and private sites. However, research data shows that candidates’ reactions to cybervetting are unfavorable, which, regardless of the risks associated with the inaccurate use of this data to predict the competence of potential employees, carries the risk of negatively affecting the company’s image, as well as the risk of some good candidates abandoning their applications.

The results of this research confirm previous scientific findings that the perceived justice of Facebook cybervetting is significantly lower than for LinkedIn cybervetting (Cohen’s d=0.71), so Polish respondents were similar in that regard to those from other Western countries (Aguado et al., 2016; Cook et al., 2020), but different from Indian respondents (Gruzd et al., 2020). Similarly, it was found that the privacy invasiveness of Facebook screening was rated significantly higher than LinkedIn’s (with Cohen’s d=0.65), which can be considered expected (Roth et al., 2016; McCarthy et al., 2017), but – to the authors’ knowledge – has only recently been studied directly (Cook et al., 2020).

Lack of privacy invasiveness is the best predictor of perceived justice of SNS screening. It uniquely accounts for 24.90% and 21.07% of the variation of the perceived justice of Facebook/LinkedIn screening, respectively, which is in line with other studies that have shown that privacy invasiveness is an important factor affecting justice perception, both for traditional screening methods (Bauer et al., 2006; Anderson et al., 2010; McCarthy et al., 2017), IT-based methods (Bauer et al., 2006; Woods et al., 2020; Balcerak & Wozniak 2021), as well as SNS-data-based methods such as cybervetting (Roth et al., 2016; Van Iddekinge et al., 2016; Cook et al., 2020).

Both models also include self-image management, although this factor uniquely accounts only for 2.89% of the variation of the perceived justice of Facebook screening and for 1.99% of the variation of the perceived justice of LinkedIn screening. This suggests that both types of SNSs are sometimes used by users to create their own professional brand, which naturally increases the acceptance of SNS screening in the selection process. If the practice of creating one’s own brand on social media or consciously managing it becomes widespread, the predictive value of SNS content for success at work is expected to decline. Furthermore, we should not expect that more information would be included on Facebook than on LinkedIn, suggesting the limited added value that information from Facebook can bring relative to that gathered on LinkedIn.

The final linear regression model predicting factors influencing acceptance of Facebook screening also included personal innovativeness that uniquely accounts for 2.46% of the variation. This factor was excluded from the model predicting the perceived justice of LinkedIn screening. Innovative candidates are more open to screening private social media, which does not change the fact that the main barrier to accepting this selection method is perceived privacy invasiveness. Indirectly, this opinion is supported by the fact that above average performance self-assessment was also excluded from both models, as it suggests that it is specific attitudes toward technology or social media privacy, rather than general psychological traits (unrelated to social media) that influence candidates’ opinions.

As already highlighted, since nearly all respondents had a Facebook account, having an account on Facebook was not taken into account for the regression analysis conducted when seeking significant predictors of the perceived justice of Facebook screening. The linear regression analysis to predict the perceived justice of LinkedIn screening yielded a model that included LinkedIn profile ownership. This predictor uniquely accounts for only 11.29% of the variation. This means that setting up a professional social media account does not guarantee acceptance for social media screening.

While privacy invasiveness proved to be the best predictor of the perceived justice of social media screening, both models excluded a general concern for internet privacy. Acceptance of social media screening is thus diminished by the knowledge that the account will be analyzed by a “potential employer,” but this does not go hand in hand with a more general concern for privacy. Those who feel a high concern for Internet privacy may be more careful about selecting the information they share. Privacy invasiveness concerns the discomfort and perceived disrespect associated with a potential employer viewing private information, even if it involves objectively secure content.


The purpose of the current study was to see how the use of two types of cybervetting was perceived by Polish users, and in particular whether it leads to a negative reaction in innovative candidates. Based on data obtained through an e-questionnaire from 150 young Internet users, we confirmed that acceptance of professional SNSs (LinkedIn) content screening is higher than private SNSs (Facebook), and that the sense of invasion of privacy has a strong impact on the perceived justice of SNS screening. We found that activities focused on creating one’s online image foster a higher acceptance of cybervetting.

Through multiple linear regression with backward elimination, we checked whether among the factors determining perceived justice are those that indirectly promote organizational entrepreneurship. For the two types of SNSs, the following predictors of perceived justice of SNS screening were taken into account: privacy invasiveness, personal innovativeness, self-image management, risk aversion, ability to control SNS information, above average performance self-assessment, general concern for internet privacy, and – in the case of LinkedIn – having an account on LinkedIn. The results of linear regression with backward elimination showed that the perceived justice of social media screening is primarily influenced by perceived privacy invasiveness. This is a negative influence. The main predictor in the obtained model, privacy invasiveness, uniquely accounts, respectively, for 24.90% and 21.07% of the variation of perceived justice of Facebook/LinkedIn screening. Acceptance of both types of SNS screening is positively influenced by self-image management, while personal innovativeness increases the acceptance of scanning private SNSs for this purpose (Facebook). In the case of LinkedIn screening, having a LinkedIn profile is also a predictor, which is understandable since one of the reasons for setting up an account on LinkedIn is to increase potential job opportunities.

From the perspective of the current study’s objective, it can be concluded that the use of Facebook cybervetting is less dangerous for companies in relation to innovative candidates, as personal innovativeness was found to be a significant predictor of perceived justice of Facebook screening. As privacy invasiveness is a major predictor of respondents’ opinions toward cybervetting, it can be said that the use of cybervetting on innovative candidates is better received by them than by other candidates.

The first practical postulate arising from the study is the need for measures to reduce the sense of potential privacy invasiveness when SNS screening is disclosed. Studies have already confirmed that increasing potential candidates’ knowledge of the scope of SNS screening, as well as the strength of the impact of screening conclusions on selection decisions, can reduce privacy invasiveness concerns as long as the organization conducts some additional activities to familiarize candidates with the selection procedure (McCarthy et al., 2017a, Truxillo et al., 2018). This approach is consistent with the general thesis already confirmed for Polish candidates that experience with a particular selection method promotes greater acceptance of that method (Balcerak & Woźniak, 2020ab; Woźniak, 2019). It is worth noting, however, that this postulate does not apply to passive candidates.

The second important conclusion of the study is related to a factor not yet studied in the area of responses to cybervetting, namely, self-image management. Our study showed that the practice of creating one’s own image on social media, or consciously managing it, is so widespread (mean 4.95 on a seven-point scale in our study) that one should expect a decrease in the predictive power of this selection method. In other words, information obtained through candidates’ SNS screening will not contribute to an accurate prediction of their achievements and attitudes at work – at least when the subject of the analysis is the content rather than the form. Only some past studies have indicated that such activities should be expected, but even then they were expected on LinkedIn (professional type portals) rather than on Facebook. Our data suggest that self-image management fosters acceptance of cybervetting for both types of SNSs, so that a mutual reinforcement of the two practices is to be expected. This means that the more frequently candidates expect cybervetting to be used in employee selection processes, the more often they will implement self-image management activities. Consequently, this suggests a decline in the predictive value of the information contained in these networks, which forces organizations to use a different way of analyzing SNS content, that is, one that analyzes not only information but the forms of relationships that these networks create. These two distinct uses of social media data – one oriented toward content and the other on the form of established relations (Gandomi & Haider, 2015; McCarthy et al., 2017; Woźniak, 2020) – are characterized by a different sensitivity to truthfulness, where the latter way is independent of it. It is to be expected that the informal use of social media as a source of content-driven information analysis will increasingly lead recruiters astray. This means that the predictive accuracy of SNS data geared toward unformalized analysis of social network content will decline. Thus, it can be concluded that this will favor the use of more formalized, analytical methods based on artificial intelligence.

Such a trend would be favorable from the perspective of the predictive validity of selection practices, as it would promote more objective analysis of SNS content, but it may carry with it further threats to candidates’ positive attitude toward selection due to reluctance toward AI use (Mirowska, 2020; Zacny et al., 2019). This would necessitate hiding information about the use of AI in selection processes or introducing other measures to improve candidate perceptions with AI-driven selection practices, such as increasing the positive ratio through pre-selection activities (McCarthy et al., 2017a, Truxillo et al., 2018), or through broader explanations of how the tools work in practice, in the form of feedback right after selection, along with broader feedback to support their understanding and use in further career planning (Konradt et al., 2017). The necessity of such practices concerning recruitment also stems indirectly from the fact that they can foster a reduced sense of privacy invasiveness, which, for those surveyed here, is the strongest predictor of perceived justice of cybervetting.

At the same time, it may be thought that the positive impact of self-image management on the perceived justice of SNS screening also suggests some other practical measures that may make it easier for potential candidates to reconcile with cybervetting, namely – prior disclosure of the scope and consequences of SNS screening in the selection process. This will allow potential candidates to trigger self-image management activities, and so – while it may indirectly lower the accuracy of inferences about competencies from SNS content –make it easier to accept such screening. In the face of this tension – between actions conducive to obtaining a positive response and provoking a decline in the informational value of posted content – it is to be expected that only the use of formalized analyses, based not so much on freely interpreted content by the recruiter, but on algorithms that examine underlying structures, can be an effective predictive cybervetting tool.

The results of the study described here expand the scientific knowledge about the applicability of SNS content analysis of Polish users, especially of innovative candidates, and allow us to make some practical recommendations to make it easier for organizations to apply SNS content analysis without discouraging candidates from applying. These results also respond to the demand that research on response to new selection tools, and cybervetting in particular, should be based on standardized measurement scales to enable methodologically valid comparison of their results and foster knowledge accumulation (McCarthy et al., 2017; Cook et al., 2020).

It should be noted that our study has a number of limitations that suggest treating the results as preliminary. They also yield findings that are inconsistent with previous findings on users from other countries, suggesting the need for further research. As the study sample was collected using snowball sampling, not only were the respondents relatively young and at the beginning of their careers, but they were also overwhelmingly female, which supports a stronger perception of the privacy risks associated with cybervetting (Gruzd et al., 2020). It was relatively rare – though in line with the overall percentage among adult Poles – for respondents to have a profile on LinkedIn, which fostered an opinion about it that was not grounded in their own experience (but was based on transferring their expectations characteristic of Facebook, where most respondents had their own profiles). The sample is relatively small and generalizations are risky, not only because of the number of respondents, but also because of the non-probability sampling. So the result should be treated as a preliminary one, and there may be a need for a similar study based on a much bigger and representative sample.

Respondents were also not put in a situation of applying for a job, which may, but does not have to (Jacobson & Gruzd, 2020), change the way they perceive selection tools based on social media. But above all – the study explored opinions formulated in response to a questionnaire, not the actual reaction when learning of an event concerning a person as a candidate for a particular workplace. This limitation of the whole fairness research paradigm has been pointed out by us before (Balcerak & Woźniak, 2020b) and the current study is not free of it.

An important limitation, which also indicates the direction of future research, is to study the responses of candidates not against the description of a certain type of company activity (as in our case – collecting information on social networks), but situate such activity in the context of the practices of other companies. The first data obtained in this way show that the type of cybervetting (juxtaposed with the candidate’s personality profile) influences such responses (Bowen et al., 2021), however, it is difficult to say to what extent the policies concerning SNS profile access are actually practiced by companies. This line of research seems promising because, as already highlighted in other research, the practice of cybervetting in employee selection has evolved from “whether companies use social media content to vet job applicants” to “to what extent companies use social media content to vet job applicants” (Bowen et al., 2021, p. 6). With the creation of norms in this area and clear standards, the strength of negative reactions to these practices is expected to decrease.

To recap, our study was the first attempt to measure attitudes toward cybervetting using the cybervetting scale proposed by Cook et al. (2020) on Polish users, which promotes comparability of data from different countries and poses questions leading to the accumulation of scientific knowledge in this area. At the same time, we demonstrated that cybervetting as a practice can be used by organizations seeking innovative candidates, and that the need for changes in the current practice of using cybervetting by organizations should be expected. For the first time in Poland, we strongly formulated a thesis that is also still absent in the world literature, namely, that regardless of the practical recommendations made here to promote a sense of fairness in candidates subjected to cybervetting by organizations, we should expect recruiters to move away from informal cybervetting in the near future.


The authors thank the editor and two referees for their helpful suggestions, as well as Mrs. Kamila Szczygieł for collecting the data and for giving us a written consent to use the data in this study.


Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215.

Aguado, D., Rico, R., Rubio, V., & Fernández, L. (2016). Applicant reactions to social network web use in personnel selection and assessment. Journal of Work and Organizational Psychology, 32, 183–190.

Ahmad, M. (2018). Review of the technology acceptance model (TAM) in internet banking and mobile banking. International Journal of Information Communication Technology and Digital Convergence, 3(1), 23-41.

Anderson, N., Salgado, J. F., & Hülsheger, U. R. (2010). Applicant reactions in selection: Comprehensive meta-analysis into reaction generalization versus situational specificity: Applicant reactions meta-analysis. International Journal of Selection and Assessment, 18, 291–304.

Balcerak, A., & Woźniak, J. (2020a). The synchronous video interviews in personnel selection processes. European Research Studies Journal, 24(2), 3-13.

Balcerak, A., & Woźniak, J. (2020b). Process favorability for different types of selection methods. In K. S. Soliman (Ed.), Education Excellence and Innovation Management: A 2025 Vision to Sustain Economic Development during Global Challenges - Proceedings of the 35th International Business Information Management Association Conference (pp. 4832-14842). Retrieved from

Balcerak, A., & Woźniak, J. (2021). Reactions to some ICT-based personnel selection tools. Economics and Sociology, 14(1), 214-231.

Bauer, T. N., Truxillo, D. M., Tucker, J. S., Weathers, V., Bertolino, M., Erdogan, B., & Campion, M. A. (2006). Selection in the information age: The impact of privacy concerns and computer experience on applicant reactions. Journal of Management, 32, 601–621.

Beckers, J. J., & Schmidt, H. G. (2003). Computer experience and computer anxiety. Computers in Human Behavior, 19(6), 785-797.

Black, S., Stone, D., & Johnson, A. (2015). Use of social networking websites on applicants’ privacy. Employee Responsibilities and Rights Journal, 27, 115-159.

Błaszczak, A. (2018). W mediach społecznościowych możesz wypłynąć, ale i karierę zatopić. Retrieved from

Bohnert, D., & Ross,W. H. (2010). The influence of social networking websites on the evaluation of job candidates. Cyberpsychology, Behavior, and Social Networking, 13(3), 341-347.

Bowen, C.-C., Stevenor, B.A. & Davidson, S. D. (2021). How people perceive different types of social media screening and their behavioral intention to pursue employment. Computers in Human Behavior Reports, 3, 100089.

Chauhan, R. S., Buckley, M. R., & Harvey, M. (2013). Facebook and personnel section: What’s the big deal? Organizational Dynamics42(2), 126-134.

Cook, R., Jones‐Chick, R., Roulin, N., & O’Rourke, K. (2020). Job seekers’ attitudes toward cybervetting: Scale development, validation, and platform comparison. International Journal of Selection and Assessment, 28(4), 383-398.

Curran, M.J., Draus, P., Schrager, M. & Zappala, S. (2014). College students and HR professionals: Conflicting views on information available on Facebook. Human Resource Management Journal, 24, 442-458.

Cubrich, M., King, R.T., Mracek, D.L., Strong, J.M.G., Hassenkamp, K., Vaughn, D., & Dudley, N.M. (2021). Examining the criterion-related validity evidence of LinkedIn profile elements in an applied sample. Computers in Human Behavior, 120, 106742.

Disatnik, D., & Steinhart, Y. (2015). Need for cognitive closure, risk aversion, uncertainty changes, and their effects on investment decisions. Journal of Marketing Research, 52(3), 349-359.

Esch, P., Black, J., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222.

Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2021). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37(4), 735-757. 1–23

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

Gardner, A.K., & Dunkin, B.J. (2019). Applicant perceptions of new selection systems are a function of their performance in the selection procedure. The American Journal of Surgery, 217(2), 272-275.

Gonzalez, R., Gasco, J., & Llopis, J. (2019). University students and online social networks: Effects and typology. Journal of Business Research, 101, 707-714.

Gruzd, A., Jacobson, J., & Dubois, E. (2020). Cybervetting and the public life of social media data. Social Media + Society, 6(2), 2056305120915618.

Jacobson, J., & Gruzd, A. (2020). Cybervetting job applicants on social media: The new normal? Ethics and Information Technology22, 175–195.

Kaplan, A.M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.

Kemp, S. (2022). Digital 2022: Global overview report. Retrieved from

Kim, J. H., Kim, M. S., Hong, R. K., & Ko, J. W. (2019). Continuous use intention of corporate mobile SNS users and its determinants: application of extended technology acceptance model. Journal of System and Management Sciences, 9(4), 12-28.

Konradt, U., Garbers, Y., Böge, M., Erdogan, B., & Bauer, T. N. (2017). Antecedents and consequences of procedural fairness perceptions in personnel selection: A three-year longitudinal study. Group and Organization Management, 42, 113–146.

Koukaras, P., Tjortjis, C., & Rousidis, D. (2020). Social media types: introducing a data driven taxonomy. Computing102, 295–340.

Langer, M., König, C.J., & Fitili, A. (2018). Information as a double-edged sword: The role of computer experience and information on applicant reactions towards novel technologies for personnel selection. Computers in Human Behavior, 81, 19-30.

Latus, K. (2018). Media społecznościowe odgrywają coraz ważniejszą rolę w procesie rekrutacji. Są szansą, ale i zagrożeniem dla pracodawców. Retrieved from,p2118127781

Levinson, P. (2010). Nowe media. Kraków: Wydawnictwo WAM.

Madera, J.M. (2012). Using social networking websites as a selection tool: The role of selection process fairness and job pursuit intentions. International Journal of Hospitality Management, 31, 1276– 1282.

McCarthy, J.M., Bauer, D.M., Truxillo, T.N., Anderson, N.R., Costa, A.C., & Ahmed, S.A. (2017). Applicant perspectives during selection: A review addressing “So what?,” “What’s new?,” and “Where to next?”. Journal of Management, 43(6), 1693–1725.

McCarthy, J. M., Bauer D.M., Truxillo, T.N., Campion, M.C, Van Iddekinge, C.H., & Campion, M.A. (2017a). Using pre-test explanations to improve test-taker reactions: Testing a set of ‘‘wise” interventions. Organizational Behavior and Human Decision Processes, 141, 43–56.

Mirowska, A. (2020). AI evaluation in selection. Effects on application and pursuit intentions. Journal of Personnel Psychology, 19(3), 142–149.

Mochi, F., Bissola, R. & Imperatori, B. (2017). Professional and non-professional social media as recruitment tools: The impact on job seekers’ attraction and intention to apply. In Bondarouk, T., Ruël, H. & Parry, E. (Eds.). Electronic HRM in the Smart Era (pp. 109-135). Bingley: Emerald.

Nikolaou, I. (2014). Social networking web sites in job search and employee recruitment. International Journal of Selection and Assessment, 22(2), 179-189.

Parasuraman, A., & Colby, C.L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59-74.

Potosky, D., & Bobko, P. (1998). The computer understanding and experience scale: A self-report measure of computer experience. Computers in Human Behavior, 14(2), 337-348.

Richey, M., Gonibeed, A., & Ravishankar, M.N. (2018). The perils and promises of self-disclosure on social media. Information Systems Frontiers20, 425–437.

Roulin, N., Langer, M., & Bourdage, J. (2021). “I” feel(s) left out: The importance of information and communication technology in personnel selection research. Industrial and Organizational Psychology14(3), 423-427.

Roulin, N., & Levashina, J. (2019). LinkedIn as a new selection method: Psychometric properties and assessment approach. Personnel Psychology, 72, 187–211.

Roth, P. L., Bobko, P., Van Iddekinge, C. H., & Thatcher, J. B. (2016). Social media in employee-selection-related decisions: A research agenda for uncharted territory. Journal of Management42(1), 269–298.

Schumann, J. H., von Wangenheim, F., & Groene, N. (2014). Targeted online advertising: Using reciprocity appeals to increase acceptance among users of free web services. Journal of Marketing, 78(1), 59-75.

Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860-873.

Suen, H. Y. (2018). How passive job candidates respond to social networking site screening. Computers in Human Behavior, 85, 396-404.

Social Media w Polsce 2021 – raport (2021). Retrieved from

Stoughton, J. W., Thompson, L. F., & Meade, A. W. (2015). Examining applicant reactions to the use of social networking websites in pre-employment screening. Journal of Business and Psychology, 30, 73–88.

Stoughton, J.W. (2016). Applicant reactions to social media in selection: Early returns and future directions. In R. Landers, & G. Schmidt (Eds.), Social Media in Employee Selection and Recruitment(pp. 249-263). Cham: Springer.

Stone, D. L., Lukaszewski, K. M., Stone-Romero, E. F., & Johnson, T. L. (2013). Factors affecting the effectiveness and acceptance of electronic selection systems. Human Resource Management Review, 23, 50–70.

Studenmund, A. H. (2001). Using econometrics: A practical guide. New York: Addison Wesley Longman Inc.

Szczygieł, K. (2021). Two types of social media platforms as a source of information for recruiters. (unpublished Master’s thesis). Faculty of Business, University of Economics and Human Sciences, Warszawa, Poland.

Truxillo, D. M., Bauer, T. N., McCarthy, J. M., Anderson, N. R., & Ahmed, S. (2018). Applicant perspectives on employee selection systems. In D. S. Ones, N. R. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The Handbook of Industrial, Work & Organizational Psychology. Thousand Oaks, CA: Sage.

Van Iddekinge, C. H., Lanivich, S. E., Roth, P. L., & Junco, E. (2016). Social media for selection? Validity and adverse impact potential of a Facebook-based assessment. Journal of Management42(7), 1811–1835.

Wiechmann, D., & Ryan, A. M. (2003). Reactions to computerized testing in selection contexts. International Journal of Selection and Assessment, 11, 215–229.

Wijesundara, T. R., & Xixiang, S. (2018). Social networking sites acceptance: The role of personal innovativeness in information technology. International Journal of Business and Management, 13(8), 75-85.

Woźniak, J. (2013). Rekrutacja—teoria i praktyka. Warszawa: PWN.

Woźniak, J. (2019). Akceptacja różnych form narzędzi selekcyjnych – przegląd literatury i wstępne wyniki badania. Zarządzanie Zasobami Ludzkimi, 5(130), 11-39.

Woźniak, J. (2020). Zarządzanie pracownikami w dobie Internetu. Warszawa: Wolters Kluwer.

Zacny, B., Kania, K., & Sołtysik, A. (2019). Stosunek potencjalnych kandydatów do wykorzystania danych z mediów społecznościowych i narzędzi AI w procesie rekrutacji. Zarządzanie Zasobami Ludzkimi, 5(130), 39–56.

Zhang, L., Van Iddekinge, C. H., Arnold, J. D., Roth, P. L., Lievens, F., Lanivich, S. E., & Jordan, S. L. (2020). What’s on job seekers’ social media sites? A content analysis and effects of structure on recruiter judgments and predictive validity. Journal of Applied Psychology, 105(12), 1530–1546.

Zdonek, I., Hysa, B. & Mularczyk, A. (2015). Self-promotion on the Internet. Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 234, 214-226.


CEL: Celem tej pracy jest analiza czynników wpływających na odbiór przez potencjalnych kandydatów przeglądu w trakcie procesu selekcji zawartości ich prywatnych (reprezentowanych przez Facebook) i profesjonalnych (LinkedIn) portali społecznościowych, a w szczególności zbadanie jak ta praktyka jest odbierana przez innowacyjnych kandydatów. METODYKA: Dane zostały pozyskane drogą e-kwestionariusza ankiety w 2021 roku. W celu ustalenia predyktorów postrzeganej uczciwości przeglądu kont na Facebooku i LinkedInie w ramach selekcji kandydatów do pracy zastosowano wielokrotną analizę regresji z eliminacją wsteczną. WYNIKI: Wyniki badań potwierdziły, że postrzegana uczciwość selekcji w oparciu o dane z mediów społecznościowych (cybervetting) kandydatów do pracy na podstawie przeglądu konta Facebook jest oceniana istotnie niżej niż w przypadku konta LinkedIn, natomiast postrzeganie naruszenie prywatności w trakcie selekcji w oparciu o dane z mediów społecznościowych jest istotnie wyższe w przypadku przeglądu konta Facebook. Wielokrotna analiza regresji z eliminacją wsteczną wykazała, że spośród przewidywanych predyktorów postrzeganej uczciwości przeglądu kont portali społecznościowych w trakcie selekcji kandydatów do pracy (poczucie naruszenia prywatności, osobista innowacyjność, zarządzanie własnym wizerunkiem w sieci, awersja do ryzyka, umiejętność kontrolowania informacji na portalu społecznościowym, ponadprzeciętna samoocena jakości pracy, ogólna troska o prywatność w internecie oraz – w przypadku LinkedIn – posiadanie konta na tym portalu) najlepszym predyktorem zarówno w przypadku prywatnych (Facebook), jak i profesjonalnych (LinkedIn) portali społecznościowych jest poczucie naruszenia prywatności. Innym istotnym predyktorem postrzeganej uczciwości przeglądu obu tych typów portali społecznościowych jest zarządzanie własnym wizerunkiem w sieci, natomiast osobista innowacyjność zwiększa akceptację skanowania w procesie selekcji portali prywatnych (Facebook). IMPLIKACJE: Niniejsze badanie przyczynia się do poszerzenia wiedzy na temat postrzeganej sprawiedliwości narzędzi selekcji opartych na technologiach informacyjno-komunikacyjnych, a  w szczególności przeglądu kont portali społecznościowych w trakcie selekcji kandydatów do pracy. Poszerza wiedzę na temat możliwości zastosowania analizy treści serwisów społecznościowych w przypadku polskich, zwłaszcza innowacyjnych, kandydatów. Artykuł zawiera również kilka praktycznych zaleceń, które mają pomóc organizacjom w przypadku stosowania analizy treści portali społecznościowych w trakcie selekcji kandydatów, by minimalizować u nich poczucie naruszenia prywatności i tym samym zwiększać postrzeganie uczciwości tego działania. ORYGINALNOŚĆ I WARTOŚĆ: Jest to pierwsze zastosowanie cybervetting scale na polskiej próbie, co jest korzystne ze względu na możliwość porównania danych z różnych krajów. Stwierdziliśmy, że działania skoncentrowane na kreowaniu własnego wizerunku w sieci sprzyjają większej akceptacji selekcji w oparciu o dane z mediów społecznościowych (cybervetting), co może zmniejszać trafność predykcyjną tego typu praktyk selekcyjnych.

Słowa kluczowe: portale społecznościowe, narzędzia selekcji oparte na technologiach informacyjno-telekomunikacyjnych, selekcja pracowników, ocena sprawiedliwości, selekcja bazująca na mediach społecznościowych

Biographical notes

Alicja Balcerak, PhD., is an Assistant Professor at the Wrocław University of Science and Technology. Her main scientific and research interests include human resource management, interactive simulation (especially management games), and knowledge management.

Jacek Woźniak, Ph.D. habil., is a Professor at the University of Human and Economics Studies in Warsaw. He conducts research on the management of professional services in companies and methods of staff development.

Alexandra Zbuchea, Ph.D. habil., is a Professor at the National University of Political Studies and Public Administration in Bucharest. She conducts research on marketing for nonprofit organizations, e-business, cultural tourism, and knowledge management. She works as Vice-Dean of Faculty of Management at the National University of Political Studies and Public Administration. She is a board member of several academic journals.

Conflicts of interest

The authors declare no conflict of interest.

Citation (APA Style)

Balcerak, A., Woźniak, J., & Zbuchea, A. (2023). Predictors of fairness assessment for social media screening in employee selection. Journal of Entrepreneurship, Management, and Innovation, 19(2), 99-126.