Social media as a place to see and be seen : Exploring factors affecting job attainment via social media

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Social media as a place to see and be seen : Exploring factors affecting job attainment via social media © 2023 the Authors Published version Laukkarinen, Matti Laukkarinen, M. (2023). Social media as a place to see and be seen : Exploring factors affecting job attainment via social media. Information Society, 39(4), 199-212. https://doi.org/10.1080/01972243.2023.2199418 2023 Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=utis20 The Information Society An International Journal ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/utis20 Social media as a place to see and be seen: Exploring factors affecting job attainment via social media Matti Laukkarinen To cite this article: Matti Laukkarinen (2023): Social media as a place to see and be seen: Exploring factors affecting job attainment via social media, The Information Society, DOI: 10.1080/01972243.2023.2199418 To link to this article: https://doi.org/10.1080/01972243.2023.2199418 © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. Published online: 18 Apr 2023. Submit your article to this journal View related articles View Crossmark data The InformaTIon SocIeTy Social media as a place to see and be seen: Exploring factors affecting job attainment via social media Matti Laukkarinen Department of Social Science and Philosophy, University of Jyväskylä, Jyväskylä, finland ABSTRACT Job seekers can utilize social media platforms to actively search for job opportunities and also receive unsolicited job offers from recruiters and employers. Using data from a representative sample of Finnish social media users, this article studies both aspects of social media job attainment by analyzing how much individuals successfully apply for jobs and get recruited to positions through social media. Results show that the prevalence of successfully applying to jobs through social media does not differ statistically between socio-economic groups, but the prevalence of getting recruited to jobs through social media is greater within higher socio-economic groups. LinkedIn users are more likely to get recruited to a job, while strategic networking and posting of professional content increase the chances of both successfully applying and getting recruited to a job through social media. The findings demonstrate that in social media-mediated job market, job seekers’ online behavior affects one’s exposure to job leads and career opportunities. Introduction Social media platforms are recognized as one of the primary intermediaries in today’s digital job market (Gandini and Pais 2018; Sharone 2017). While many people join these platforms for enjoyment and leisure purposes, research has shown that users can gain professional career benefits from social media usage (Davis et al. 2020; Nikitkov and Sainty 2014; Utz 2016). The significant role of social media in labor market matchmaking can be attributed to its capacity to offer a cost-efficient avenue for job seekers and employers to exchange job-related information (Brown and Vaughn 2011; Chiang and Suen 2015). On the supply-side of the labor market, job seekers utilize social media features and personal contacts for online job search (Garg and Telang 2018; Karaoglu, Hargittai, and Nguyen 2022). On the demand-side, employers leverage social media platforms as a talent attraction and selection tool (Bohnert and Ross 2010; McDonald et al. 2022; Ollington, Gibb, and Harcourt 2013; Phillips and Gully 2015). Most of the literature characterizes job search as a process in which job seekers acquire information about potential job opportunities through instrumental job search activities, i.e., purposive investments to information seeking (McDonald 2010). In the social media-mediated job market, this framework proves inadequate, as recruiters can proactively search for potential job candidates, allowing job seekers to receive information about job opportunities even if they are not actively searching for them. Some users know this and build their online presence with this premise in mind (Bangerter, Roulin, and König 2012; Berkelaar 2014). Even though changing jobs without actively engaging in a job search is not a novel concept (see Granovetter 1995), Social media platforms have broadened the scope of social media “headhunting” to include a wider variety of occupational groups and positions (Kroll, Veit, and Ziegler 2021; McDonald et al. 2019). Several authors have flagged the lack of research interest in how social media affects individuals’ career transitions and labor market outcomes (Roth et al. 2016; Sullivan and Al Ariss 2021; Treem and Leonardi 2013; Utz and Breuer 2016). This article introduces a novel framework for studying social media job attainment by recognizing that job seekers can use social media platforms to acquire job-related © 2023 The author(s). Published with license by Taylor & francis Group, LLc. CONTACT matti Laukkarinen matti.m.laukk[email protected] Department of Social Science and Philosophy, University of Jyväskylä, opinkivi, Keskussairaalantie 2, Jyväskylä 40014, finland https://doi.org/10.1080/01972243.2023.2199418 This is an open access article distributed under the terms of the creative commons attribution-noncommercial-noDerivatives License (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the accepted manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 10 march 2022 accepted 2 april 2023 KEYWORDS Impression management; LinkedIn; online job search; social media; social networking 2 M. LAUKKARINEN information and conduct online job searches, as well as enhance their visibility and attract the attention of recruiters and potential employers. Consequently, job seekers must have not only the skills to seek out professionally relevant information, but also possess cultural capital and knowledge to present themselves in a manner that captures the attention of recruiters (Bills, Di Stasio, and Gërxhani 2017; Sharone 2017). In this framework, job-seeking through social media is simultaneously a purposive process of information-seeking as well as a process partly mediated by chance. The research on serendipitous job matching has suggested that social interaction can have unintended consequences for career outcomes (Bright, Pryor, and Harpham 2005), which is likely to be an even bigger factor on social media, where the chances for serendipitous encounters is particularly high. While receiving job leads through one’s social media network can seem an unexpected event from the user’s point of view, research has shown that individual and contextual factors influence the receipt of unsolicited job information (McDonald 2010). This article examines what factors affect the probability of receiving jobs through social media. The first goal of the article is to investigate the success rates of job seekers from different socio-economic groups in applying for jobs and getting recruited to jobs through social media. The second goal is to analyze the factors that impact the likelihood of job attainment via social media. To address these questions, a nationally representative sample of Finnish social media users was analyzed. The evolution of social media and their impact on online job search and recruitment The landscape of social media platforms has evolved rapidly during the last decade. The introduction of various technologies has affected the ways how platforms process content and how users gain access to information (Ellison and Vitak 2015; Kane 2017). These changes have also impacted how job seekers and recruiters obtain professionally relevant information through social media. Although social media platforms continue to evolve and remain a moving target for researchers, it is important to identify some significant changes that have influenced contemporary social media job search and recruiting. Most of the social media-related literature centers on the term social network site. In a widely utilized definition, boyd and Ellison (2007, 211) define social network sites (SNS) as “web-based services that allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.” Subsequently, in their revised definition, Ellison and boyd (2014) acknowledge that social network sites have evolved from their “friend” or “follower” based origins more toward algorithmic-curated streams of content. Even though the revised definition1 takes into account that social network sites aren’t as profile-centric as they used to be, Kane et al. (2014) argue that the term social network “site” is misleading given the current state of social media. Instead, they propose a replacement term social media network, which they define as having “four essential features such that users (1) have a unique user profile that is constructed by the user, by members of their network, and by the platform; (2) access digital content through, and protect it from, various search mechanisms provided by the platform; (3) can articulate a list of other users with whom they share a relational connection; and (4) view and traverse their connections and those made by others on the platform” (278, italics in original). This article acknowledges that social media platforms have evolved from their initial “bounded” single website origins to more open data analytic-driven infrastructures, which have enabled a broader use of user data in labor market matchmaking (see Köchling and Wehner 2020). For example, on LinkedIn, platform-provided search tools enable recruiters to gain access to a targeted pool of potential job candidates based on the information presented on the user’s LinkedIn profile (McDonald etal. 2019). These matching processes are largely mediated by algorithm-based predictive analytics, which remains an understudied subject in online job market research (see Shrestha and Yang 2019). In professional context, Facebook, LinkedIn, and Twitter are among the most studied social media platforms (see Utz 2016). These platforms differ in their architecture and intended use (Papacharissi 2009). Facebook’s user interface is geared more toward personal self-expression, whereas LinkedIn’s user interface is designed for professional self-presentation (Van Dijck 2013). Facebook’s interface incentivizes users to publish personal data (hobbies, interests, etc.). LinkedIn, on the other hand, provides a uniform and chronological interface for publishing professional career-oriented information. Twitter provides users limited possibilities for sharing profile information and instead allows users to post short textual messages to a network of “followers”. Twitter’s referral-based interface promotes dialogical communication, even ThE INFoRMATIoN SocIETy 3 though there are no technical requirements or social expectations for reciprocal communication (Marwick and boyd 2011). Users network compositions also vary by platform. On Facebook, networks are typically comprised of an existing group of friends and acquaintances, and the platform is used to support preexisting offline ties. As a career-oriented platform, LinkedIn promotes professional networking, and users typically connect with work-related acquaintances or experts in their fields (boyd and Ellison 2007). On Twitter, the network compositions are generally asymmetrical, as the platforms’ following function doesn’t necessitate reciprocal connection. The asymmetry of networks entails that on Twitter, content reach is hard to define, especially if users’ privacy settings are unrestricted (Marwick and boyd 2011). Social media as a place to see Generally speaking, online job search refers to any instance when people utilize the internet in their job search, ranging from online job boards (e.g., Monster. com) to social media platforms (Stevenson 2009). Previous research has established that online job search reduces the duration of unemployment and might help job seekers find more prestigious jobs (DiMaggio and Bonikowski 2008; Faberman and Kudlyak 2016). According to Kuhn and Mansour (2014), unemployed people who look for work online reemploy approximately 25% faster than those who do not search for work online. Even though the authors did not specifically review the effects of social media platforms on job attainment, they concluded that “contacting friends and relatives online is highly correlated with job-finding rates” (1231). These findings accord with previous literature, which has established that besides casual socializing activities, people utilize social media for information-seeking purposes (Brandtzæg and Heim 2009; Vitak and Ellison 2013). For example, on LinkedIn, users can search for posted and advertised jobs as well as reach out to their network contacts for job leads and referrals (Garg and Telang 2018). Comparative studies have shown that LinkedIn seems to yield most professional benefits for its users (Nikitkov and Sainty 2014). Utz’s (2016) study of Dutch online users also supports this view. In the study, LinkedIn users reported the highest professional information benefits (timely access to relevant information and being referred to career opportunities), followed by Twitter users and lastly Facebook users. Even though Utz’s initial study predicted professional information benefits for various measures, such as posting of professional content and the number of strong network ties, the follow-up longitudinal study (Utz and Breuer 2016) showed long-term professional information benefits only for strategic networking. According to the authors, the lack of long-term benefits is linked to how individuals share and seek information on social media. Social media users anticipate prompt feedback to their queries and posts, which is why interactions rarely yield information benefits after a certain period has passed. Social media as the place to be seen In addition to job seekers, employer organizations also utilize social media platforms for information-seeking purposes (Davison, Maraist, and Bing 2011). Employers utilize social media for various purposes throughout the recruitment process, and it is important to identify whether they are using it to attract candidates or evaluate them. Online screening or “cybervetting” is a process where employers evaluate job candidate’s potential job performance and characteristics by utilizing the information available on the internet (Brown and Vaughn 2011; McDonald et al. 2022). In practice, this is done by reviewing candidate’s social media profile or “googling” the candidate. These assessments are related to the selection phase of the recruitment process (Roth et al. 2016), meaning that recruiters use online screening as an additional information source after the evaluation of preliminary information, i.e., CV, application form, or first screening interview (Nikolaou 2014). Before the selection phase, employers also use social media for active sourcing, which involves the proactive search and reaching out to potential job candidates (Breaugh 2008; Kroll, Veit, and Ziegler 2021, Ollington, Gibb, and Harcourt 2013). McDonald etal. (2019) presented an insightful analysis and discussion on this scarcely researched phase of recruitment. In their qualitative study, the authors described how recruiters use LinkedIn to identify passive job candidates, i.e., employed individuals who are not looking for work but might be willing to change jobs. By utilizing LinkedIn as a “workforce database”, recruiters can search the user base and their network connections to find potential job candidates. Paid services are also offered by the platform, which provides recruiters with access to various search functions and predictive analytic tools. These features allow recruiters to scan and filter the entire user base based on the information provided in the user’s LinkedIn profile. Recruiters can filter the user base by their professional experience and competence and also 4 M. LAUKKARINEN leverage algorithm-based predictive analytics tools to sort individuals by their sociodemographic background and interpreted personality traits. Authors suggest that algorithmic search tools, among other internet technologies, have evolved into a new source of labor-market polarization. In this new kind of “winner-take-all labor market”, active sourcing practices increasingly favor those who can digitally signal competence and match employers’ subjective perceptions of optimal performance, while competition amongst other workers is getting increasingly intensified (McDonald et al. 2019, 96). Similarly, Sharone (2017) argues that for job seekers, employer´s use of social media is a double-edged sword. On one hand, social media platforms offer job seekers the opportunity to increase their visibility to recruiters. On the other hand, as employers increasingly use social media for active sourcing, job seekers must invest more time and effort into their online presence to avoid missing out on professional opportunities. While employers have always been inclined to avoid stigmatized applicants in favor of privileged applicants (Bills, Di Stasio, and Gërxhani 2017), advancements in technology and wider access to open data have resulted in a significant increase in the number of candidates that employers can now reach using algorithmic tools. In this active sourcing process, minor variations in digital signals can have considerable implications for job market outcomes. There is evidence that contemporary recruitment practices, such as active sourcing, are susceptible to both implicit and explicit discrimination. (Kroll, Veit, and Ziegler 2021). Employers’ proactive use of social media for identifying and approaching potential job candidates, i.e. active sourcing, increases the likelihood of job offers being extended to job seekers who have not actively participated in the job-seeking process. This raises the question of what factors affect the chances of getting found and contacted by an employer. Research on employer’s use of social media has indicated that recruiters utilize job seeker’s self-presentation signals to assess their suitability for the job and the organization they’re hiring for (Chiang and Suen 2015; McDonald et al. 2022; Roulin and Bangerter 2013). Social media platforms provide users with the opportunity to efficiently edit and distribute information, which is why these platforms are often described as ideal platforms for professional impression management (Roulin and Levashina 2016). Previous studies on professional impression management have focused mainly on LinkedIn. Roulin and Levashina (2019) found that on LinkedIn, profile length, profile photo, and number of connections were positively related to platform-based hiring recommendations, which were associated with higher career success. A recent study on professional impression management concluded that on LinkedIn, a more extended profile summary and a profile photo with higher facial prominence were positively related to receiving more job offers (Krings et al. 2021). Together, these studies indicate that the content on job seekers’ social media profiles plays a role in active sourcing. Socio-economic differences of social media job attainment Prior research on the effects of socio-economic factors on job search has shown that job seekers of higher socio-economic groups possess higher professional self-efficacy, which translates into more active and varied job-seeking activities (DeOrtentiis, Van Iddekinge, and Wanberg 2022; Hu etal. 2022; Huang and Hsieh 2011). Although only a few studies have specifically examined the effects of socio-economic factors on online job search, some studies have highlighted the effects of individual sociodemographic factors, such as age and education. Green etal. (2012) found that job seekers with higher educational qualifications were more likely to use the internet for job search. Karaoglu, Hargittai, and Nguyen (2022) found that job seekers with lower income and education levels were less likely to use social media for job search. They also concluded that in addition to sociodemographic factors, “digital job-search skills” correlate strongly with the use of the internet and social media for job search. Nikolaou (2014) found out that males and job seekers with higher education tend to use LinkedIn more, whereas younger job seekers prefer to use Facebook in their job search. The study also found out that compared to active job seekers, so-called passive job seekers, i.e., employed individuals who are not looking for work but might be willing to change jobs, benefited more from LinkedIn even though active job seekers used social media platforms more actively compared to passive job seekers. Very little was found in the literature on the question of socio-economic differences in getting recruited to a job through social media. Previous research on executive search, informally known as “headhunting”, has established that recruiters identify and attract potential job candidates, especially when hiring for executive positions (Hamori 2010, 2014). It has been proposed that employers also utilize social media for identifying and attracting purposes primarily when recruiting for higher positions ThE INFoRMATIoN SocIETy 5 (see McDonald et al. 2019). This suggests that job seekers from higher socio-economic backgrounds might be more commonly targeted for recruitment through social media compared to those from lower socio-economic backgrounds, potentially leading to a greater chance of getting recruited to a job. Aims Informed by the above-discussed literature, this article approaches social media platforms firstly as venues where job seekers can actively search information about potential job opportunities and apply to them, and secondly as channels through which job seekers can manage their professional image by sharing information about themselves, thereby increasing their visibility to potential employers. Specifically, the research questions are as follows: RQ1: What is the prevalence of successful application to a job through social media, and does this vary by socio-economic status? RQ2: What is the prevalence of recruitment to a job through social media, and does this vary by socio-economic status? RQ3a: What factors affect the probability of successful application to a job through social media? RQ3b: What factors affect the probability of recruitment to a job through social media? Data and methods The respondents were recruited through two large online research panels. From these panels, a professional research company administered the survey to reach the desired sample of 5000 respondents, a nationally representative sample of the Finnish population aged 18-69. The survey asked a wide range of questions regarding work life in general, including online and offline job search2. The original sample was narrowed to suit the research aims better. The sample was restricted to the active labor force, so students and retirees were excluded from the analysis. Entrepreneurial groups were also excluded from the analysis as self-employed persons’ contract-based employment relationships produce qualitative differences in job-seeking behavior compared to wage earners. Therefore, the analysis was restricted to wage earners (blue-collar, lower white-collar, upper white-collar, and upper management). As the focus was on social media platforms, the sample was further restricted to exclude nonusers of Facebook, LinkedIn, or Twitter from the analysis. The final sample consisted of 2138 respondents, a sub-sample of the active labor force in Finland, comprised of wage earners who also use Facebook, Twitter, or LinkedIn. Measures Dependent variables The phenomenon under study, job attainment via social media, was examined through two dependent variables. Respondents were asked “have you found a job or assignment through social media” and “have you been found to a job or position through social media”. The first variable indicated whether respondents themselves had successfully applied to a job through social media. The second variable indicated whether respondents had been contacted and recruited to a job through social media. Both variables were categorical yes-or-no questions.3 Independent variables • Sociodemographics: Gender was coded as a binary variable (female = 0, male = 1), as they were the only options offered on the survey. Age was asked in years. Respondents indicated their level of education on a 6-point scale, which was recoded into three categories (secondary degree or lower, bachelor’s degree, master’s degree or higher). Respondents indicated their self-assessed socio-economic group on a scale based on a standard socio-economic classification used by Statistics Finland. The classification is based on international standards4 and is formed considering a person’s stage of life, occupation, and occupation status. Dummy variables were created to indicate the respective socio-economic group (0 = no, 1 = yes). • Strategic networking: Respondents indicated how much they had spent time consciously networking with people who could be valuable to job search during the last year. Answer categories were on a 5-point scale ranging from “not at all”, “less than one workday”, “1-3 workdays”, “3-5 workdays” to “more than 5 workdays”. In the model, higher values indicated more active strategic networking. • Job search activity: In parallel to strategic networking, respondents indicated how much they had spent time browsing various job search-related platforms and services during 6 M. LAUKKARINEN the last year. Answer categories were on a 5-point scale ranging from “not at all”, “less than one workday”, “1-3 workdays”, “3-5 workdays” to “more than 5 workdays”. In the model, higher values indicated higher job search activity. • Posting of professional content: Respondents indicated how often they post content related to their work or skills on social media, such as writing an update, sharing an article, or publishing an image. The 7-point scale answer categories ranged from “daily or almost daily” to “never”. In the model, higher values indicated more active posting of professional content. • Platform usage: Respondents indicated what social media platforms they used. Facebook, LinkedIn, and Twitter were selected for the study as they are among the most popular social media platforms, and these platforms have been previously studied in the professional context. Respondents with a profile on Facebook, Twitter, and LinkedIn were coded into dummy variables to represent the use (1) or nonuse (0) of the specific platform. Sample Approximately half of the respondents were female (48.8%). The mean age for the sample was 43.2. In all, 46.8% of the respondents had a secondary degree or less, 25.8% had a bachelor’s degree, and 27.4% had a master’s degree or higher. Further, 49.6% of the respondents were blue-collar workers, 21.2% were lower white-collar workers, 24.4% were upper white-collar workers, and 4.8% were upper management workers. The most popular social media platform was Facebook (70.4%), followed by LinkedIn (28.3%) and lastly Twitter (16.8%). It should be noted that some of the respondents used multiple platforms simultaneously. Descriptive statistics of the sample are presented in Table 1. Analytical strategy Cross-tabulations and chi-square statistics were used to examine the prevalence and association between socio-economic groups and the respective job attainment method (RQ2, RQ2). Two separate logistic regression analyses were conducted to examine what factors affect the probability of successfully applying (RQ3a) and getting recruited (RQ3b) to a job through social media. (Both analyses started with the baseline of sociodemographic variables (Model 1). In the second step, strategic networking, job search activity, and the posting of professional content were added (Model 2). In the final step, the usage of a specific platform (Facebook, LinkedIn, or Twitter) was added (Model 3). Results are reported stepwise and summarized in Tables 2 and 3. Results Descriptive statistics During the last year, over 40% of respondents indicated spending at least some time consciously networking with people who could be valuable in regard to job search (42.7%). Younger age and higher education were positively associated with strategic networking. Upper white-collar and upper management workers were likelier, and blue-collar workers were less likely to practice strategic networking. Two out of three respondents had spent at least some amount of time browsing various job search-related platforms and services during the last year (66.4%). Women, younger respondents, and respondents with higher education were more likely to spend time searching for a job. Over fifty percent of respondents had posted content related to their work or skills on social media (52.7%). Upper management workers and individuals with higher education were more likely to post professional-related content on their social media profiles. Regarding platform usage and socio-economic status, blue-collar workers were likelier to use Facebook, whereas for upper white-collar and upper management workers, the usage was less likely. With Table 1. Sample, descriptive statistics. Percent / m (SD) female 48.8 age 43.2 (11.6) 18–24 5.8 25–34 21.5 35–44 25.5 45–54 27.5 55–64 18.7 65–69 1,0 education level Secondary degree or less 46.8 Bachelor´s degree 25.8 master´s degree or higher 27.4 Socio-economic status Blue-collar worker 49.6 Lower white-collar worker 21.2 Upper white-collar worker 24.4 Upper management worker 4.8 Social media usage Uses facebook 70.4 Uses LinkedIn 28.3 Uses Twitter 16.8 n = 2138, m = mean, SD = Standard Deviation. ThE INFoRMATIoN SocIETy 7 LinkedIn and Twitter, the situation is opposite, as both platforms were less likely used by blue-collar workers and more likely used by upper white-collar and upper management workers. Interestingly, there aren’t any statistically significant correlations with lower white-collar worker’s platform usage. The means, standard deviations, and pair-wise correlations of the variables included in the analysis are displayed in Table 4. Applying to jobs through social media The first research question (RQ1) looked for how successful different socio-economic groups are in applying to jobs through social media. Cross-tabulations (see Figure 1) show slight variation between socio-economic groups in the prevalence of successfully applying to a job through social media. In the groups of blue-collar (10.1%), lower-white collar Table 2. Logistic regression analysis on successfully applying to a job through social media. model 1 model 2 model 3 BSe odds ratio BSe odds ratio BSe odds ratio 1.Gender (female = 0 male = 1) −0.422** 0.152 0.656 −0.342** 0.160 0.647 −0.375** 0.167 0.687 2. age −0.035*** 0.007 0.966 −0.028*** 0.007 0.970 −0.027*** 0.008 0.973 3. education 0.098 0.099 1.103 −0.066 0.103 0.950 −0.097 0.107 0.907 4. Socio-economic status (base: blue-collar) 5. Lower white-collar 0.076 0.200 1.079 0.100 0.209 1.106 0.063 0.212 1.066 6. Upper white-collar 0.219 0.215 1.245 0.154 0.222 1.133 0.084 0.230 1.088 7. Upper management 0.489 0.351 1.631 0.129 0.378 1.067 0.066 0.371 1.068 8. Strategic networking 0.338*** 0.066 1.402 0.324*** 0.060 1.382 9. Job search activity 0.249*** 0.064 1.282 0.244*** 0.065 1.277 10. Professional content 0.274*** 0.035 1.315 0.273*** 0.037 1.314 11. facebook usage (no = 0 yes = 1) −0.050 0.294 0.951 12. LinkedIn usage (no = 0 yes = 1) 0.211 0.189 1.234 13. Twitter usage (no = 0 yes = 1) 0.005 0.188 1.005 chi-square (df) 37.737*** (6) 191.407*** (9) 192.821*** (12) nagelkerke Pseudo-r² 0.037 0.182 0.183 Notes: n = 2138. *p < .05, **p < .01, ***p < .001. Table 3. Logistic regression analysis on getting recruited to a job through social media. model 1 model 2 model 3 BSe odds ratio BSe odds ratio BSe odds ratio 1.Gender (female = 0 male = 1) −0.010 0.186 1.010 0.009 0.197 1.010 −0.184 0.205 0.832 2. age −0.015 0.009 0.985 −0.008 0.009 0.992 −0.006 0.010 0.994 3. education 0.171 0.124 1.187 0.069 0.127 1.072 −0.067 0.131 0.936 4. Socio-economic status (base: blue-collar) 5. Lower white-collar 0.691** 0.255 1.995 0.758** 0.263 2.135 0.588* 0.268 1.800 6. Upper white-collar 0.812** 0.272 2.252 0.699** 0.275 2.013 0.396 0.283 1.486 7. Upper management 1.358*** 0.374 3.890 0.939** 0.398 2.557 0.632 0.405 1.882 8. Strategic networking 0.596*** 0.080 1.814 0.545*** 0.083 1.724 9. Job search activity −0.104 0.085 0.901 −0.124 0.086 0.884 10. Professional content 0.225*** 0.043 1.252 0.211*** 0.046 1.235 11. facebook usage (no = 0 yes = 1) −0.287 0.289 0.750 12. LinkedIn usage (no = 0 yes = 1) 0.858*** 0.239 2.359 13. Twitter usage (no = 0 yes = 1) 0.275 0.216 1.316 chi-square (df) 29.590*** (6) 135.016*** (9) 154.653** (12) nagelkerke Pseudo-r² 0.038 0.168 0.192 Notes: n = 2138. *p < .05, **p < .01, ***p < .001. 14 M. LAUKKARINEN Roulin, N., and A. Bangerter. 2013. Social networking websites in personnel selection: A signaling perspective on recruiters’ and applicants’ perceptions. Journal of Personnel Psychology 12 (3):143–51. doi: 10.1027/1866-5888/a000094. Roulin, N., and J. Levashina. 2016. Impression management and social media profiles. In Social media in employee selection and recruitment: Theory, practice, and current challenges, eds. R. N. Landers and G. B. Schmidt, 223–48. Cham, Switzerland: Springer. Roulin, N., and J. Levashina. 2019. 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