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Futureemploymentselectionmethodsevaluatingsocialnetworkingwebsites.pdf
Journal of Managerial Psychology Future employment selection methods: evaluating social networking web sites Donald H. Kluemper, Peter A. Rosen,
Article information: To cite this document: Donald H. Kluemper, Peter A. Rosen, (2009) "Future employment selection methods: evaluating social networking web sites", Journal of Managerial Psychology, Vol. 24 Issue: 6, pp.567-580, https:// doi.org/10.1108/02683940910974134 Permanent link to this document: https://doi.org/10.1108/02683940910974134
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Future employment selection methods: evaluating social
networking web sites Donald H. Kluemper
Louisiana State University, Baton Rouge, Louisiana, USA, and
Peter A. Rosen University of Evansville, Evansville, Indiana, USA
Abstract Purpose – The use of social networking web sites (SNWs), like Facebook and MySpace, has become extremely popular, particularly with today’s emerging workforce. Employers, aware of this phenomenon, have begun to use the personal information available on SNWs to make hiring decisions. The purpose of this paper is to examine the feasibility of using applicant personal information currently available on SNWs to improve employment selection decisions.
Design/methodology/approach – A total of 378 judge ratings (63 raters £ 6 subjects) are evaluated to determine if raters can reliably and accurately determine the big-five personality traits, intelligence, and performance based only on information available on SNWs. Interrater reliability is assessed to determine rater consistency, followed by an assessment of rater accuracy.
Findings – Based solely on viewing social networking profiles, judges are consistent in their ratings across subjects and typically able to accurately distinguish high from low performers. In addition, raters who are more intelligent and emotionally stable outperformed their counterparts.
Practical implications – Human resource (HR) professionals are currently evaluating social networking information prior to hiring applicants. Since SNWs contain substantial personal information which could be argued to cause adverse impact, academic studies are needed to determine whether SNWs can be reliable and valid predictors of important organizational criteria.
Originality/value – This paper is the first, as far as the authors are concerned, to address the use of SNWs in employment selection, despite their current utilization by HR practitioners.
Keywords Selection, Recruitment, Social networks, Internet
Paper type Research paper
Within the past few years, the phenomenon of social networking web sites (SNWs) on the internet has exploded into the mainstream. Further, this online information has begun to be used for purposes beyond its intended use. Owing to the vast amount of personal information on these web sites, employers have begun to tap into this information as a source of applicant data in an effort to improve hiring decisions. This study evaluates the use of the SNWs in employment selection. Specifically, can trained judges consistently and accurately assess important organizational characteristics such as personality, intelligence, and performance using only a target’s SNW information? In addition, the use of this information may lead to discrimination against applicants, given the wide range of available personal information such as gender, race, age, religion, and disability status otherwise illegal to use when making employment decisions.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0268-3946.htm
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Journal of Managerial Psychology Vol. 24 No. 6, 2009
pp. 567-580 q Emerald Group Publishing Limited
0268-3946 DOI 10.1108/02683940910974134
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Social networking web sites SNWs focus on building online communities of people who share interests and activities, or who are interested in exploring the interests and activities of others. Most provide a variety of ways for users to interact, such as e-mail and instant messaging services. SNWs are designed to connect users to each other and to visually display each individual’s network of friends. The number of users for these web sites and the daily traffic created by these web sites are staggering. According to the “About Us” section of the various sites, MySpace is the largest with over 248 million registered users. Other SNWs also have millions of users registered, such as Facebook (110 million), Friendster (85 million), Hi5 (80 million), Orkut/Google (37 million), and LinkedIn (25 million).
While these sites differ in the features that are available, most have a mechanism for posting pictures, music and videos, keeping blogs, sharing links, and displaying interests. These sites vary in user demographics. For example, although Facebook is currently open to anyone, it started as a high school and college web site exclusively, with about 90 percent of students registered for the site (van der Werf, 2006).
Social networking web sites in selection Owing to the increasing prevalence of SNWs in conjunction with the large volume of information available to the viewer, employers have begun using SNWs to assist in the selection process for new employees. About 50 percent of the employers attending college career fairs use online technology, including both search engines and SNWs to screen candidates (Shea and Wesley, 2006). Currently, between 20 (Framingham, 2008) and 25 percent (Taylor, 2007; NACE, 2006) of managers are using SNWs to screen candidates, with 40 percent indicating that they are likely to use them within the next year (Zeidner, 2007). These employers apparently feel justified in electronic screening using SNWs. The president of a small Chicago consulting firm, when asked about an applicant that applied for an internship and had questionable content on his page, replied, “A lot of it makes me think, what kind of judgment does this person have? Why are you allowing this to be viewed publicly?” (Finder, 2006).
While it may be common practice to monitor web site content it may not be legal. Lance Chou, Director of Career Development at Stanford University, noted that:
[. . .] some employers might try and learn something about the student’s personality and whether it would be appropriate for the job. However, there is information on Facebook that is not relevant to the job, but may be used inappropriately by employers to assess a candidate (Fuller, 2006).
According to George Lenard, an employment attorney, employers can inadvertently learn about matters such as candidates’ age, marital status, and other topics typically are off limits in job interviews, and organizations can be sued for discrimination if these candidates are not hired (Frauenheim, 2006). The question of whether employer monitoring of SNWs is illegal may relate to equal employment opportunity (EEO) law. EEO severely limits the type of information an interviewer may ascertain and use. For example, during interviews employers may not ask questions regarding race, religion, sexual preference, or marital status. However, all of this information can be easily located using SNWs (Kowske and Southwell, 2006). The ethics and legality behind electronic screening using SNWs has become an important issue for human resource (HR) practitioners (Zeidner, 2007).
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Since no academic studies, to our knowledge, have assessed whether using SNWs in employment selection are a reliable and valid predictor of important organizational outcomes, their value in employment screening is unknown. Until the reliability and validity of information on SNWs is established, employers should use caution when using SNWs to make hiring decisions. Just as with structured interview methods (Campion et al., 1997), focusing on job-related information in SNWs should minimize the use of less job-relevant information that might bias the hiring decision. This study focuses on showing the validity and reliability of three such categories of job-relevant characteristics which have potential to be rated consistently and accurately and to serve as valid predictors of job performance. The assessment of personality, intelligence, and a global measure of performance through SNWs provide multiple possible avenues regarding validity generalization of job-relevant characteristics, and thereby justifying the use of SNWs in the screening process.
Assessment of personality The 1990s have seen a huge growth in the use of personality assessment within personnel selection practice and research (Barrick and Mount, 1991; Frei and McDaniel, 1997; Ones et al., 1993; Salgado, 1998; Tett et al., 1991). These studies provide positive evidence for the criterion-related validity of personality. When it comes to the prediction of overall job performance, conscientiousness was found to be the best predictor, showing consistent predictions across all occupational groups. In addition, extroversion, agreeableness, and neuroticism were shown to predict job performance in certain jobs. Finally, although typically unrelated to job performance, openness to experience has been found to predict training performance.
Beyond the predominant focus on self-reported personality assessment, personality can also be measured using other-ratings. It is well established that people can assess the personality of others, even after relatively short exposure, but that the accuracy of these assessments depends on the information available to the observer (Barrick et al., 2000). Observer ratings may be more valuable than self-ratings in employment selection, particularly when targets are not available for self-reports, self-reports are untrustworthy, and researchers wish to improve accuracy by aggregating multiple raters (Hofstee, 1994; McCrae and Weiss, 2007). Other-rated personality via SNWs seems quite promising in the selection context, since other-ratings of personality have been found to predict job performance. Motowidlo and colleagues (1996) found that interviewer-rated extroversion and conscientiousness (r ¼ 0.27 and 0.20, respectively) significantly predict supervisor rated job performance. In addition, Mount et al. (1994) show that observer ratings of extroversion and conscientiousness from supervisors, coworkers, and customers significantly predicts sales performance, even beyond self-rated personality.
The validity of other-rated personality, however, can depend on the relationship the subject has with the rater and the quality of the information available to the rater. A meta analysis conducted by Connolly and Viswesvaran (1998) show low accuracy for strangers in predicting personality and moderate prediction by peers for each of the big-five traits. In addition, Barrick et al. (2000) developed a personality-based job interview for the purpose of assessing the personality of the applicant. They found that personality-based job interviews could be used to accurately predict three of the big-five. Considering the average interview is approximately 40 minutes in length
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(Campion et al., 1997), it appears that an interviewer can assess some aspects of personality as effectively as a close acquaintance of the applicant. We propose that the information available in SNWs provides a similar means to assess personality.
Furthermore, since SNW ratings are obtained from a wide range of personal information that is a reflection of ongoing behaviors and interactions with other users of the networks, web sites may actually provide unique information not found with other selection methods. Recent issues of Personnel Psychology (Morgeson et al., 2007a, b; Ones et al., 2007; Tett and Christiansen, 2007) and Industrial and Organizational Psychology (Hough and Oswald, 2008; Griffith and Peterson, 2008) have focused on the limited criterion validity of self-reported personality measures as well as the complex issue of social desirability and faking of self-reported personality measures. To increase validity and address issues of socially desirable responding “we should look at other ways of assessing personality. There are a variety of ways of finding out about people’s stable pattern of behavior” (Morgeson et al., 2007a, b, p. 719). SNWs may prove to be an appropriate means of assessing personality in this way. Beyond personality assessment, similar issues emerge in relation to a host of employment selection methods. Resumes, interviews, job applications, and many other forms of employee selection include a certain element of self-presentation, reflecting “maximal” instead of “typical” work performance (Sackett, 2007; Sackett et al., 1988). Employment selection methods using social networking are likely to be based on “typical” behaviors, and therefore may be more accurate than other selection methods. At a minimum, this method should provide information that is distinct from “maximal” selection methods, thereby allowing for a stronger likelihood that using SNWs will yield incremental validity beyond established methods of employment selection[1].
This is not to say that SNWs are not susceptible to manipulation and faking, similar to that of self-report personality measures and job interviews. In fact, as users of SNWs become more aware that their profiles are being evaluated by potential employers, information provided on profiles is likely to be skewed in an effort to be viewed more favorably. However, there are aspects of SNWs which would make the process of skewing information difficult. Much of the information present in a given social networking profile is submitted by other members of the network, such as tagged photos and writing on another’s wall[2]. Although some negative information can be deleted by the user, the user has more limited control over this aspect of their profile. In addition, some of the information controlled by the users themselves would be difficult to fake. For example, extroversion may be tied to the number of friends a user has in the social network. Artificially inflating a substantial number of friends in the network would pose great difficulty, as the user cannot control who accepts their friend request. As another example, rater assessment of personality traits might be drawn in part from photos, which are similarly difficult to fake. Thus, while faking would appear to be an issue, it is likely that the impact of faking is less than with other selection methods. Future research should assess the impact of faking in the context of SNWs.
Despite the potential for faking, Vazire and Gosling (2004) used personal web sites to accurately assess personality. Although personal web sites are similar in some ways to SNWs, they are used by such a small percentage of potential applicants that they are impractical for purposes of employment selection. In addition, SNWs provide additional information not included in personal web sites, such as a list of the user’s friends and a list of the interest groups a user has joined. However, Vazire and Gosling
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provide initial evidence for the accurate prediction of personality using personal information available on the internet.
The types of information available on SNWs may be particularly effective in predicting the big-five personality traits. SNWs contain various sources of information which could be used to assess behaviors related to personality. For example, the types and number of interest groups the user has joined, comments that have been left for the user, comments made by the user on other people’s “walls,” “tagging” photos, updating “status messages”[3], and listing books and intellectual interests in the “personal information” section[4]. These examples provide only a very preliminary introduction to the various sources of personal information available in SNWs which might indicate an individual’s personality. It should also be noted that the use of SNWs may vary based on characteristics beyond personality. For example, an individual who is more adept with this form of technology may be more likely to participate in social networking and may post more information more frequently than those individuals lacking in these technological skills. Since age has been shown to relate to technology acceptance and use (Morris and Venkatesh, 2000), this also brings the issue of age into the possible groups which could be adversely impacted by the use of this type of technology by HR departments in hiring. Future research should explore this issue further.
Assessment of intelligence Since the very earliest research on personnel selection, cognitive ability has been one of the major methods used to attempt to discriminate between candidates and to predict subsequent job performance (Robertson and Smith, 2001). Intelligence is the single most effective predictor known of individual performance at school and on the job (Gottfredson, 1998), accounting for approximately 25 percent of the variance in job performance (Hunter and Hunter, 1984). Cognitive ability provides criterion-related validity that generalizes across more or less all occupational areas (Robertson and Smith, 2001). In addition, judge ratings of intelligence have been shown to predict intelligence test scores (Borkenau et al., 2004). Furthermore, biographical data have been shown to predict intelligence (Schmidt and Hunter, 1998). These results provide some evidence that assessment of intelligence should be viable within the context of SNWs.
Assessment of global performance Beyond personality and intelligence, SNWs may also contain additional information which may be useful in employment selection. Owing to the large volume of information contained in SNWs, information may also be obtained which relate to the user’s writing skills, job experiences, or a variety of knowledge, skills, abilities, or other criteria which might relate to job or organizational fit in a given employment selection context. A more global assessment of performance would include a variety of information. In fact, due to the broad range of information available on SNWs and the lack of consistency in this information across individuals, the approach of assessing broad characteristics is likely to be more practical than assessing more narrow aspects of social networking profiles that may be unavailable and/or inconsistent for a large segment of the profiles.
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Rater consensus/interrater agreement Furr and Funder (2007) identify rater consensus as the first psychometric concern when assessing characteristics through judge-rated behavioral observation. Thus, the primary focus of this paper, as the first to address the potential use of SNW information for the purpose of employment selection, is to focus on the issue of rater consensus.
To the degree to which SNWs provide a distinguishable and consistent basis of evaluation, judges will reach a certain level of consensus in their impressions of these SNWs. Thus, consensus is expected across judges:
H1. Ratings of the big-five dimensions of personality, intelligence, and performance based on SNW information are consistent across raters.
Accuracy of ratings A second psychometric concern is that of validity (Furr and Funder, 2007). If rater consensus is established, the next step will be to establish various forms of validity. This study evaluates validity by assessing whether judges are accurate in their assessment of personality intelligence, and performance.
Personality, intelligence, and performance impressions based on judge assessments have been shown to be quite accurate, even when the amount of information is limited (Borkenau et al., 2004). This growing body of research suggests that people have a natural talent for judging one another accurately (Vazire and Gosling, 2004). Given the high volume of information available to assess behavioral cues on SNWs, a suitable level of accuracy is expected:
H2. Raters assessing an individual’s personality, intelligence, and performance through SNWs are able to distinguish between those individuals who are high on each characteristic from those who are low on that characteristic.
Method Participants and procedures This study was conducted at a large public university in the southern USA. A total of 63 students enrolled in an employment selection course participated in this project for course credit. Participants were 49 percent male, 90 percent Caucasian, averaged 24 years of age, and worked an average of 26 hours per week. Participants had prerequisites in HRs and statistics. As part of the employment selection course, these participants were trained in both personality/intelligence testing and effective utilization of rating scales, participated in a one hour training session for this project (reviewing the definitions of the big-five personality traits, general mental ability, and academic performance; viewing Facebook profiles to identify specific information which could be used to assess the focal characteristics of the study; and familiarizing the participants with the rating form to be used when conducting the assessments), and participated in a series of practice assessments prior to conducting the ratings for this study (an assignment to evaluate SNWs and identify specific information that could be used to assess each of the focal characteristics of the study, follow-up class discussion of these observations, and a practice session in which each participant conducted two assessments of current Facebook users by using the researcher designed rating form). All participants had personal involvement with SNWs. Participants were asked to spend ten minutes evaluating each of the six social networking profiles, consider multiple
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aspects of the profiles which could relate to a specific trait, then complete the rating form based on their overall impression of the social networking profile.
The choice of the three male and three female social networking profiles was randomly generated from a list of volunteers in an introductory management course. Along with volunteering to have their SNWs evaluated[5], the volunteers also completed demographics and personality questionnaires, an intelligence test, and consented to allow the researchers to obtain their grade point average (GPA) from the university registrar.
Measures Judge ratings of big-five personality traits – measured with 25 items from the bipolar adjective checklist (Goldberg, 1992) on a nine-point scale.
Judge ratings of general mental ability – a single item measure was used to measure intelligence quotient (IQ) based on Reilly and Mulhern (1995). Judges were asked to “Estimate the user’s IQ. Remember that the average IQ is 100, and one-sixth of the population have IQs less than 85, with one-sixth scoring over 115.”
Judge ratings of performance – a single item measure was used to measure academic performance based on the format used to measure IQ. Judges were asked to “Estimate the user’s GPA. Remember that an average GPA is 3.0 and the maximum is 4.0.”
Ratee self-reported big-five personality traits (referred to as true scores) – measured with 150 items from the international personality item pool – IPIP (Goldberg et al., 2006) on a five-point scale ranging from strongly disagree to strongly agree. The as ranged from 0.92 for conscientiousness to 0.81 for openness.
Ratee general mental ability (IQ true score) – measured with the Wonderlic personnel test (Wonderlic, 2000), a 12 minute/50 question timed test of intelligence.
Ratee performance (performance true score) – academic performance was obtained via GPA from the University Registrar. Although academic performance is less ideal than job performance in the context of employment selection, it represents an objective measure to test the hypotheses presented.
Results The as for the judge ratings of personality were calculated for each of the six ratees. These six as were then averaged for each of the big-five to estimate the overall internal consistency of the scales. In order to assess H1, interrater agreement in the form of average measures intraclass correlation coefficients (ICCs) for the judge ratings are included in Table I. The scaled scores for the big-five personality traits and the single item scores for IQ and performance were evaluated for interrater agreement. The 378 total ratings (63 raters £ 6 ratings each) were used to calculate the ICCs. The ICC values were all adequate, ranging from 0.93 for extroversion to 0.99 for conscientiousness and performance. Since ICCs are expected to be higher with a larger number of raters, Table II also includes the number of raters for each characteristic which would be necessary to achieve a 0.50 ICC value. Although there are no guidelines for level of agreement, 0.50 was used in the analyses as it should provide a minimum level of acceptable agreement across judges. The Spearman Brown prophecy formula was used to determine how many raters would be required to obtain an adequate (0.50) ICC value. Based on the 63 raters from this study, it was determined that between two (for conscientiousness and performance) and six (for emotional stability
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and extroversion) raters would be required to obtain a satisfactory level of interrater agreement.
H2 was evaluated by conducting t-tests on score means in order to determine whether or not the means are statistically different from one another. In order to determine which means to test, the true scores (self-reported big-five personality scores, intelligence scores, and GPA) of the six rated subjects were evaluated. For each of the seven characteristics, the individual with the highest true score and the individual with the lowest true score were selected for analysis. Judge mean ratings for these subjects were then compared to determine whether or not raters are able to distinguish individuals high on a characteristic from those low on the same characteristic. This method also allows for evaluation of the direction of the relationship, such that (in addition to evaluating mean differences) the judge rating of the subject with the higher true score should be higher than the judge rating of the lower true score. Results demonstrate that the mean judge ratings for the subject highest on the seven characteristics were statistically different from the subjects lowest on those characteristics. In addition, with the exception of openness to experience, the judges mean ratings were higher for those with the highest true score, indicating the ability of judges to distinguish the traits of conscientiousness, emotional stability, agreeableness, extroversion, intelligence, and performance by evaluating SNWs[6].
Post hoc analyses were conducted to determine the impact of intelligence and personality on judge consistency and accuracy. Prior research has demonstrated mixed findings related to the impact of personality traits of the rater on rating accuracy.
High ratee score Low ratee score n S# SS RM S# SS RM T
Conscientiousness 63 6 4.07 8.03 2 3.50 7.65 2.77 *
Emotional stability 63 5 3.33 6.29 3 2.17 5.80 3.00 *
Agreeableness 63 6 4.27 8.04 5 3.57 7.26 6.12 *
Openness 63 3 3.78 5.51 1 3.46 6.37 6.34 *
Extroversion 63 1 4.10 7.65 2 3.40 6.85 5.99 *
IQ 63 6 26 110.4 3 17 94.7 13.53 *
Performance 63 6 3.94 3.57 2 1.81 3.46 2.78 *
Notes: *p , 0.05; S#, subject; SS, subject score (true scores); RM, rater mean score
Table II. Differences in judge ratings means comparing high and low ratee scores
a ICC No. of raters
Conscientiousness 0.92 0.99 2 Emotional stability 0.83 0.94 6 Agreeableness 0.80 0.98 3 Openness to experience 0.87 0.98 3 Extroversion 0.85 0.93 6 IQ 0.98 3 Performance 0.99 2
Notes: n ¼ 378 ratings (63 raters £ 6 ratings each); number of raters indicates the number of raters which would be required in a future study in order to obtain a 0.50 ICC value
Table I. Judge rating as and ICCs
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Ambady et al. (1995) found that less sociable (extroverted) raters were more accurate, while Lippa and Dietz (2000) found that only openness indicated more accurate raters. In addition, narcissistic raters have been found to be less accurate (John and Robbins, 1994), which may be relevant to the big-five since narcissism relates strongly to neuroticism. Finally, intelligence has also been reported to positively relate to rater accuracy (Lippa and Dietz, 2000). In the current study, the 63 judges were asked to take the same intelligence and personality tests as the SNW subjects. The analyses conducted above were then re-evaluated based on high versus low groups based on intelligence and the big-five. Results show no difference in interrater agreement based on these characteristics. However, judges who are more intelligent and more emotionally stable were shown to be more accurate in their judgments. More specifically, when the raters were split into high and low groups based on intelligence scores (the 31 highest scores versus the 31 lowest scores), the high intelligence group significantly and more accurately differentiated between high and low characteristics for conscientiousness, emotional stability, openness, and performance. For example, with all 63 raters combined, the difference between rater means for conscientiousness in Table II is 0.38 (8.03 for the high ratee score and 7.65 for the low ratee score). When assessing high and low intelligence raters independently, the mean difference for the 31 high intelligence raters is 0.61, but only 0.14 for the 31 low intelligence raters. Thus, more intelligent raters seem to be more capable of assessing this trait than less intelligent raters. Similarly, raters who are the most emotionally stable also rate more accurately for conscientiousness, emotional stability, openness, and performance. For example, the mean difference across raters for high and low ratee conscientiousness is again 0.38, but is 0.73 for the 31 raters who are the most emotionally stable and 0.03 for the 31 raters who are the least emotionally stable. These results indicate the potential need for researchers to consider intelligence and emotional stability when selecting individuals who will serve as raters of characteristics such as personality.
Discussion Based on the large volume of personal information available on SNWs, judges’ ratings of the big-five dimensions of personality, intelligence, and global performance were consistent across the 63 raters in this study, demonstrating adequate internal consistency reliability and interrater agreement. In addition, the trained raters were able to accurately distinguish between individuals who scored high and individuals who scored low on four of the big-five personality traits, intelligence, and performance, providing initial evidence that raters can accurately determine these organizationally relevant traits by viewing SNW information.
As stated earlier, other rated personality has been shown to predict job performance. Considering that other methods of other-reported personality are unlikely to be viable in an employment selection context, SNW ratings of personality may be a practical approach. Owing to the theoretical and methodological differences between self-reported and other-rated personality, it is likely that ratings of personality via SNWs will provide a context for incremental prediction of job performance beyond the predominant self-report approach. In addition, the differences in context between SNWs and a job interview (i.e. socially desirable responding in the job interview as well as the unique nature of information contained in SNWs) should similarly allow for unique prediction of job performance beyond what can be evaluated through
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personality assessment in the employment interview. This approach may be particularly valuable since these assessments take only a fraction of the time involved with other selection methods.
This study is not without limitations. Although the analyses testing the consistency of the relationships of SNW ratings are based on 378 judge ratings from 63 raters, the analyses testing rater accuracy were conducted by testing for significant differences between the high and low performer on the seven characteristics for only six subjects. Future research should assess accuracy over a larger sample of subjects.
We hope that the results of this preliminary study will not be used by organizations to support their use of SNWs in employment selection. Without further validation in a variety of studies, with larger samples and in a variety of organizational contexts, caution should be used when interpreting the implications of this study. This is particularly true given the potential for employer legal liability due to the vast amount of personal information available on SNWs. Information regarding gender, race, age, disabilities, and other criteria which should not be used when making hiring decisions will most certainly, consciously or not, influence who gets hired. Even if this information does not bias the hiring decision, disparate impact issues may still exist. Future research should also examine the potential issues of adverse impact and potentially illegal information in hiring decisions using personal information from SNWs. In addition, research should be conducted to compare assessments of SNWs to other employment selection methods, such as personality assessment, intelligence testing, and employment interviews.
Based on the relative absence of research evidence in this newly developing area, particularly regarding the potential for adverse impact and the lack of validity evidence, we believe the most important practical implication of this paper is for organizations to use SNWs with these issues in mind. Organizational representatives assessing SNWs should ask themselves two important questions. First, is the organization assessing (or could be perceived as assessing) information which could lead to discrimination against a legally protected group? Second, is the specific social networking information used to help make a hiring decision valid in determining who will perform better on the job? The approach used in this paper of assessing personality traits, intelligence, or general performance begin to provide answers to these questions.
Notes
1. Special thanks to an anonymous reviewer for pointing out the “maximal”/“typical” performance distinction.
2. A wall, similar to a guestbook on other web sites, is a forum for the friends of the user to post comments to the user in an open forum where all of the user’s friends can see. This is different from a message that goes only to the user, and cannot be seen by others. By tagging a photo, both the user and the user’s friends have the ability to indicate that the particular user appears in a photo. Both user posted photos and photos where the user has been tagged appear on the users profile in the photos section. The user has the ability to “untag” or remove the link between the picture and his or her profile, but the picture will still remain on the page of the friend who uploaded the picture.
3. Status messages are a way for the user to briefly tell their friends what they are currently doing, how they are feeling, or any other short message that they would like to convey to their friend list. Status messages are often times the area of the social networking page that
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gets updated most frequently, with some users changing their status message multiple times a day.
4. Personal information lists the user’s activities, interests, and favorite movies, books, television shows, and quotations, and is a way for the user’s friends to get a better understanding of the user.
5. It is unknown whether or the degree to which these participants made modifications to their Facebook profile after agreeing to allow their profiles to be evaluated for research purposes. However, the potential for altering a profile parallels situations in which these Facebook users might choose to alter their profile when applying for a job, since it is now widely known that employers may potentially assess these profiles during the employment selection process. Future research should assess the degree to which SNW users attempt to modify their profiles in these situations.
6. Based on a suggestion from one of the anonymous reviewers, we also assessed H2 based on the magnitude of the correlation coefficients between Facebook-ratings and “true scores,” since this approach includes all six of the Facebook users in the sample instead of just the high and low performer for each characteristic. Owing to the extremely small sample size, this approach is problematic, even considering the large number of items and raters used to generate the scores. However, due to the novelty of the research question and the exploratory nature of this study, we agree that this analysis may provide additional insight into the proposed relationships. Results indicate that four of the seven proposed relationships have medium to large effect sizes (Cohen, 1988). Specifically, conscientiousness (0.40), agreeableness, (0.38), extroversion (0.52), and performance (0.32). Emotional stability and IQ were small (0.07 and 20.01, respectively), while openness was once again negatively associated. These results provide additional evidence that measuring job-relevant characteristics using SNWs may be a valid method of assessment.
References
Ambady, N., Hallahan, M. and Rosenthal, R. (1995), “On judging and being judged accurately in zero-acquaintance situations”, Journal of Personality and Social Psychology, Vol. 69, pp. 518-29.
Barrick, M.R. and Mount, M.K. (1991), “The big five personality dimensions and job performance: a meta-analysis”, Personnel Psychology, Vol. 44, pp. 1-26.
Barrick, M.R., Patton, G.K. and Haugland, S.N. (2000), “Accuracy of interviewer judgements of job applicant personality traits”, Personnel Psychology, Vol. 53, pp. 925-51.
Borkenau, P., Mauer, N., Rieman, R., Spinath, F.M. and Angleitner, A. (2004), “Thin slices of behavior as cues of personality and intelligence”, Journal of Personality and Social Psychology, Vol. 86, pp. 599-614.
Campion, M.A., Palmer, D.K. and Campion, J.E. (1997), “A review of structure in the selection interview”, Personnel Psychology, Vol. 50, pp. 655-702.
Cohen, J. (1988), Statistical Power Analyses for the Behavioral Sciences, Lawrence Erlbaum Associates, Hillsdale, NJ.
Connolly, J.J. and Viswesvaran, C. (1998), “The convergent validity between self- and observer ratings of personality”, poster (71-08) presented at the 13th Annual Conference of the Society for Industrial and Organizational Psychology, Dallas, TX.
Finder, A. (2006), “For some, online persona undermines a resume”, New York Times, available at: www.nytimes.com/2006/06/11/us/11recruit.html?scp¼1&sq¼ForþSome%2CþOnline þPersonaþUnderminesþa þ R%E9sum%E9 (accessed January 10, 2008).
Employment selection methods
577
D ow
nl oa
de d
by V
IC T
O R
IA U
N IV
E R
SI T
Y (A
us tr
al ia
) A t 2
2: 53
1 2
M ar
ch 2
01 8
(P T
)
Framingham, H.H. (2008), “Employers use social networks in hiring process”, Computerworld, available at: http://computerworld.co.nz/news.nsf/care/63C6E9BE6A2CD920CC2574C9000 3ADDD (accessed October 16, 2008).
Frauenheim, E. (2006), “Caution advised when using social networking web sites for recruiting, background checking”, Workforce Management, Vol. 24, available at: www.workforce. com/archive/feature/24/58/49/245851.php (accessed October 15, 2008).
Frei, R.L. and McDaniel, M.A. (1997), “Validity of customer service measures in personnel selection: a review of criterion and construct evidence”, Human Performance, Vol. 11, pp. 1-27.
Fuller, A. (2006), “Employers snoop on Facebook”, Stanford Daily, available at: http://daily. stanford.edu/article/2006/1/20/employersSnoopOnFacebook (accessed January 10, 2008).
Furr, R.M. and Funder, D.C. (2007), “Behavioral observation”, in Robins, R.W., Fraley, R.C. and Krueger, R.F. (Eds), Handbook of Research Methods in Personality Psychology, Guilford Press, New York, NY.
Goldberg, L., Johnson, J., Eber, H., Hogan, R., Ashton, M., Cloninger, C., Gough, H. and Johnson, J. (2006), “The international personality item pool and the future of public-domain personality measures”, Journal of Research in Personality, Vol. 40, pp. 84-96.
Goldberg, L.R. (1992), “The development of markers for the big-five factor structure”, Psychological Assessment, Vol. 4, pp. 26-42.
Gottfredson, L.S. (1998), “The general intelligence factor”, Scientific American Presents Intelligence, Vol. 9, pp. 24-9.
Griffith, R.L. and Peterson, M.H. (2008), “The failure of social desirability measures to capture applicant faking behavior”, Industrial and Organizational Psychology: Perspectives on Science and Practice, Vol. 1, pp. 308-11.
Hofstee, W.K. (1994), “Who should own the definition of personality?”, European Journal of Personality, Vol. 8, pp. 149-62.
Hough, L.M. and Oswald, F.L. (2008), “Personality testing and industrial-organizational psychology: reflections, progress, and prospects”, Industrial and Organizational Psychology: Perspectives on Science and Practice, Vol. 1, pp. 272-90.
Hunter, J.E. and Hunter, R.F. (1984), “Validity and utility of alternative predictors of job performance”, Psychological Bulletin, Vol. 96, pp. 72-98.
John, O.P. and Robins, R.W. (1994), “Accuracy and bias in self-perception: individual differences in self-enhancement and narcissism”, Journal of Personality and Social Psychology, Vol. 66, pp. 206-19.
Kowske, B. and Southwell, M. (2006), “E-screening proves ‘e-resistible’: but at what cost?”, Human Resource Executive Online, available at: www.hreonline.com/HRE/story.jsp? storyId¼6835642 (accessed January 10, 2008).
Lippa, R.A. and Dietz, J.K. (2000), “The relation of gender, personality, and intelligence to judges’ accuracy in judging strangers’ personality from brief video segments”, Journal of Nonverbal Behavior, Vol. 24, pp. 25-43.
McCrae, R.R. and Weiss, A. (2007), “Observer ratings of personality”, in Robins, R.W., Fraley, R.C. and Krueger, R.F. (Eds), Handbook of Research Methods in Personality Psychology, Guilford Press, New York, NY.
Morgeson, F.P., Campion, M.A., Dipboye, R.L., Hollenbeck, J.R., Murphy, K.R. and Schmitt, N. (2007a), “Are we getting fooled again? Coming to terms with limitations in the use of personality tests for personnel selection”, Personnel Psychology, Vol. 60, pp. 1029-49.
JMP 24,6
578
D ow
nl oa
de d
by V
IC T
O R
IA U
N IV
E R
SI T
Y (A
us tr
al ia
) A t 2
2: 53
1 2
M ar
ch 2
01 8
(P T
)
Morgeson, F.P., Campion, M.A., Dipboye, R.L., Hollenbeck, J.R., Murphy, K.R. and Schmitt, N. (2007b), “Reconsidering the use of personality tests in personnel selection contexts”, Personnel Psychology, Vol. 60, pp. 683-729.
Morris, M.G. and Venkatesh, V. (2000), “Age differences in technology adoption decisions: implications for a changing workforce”, Personnel Psychology, Vol. 53, pp. 375-403.
Mount, M.K., Barrick, M.R. and Strauss, J.P. (1994), “Validity of observer ratings of the big five personality factors”, Journal of Applied Psychology, Vol. 79, pp. 272-80.
National Association of Colleges and Employers Newsletter (2006), “Quick poll results: more than one-quarter of organizations have ‘Googled’/reviewed candidate profiles on social networking sites”, available at: www.naceweb.org/pubs/spotlightonline/2006/c070606. htm#1 (accessed January 10, 2008).
Ones, D.S., Viswesvaran, C. and Schmidt, F.L. (1993), “Comprehensive meta-analysis of integrity test validities: findings and implications of personnel selection and theories of job performance”, Journal of Applied Psychology, Vol. 83, pp. 35-42.
Ones, D.S., Dilchert, S., Viswesvaran, C. and Judge, T.A. (2007), “In support of personality assessment in organizational settings”, Personnel Psychology, Vol. 60, pp. 995-1027.
Reilly, J. and Mulhern, G. (1995), “Gender difference in self-estimated IQ: the need for care in interpreting group data”, Personality and Individual Differences, Vol. 18, pp. 189-92.
Robertson, I.T. and Smith, M. (2001), “Personnel selection”, Journal of Occupational & Organizational Psychology, Vol. 74, pp. 441-72.
Sackett, P.R. (2007), “Revisiting the origins of the typical-maximum performance distinction”, Human Performance, Vol. 20, pp. 179-85.
Sackett, P.R., Zedeck, S. and Fogli, L. (1988), “Relations between measures of typical and maximum job performance”, Journal of Applied Psychology, Vol. 73, pp. 482-6.
Salgado, J.F. (1998), “Big five personality dimensions and job performance in army and civil occupations: a European perspective”, Human Performance, Vol. 11, pp. 271-88.
Schmidt, F.L. and Hunter, J.E. (1998), “The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 85 years of research findings”, Journal of Applied Psychology, Vol. 124, pp. 262-74.
Shea, K. and Wesley, J. (2006), “How social networking sites affect students, career services, and employers”, National Association of Colleges and Employers Journal, Vol. 66 No. 3, pp. 26-32.
Taylor, A. (2007), “Careless online talk can cost candidates jobs”, Financial Times, available at: www.ft.com/cms/s/0/0dd40300-dcca-11db-a21d-000b5df10621.html (accessed January 10, 2008).
Tett, R.P. and Christiansen, N.D. (2007), “Personality tests at the crossroads: a response to Morgeson, Campion, Dipboye, Hollenbeck, Murphy, and Schmitt (2007)”, Personnel Psychology, Vol. 60, pp. 967-93.
Tett, R.P., Jackson, D.N. and Rothstein, M. (1991), “Personality measures as predictors of job performance: a meta-analytic review”, Personnel Psychology, Vol. 44, pp. 703-42.
van der Werf, M. (2006), “Beware of using social-networking websites to monitor students, lawyers say”, Chronicle of Higher Education, Vol. 53 No. 26, p. A28.
Vazire, S. and Gosling, S.D. (2004), “E-perceptions: personality impressions based on personal websites”, Journal of Personality and Social Psychology, Vol. 87, pp. 123-32.
Wonderlic & Associates (2000), Wonderlic Personnel Test and Scholastic Level Exam: User’s Manual, Wonderlic & Associates, Libertyville, IL.
Zeidner, R. (2007), “How deep can you probe?”, HR Magazine, Vol. 52 No. 10, pp. 57-62.
Employment selection methods
579
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nl oa
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Y (A
us tr
al ia
) A t 2
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Further reading
McCain, J. (2008), “McCain says using Google to vet VP candidates”, Yahoo News, available at: http://news.yahoo.com/s/nm/20080609/wr_nm/usa_politics_mccain_google_dc (accessed June 12, 2008).
Motowidlo, S.J., Burnett, J.R., Maczynski, J., Witkowski, S., Wojtachnio, A. and Chelpa, S. (1996), “Predicting managerial job performance from personality ratings based on structured interview: an international replication”, Polish Psychological Bulletin, Vol. 27, pp. 139-51.
About the authors Donald H. Kluemper is an Assistant Professor and Robert and Patricia Hines Professor of Management at Louisiana State University. He received his PhD from the Department of Management at Oklahoma State University. He is a member of the Academy of Management, American Psychological Association, Southern Management Association, and the Society for Industrial and Organizational Psychology. His research interests include employment selection, personality testing, intelligence testing, and emotional intelligence. His research has been published in the Journal of Organizational Behavior, International Journal of Selection and Assessment, and Personality and Individual Differences. Donald H. Kluemper is the corresponding author and can be contacted at: [email protected]
Peter A. Rosen is an Assistant Professor of Management Information Systems at the University of Evansville, Indiana. He received his PhD from the Department of Management Science and Information Systems at Oklahoma State University. He is a member of the Decision Sciences Institute, Association for Information Systems, INFORMS, and the Academy of Management. His research interests include social networking technology, data privacy and security, technology acceptance, personal innovativeness, and statistics in sports. His research has been published in the Journal of Database Management, the International Journal of Information and Computer Security, the Journal of Quantitative Analysis in Sports, and the Journal of the Academy of Business Education.
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nl oa
de d
by V
IC T
O R
IA U
N IV
E R
SI T
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us tr
al ia
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2: 53
1 2
M ar
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01 8
(P T
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57. Nicolas Roulin, Adrian Bangerter. 2013. Social Networking Websites in Personnel Selection. Journal of Personnel Psychology 12:3, 143-151. [Crossref]
58. Julia Heidemann, Mathias Klier, Florian Probst. 2012. Online social networks: A survey of a global phenomenon. Computer Networks 56:18, 3866-3878. [Crossref]
59. Juan M. Madera. 2012. Using social networking websites as a selection tool: The role of selection process fairness and job pursuit intentions. International Journal of Hospitality Management 31:4, 1276-1282. [Crossref]
60. Jose M. Cortina, Joseph N. Luchman. Personnel Selection and Employee Performance . [Crossref] 61. Wen Chang, Juan M. Madera. 2012. Using Social Network Sites for Selection Purposes: An Investigation
of Hospitality Recruiters. Journal of Human Resources in Hospitality & Tourism 11:3, 183-196. [Crossref] 62. Hanna Krasnova, Natasha F. Veltri, Oliver Günther. 2012. Die Rolle der Kultur in der Selbstoffenbarung
und Privatsphäre in sozialen Onlinenetzwerken. WIRTSCHAFTSINFORMATIK 54:3, 123-133. [Crossref]
63. Greet Van Hoye. 2012. Recruitment sources and organizational attraction: A field study of Belgian nurses. European Journal of Work and Organizational Psychology 21:3, 376-391. [Crossref]
64. Hanna Krasnova, Natasha F. Veltri, Oliver Günther. 2012. Self-disclosure and Privacy Calculus on Social Networking Sites: The Role of Culture. Business & Information Systems Engineering 4:3, 127-135. [Crossref]
65. DONALD H. KLUEMPER, PETER A. ROSEN, KEVIN W. MOSSHOLDER. 2012. Social Networking Websites, Personality Ratings, and the Organizational Context: More Than Meets the Eye? 1. Journal of Applied Social Psychology 42:5, 1143-1172. [Crossref]
66. Robert E. Wilson, Samuel D. Gosling, Lindsay T. Graham. 2012. A Review of Facebook Research in the Social Sciences. Perspectives on Psychological Science 7:3, 203-220. [Crossref]
67. H. Kristl Davison, Catherine C. Maraist, R. H. Hamilton, Mark N. Bing. 2012. To Screen or Not to Screen? Using the Internet for Selection Decisions. Employee Responsibilities and Rights Journal 24:1, 1-21. [Crossref]
68. Ross Slovensky, William H. Ross. 2012. Should human resource managers use social media to screen job applicants? Managerial and legal issues in the USA. info 14:1, 55-69. [Abstract] [Full Text] [PDF]
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) A t 2
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)
69. KATIE G. EGAN, MEGAN A. MORENO. 2011. Prevalence of Stress References on College Freshmen Facebook Profiles. CIN: Computers, Informatics, Nursing 29:10, 586-592. [Crossref]
70. Katie G. Egan, Megan A. Moreno. 2011. Alcohol References on Undergraduate Males’ Facebook Profiles. American Journal of Men's Health 5:5, 413-420. [Crossref]
71. Lauren E. McEntire, Tiffany M. Greene-Shortridge. 2011. Recruiting and Selecting Leaders for Innovation. Advances in Developing Human Resources 13:3, 266-278. [Crossref]
72. Azy Barak. Internet-based Psychological Testing and Assessment 225-255. [Crossref] 73. Bart Rienties, Dirk Tempelaar, Miriam Pinckaers, Bas Giesbers, Linda Lichel. 2010. The Diverging
Effects of Social Network Sites on Receiving Job Information for Students and Professionals. International Journal of Sociotechnology and Knowledge Development 2:4, 39-53. [Crossref]
74. Megan A. Moreno. 2010. Social Networking Sites and Adolescents. Pediatric Annals 39:9, 565-568. [Crossref]
75. Katherine Karl, Joy Peluchette, Christopher Schlaegel. 2010. Who's Posting Facebook Faux Pas? A Cross- Cultural Examination of Personality Differences. International Journal of Selection and Assessment 18:2, 174-186. [Crossref]
76. Daniel Bohnert, William H. Ross. 2010. The Influence of Social Networking Web Sites on the Evaluation of Job Candidates. Cyberpsychology, Behavior, and Social Networking 13:3, 341-347. [Crossref]
77. Bridgette Waite, Elsa-Sofia Morote. Hiring Practices, Uses of Social Media, and Teacher Retention Differences 275-292. [Crossref]
78. Maura Cherney, Daniel Cochece Davis, Sandra Metts. Surf's Up 270-290. [Crossref] 79. Jared Z. Ferrell, Jacqueline E. Carpenter, E. Daly Vaughn, Nikki M. Dudley, Scott A. Goodman.
Gamification of Human Resource Processes 108-139. [Crossref] 80. Ryan J Petty, Laxmikant Manroop, Sara Linton. Integration of Micro-Blogs into the Human Resource
Management (HRM) Areas of Recruitment and Selection 226-250. [Crossref] 81. Karen Abney Korn. Facing Facebook in Higher Education 1-53. [Crossref] 82. Tanya Bondarouk, Huub J. M. Ruël, Paul Timmermans. Determining the Requirements for E-Selection
in a Small Recruitment Company 1-37. [Crossref] 83. William Ross, Ross Slovensky. Using the Internet to Attract and Evaluate Job Candidates 537-549.
[Crossref] 84. Ralf Caers, Claudia Vigna, Tim De Feyter. Recruitment and Selection Using Social Network Sites
550-559. [Crossref] 85. Andreas Eckhardt, Wolfgang Brickwedde, Sven Laumer, Tim Weitzel. The Need for a Recruiter 2.0 for
Hiring IT Talent 325-339. [Crossref] 86. Bart Rienties, Dirk Tempelaar, Miriam Pinckaers, Bas Giesbers, Linda Lichel. The Diverging Effects of
Social Network Sites on Receiving Job Information for Students and Professionals 202-217. [Crossref]
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