Social media in employee selection and recruitment

Richard N. Landers · Gordon B. Schmidt Editors Social Media in Employee Selection and Recruitment Theory, Practice, and Current Challenges Social Media in Employee Selection and Recruitment Richard N Landers • Gordon B Schmidt Editors Social Media in Employee Selection and Recruitment Theory, Practice, and Current Challenges Editors Richard N Landers Department of Psychology Old Dominion University Norfolk, VA, USA Gordon B Schmidt Division of Organizational Leadership and Supervision Indiana University-Purdue University Fort Wayne Fort Wayne, IN, USA ISBN 978-3-319-29987-7 ISBN 978-3-319-29989-1 DOI 10.1007/978-3-319-29989-1 (eBook) Library of Congress Control Number: 2016938158 © Springer International Publishing Switzerland 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland I dedicate this book to Owen, who will probably find it delicious —Richard Landers This book is dedicated to my wife and our family of Wiegands, Smiths, and Schmidts; especially my mother, Priscilla; sister, Brenda; and the memory of my father, Eugene —Gordon Schmidt Contents Part I Social Media in Employee Selection and Recruitment: An Overview Richard N Landers and Gordon B Schmidt Part II Introduction Current Applications Social Media as a Personnel Selection and Hiring Resource: Reservations and Recommendations H Kristl Davison, Mark N Bing, Donald H Kluemper, and Philip L Roth Theoretical Propositions About Cybervetting: A Common Antecedents Model Julia L Berger and Michael J Zickar An Uncertainty Reduction Approach to Applicant Information-Seeking in Social Media: Effects on Attributions and Hiring Caleb T Carr 15 43 59 Social Media Use: Antecedents and Outcomes of Sharing Nathan Weidner, Kimberly E O’Brien, and Kevin T Wynne 79 Game-Thinking Within Social Media to Recruit and Select Job Candidates 103 Andrew B Collmus, Michael B Armstrong, and Richard N Landers vii viii Contents Part III Practical Guidelines Social Media, Big Data, and Employment Decisions: Mo’ Data, Mo’ Problems? 127 Sarah Guilfoyle, Shawn M Bergman, Christopher Hartwell, and Jonathan Powers Comparing the Social Media in the United States and BRIC Nations, and the Challenges Faced in International Selection 157 Brandon Shields and Julia Levashina Social Media and Employee Recruitment: Chasing the Run Away Bandwagon 175 Yalcin Acikgoz and Shawn M Bergman 10 How to Stay Current in Social Media to Be Competitive in Recruitment and Selection 197 Stephanie L Black, Montressa L Washington, and Gordon B Schmidt Part IV Challenges and Limitations 11 Impression Management and Social Media Profiles 223 Nicolas Roulin and Julia Levashina 12 Applicant Reactions to Social Media in Selection: Early Returns and Future Directions 249 J William Stoughton 13 Legal Concerns When Considering Social Media Data in Selection 265 Gordon B Schmidt and Kimberly W O’Connor 14 Online Exclusion: Biases That May Arise When Using Social Media in Talent Acquisition 289 Enrica N Ruggs, Sarah Singletary Walker, Anita Blanchard, and Shahar Gur 15 Is John Smith Really John Smith? Misrepresentations and Misattributions of Candidates Using Social Media and Social Networking Sites 307 Noelle B Frantz, Elizabeth S Pears, E Daly Vaughn, Jared Z Ferrell, and Nikki M Dudley Contents Part V 16 ix Future Directions Social Media in Employee Selection and Recruitment: Current Knowledge, Unanswered Questions, and Future Directions 343 Richard N Landers and Gordon B Schmidt Index 369 16 Social Media in Employee Selection and Recruitment… 361 applicants should know their social media data is being examined This could be considered from both the practical level of applicant reactions as well as from an ethical level of what is morally appropriate for an organization to If an organization informs applicants that social media data will be examined in the selection process, the next step is to decide how much information is given Some organizations may only go so far as to inform applicants that social media data may be accessed during the process Other organizations might offer information on what types of social media data will be sought, such as for assessing personality, discovering illegal actions, checking for racist statements, finding relevant colleague connections, or determining person–organization fit Organizations could even provide information on the social media sites they look at in the process Such elaboration may make applicants feel that social media data is being used in a fair way and for reasonable purposes, although it would increase the opportunity for faking Organizations may also consider how open they are related to the results of such searches If an applicant has negative social media content arise during such a search does the employer inform the applicant? Does the employer inform someone who was screened out due to social media content? An organization could simply tell applicants they have been screened out or provide more direct feedback and guidance on why Applicants may have greater acceptance when screened out if they are aware of the reason In practice, many organizations assume withholding such information is the preferred approach This, however, is an empirical question that needs to be tested Entwined with open social media data use policies are questions of accuracy and interpretation of information that appears contradictory (see Carr, 2016, for an example) Black et al (2016) discuss this with regard to evaluations of the credibility of social media content However, this represents a fairness question as well, as some social media might besmirch an individual’s reputation while factually inaccurate For example, a picture that could be interpreted as an individual engaging in drunken behavior may in fact be a picture of someone with a serious illness whose medication has led to such a presentation Even if the image is presented with text providing context, there is no guarantee that a viewer will read, interpret, or believe such text This raises further fairness questions related to whether an applicant should be able to defend or explain social media content discovered In an open process, an organization might directly ask an applicant about potentially disqualifying social media content found online The applicant could then correct an error if one was made or give explanation, and this could be done before or after the screen was conducted In a closed process where the applicant does not even know social media screening is happening, the misattributed picture or content might result in their exclusion without any chance for appeal In considering fairness, organizations may want to consider instituting appeals processes for applicants Practical responses of applicants must be considered as well If applicants are told that their social media content will be examined, applicants may close their social media accounts or engage in impression management These are questions 362 R.N Landers and G.B Schmidt organizations will want to consider as they decide how open they want their processes to be and how “cleaned up” profiles help or hurt the degree social media content predicts important organizational outcomes from applicants If the applicants with information most likely to flag them negatively are also the applicants most likely to change their profiles, this may have validity implications as well One potential result of knowledge of social media use in selection processes could be an arms race Chiang and Suen (2015) found that social media content impacted recruiters’ perceived fit of that candidate with the organization, and services have already appeared that modify social media profiles to increase hirability Thus, on one side, organizations will try to secure accurate information about applicants On the other, applicants will try to make good impressions, potentially regardless of accuracy This may result in warring technologies, each attempting to outwit the other in each iteration Roulin and Levashina (2016) delve into many of such issues created by applicant impression management Some organizations are already concerned about applicant faking, which makes the question of fairness more complicated Such concern led to organizations asking applicants for passwords to their social media accounts, a practice described by Schmidt and O’Connor (2016) and subsequently banned in approximately 20 states in the United States (Drouin, O’Connor, Schmidt, & Miller, 2015; Pate, 2012) If organizations think that impression management will lead to fake profiles, organizations will be less likely to be transparent about their social media screening procedures We are also likely to see organizations engage in new strategies and methods over time in order to combat this Importantly, research is not yet clear on the degree or incidence rate of social media impression management tactics in the selection process, so organizations in such practices may be chasing shadows This highlights the importance of further research in this area 16.3.4 Question 4: What Changes Outside the Context of the United States? A final and severely understudied question in this domain regards the generalizability of social media-based selection research conducted in the United States to other nations We invited two contributions in this area Shields and Levashina (2016) considered social media in BRIC (Brazil, Russia, India, and China) countries, whereas Schmidt and O’Connor (2016) provided examples of how non-US laws could impact social media selection processes More needs to be done, however, with a significant need for empirical work The three questions discussed above all may play out differently depending upon culture and legal system In the present economy, dominant companies are multinationals with needs to balance workforces and customers all over the globe As such, we need to couch our understanding of selection procedures within this global context, and the added dimension of social media which themselves vary in popularity by location makes this especially important in this context 16 Social Media in Employee Selection and Recruitment… 363 As noted by Shields and Levashina (2016), social media site popularity varies significantly by nation In some cases, particular sites may not be allowed by national policy, such as the forbiddance of Facebook and Twitter in China (The Economist, 2013) This has significant effects on how organizations engage in social media data collections and examination For example, Facebook data about a candidate from the United States may not provide the same information about that job candidate as data about a Chinese national job candidate on RenRen provides about that job candidate The censorship environment in China in addition to cultural differences in long-term orientation (Hofstede, Hofstede, & Minkov, 1997) might result in substantial range restriction on numerous traits of interest Cross-cultural and cross-platform comparisons need to be made on social media data A social media data analysis system that works well for Facebook data may not work as well for sites with different structures Organizations may combat such issues by focusing their processes on the affordances of social media (Collmus et al., 2016) rather than specific features or by focusing upon particular types of work-related behaviors such as those of Van Zoonen et al (2016), but the international context will work to complicate matters Language structures, differences in language formality, and etiquette expectation differences all can make comparisons of social media data across nations difficult One area in particular need of additional research focuses upon differences in applicant reactions by culture and country While there is existing evidence for some uniformity in selection tool reactions across countries (e.g Ryan et al., 2009), different values and expectations (e.g., privacy) will play a role in how social media selection processes are seen Organizations may need to balance national preferences with organizational desire for uniform systems of assessment A social media process that is seen as fair in one country might be seen as unfair in another Research comparing applicant reactions to social media data use in selection processes across different country contexts would be valuable for beginning to understand what differences exist International differences in candidate behaviors are also a high research priority Cultures defined by restraint may be more likely to engage in impression management techniques in comparison to cultures that tend toward indulgence (Hofstede et al., 1997) Content seen as a “red flags” in a restrained culture may be innocuous in an indulgent one, influencing which candidates are screened out for objectively identical infractions Behaviors engaged in by candidates may also be impacted by technology and infrastructure in a country Job candidates from areas with limited Internet access are less likely to have robust online social media profiles and general online presence Social media data collection policies completely standardized across nations may be detrimental to validity given such differences, depending upon the information sought Finally, differences in laws across countries will also have an impact on how social media selection processes are engaging in successfully and legally Schmidt and O’Connor (2016) offer some illustrations of the impact of national laws, such as the European Union’s Right to Be Forgotten, but more systematic legal examination is needed 364 16.4 R.N Landers and G.B Schmidt Conclusion In this chapter, we used the results of our author survey to develop several stances on the current state of the literature Specifically, experts are in general agreement that establishing a shared, interdisciplinary science is a high priority in order to determine the overall value and potential of social media in selection Such tactics are necessary to remain relevant to modern organizational practices given the quickly changing nature of social media It is additionally recognized that organizations are currently using social media in ways that are nonoptimal, if not harmful, to organizational goals, that there is pressure to continue doing so, and that practitioners face many of the same pressures that academics face The difference is that practitioners are more likely to adopt these technologies despite the lack of evidence, while academics are likely to call for more research All experts surveyed, whether practitioners or academics, expressed reservations about the use of social media in selection Here, however, there was some disagreement; some experts condemned the use of social media outright, whereas others suggested great potential somewhere in the future It is within the gap between those perspectives that future research in this domain will have the greatest impact From the chapters in this text, we furthermore developed four key questions of greatest importance for future research First, we must determine what useful information can be obtained from social media data This may be in the form of personal characteristics, like personality and cognitive ability, or it may be in the form of behaviors, such as social media endorsements and content counts Second, we must explore the technical details of incorporating this information into selection systems Specifically, we may take a more traditional organizational sciences approach, collecting specific theory-driven measures from existing social media, or we may take a more modern data science approach, extracting whatever information might be contained within social media data that is useful in parsimonious prediction of outcomes of interest Third, even if we can figure out what to measure and how to implement it, we must consider how applicants will react to it, and if our implementations are ethical Although great troves of data may be available, there may be lines that organizations simply should not cross Some data, perhaps, should just be off-limits Fourth and finally, we must consider how answers to the first three questions change as a result of location Both culture and legal context influence how social media data might be used by organizations, and researchers should pay closer attention to such differences Overall, we conclude from this that the future is quite bright for research on social media in selection Although this new predictor class is unproven and untested, there is sufficient enthusiasm from both academics and practitioners to suggest that future value may be obtained Just as it took decades to develop rock solid recommendations for other selection methods, especially considering many of those debates are on-going even now, we should not expect that the challenges of social media-based selection should already be solved If there is value to be found, it will take time to find it, and we hope that the questions posed here and the issues discussed will be a strong first step 16 Social Media in Employee 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Validity and adverse impact potential of a Facebook-based assessment Journal of Management doi:10.1177/0149206313515524 Van Zoonen, W., Verhoeven, J W M., & Vliegenthart, R (2016) How employees use Twitter to talk about work: A typology of work-related tweets Computers in Human Behavior, 55, 329–339 Youyou, W., Kosinski, M., & Stillwell, D (2015) Computer-based personality judgments are more accurate than those made by humans Proceedings of the National Academies of Science, 112, 1036–1040 Index A Academic conferences, 346 Academic performance, 91, 354 Academic publishing, 346 Academic success, 31 Academy of Management (AOM), 206 Accountability, 90, 95, 144 Acculturation, 90 Accuracy, 4, 20, 30, 112, 119, 141, 143, 146, 159, 189–191, 358, 359, 361, 362 Adaptability, 354 Advancement opportunities, 293 Adverse impact, 22, 24, 32, 35–38, 84, 94, 146, 300, 302, 354 Affective prejudices, 322 Affirmative action, 266, 349 Affordances, 8, 106–108, 110, 119, 355, 363 Age Discrimination in Employment Act of 1967 (ADEA), 21, 269, 271, 272 Agreeableness, 30, 45, 67, 227, 239, 253, 256–258 Alcohol use, 30 Alexa.com, 203 Algorithmic approaches, Alumni, 81 Amazon Mechanical Turk, 205 Americans with Disabilities Act of 1990 (ADA), 21, 269–271 Analytics, 9, 116, 127–130, 135–139, 141–143, 145–150, 203, 204, 213 Angel investor, 202 Anti-harassment laws, 267 Appeals processes, 361 Apple, 296 Applicant reactions, 6, 36, 91, 108, 115, 119, 250–254, 257, 258, 260, 361, 363 Applications, 5, 21, 33, 60, 63, 65, 80, 94, 104–107, 109, 128, 129, 138, 175, 178, 189, 203, 213, 229, 274, 279, 295, 298, 299, 358–360 Assertive tactics, 225, 233, 234 Assessment centers, 225, 297 Assessor training, 32 Assimilation, 62 Attraction, 10, 81, 291, 295–297 Attraction-Selection-Attrition (ASA), 81, 296, 297 Attributional certainty, 60, 61, 63, 66, 71, 72 Attribution error, 70 Attribution theory, 94 Auditing, 199, 358, 360 Authenticity, 159, 162, 163, 167–168 Awareness, 4, 21, 110, 176, 310, 311, 325, 328, 332 B Background check, 8, 20, 29, 38, 39, 64, 95, 230, 238, 274, 298, 300, 303 Badmouthing, 30, 230 Bandwagon effect, 88 Bank of America, 132, 296, 298 Behavioral meta-data, 117 Best practices, 9, 17, 18, 34, 36, 177, 217, 321, 344, 351, 352 Bias, 23, 37, 51, 86, 96, 114, 128, 166, 209, 242, 243, 266, 282, 291, 292, 294, 295, 299, 303, 319, 320, 322, 323, 325, 327, 328, 330 © Springer International Publishing Switzerland 2016 R.N Landers, G.B Schmidt (eds.), Social Media in Employee Selection and Recruitment, DOI 10.1007/978-3-319-29989-1 369 370 Big data, 9, 109, 116, 117, 127–135, 137–139, 141–150, 301, 348 Big-five personality, 30 Biodata, 32, 111, 112, 225, 227, 298, 315, 318 Blacks, 32, 35, 293, 299, 331 Blogs, 5, 80, 207 Bookmarks, 207 Boundary conditions, 354 Brand equity theory, 108, 119 Brazil, 9, 158, 362 Brazil, Russia, India, and China (BRIC) Nations, Business Horizons, 205 C CareerBuilder.com, 30, 182, 183 Casual SNS, 298 Censorship, 163, 279, 280, 317, 363 CEO, 16, 18, 73, 86, 210, 216, 357 Cheating, 114 China, 9, 64, 158–160, 166–168, 172, 226, 279–281, 362, 363 Civil Rights Act of 1964, 21, 269–270 Civil Service Reform Act of 1978, 22, 273 Clean profile, 237 Clorox, 296 Cognitive ability, 17, 30, 33, 47, 105, 111, 112, 227, 259, 266, 364 Cognitive evaluation theory, 115 Cognitively loaded stereotypes, 322 Collaboration, 80, 89, 104, 350 Collaborative practices, 80 Colleague connections, 361 College placement officers, 293, 297 CollegeRecruiter.com, 202 College students, 80, 258, 299 Common antecedents model, 8, 44, 45, 49 Communication privacy management theory (CPM), 93 Communities of interest, 80 Community data, 131, 132 Computational linguistics, 136, 141 Computer applications, 358 Computer automated algorithms, 135 Computer information systems, 148, 149 Computer science, 110, 113, 148, 228, 346 Confidential information, 29–30, 271, 281, 359 Confirmation bias, 323 Conscientiousness, 27, 30, 36, 45, 48, 67, 226, 227, 231, 235, 242, 253, 257, 258 Consumer report, 20, 283 Content analysis, 345–349 Content expertise, 351 Index Context collapse, 92, 317 Contextualizing, 207 Continental Airlines, 267 Cookies, 132 Corporate websites, 177 Counterproductive workplace behavior (CWB), 30, 44, 45 Counter-stereotypic imaging, 331 Cover letters, 60, 63, 68, 74, 128, 136, 314, 329 Creativity, 28, 30, 85, 89, 145, 162, 163, 299, 302, 354 Credibility, 9, 67, 74, 108, 118, 183, 189–193, 208–211, 238, 361 social media content, 209–211 social media sites, 208, 210 Cultural differences, 166, 294, 363 Cultural tastes, 294 Culture, 61, 81, 139, 142, 149, 150, 158, 201, 204, 290, 294, 296, 362–364 Customer engagement, 85 Cyber-behavior, 44–49 Cyberbullying, 90 Cybervetting, 8, 43, 44, 46, 50, 51 CyWorld, 69 D Data capture, 106, 118, 128, 351 Data exhausts, 131 Data mining, 208, 217, 359 Data Privacy Directive, 21 Data science, 350, 351, 358, 360, 364 Defamation, 19, 144, 281 Defensive tactics, 225, 226, 233, 236, 243, 244 Descriptive analytics, 128, 135–137 Diagnostic analytics, 128, 136 Digital footprint, 129, 145, 330 Dirty data, 146 Disability, 21, 84, 268, 270, 320, 327, 352 Disclose their usernames and passwords, 19 Discrimination, 20–23, 32, 35, 84, 166, 267–274, 283, 291, 293, 299, 301, 302, 319–327, 331 Discriminatory hiring practices, 323, 330 Disparage of a previous employer, 298 Disparate impact, 321, 323 Disparate treatment, 21, 22, 37 Diversity, 83, 131, 139, 296, 301, 328, 332, 349 Doctrine of negligence, 267 Documentation, 38, 266, 321, 352 Dynamic profile content, 159, 160, 162–164 Index E Educational plagiarism applications, 359 eFinancialCareers, 202 Electronic Communications Privacy Act of 1986, 20 Ello, 202 Emojis, 296 Emotionally loaded prejudices, 322 Empathy, 81, 86 Enterprise social media (ESM), 85, 86, 94, 95 Envy, 87 Equal Employment Opportunity Act, 165 Equal Employment Opportunity Commission (EEOC), 22, 28, 147, 149, 166, 269, 272, 273, 301 Ethnic sounding names, 299 European Union, 21, 277–279, 363 Evangelical, 269 Evidence-based practices, 128 Exclusion, 291, 361 Executives, 357 Exemplars, 331 Extra-role behaviors, 45 Extraversion, 27, 30, 36, 45, 48, 67, 112, 227, 231, 235, 239, 242, 255, 257, 328 F Facebook, 5, 18, 19, 21–23, 25–27, 29–35, 43, 45, 46, 48, 51, 52, 55, 64, 68–73, 80–83, 85, 87, 89–91, 104, 110, 112, 117, 129, 130, 137–144, 147, 158–161, 163–165, 167–171, 176, 178–182, 184, 185, 187, 188, 190, 192, 193, 198, 200–202, 204, 207–211, 215, 216, 223, 229, 231, 237–239, 242, 243, 251, 253, 255, 256, 258, 259, 267, 271, 274, 277, 279–281, 283, 290, 292–294, 296, 298, 299, 315, 323, 324, 329, 348, 355, 357–359, 363 likes, 131, 358 Fads, 344 Fair Credit Reporting Act (FCRA), 20, 38, 144, 145, 283 Fair information policies, 213, 214 Faking, 34, 86, 114, 224–226, 310, 313, 361, 362 False consensus effect, 88 False identities, False positives, 359 Falsified information, 298 Farcing attacks, 231 Faux pas, 230, 237, 240, 241, 259, 296 Favoritism, 266, 282, 293, 326, 327 371 Federal laws, 19 Feedback, 61, 105, 107, 109, 110, 115, 118, 176, 203, 213, 251, 296, 309, 361 Fidelity, 28, 29, 35, 60, 112, 113, 146 FinancialJobBank, 202 Firm reputation, 109 Following, social media, 314 Foundational research, 346 Four-fifths Rule, 266 FourSquare, 200 Fourth Amendment of the U S Constitution, 19 G Game-thinking, 6, 8, 104–108, 110, 111, 113, 116–118 Gamification, 107 Gay, 273, 357 Gender identity, 273, 274 Generalizability, 33, 46, 94, 95, 255, 256, 362 General mental ability, 8, 44 Generation X, 318 Genetic Information Nondiscrimination Act (GINA), 21 Germany, 158, 277 GitHub, 216, 238 GlassDoor.com, 62 Goal setting theory, 94 Golden Shield Project, 279 Google, 17, 18, 110, 129, 131, 132, 140, 158–161, 164, 165, 167, 169, 203, 207, 238, 274, 277, 278 Google+, 129, 158–161, 164, 165, 167, 169, 238 Google’s Display Planner, 203 Googling, Great Cannon, 279–281 Great Firewall, 279 GreatPlaceToWork.com, 62 Griggs v Duke Power Co 1971, 32 Group identification, 294 Groups, social media, 32, 35, 69, 71 H Harassment, 19, 91, 267 Hashtags, 161, 176 Health, 22, 88, 104, 117, 145, 270, 271, 311 HeForShe campaign, 329 Hertz, 267, 357 Hirability, 354, 362 Hispanics, 32, 35, 293, 294, 299 Homogeneous networks, 293 Honesty, 227, 251 372 Hospitals, 357 Human resource information system (HRIS), 133, 135, 142 Hypermedia, 161 Hyper-personalization, 299 Hyves, 69 I IBM, 144, 204 Idealized self, 309 Identity claim, 66, 67, 73 Illegal behavior, 359 Illegal drug use, 29 Illusion of transparency, 88 Implicit association test (IAT), 326 Implicit bias, 303, 308, 319, 321–323, 325–327, 331 Implicit measures, 326 Implicit race bias, 327 Impression management (IM), 34, 48, 49, 139, 167, 224–244, 276, 361–363 Inappropriate pictures, 298 Incremental algorithms, 360 Incumbents, 110, 118, 252, 254, 298, 313 India, 9, 158, 362 Individuation, 331 Industrial/organizational psychology, 344 Information overload, 90 Information seeking, 60, 61, 63–65, 71–73, 83, 316 Information systems, 9, 159 Information technology professionals, 141, 148, 149 Ingratiation, 235 In-group, 293, 323, 326, 327, 356 Instagram, 88, 129, 158, 160–165, 167, 169, 171, 176, 199, 200, 214, 223, 238, 239, 298 Intelligence, 28, 80, 105, 298, 354 Intentions to apply, 84, 108 Interactivity, 65, 107 Interdisciplinary perspective, 349–351 Interdisciplinary research, 351, 358 Intergroup conflict, 319 Internal consistency reliability, 26, 28 Internal referrals, 108, 110, 111, 118 Internal selection, 23, 86, 357 Internal SNS, 85, 141 International Personality Item Pool (IPIP), 117 International selection, 9, 158, 159, 163, 168, 172 Internet of Things (IoT), 117 Index Interpersonal treatment, 251 Interrater reliability, 26, 27 Interviews, 18, 24, 37, 38, 51, 63, 68, 74, 140, 215, 224–227, 230, 232–234, 236, 241, 242, 270, 297, 301, 303, 314, 320, 330 structured, 38, 227, 303, 320 unstructured, 24, 303 Intolerance, 320 Intranets, 142 Introverts, 48 Intrusion upon seclusion, 20 Invasion of privacy, 6, 92, 144, 145, 253, 256, 278 J Job analysis, 28, 29, 37, 38 Job fairs, 177, 293, 297 Job history, 80 Job performance, 5, 16, 17, 22, 28, 29, 31–33, 35–37, 44, 45, 49, 60, 88–90, 111–114, 117, 147, 181, 211, 228, 230, 302, 346, 352, 354–356 Job postings, 177, 183, 186, 187, 198, 199, 201, 202, 215, 301 Job relatedness, 251 Job relevance, 17, 348 Job-relevant skills, 19 Job-seeking behaviors, 83 Job shadowing, 62 Justifications, 225, 251 K Key connectors, 293 Keyword tagging, 134 Knelf, 115 Knowledge, skills, abilites (KSA), 62, 63, 66, 68, 70, 74 Knowledge, skills, abilites, other (KSAO), 166 L Language analysis, 25 Language formality, 363 Law China, 9, 158, 159, 166 European Union, 277, 278 United Arab Emirates, 281 United States, 266, 268 Leadership, 30, 73, 117, 199, 324 Legality, 6, 8, 17, 23, 59, 144, 277, 284, 347, 352 Lie scales, 313 Index LinkedIn, 5, 18, 19, 21, 23, 25, 33, 36, 37, 64, 66–68, 73, 80, 83, 84, 88, 90, 104, 111, 114, 129, 131, 134, 137–140, 143, 144, 158–163, 165, 167, 168, 170–172, 176, 178–182, 184, 185, 187, 188, 190–192, 200–202, 204, 209, 211, 215, 216, 223, 230, 233, 235, 236, 238, 239, 242, 253, 255, 258, 259, 272, 277, 280, 283, 290, 292, 293, 296–298, 300, 323, 324 LinkedIn endorsements, 111, 131 Litigation, 36, 145, 146, 253, 257, 323, 352 Log files, 132 Loneliness, 91 Long term orientation, 363 Low-income workers, 291 M Machiavellianism, 227, 242 Machine learning, 117, 135 Maladjusted individuals, 313 Malware, 279–281 Managerial role, 357 Marginalized groups, 291–296, 299, 301 Mashable, 203, 204 Masspersonal, 65, 66, 68, 73 Meta-analyses, 44, 324, 327 Millennials, 215 Minority job applicants, 10, 291–293, 295 Misattribution, 10, 307–333 Mobile platforms, 106 Moderators, 83, 88, 254, 257, 258, 354 Monster.com, 177, 182, 183, 188, 290 Motivations, 26, 60, 72, 83, 84, 88, 311, 355 Mozilla, 357 Multidisciplinary, 44 MySpace, 18, 19, 198 N Narcissism, 30, 84, 227, 242, 312 National Labor Relations Act (NLRA), 22, 23 National Labor Relations Board (NLRB), 22 National origin, 6, 21, 166, 269, 321, 352 Need for self-presentation, 82 Need to belong, 82 Negative bias, 10, 291–293, 295, 298–300, 303 Negligent hiring, 35, 37, 201, 267, 357 Negligent retention, 267 Negligent supervision, 267 Networking, 10, 172, 177–182, 184–185, 293 Neuroticism, 45, 48, 87, 227, 231, 242 New technology, 198, 199, 203, 240, 349, 358 Number of connections, social media, 162 373 O Odnoklassniki, 159–161, 165, 167, 169, 171 Offline networks, 294 Older workers, 291 Onboarding, 80, 104 Online aliases, 309 Online audience, 315, 316 Online behaviors, 5, 8, 44, 46, 47, 49, 50, 315, 357 Online exlcusion, 10, 289–303 Online groups, 48 Online identity, 10 Online job boards, 177, 182, 183, 185–187, 190–193, 290 Online portfolios, 65 Openness to experience, 45, 49, 239, 255, 257, 258, 302 Open-vocabulary analysis program, 358 Optimal Distinctiveness Theory, 319, 322 Organizational attraction, 84, 95, 109, 110, 118, 145, 252, 253, 255, 256 Organizational citizenship behavior, 113, 356 Organizational commitment, 88, 110, 134 Organizational culture, 109, 295, 296 Organizational entry, 61 Organizational exit, 61 Organizational image, 91, 108 Organizational justice, 250, 253, 256 Organizational policies, 214, 292, 321 Organizational sciences, 344, 346, 348–351, 358, 364 Other-focused tactics, 233, 241 Other-rating, 111, 112, 118 Out-group, 322, 323, 326, 331 Overclaiming, 34 P Paper trail, 352 Passive candidates, 178 Passive recruitment, 181, 185, 295 Passwords, 38, 257, 275, 276, 300, 362 Personal brand, 233 Personality, 5, 8, 18, 25–28, 30–34, 38, 44–46, 48, 63, 64, 66, 67, 69–72, 74, 81, 82, 84, 88, 94, 105, 107, 108, 111, 112, 114, 117, 118, 134, 136, 139–141, 147, 158, 162, 163, 223–228, 230, 231, 233, 235, 238–244, 257–259, 300, 302, 313–315, 321, 327, 328, 348, 351, 354, 358, 359, 361, 364 psychology, 351 testing, 18 Personal social media, 19, 73, 229, 238, 239, 241, 267, 275–277, 300 374 Person-job fit, 62, 239, 242 Person-organization fit, 16 Person-situation interaction, Perspective taking, 238, 331 Pew Research Center, 162, 179, 204, 292, 308 Photos, 48, 49, 51–53, 70, 80, 84, 160, 161, 165, 169, 171, 229, 230, 238 Physical attractiveness, 51, 320 Pinterest, 158, 160, 161, 164, 167, 199, 202 Plagiarism, 359 Positive content bias, 87 Practice-research gap, 347 Predictive analytics, 128, 135, 137, 139, 142 Preexisting beliefs, 322 Pregnancy, 268, 272, 273, 352 Pregnancy Discrimination Act of 1978 (PDA), 272, 273 Prejudicial attitudes, 291, 294 Prescriptive analytics, 137–139 Principles for the Validation and Use of Personnel Selection Procedures (SIOP Principles), 147 Privacy concern, 84, 92–94, 162, 252, 275 Privacy laws, 300 Privacy orientation, 91 Privacy settings, 18, 19, 23, 46, 55, 92, 144, 146, 159, 170–172, 182, 237, 238, 240–242, 244, 254, 274–276, 317, 355 Private forums, 131, 250, 253 Private messaging, 355 Problem solving, 33, 45, 80, 117, 119 Procedural justice, 36, 250–252, 255–257, 259, 260 Productivity, 89, 94, 149, 327 Professional advancement, 355 Professional cyber-behavior, 46 Professional image, 299 Professional networking sites, 66 Professional social media, 229, 230, 239, 241 Project Implicit website, 328 Proprietary intellectual property, 347 Protected class, 21, 22, 25, 38, 51, 84, 94, 146, 214, 266, 269, 270, 273, 283, 298, 321–323, 325, 330, 332, 352, 353 Protected concerted activity, 22, 23 Psychological needs, 82, 114, 115 Psychology, 44, 45, 228, 255, 256, 258, 319, 320, 322, 331, 345, 350, 351 Psychometric properties, 5, 17, 24, 33 Psychometrics, 150 Publically available data, 129 Public forums, 131 Public self-disclosure, 48 Pymetrics, 105 Index Q Qualifications, 4, 29, 30, 50, 200, 223, 225, 274, 298, 327 Quantitative methods, 128, 131, 135 Quizzes, social media, 104 Quora, 16 R Racial homogeneity, 293 Racial identities, 294 Racioethnic minorities, 298 Range restriction, 363 Rater training, 30, 328 Realism hypothesis, 189 Realistic accuracy model, 112 Realistic job preview, 62, 182, 183, 190 Reasonable accommodation, 270 Reasonable expectation of privacy, 20, 274, 275 Reconsideration, 251 Recruitment, 4, 7–11, 45, 80, 82, 84, 85, 94, 104, 105, 107–110, 112, 116, 118, 119, 127, 138, 145, 176–178, 181–189, 191–193, 198–201, 203, 205, 206, 209–211, 213, 215, 217, 254, 258, 259, 290, 292, 293, 295–299, 301, 320, 344, 349, 352–354 Red flags, 363 References, 60, 63, 66, 74, 211, 299, 315 Referrals, 110, 118, 177, 178, 200, 213, 293, 297 Relational contexts, 65, 69, 71 Relationship orientation, 82 Relationship status, 164 Relevancy, 317, 359 Reliability, 5, 8, 17, 24–28, 33, 147, 228, 242, 345, 347, 353 Religion, 6, 21, 84, 269, 270, 319, 321, 352 RenRen, 159–161, 165, 167, 169, 170, 172, 363 Replication, 32 Reputation, 26, 83, 85, 88, 95, 115, 146, 150, 163, 215, 230, 240, 278, 290, 316, 361 VIP, 278 ResearchGate, 238 Respondeat superior, 267 Response distortion, 225, 310, 313 Restraint, 363 Resumes, 23, 60, 63, 65, 66, 68, 70, 74, 128, 136, 140, 176, 198, 201, 211, 234, 239, 298, 299, 314, 324, 329 Retention, 80, 105, 142, 357 Retweet, 178 Right to be forgotten, 277–279, 363 Right to privacy, 19, 274, 317 Role support, 104 Russia, 9, 158, 159, 166–168, 172, 362 375 Index S SafeAssign, 359 Science-practitioner model, 128 Scoring, social media data, 24, 27 Screening, 17–27, 29, 30, 32, 34, 35, 37–39, 50, 64, 105, 131, 145, 146, 148, 204, 214, 224, 232, 242, 244, 250–260, 265–267, 273, 277, 278, 280, 281, 283, 290, 298, 300, 302, 308, 319, 329, 330, 347, 348, 353, 357, 361, 362 Selection Procedural Justice Scale (SPJS), 252 Self-actualization, 312 Self-categorization theory, 319, 322 Self-censorship, 317 Self-deception, 34, 225, 309–314 Self-determination theory, 115 Self-disclosure, 73, 93, 140, 318 Self-discrepancy theory, 311 Self-enhancement, 235 Self-esteem, 81, 82, 231 Selfies, 231 Self-monitoring behavior, 49 Self-promotion, 83, 88, 225, 226, 233–235, 239, 240, 243, 244, 315 Self-quantification data, 132 Self-ratings, 111, 119 Semi-structured data, 134 Serious games, 107 Sexual harassment, 90 Sexual orientation, 22 Shared science, 345 Similarity-attraction principle, 327 Simulation, 112, 137 Sina Weibo, 159–161, 165, 167, 169, 171, 172 Site features, 355 Situational judgment tests, 114 Slang, 53, 298 Smartphones, 106, 215 Snapchat, 129, 172, 238 Social adjustment, 88 Social anxiety, 81, 91 Social bookmarking, 80 Social capital, 30, 87, 89–91, 162, 316 Social desirability, 114, 225, 310 Social Identity Theory, 319, 322 Social Information Processing Theory, 10, 291 Socialization, 90, 200 Social media, 3–11, 16–24, 29, 34–39, 44, 51, 59, 60, 64–74, 80–95, 104–116, 118, 119, 127–130, 132, 133, 137–150, 157–162, 164–169, 171, 175, 176, 179, 181, 182, 186, 190–193, 198–217, 223, 224, 226, 228–244, 250–260, 265–268, 271–284, 290–294, 296, 298–303, 308, 309, 313, 314, 318, 319, 329, 330, 344–364 addiction, 91 fatigue, 90, 92 policies, 89 team, 86 users, 10, 80, 81, 87, 92–94, 144, 158, 162, 224, 229–231, 234, 241, 274 Social network sites (SNS) presence, 4, 8, 10, 69, 80, 129, 157, 161, 172, 176–182, 184–185, 190, 191, 198, 200, 207, 209, 236, 277, 290–293, 295–298, 300, 307–333, 348, 352 Social norms, 48, 296 Social psychology, 332, 351 Social recruiting, 200, 213 Social support, 88, 229 Society for Human Resource Management (SHRM), 17, 18, 59, 141, 148, 166, 167, 290, 292, 298 Society for Industrial and Organizational Psychology (SIOP), 147, 149, 344, 352 Sony Pictures hack, 281 Special care and protection duty, 357 Sproutsocial, 203 StackExchange, 113 Standardization, 8, 17, 20, 24–26, 38, 94, 352, 359 Standardized group differences, 32, 35 State laws, 275, 276, 284, 324, 349, 356 Static profile content, 159, 164–167, 173 Status networks, 293 Status updates, 84, 130, 238, 266, 355 Stereotypes, 37, 294, 295, 298, 299, 302, 303, 320–324, 326, 327, 330–332 replacement, 331 Stigma, 294 theory, 10, 291, 293 Stored Communications Act, 20, 144, 145 Stress, 45, 90, 132, 311 Structured assessment, 30 Structured data, 133 Subtle discrimination, 295 Suitability, 294, 298, 300, 302, 354 Super-profile, 139 Supreme Court, 206, 269, 272, 284, 329, 330 Supreme Court, United States (SCOTUS), 206, 269, 272 Surface characteristics, 210 T Tablets, 106, 215 Tagging, 106, 169, 171, 207 Target, 274 audience, 83, 203, 217, 301, 352 Taxonomy, 44–46, 49 376 TechCrunch, 203 Technological expertise, 351 Technological progress, 345–347 Technology overload, 90 Temporary internet files, 132 Test anxiety, 108, 115 Test-retest reliability, 26, 27 Test-taking motivation, 84 Text analytics, 128, 136, 141, 147 Third-party contributions, 159, 168–170 Third-party screening, 20 Timeliness of information, 143 Title VII, 6, 21, 269–270, 272–274, 321, 325 Torts, 20 Trade secrets, 19 Training, 6, 17, 45, 63, 86, 104, 107, 132, 148, 259, 267, 271, 303, 316, 327, 328, 330–333, 354 Transgender, 273 Transparency, 80 Tumblr, 158, 160, 161, 164, 167, 170, 238 Turnover intentions, 110, 354 Twitter, 5, 18, 19, 21–23, 25, 29, 33, 34, 64, 71, 73, 80, 83, 95, 104, 129–131, 134, 137, 138, 140, 143, 144, 158–162, 164, 165, 167–169, 171, 176, 178–182, 184, 185, 187, 188, 190, 192, 193, 199–202, 204, 208, 210, 215, 216, 233, 236, 238, 239, 253, 255, 258, 279, 280, 283, 290, 292, 296, 355, 363 U Uncertainty, 8, 60–68, 71–74 reduction theory, 8, 60, 61 Unfriend, 237 Uniform Guidelines for Employee Selection (Uniform Guidelines), 28, 30–32, 35, 37, 147, 149, 302 United Arab Emirates, 281–282 United Kingdom, 158, 284 United States (USA), 158, 159, 166, 168, 226, 266, 268, 278, 279, 281, 284, 344, 349, 353, 362–363 Unstructured data, 133 User generated content, 106, 308 V Validity, 5, 8, 17, 22, 24, 25, 28–33, 35–38, 44, 46, 81, 94, 105, 111–116, 140, 142, 143, 147, 150, 211, 224, 228, 242, 243, 267, 302, 309, 313, 318, 327, 328, 331, 345, 347, 348, 353, 358, 362, 363 Index construct, 29–31, 105, 114, 115, 147 content, 28–29, 35 criterion-related, 22, 31–32, 35, 36, 38, 113–116, 147 face, 115, 116 incremental, 31, 33, 111, 147, 353, 358 Values, 16, 30, 47, 70, 81, 95, 108, 185, 190, 191, 201, 225, 227, 233, 235, 236, 239, 240, 295–297, 317, 327, 345, 358, 363 Vernacular, 298 Videos, 95, 129, 130, 161, 165, 182, 214, 229 games, 104, 164 Vigilance-related tasks, 354 Vine, 172 Virtual groups, 324 Vkontakte, 159 Voice, 36, 83, 176, 250, 251 Voluntary on-the-job learning behaviors, 90 W War for talent, 177 Warranting value, 67, 68, 73, 74 Web browsing histories, 132 Web scraping software, 359 Webtrends, 204 Web 2.0, 3, 5, 106, 128, 132 Well-being, 87, 88, 115, 229, 295, 310 Wikipedia, 214 Women, 92, 162, 166, 272, 291, 293, 294, 297, 354 Work context, 355 Work-family conflict, 90 Workplace behaviors, 8, 44, 45 Workplace norms, 62 Work-related social media content, 355 Work-related Social Media Questionnaire (WSMQ), 94 Work sample test, 352 Written permission, 38 X Xing, 66, 68, 73 Y YouTube, 71, 130, 214 Z Zenefits, 16, 18 Zerply, 66 .. .Social Media in Employee Selection and Recruitment Richard N Landers • Gordon B Schmidt Editors Social Media in Employee Selection and Recruitment Theory, Practice, and Current Challenges... Social Media and Employee Recruitment: Chasing the Run Away Bandwagon 175 Yalcin Acikgoz and Shawn M Bergman 10 How to Stay Current in Social Media to Be Competitive in Recruitment and Selection. .. Hartwell, and Jonathan Powers Comparing the Social Media in the United States and BRIC Nations, and the Challenges Faced in International Selection 157 Brandon Shields and Julia Levashina Social