Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-15T20:49:00.589Z Has data issue: false hasContentIssue false
  • English
  • Français

Gaps in research and practice in social media-facilitated practices at work

Published online by Cambridge University Press:  22 September 2021

Julia Hylton WhitakerOpen the ORCID record for Julia Hylton Whitaker[Opens in a new window],
Amber Nicole SchroederOpen the ORCID record for Amber Nicole Schroeder[Opens in a new window] and
Traci Megan BrickaOpen the ORCID record for Traci Megan Bricka[Opens in a new window]
Julia Hylton Whitaker*
Affiliation:
The University of Texas at Arlington–Department of Psychology
Amber Nicole Schroeder
Affiliation:
The University of Texas at Arlington–Department of Psychology
Traci Megan Bricka
Affiliation:
The University of Texas at Arlington–Department of Psychology
*
*Corresponding author. Email: julia.whitaker@mavs.uta.edu
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

Information

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 14 , Issue 3 , September 2021, pp. 436 - 440
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Society for Industrial and Organizational Psychology

One of the most significant information and communication technologies (ICTs) affecting society today is online social media (Wharton, 2019). Yet, as is the case with most ICT research (Chamorro-Premuzic et al., Reference Chamorro-Premuzic, Winsborough, Sherman and Hogan2016), empirical work on social media within the workplace often lags behind the fast pace of the business world. In addition, as social media research is conducted across a wide array of scientific disciplines (Wilson et al., Reference Wilson, Gosling and Graham2012), research fragmentation is likely. As social media plays a key role in a number of distinct organizational practices that can affect both organizations and individuals (SHRM, 2016), the purpose of this paper is to build on Hu et al.’s (Reference Hu, Barber, Park and Day2021) work not only by considering other ways that social media use is relevant to the workplace beyond its role in contributing to counterproductive behavior but also, more specifically, by discussing how two ICT perspectives (i.e., the technology behavior and technology experience perspectives) have produced research fragmentation and gaps between science and practice within this domain.

Adapting Hu et al.’s (Reference Hu, Barber, Park and Day2021) taxonomy to organization-level practices, we define the technology behavior perspective as the consideration of how technology can be used within organizational practices to predict individual behavior, whereas the technology experience perspective refers to the examination of the psychological experiences resulting from technology-facilitated organizational practices. Using the technology perspective framework, the current paper highlights challenges and opportunities for both researchers and practitioners related to three distinct social-media-facilitated organizational practices—job candidate vetting, employee monitoring, and brand management—each practice of which is designed to focus on a select group of individuals—job candidates, incumbents, or customers and online freelancers, respectively.

Social media-facilitated job candidate vetting

Social media has influenced the way in which some organizations screen job candidates, as cybervetting approaches (i.e., web-based job applicant screening) are becoming increasingly common (SHRM, 2016). Related to the technology behavior perspective, cybervetting studies that examine whether social media-based assessments can predict individual characteristics or future behaviors often use either (a) subjective human assessment techniques in which raters form holistic judgments or (b) data-mining approaches where specific social media content is linked to human attributes or behaviors. Human assessment techniques have often been the focus of research in industrial-organizational (I-O) psychology. Some studies have suggested that rater-derived cybervetting assessments can be used to determine candidates’ personality profiles and predict job performance (Kluemper et al., Reference Kluemper, Rosen and Mossholder2012), whereas other research has suggested the opposite (Van Iddekinge et al., Reference Van Iddekinge, Lanivich, Roth and Junco2016). Data-mining approaches (e.g., machine learning) for predicting individual attributes are more commonly seen within computer science research (Kosinski et al., Reference Kosinski, Stillwell and Graepel2013) and may be more effective than human-based techniques (Youyou et al., Reference Youyou, Kosinski and Stillwell2015).

Although cybervetting research that focuses on the technology experience perspective is more limited, several studies have indicated that job applicants may perceive cybervetting to be a privacy invasion, which can produce stress and injustice perceptions and adversely affect outcomes such as organizational attraction and intentions to litigate (Stoughton et al., Reference Stoughton, Thompson and Meade2015). Notably, applicant and employer perspectives on cybervetting often diverge (i.e., applicant opinions are generally more negative; Berkelaar, Reference Berkelaar2014), thereby highlighting the importance of considering all relevant stakeholder attitudes. Additionally, research has indicated that data mining-based cybervetting approaches may have the potential to produce discriminatory hiring decisions (Weissmann, Reference Weissmann2018), as such techniques can rely on seemingly job-irrelevant information to maximize predictive potential (e.g., social media content indicating one’s appreciation for curly fries has been linked to intelligence, Kosinski et al., Reference Kosinski, Stillwell and Graepel2013). Thus, more integrative approaches that incorporate both human judgment and algorithmic techniques into cybervetting practices are warranted (Willford, Reference Willford2019).

Social media-facilitated employee monitoring

Organizations also use social media to monitor employees’ online behavior. Notably, as there is limited research that is specifically focused on social media-facilitated monitoring, we can extrapolate from research on electronic performance monitoring (ePM) in general to draw links to the technology behavior perspective. Namely, ePM is often used to incentivize performance, protect organizations from liability, and enhance organizational surveillance mechanisms (Ravid et al., Reference Ravid, Tomczak, White and Behrend2020). Looking specifically at ePM of social media, there have been many publicized job terminations resulting from social media activity (see, e.g., Toropin & Asmelash, Reference Toropin and Asmelash2020). Practitioners have argued that monitoring social media is part of an organization’s responsibility (Bell, Reference Bell2018), as nonwork behavior can damage an organization’s reputation (Umphress et al., Reference Umphress, Tihanyi, Bierman and Gogus2013).

From the technology experience perspective, studies have highlighted that employees view social media monitoring as an invasion of privacy (Sayre & Dahling, Reference Sayre and Dahling2016) and that it may be an infringement on employee rights that can result in legal consequences (Lam, Reference Lam2016). Social media-facilitated employee monitoring has been underinvestigated by academics, and we lack knowledge regarding how employees react to it or how it is carried out in practice (e.g., are automated data extraction techniques used to flag content, or are human resource professionals monitoring employee activity manually?). Although organizations have been favored in the majority of the federal cases that have been related to terminations that involved social media (Schroeder & Lile, Reference Schroeder and Lile2016), the legal boundaries surrounding social media-facilitated ePM are unclear, creating a somewhat gray territory (Lam, Reference Lam2016), and there is a lack of understanding regarding fair and effective policies. Thus, although organizations may practically be able to monitor employees’ social media activity, the adverse employee reactions that this practice produces could result in greater negative consequences downstream (e.g., intentions to litigate; Stoughton et al., Reference Stoughton, Thompson and Meade2015).

Social media-facilitated brand management

In addition, it is important to consider organizational use of social media to facilitate brand management. As social media marketing has been identified as a modern brand management tactic (Godey et al., Reference Godey, Manthiou, Pederzoli, Rokka, Aiello, Donvito and Singh2016), organizations are facing increasing pressure to use social media in digital marketing practices. From a technology behavior perspective, organizations are turning to social media marketing for a number of purposes, such as enhancing a brand’s reputation (Godey et al., Reference Godey, Manthiou, Pederzoli, Rokka, Aiello, Donvito and Singh2016) and promoting products or services to customers via social media influencers (SMIs; i.e., individuals who shape audience opinions via strategic social media posts; Freberg et al., Reference Freberg, Graham, McGaughey and Freberg2011). Customers are becoming increasingly averse to traditional marketing efforts (e.g., advertisements), and they instead often turn to recommendations that are made by a trusted individual, such as an SMI. Notably, SMIs can significantly influence consumer purchasing decisions (Chatterjee, Reference Chatterjee2011), and organizational use of a popular, trusted SMI can greatly benefit the brand (De Veirman et al., Reference De Veirman, Cauberghe and Hudders2017). Therefore, organizations’ use of SMIs for marketing purposes is a unique application of ICT behavior that is used to predict or influence consumer behavior.

Despite the benefits that are offered by organizations’ use of SMIs in marketing efforts, the work experiences of online freelancers, such as SMIs, can be less than ideal. More specifically, examining SMIs through a technology experience lens reveals that there are significant challenges for this sector of the workforce, including job precarity (Sutherland et al., Reference Sutherland, Jarrahi, Dunn and Nelson2020) and competition to secure work (Dunn et al., Reference Dunn, Stephany, Sawyer, Munoz, Raheja, Vaccaro and Lehdonvirta2020). Further, number of hours worked has been negatively linked to work–life balance and life satisfaction among online freelancers (Davis et al., Reference Davis, Shevchuk and Strebkov2014), demonstrating that the lack of standard work hours in such roles may be detrimental. Taken together, the technology experience perspective offers important insight into the work experiences of online freelancers (e.g., SMIs).

Conclusion

The majority of what is known about social media-facilitated organizational practices can be categorized into Hu et al.’s (Reference Hu, Barber, Park and Day2021) technology behavior and technology experience perspectives. Yet, in doing so, a number of gaps between research disciplines as well as between science and practice are brought to light. For example, disjointed cybervetting research efforts in line with the technology behavior perspective have resulted in calls for increased interdisciplinary research and scientist–practitioner collaborations that investigate the combined efforts of data mining and human judgment techniques (Willford, Reference Willford2019). For example, organizations could use data mining techniques to extract attribute-relevant content, thereby eliminating human raters’ exposure to irrelevant social media content (e.g., religious posts). In addition, many scholars caution the use of cybervetting as a selection device (Van Iddekinge et al., Reference Van Iddekinge, Lanivich, Roth and Junco2016), yet cybervetting is commonly used in preemployment screening, thereby further highlighting the gap between science and practice.

There is a lack of research that considers the technology behavior perspective with respect to social media-facilitated ePM. Moreover, science and practice in this area are not well integrated and a substantial gap between the technology behavior and experience perspectives warrants more attention. Future research should attempt to identify an appropriate balance between managing fairness and protecting the organization from liability, thereby reducing conflicting perspectives among employers and employees. Notably, as organizations increase their monitoring activities, employees are likely to engage in more online privacy management behaviors to conceal themselves from employer scrutiny (Roulin & Levashina, Reference Roulin, Levashina, Landers and Schmidt2016). Employers may then perceive the need to take more extreme measures for employee monitoring, which could exacerbate perceptions of trust violation.

Regarding brand management, there has been a primary focus on the technology behavior perspective, which has contributed to a science–practitioner gap, as well as a predominantly marketing-driven perspective in considering how social media affects stakeholders who are external to the organization. Thus, there are many opportunities for the field of I-O psychology to address technology experiences that are related to SMIs who are essential for company brand management as well as customer reactions to organizations that maintain a social media presence. For example, just as social media marketing efforts can influence customer perceptions of the brand (Godey et al., Reference Godey, Manthiou, Pederzoli, Rokka, Aiello, Donvito and Singh2016), the way in which organizations present themselves and interact with customers is also likely to affect other important business functions, such as talent acquisition.

Taken together, it is important that organizations acknowledge the advantages that social media provides to facilitate organizational practices while being cognizant of the potential disadvantages that accompany it. We caution organizations to avoid implementing ICT practices without considering the downstream effects related to how employees and relevant stakeholders experience the technology and extend the sentiments of Hu et al. (Reference Hu, Barber, Park and Day2021) by calling on researchers and practitioners alike to engage in efforts that contribute to the “defragging” of our knowledge of social media-facilitated organizational practices.

Footnotes

†

Amber Schroeder is now affiliated with Amazon. All activities relating to this article were completed prior to her change in affiliation.

References

Bell, J. D. (2018, August 24). Firing for online behavior. SHRM. https://www.shrm.org/hr-today/news/hr-magazine/0918/pages/firing-for-online-behavior-.aspx Google Scholar
Berkelaar, B. L. (2014). Cybervetting, online information, and personnel selection: New transparency expectations and the emergence of a digital social contract. Management Communication Quarterly, 28(4), 479–506. https://doi.org/10.1177/0893318914541966 CrossRefGoogle Scholar
Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New talent signals: Shiny new objects or a brave new world? Industrial and Organizational Psychology: Perspectives on Science and Practice, 9(3), 621–640. https://doi.org/10.1017/iop.2016.6 CrossRefGoogle Scholar
Chatterjee, P. (2011). Drivers of new product recommending and referral behavior on social network sites. International Journal of Advertising, 30(1), 77–101. https://doi.org/10.2501/IJA-30-1-077-101 CrossRefGoogle Scholar
Davis, S. N., Shevchuk, A., & Strebkov, D. (2014). Pathways to satisfaction with work-life balance: The case of Russian-language Internet freelancers. Journal of Family and Economic Issues, 35(4), 542–556. https://doi.org/10.1007/s10834-013-9380-1 CrossRefGoogle Scholar
De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828. https://doi.org/10.1080/02650487.2017.1348035 CrossRefGoogle Scholar
Dunn, M., Stephany, F., Sawyer, S., Munoz, I., Raheja, R., Vaccaro, G., & Lehdonvirta, V. (2020). When motivation becomes desperation: Online freelancing during the COVID-19 pandemic. https://osf.io/preprints/socarxiv/67ptf/ CrossRefGoogle Scholar
Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011). Who are the social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90–92. https://doi.org/10.1016/j.pubrev.2010.11.001 CrossRefGoogle Scholar
Godey, B., Manthiou, A., Pederzoli, D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media marketing efforts of luxury brands: Influence on brand equity and consumer behavior. Journal of Business Research, 69(12), 5833–5841. https://doi.org/10.1016/j.jbusres.2016.04.181 CrossRefGoogle Scholar
Hu, X., Barber, L K., Park, Y., & Day, A. (2021). Defrag and reboot? Consolidating information and communication technology research in I-O psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 14(3), 371–396.Google Scholar
Kluemper, D. H., Rosen, P. A., & Mossholder, K. W. (2012). Social networking websites, personality ratings, and the organizational context: More than meets the eye? Journal of Applied Social Psychology, 42(5), 1143–1172. https://doi.org/10.1111/j.1559-1816.2011.00881.x CrossRefGoogle Scholar
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences–PNAS, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110 CrossRefGoogle ScholarPubMed
Lam, H. (2016). Social media dilemmas in the employment context. Employee Relations, 38(3), 420–437. https://doi.org/10.1108/ER-04-2015-0072 CrossRefGoogle Scholar
Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126. https://doi.org/10.1177/0149206319869435 CrossRefGoogle Scholar
Roulin, N., & Levashina, J. (2016). Impression management and social media profiles. In Landers, N. R. & Schmidt, B. G. (Eds.), Social media in employee selection and recruitment: Theory, practice, and current challenges (pp. 223–248). Springer International Publishing.Google Scholar
Sayre, G. M., & Dahling, J. J. (2016). Surveillance 2.0: How personality qualifies reactions to social media monitoring policies. Personality and Individual Differences, 90, 254–259. https://doi.org/10.1016/j.paid.2015.11.021 CrossRefGoogle Scholar
Schroeder, A. N., & Lile, C. R. (2016). Legal trends in organizational online social media use. Industrial and Organizational Psychology: Perspectives on Science and Practice, 9(3), 682–689. https://doi.org/10.1017/iop.2016.71 CrossRefGoogle Scholar
Society for Human Resource Management (SHRM). (2016, January 19). Managing and leveraging workplace use of social media. https://www.shrm.org/resourcesandtools/tools-and-samples/toolkits/pages/managingsocialmedia.aspx Google Scholar
Stoughton, J. W., Thompson, L. F., & Meade, A. W. (2015). Examining applicant reactions to the use of social networking websites in pre-employment screening. Journal of Business and Psychology, 30(1), 73–88. https://doi.org/10.1007/s10869-013-9333-6 CrossRefGoogle Scholar
Sutherland, W., Jarrahi, M. H., Dunn, M., & Nelson, S. B. (2020). Work precarity and gig literacies in online freelancing. Work, Employment and Society, 34(3), 457–475. https://doi.org/10.1177/0950017019886511 CrossRefGoogle Scholar
Toropin, K., & Asmelash, L. (2020, November 30). Nurse put on administrative leave after her TikTok bragging about breaking COVID-19 rules goes viral. CNN. https://www.cnn.com/2020/11/30/us/tik-tok-nurse-viral-salem-hospital-trnd/index.html Google Scholar
Umphress, E. E., Tihanyi, L., Bierman, L., & Gogus, C. I. (2013). Personal lives? The effects of nonwork behaviors on organizational image. Organizational Psychology Review, 3(3), 199–221. https://doi.org/10.1177/2041386612473014 CrossRefGoogle Scholar
Van Iddekinge, C. H., Lanivich, S. E., Roth, P. L., & Junco, E. (2016). Social media for selection? Validity and adverse impact potential of a Facebook-based assessment. Journal of Management, 42(7), 1811–1835. https://doi.org/10.1177/0149206313515524 CrossRefGoogle Scholar
Weissmann, J. (2018, October 10). Amazon created a hiring tool using A.I. It immediately started discriminating against women. Slate. https://slate.com/business/2018/10/amazon-artificial-intelligence-hiring-discrimination-women.html Google Scholar
Wharton. (2019, July 26). The impact of social media: Is it irreplaceable? University of Pennsylvania. https://knowledge.wharton.upenn.edu/article/impact-of-social-media/ Google Scholar
Willford, J. C. (2019, July 17). Enhancing judgment: The case for human-algorithm collaboration. Society for Industrial-Organizational Psychology. https://www.siop.org/Research-Publications/Items-of-Interest/ArtMID/19366/ArticleID/3059/Enhancing-Judgment-The-Case-for-Human-Algorithm-Collaboration Google Scholar
Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of Facebook research in the social sciences. Perspectives on Psychological Science, 7(3), 203–220. https://doi.org/10.1177/1745691612442904 CrossRefGoogle ScholarPubMed
Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences–PNAS, 112(4), 1036–1040. https://doi.org/10.1073/pnas.1418680112 CrossRefGoogle ScholarPubMed