Social media profiles, recruiters and recruitment decisions

Last registered on January 28, 2022

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Pre-Trial

Trial Information

General Information

Title

Social media profiles, recruiters and recruitment decisions

RCT ID

AEARCTR-0008793

Initial registration date

January 28, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

January 28, 2022, 10:31 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

United States of America

Region

Online

Primary Investigator

Name

Yuval Ofek-Shanny

Affiliation

Friedrich-Alexander-Universität Erlangen-Nürnberg

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Other Primary Investigator(s)

PI Name

Veronika Grimm

PI Affiliation

Friedrich-Alexander-Universität Erlangen-Nürnberg

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PI Name

Edmund Baker

PI Affiliation

Friedrich-Alexander-Universität Erlangen-Nürnberg

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Additional Trial Information

Status

In development

Start date

2022-02-01

End date

2022-06-01

Keywords

Behavior, Health, Labor

Additional Keywords

social media, recruitment, mental health, honesty, discrimination

JEL code(s)

J71, M51

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

The importance of social media in recruitment is increasing. More and more people are open about many aspects of their personal life in their SM profiles and evidence from employers surveys and correspondence studies suggests data from social media is increasingly used and increasingly important in the hiring process.
However, correspondence studies do not identify the individual making the decision, and so, are limited in their ability to describe the mechanisms behind this result.
In our study, we use an experiment simulating the screening of job applicants to elucidate how the recruiter evaluation of the candidate is affected by the interaction between recruiter characteristics such as gender, education, and attitudes toward the social media, applicant characteristics available through social media profiles and lack of social media profile.

External Link(s)

Registration Citation

Citation

Baker, Edmund, Veronika Grimm and Yuval Ofek-Shanny. 2022. "Social media profiles, recruiters and recruitment decisions." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.8793-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-02-01

Intervention End Date

2022-06-01

Primary Outcomes

Primary Outcomes (end points)

Application Score

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Time spent on an application, Time spent on CV, time spent on SM, note length, questionnaire information

Secondary Outcomes (explanation)

While we don't measure time on CV and time on SM data separately, we intend to use the recording of the mouse click to give proxies for these measures.

Experimental Design

Subjects are asked to read a job description for an office manager and then to assess a number of randomly generated combinations of CVs and SM data that vary according to work experience, education, interests, and gender. Subjects are asked to give each CV a score from one to ten. SM information includes - Control SM profile, Bad SM profile, Mental health issues, Employment gap, and lack of SM profiles.

Experimental Design Details

Randomization Method

Randomization by a computer program.

Randomization Unit

application

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

1500

We intend to start with 100 participants, make sure the output is technically correct and then continue to the rest of the experiment.

Sample size: planned number of observations

1500

Sample size (or number of clusters) by treatment arms

1500X Control SM data
1500X Bad SM data
1500X Mental health issues SM data
1500X Gap (as in CV) SM data
1500X Gap (different from CV) SM data
1500X No SM data

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We calculate a power of 80% for detecting a 0.36 effect size (units: applicant score from 0 to 10) (Given Bonferroni correction for five hypotheses.)

Supporting Documents and Materials

IRB