Date of Award

Summer 2023

Degree Type

Restricted to Claremont Colleges Dissertation

Degree Name

Psychology, PhD


School of Social Science, Politics, and Evaluation

Advisor/Supervisor/Committee Chair

Stephen Gilliland

Dissertation or Thesis Committee Member

Michelle Bligh

Dissertation or Thesis Committee Member

Michael Campion

Dissertation or Thesis Committee Member

Stewart Donaldson

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2023 Benjamin Falls


applicant reactions, asynchronous video interviews, data privacy concerns, Organizational justice

Subject Categories

Organizational Behavior and Theory


Advancements in technology have transformed the landscape of organizational selection systems. In order to remain competitive and identify top talent, organizations have increasingly adopted new tools like automated interviews and evaluations to help in the decision-making process (Jaser et al., 2022). One such tool is asynchronous video interviews (AVIs), which offer the benefits of streamlining the hiring process and efficiently reviewing a large pool of candidates (Saurabh, 2022). However, applicant reactions to these systems have generally been negative, with concerns that automation may negatively impact perceptions of fairness (e.g., Langer et al., 2021). Explanations have been found to be a useful approach for improving applicant reactions, however, not all explanations are equally effective (Gilliland and Steiner, 2012; Langer et al., 2021). Therefore, this study aims to test Gilliland's (1993) model of applicants' reactions to employment selection systems in the context of AVIs, specifically focusing on the aftermath of receiving a negative selection decision. Data were collected from 380 individuals in the USA and the UK through online crowdsourcing platforms. These participants took part in a brief AVI session consisting of three interview questions. Subsequently, all participants received the same negative selection decision, accompanied by two different “should” explanations tailored to address the should counterfactual for each condition (Folger & Cropanzano, 2001). The strength-based explanation focused on aspects of the automated interviewing and evaluation that are considered strengths of the system, such as its consistency in evaluating applicants and efforts to mitigate bias (e.g., Noble et al., 2021). Conversely, the weakness-based explanation focused on addressing the system's shortcomings and emphasized features that could assist applicants in showcasing their KSAOs. The explanations were manipulated using a 2 (strength-based vs. no strength-based) x 2 (weakness-based vs. no weakness-based) design, such that individuals received either a strength-based, weakness-based, combined, or no additional explanation. The combined explanation was found to indirectly impact organizational attraction, pursuit intentions, and recommendation intentions through perceptions of overall procedural justice and interpersonal treatment. There were no significant effects found for litigation intentions and social media intentions. Additionally, this study examined the moderating roles of data privacy concerns and power distance in the relationships between fairness perceptions and applicant reactions. Data privacy concerns were found to moderate several justice-outcome relationships, whereas power distance did not demonstrate a moderating effect on these relationships. Overall, this study underscores the significance of providing more comprehensive rejection letters to enhance applicant reactions to AVIs and highlights the importance of considering data privacy concerns within the literature on applicant reactions.