Oxford Chapter

Oxford University Press · Book chapter · 2026

Applicant Reactions to Testing and Selection

Thirty-five years of research on how applicants experience assessment—and what changes when technology and artificial intelligence enter the process.

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Gilliland, S. W., Liu, W., & Steiner, D. D. (2026). “Applicant Reactions to Testing and Selection.” In N. Schmitt & A. M. Ryan (Eds.), The Oxford Handbook of Personnel Assessment and Selection (2nd ed., Ch. 29). Oxford University Press.

DOI: 10.1093/9780197809013.003.0029

Chapter overview

A field moves from selection fairness to a broader applicant perspective.

The chapter reviews how applicant-reactions research developed into a theoretically grounded and productive field. Organizational justice remains a major foundation, alongside research on test motivation, social psychology, technology, and applicant self-perceptions.

Across demographic groups and cultures, applicants tend to respond most favorably to work samples and interviews. They respond less favorably when selection removes personal contact, as in many asynchronous video and automated processes.

AI and selection

Consistency is valued. Impersonality is not.

Reactions to AI-enabled selection are mixed. Applicants may appreciate consistency and efficiency while worrying about privacy, opacity, loss of individual attention, and whether an automated system can recognize their full capabilities.

This tension directly informs William’s continuing research on trust, organizational identification, and human–AI selection systems.

William’s contribution

Connecting the established field to its next questions.

As a co-author, William contributed to the updated synthesis of applicant-reactions scholarship and its future direction. The chapter’s discussion of AI, personal contact, justice, and applicant outcomes provides the broader scholarly context for his master’s thesis and doctoral research agenda.

Keywords

Applicant reactionsTest fairnessOrganizational justicePost-hire behaviorArtificial intelligenceSelection technology