/pdf/technology-in-the-employment-interview-a-meta-analysis-and-1ijr29fs9q.pdf

Technology in The employmenT inTerview: A meTA-AnAlysis And FuTure reseArch AgendA

The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias.,Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias.,Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users.,This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems.,This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.

Algorithmic equity in the hiring of underrepresented IT job candidates

Implications of a changing workforce and workplace for justice perceptions and expectations

While the influence of technology and medium of assessment administration on applicant reactions has been a topic for recent discussion, scant research has considered reactions to various forms of media types in employee character-based simulations. In a series of two studies, we focused on the influence of various media types on a variety of applicant reaction criteria. In Study 1, we explored (1) differences in procedural justice perceptions and company impressions between a text and 3D animated simulation, (2) spillover mechanisms by which applicant reactions influences company perceptions, and (3) the influence of media richness on perceptions of other assessments types within a battery. In a second study, we focused on applicant reactions to and rankings of three media types (i.e., 2D animation, 3D animation, and live-action video) in a character-based simulation. Our results indicated support for a mediated effect of procedural justice rules on company perceptions. Across studies, favorable reaction ratings and rankings for 3D animation and live-action video were found.

Examining Applicant Reactions to Different Media Types in Character-based Simulations for Employee Selection

The use of game-based assessments (GBAs) is growing in selection contexts, yet test-takers have varying reactions to such assessments, which have important implications for applicant behavior. This paper reviews the literature on applicant reactions and explores classic assessment models in the context of GBAs, identifying best practice recommendations and pitfalls for enhancing the candidate experience.,A sample of 374 participants from MTurk completed cognitive GBAs and questionnaires regarding test-taker reactions (job-relatedness, perceived opportunity to perform, provision of selection information, face validity, task engagement, task motivation and willingness to refer others to the company), technology self-efficacy, and game/technology experience.,Fairness mediated the relationship between procedural justice rules and willingness to recommend the company to others. Technology self-efficacy was significantly related to fairness perceptions and procedural justice perceptions. Males had significantly higher procedural justice perceptions of GBAs than females.,The study underscores the importance of considering fairness perceptions and individual differences in reactions to GBAs. Future research should study participants within high-stakes hiring situations and examine other individual difference factors such as ethnicity.,GBAs are a viable assessment method for personnel selection, yet organizations must recognize that individuals are more likely to respond positively to GBAs if they perceive such assessments as fair and job-related, and perceive themselves as capable of performing well on the assessment.,This study tests a classic model of procedural fairness in a novel and timely assessment context.

Game on! Exploring reactions to game-based selection assessments

Applicant Reactions to Technology‐Based Selection: What We Know So Far

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kyle Mack

Author Tools

Chat about Author

Papers

Applicant Reactions to Technology‐Based Selection: What We Know So Far