The Role of Self-Threat in the Decision to Use Algorithm Hiring Aids
Bibliographic record
Abstract
Given their accuracy, reliability, and efficiency, algorithms are now a fundamental part of many decision-making processes in both personal and professional domains (Castelo et al., 2019; Prahl & Swol, 2016). They facilitate consumers’ decisions by providing information, suggestions, recommendations, or candidates (Hou & Jung, 2021). However, researchers find that employees are reluctant to use algorithm decision aids, referred to as algorithm aversion (Burton et al., 2020; Dietvorst et al., 2015). The current paper explores the role of self-threat in explaining and overcoming algorithm aversion within a hiring context. We reason that many employees perceive algorithms as threats to their self-concepts because these aids threaten core components of their working selves, such as autonomy, job roles, expertise, status, and job security. As a result, employees respond with a defensive aversion towards these algorithms. Through a randomized experiment, we show that when algorithm decision aids do not pose a threat to the self, employees are more likely to accept these aids and use them. This has implications for the literature on algorithm aversion because we proposed a new way of looking at the underlying cause of this aversion. Our paper also has practical implications as organizations can take steps when introducing algorithm aids to ensure they are not perceived as threats by employees, which can increase their acceptance and usage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".