The use of Artificial Intelligence (AI) in the hiring process: Job applicants’ perceptions of procedural justice
Using Artificial Intelligence (AI) for hiring is becoming a common practice among the employers. However, there is a lack of empirical evidence in academia about how job applicants react to such utilization. In this empirical study, we tried to investigate the relationship between perceived (by job...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Computers in Human Behavior Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958825001289 |
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| Summary: | Using Artificial Intelligence (AI) for hiring is becoming a common practice among the employers. However, there is a lack of empirical evidence in academia about how job applicants react to such utilization. In this empirical study, we tried to investigate the relationship between perceived (by job applicants) use of AI in the hiring process (PUAHP) and job applicants’ perceived procedural justice (PPJ) using an extension of the Technology Acceptance Model (TAM) as mediators [perceived ease of use (PEU), perceived usefulness (PU) and perceived trust (PT)]. The study was conducted based on primary data collected from 418 job applicants in Bangladesh. We used SPSS 26 for descriptive statistics and partial least squares structural equation modeling (PLS-SEM) through SmartPLS 4.0. The study identified that, first of all, PUAHP has a significant positive relationship with the dependent variable, PPJ. Second, PUAHP was also found to be significantly correlated with all three mediators (PEU, PU and PT). Third, PEU, PU and PT were identified to have significant positive relationships with PPJ. Finally, regarding the mediating effects, we found that all three mediators partially mediated the relationship between PUAHP and PPJ. Although, there are sufficient empirical studies that have focused on the use of AI in the hiring process, comparatively less attention has been put so far particularly on job applicants' percpetions of such an use of AI in the hiring process. Therefore, this study can add value to the existing knowledge by reducing such lacking. Moreover, we used three components of the extended TAM as mediators which is an unique effort in academia. Furthermore, the study was conducted based on primary responses in an emerging economy which is also rare in academia. |
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| ISSN: | 2451-9588 |