ABSTRACT:
Hiring a new employee is traditionally thought to be an uncertain investment. This uncertainty is lessened by the presence of signals that indicate job fitness. Ideally, job applicants objectively signal their qualifications, and those signals are correctly assessed by the hiring team. In reality, signal manipulation is pervasive in the hiring process, mitigating the reliability of signals used to make hiring decisions. To combat these inefficiencies, we propose and evaluate SIGHT, a theoretical class of systems affording more robust signal evaluation during the job application process. A prototypical implementation of the SIGHT framework was evaluated using a mock-interview paradigm. Results provide initial evidence that SIGHT systems can elicit and capture qualification signals beyond what can be traditionally obtained from a typical application and that SIGHT systems can assess signals more effectively than unaided decision-making. SIGHT principles may extend to domains such as audit and security interviews.
Key words and phrases: signaling theory, job-application assessment, behavioral assessment, automated interviewing, virtual-agent-based interviewing, deception detection, human-risk assessment, NeuroIS, design science