English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Job interviews are a fundamental activity for most corporations to acquirepotential candidates, and for job seekers to get well-rewarded and fulfillingcareer opportunities. In many cases, interviews are conducted in multipleprocesses such as telephone interviews and several face-to-face interviews. Ateach stage, candidates are evaluated in various aspects. Among them, gradeevaluation, such as a rating on a 1-4 scale, might be used as a reasonablemethod to evaluate candidates. However, because each evaluation is based on asubjective judgment of interviewers, the aggregated evaluations can be biasedbecause the difference in toughness of interviewers is not examined.Additionally, it is noteworthy that the toughness of interviewers might varydepending on the interview round. As described herein, we propose an analyticalframework of simultaneous estimation for both the true potential of candidatesand toughness of interviewers' judgment considering job interview rounds, withalgorithms to extract unseen knowledge of the true potential of candidates andtoughness of interviewers as latent variables through analyzing grade data ofjob interviews. We apply a Bayesian Hierarchical Ordered Probit Model to thegrade data from HRMOS, a cloud-based Applicant Tracking System (ATS) operatedby BizReach, Inc., an IT start-up particularly addressing human-resource needsin Japan. Our model successfully quantifies the candidate potential and theinterviewers' toughness. An interpretation and applications of the model aregiven along with a discussion of its place within hiring processes inreal-world settings. The parameters are estimated by Markov Chain Monte Carlo(MCMC). A discussion of uncertainty, which is given by the posteriordistribution of the parameters, is also provided along with the analysis.

Applying Bayesian Hierarchical Probit Model to Interview Grade Evaluation