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Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Organizations perform better when the right candidates are hired. Through person-environment fit (P-E fit), firms can enhance recruitment and selection by ensuring appropriate fit at different levels of the construct.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of the study is to design economical shock tube. It is an instrument used for experimental investigations not only related to shock phenomena but also for the behavior of the material when it is subjected to high-speed flow. The material used here in this shock tube is stainless steel ss304 and aluminum. A shock tube consists of two sections, namely, the driver and the driven. The gas in the driven and driver is filled with atmospheric air and nitrogen, respectively, under the predominant condition.

The focus of the study is on the design and fabrication of shock tubes. a shock tube is a research tool to make an aerodynamic test in the presence of high pressure and temperature by generating moving normal shock waves under controlled conditions.

The main necessity for instrumentation in the shock tube experiment is to know the velocity of the moving shock wave from which the other parameters can be calculated. the pressure transducers are located in the shock tube in various locations to measure aerodynamic parameters in terms of pressure.

The main objective of this project work is to make an experimental setup to produce supersonic velocity with the readily available material in the market in a highly safe manner.