Online job vacancy attractiveness: Increasing views, reactions and conversions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F22%3A00127160" target="_blank" >RIV/00216224:14560/22:00127160 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S156742232200076X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S156742232200076X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.elerap.2022.101192" target="_blank" >10.1016/j.elerap.2022.101192</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Online job vacancy attractiveness: Increasing views, reactions and conversions

Popis výsledku v původním jazyce

E-commerce development has not bypassed the labor market, and employers seeking to hire are posting vacancies on specialized online platforms. Operators of such online platforms and employers are assessing the attractiveness of job vacancies, as they are interested in making online job vacancies (OJVs) more attractive to job seekers, i.e. to increase the number of views of the posted job ad, the number of times job seekers submit an application form and conversions (the ratio of the two). However, given that job types and job seekers are extremely heterogeneous, it is difficult for employers and specialized online platform operators to design attractive job ads. In collaboration with a leading OJV platform in Slovakia, we study whether machine-learning methods can improve the predictions of OJV attractiveness on a sample of 32482 OJVs that offer as many as 883 job features. Our study shows that, as opposed to various linear models, considerable prediction improvements can be achieved using random forest, which exploits possible nonlinear relationships between combinations of explanatory variables and measures of attractiveness. Based on this insight, we perform a statistical evaluation of key variables of importance. We find that the job classification, job benefits, and variables related to a simple morphological description of the job and job title are relevant. The results of this study can help the operators of specialized job vacancy platforms and employers improve their job ads to attract more job seekers.

Název v anglickém jazyce

Online job vacancy attractiveness: Increasing views, reactions and conversions

Popis výsledku anglicky

E-commerce development has not bypassed the labor market, and employers seeking to hire are posting vacancies on specialized online platforms. Operators of such online platforms and employers are assessing the attractiveness of job vacancies, as they are interested in making online job vacancies (OJVs) more attractive to job seekers, i.e. to increase the number of views of the posted job ad, the number of times job seekers submit an application form and conversions (the ratio of the two). However, given that job types and job seekers are extremely heterogeneous, it is difficult for employers and specialized online platform operators to design attractive job ads. In collaboration with a leading OJV platform in Slovakia, we study whether machine-learning methods can improve the predictions of OJV attractiveness on a sample of 32482 OJVs that offer as many as 883 job features. Our study shows that, as opposed to various linear models, considerable prediction improvements can be achieved using random forest, which exploits possible nonlinear relationships between combinations of explanatory variables and measures of attractiveness. Based on this insight, we perform a statistical evaluation of key variables of importance. We find that the job classification, job benefits, and variables related to a simple morphological description of the job and job title are relevant. The results of this study can help the operators of specialized job vacancy platforms and employers improve their job ads to attract more job seekers.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Electronic Commerce Research and Applications

ISSN

1567-4223

e-ISSN

1873-7846

Svazek periodika

55

Číslo periodika v rámci svazku

September - October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000863311700002

EID výsledku v databázi Scopus

2-s2.0-85136557119