What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages

Information on a company's employment webpage sends signals about the employer image the company intends to project to applicants. Nonetheless, we know little about the content of recruitment signals sent via company employment webpages. This study develops a method to measure companies' p...

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Main Authors: THEURER, Christian P., SCHAPERS, Philipp, TUMASJAN, Andranik, WELPE, Isabell M., LIEVENS, Filip
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/6851
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7850/viewcontent/hrm.22085.pdf
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spelling sg-smu-ink.lkcsb_research-78502023-09-18T06:00:59Z What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages THEURER, Christian P. SCHAPERS, Philipp TUMASJAN, Andranik WELPE, Isabell M. LIEVENS, Filip Information on a company's employment webpage sends signals about the employer image the company intends to project to applicants. Nonetheless, we know little about the content of recruitment signals sent via company employment webpages. This study develops a method to measure companies' projected employer image attributes based on their employment webpages. Specifically, we analyze companies' projected employer image attributes by applying computer-aided text analysis (CATA) to the employment webpages of 461 Fortune 500 companies (i.e., more than 11,100 individual pages). Our results show that projected employer image attributes remain relatively stable over time. Moreover, we find relatively low levels of employer image differentiation between companies and between industries. Only a small group of companies (<20%) use distinct employer attribute signals to communicate their projected employer image. Finally, there is limited convergence between projected employer image attributes based on employment webpages and ratings on similar attributes on employer review websites. Generally, our results show that CATA is a viable method for assessing companies' projected employer image in the context of employer image management and engineering. 2022-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/6851 info:doi/10.1002/hrm.22085 https://ink.library.smu.edu.sg/context/lkcsb_research/article/7850/viewcontent/hrm.22085.pdf http://creativecommons.org/licenses/by-nc-sa/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University CATA content analysis employer branding employer image third party employment branding Business and Corporate Communications Human Resources Management Organizational Behavior and Theory
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic CATA
content analysis
employer branding
employer image
third party employment branding
Business and Corporate Communications
Human Resources Management
Organizational Behavior and Theory
spellingShingle CATA
content analysis
employer branding
employer image
third party employment branding
Business and Corporate Communications
Human Resources Management
Organizational Behavior and Theory
THEURER, Christian P.
SCHAPERS, Philipp
TUMASJAN, Andranik
WELPE, Isabell M.
LIEVENS, Filip
What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
description Information on a company's employment webpage sends signals about the employer image the company intends to project to applicants. Nonetheless, we know little about the content of recruitment signals sent via company employment webpages. This study develops a method to measure companies' projected employer image attributes based on their employment webpages. Specifically, we analyze companies' projected employer image attributes by applying computer-aided text analysis (CATA) to the employment webpages of 461 Fortune 500 companies (i.e., more than 11,100 individual pages). Our results show that projected employer image attributes remain relatively stable over time. Moreover, we find relatively low levels of employer image differentiation between companies and between industries. Only a small group of companies (<20%) use distinct employer attribute signals to communicate their projected employer image. Finally, there is limited convergence between projected employer image attributes based on employment webpages and ratings on similar attributes on employer review websites. Generally, our results show that CATA is a viable method for assessing companies' projected employer image in the context of employer image management and engineering.
format text
author THEURER, Christian P.
SCHAPERS, Philipp
TUMASJAN, Andranik
WELPE, Isabell M.
LIEVENS, Filip
author_facet THEURER, Christian P.
SCHAPERS, Philipp
TUMASJAN, Andranik
WELPE, Isabell M.
LIEVENS, Filip
author_sort THEURER, Christian P.
title What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
title_short What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
title_full What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
title_fullStr What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
title_full_unstemmed What you see is what you get? Measuring companies' projected employer image attributes via companies' employment webpages
title_sort what you see is what you get? measuring companies' projected employer image attributes via companies' employment webpages
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/lkcsb_research/6851
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7850/viewcontent/hrm.22085.pdf
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