Pre-entry promissory signalling in digital recruitment: Evidence from LinkedIn job advertisements in global hotel brands

Addressing limited research on promissory digital recruitment communication, this study examines LinkedIn job advertisements as pre-entry promissory communication texts through which organisations communicate explicit and implicit employment promise signals. Drawing on a qualitative interpretive approach and reflexive thematic analysis, the study analyses 100 LinkedIn job advertisements from ten selected global hotel brands. The findings identify six recurring categories of employment promise signals: belonging and identity, growth and mobility, rewards and security, wellbeing, purpose and meaning, and prestige and work environment. These signals are communicated through distinct discursive framing strategies and are theoretically organised into six implied employment-relationship expectation domains. The study proposes the Digital Recruitment Promise–Expectation Signalling Model (DR-PESM), a bounded, empirically grounded conceptual model integrating identified promise signals and discursive framing with theoretically proposed applicant interpretation and implied expectation domains. The study contributes to signalling theory, psychological contract scholarship, and digital recruitment communication by showing how LinkedIn job advertisements can make employment-relationship meanings communicatively available before organisational entry and guides future applicant-focused research.

Authors

Institutions

Publication Details

Journal
Social Sciences & Humanities Open
Published
2026-11-09
DOI
https://doi.org/10.1016/j.ssaho.2026.103974
Primary Topic
Employer Branding and e-HRM
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Pre-entry promissory signalling in digital recruitment: Evidence from LinkedIn job advertisements in global hotel brands

Malik Muhammad Sheheryar Khan, Rashid Ashraf, AbdulQuddus Mohammed, Syed Afzal Shah et al.
Social Sciences & Humanities Open
Employer Branding and e-HRM
article

Pre-entry promissory signalling in digital recruitment: Evidence from LinkedIn job advertisements in global hotel brands

Malik Muhammad Sheheryar Khan, Rashid Ashraf, AbdulQuddus Mohammed, Syed Afzal Shah, Said Badreddine, Hamsa Al Ammari
article en

Abstract

Addressing limited research on promissory digital recruitment communication, this study examines LinkedIn job advertisements as pre-entry promissory communication texts through which organisations communicate explicit and implicit employment promise signals. Drawing on a qualitative interpretive approach and reflexive thematic analysis, the study analyses 100 LinkedIn job advertisements from ten selected global hotel brands. The findings identify six recurring categories of employment promise signals: belonging and identity, growth and mobility, rewards and security, wellbeing, purpose and meaning, and prestige and work environment. These signals are communicated through distinct discursive framing strategies and are theoretically organised into six implied employment-relationship expectation domains. The study proposes the Digital Recruitment Promise–Expectation Signalling Model (DR-PESM), a bounded, empirically grounded conceptual model integrating identified promise signals and discursive framing with theoretically proposed applicant interpretation and implied expectation domains. The study contributes to signalling theory, psychological contract scholarship, and digital recruitment communication by showing how LinkedIn job advertisements can make employment-relationship meanings communicatively available before organisational entry and guides future applicant-focused research.

Social Sciences & Humanities OpenVol. 14
Higher Colleges of Technology (AE)
Employer Branding and e-HRM
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon