Trust Lost, Ethics Found? Rethinking LinkedIn’s Algorithmic Marketing Practices

The study conducted explains the moral responsibilities of algorithms involved in the view count notifications of LinkedIn profiles while placing emphasis on the variation caused during or after buying a premium. Utilizing the Trust model by Mayer et al. (1995) and dark patterns theory as a foundation, the study employs longitudinal surveys, qualitative interviews, and comparative analysis to assess LinkedIn’s practices against Facebook, TikTok, Netflix and Spotify. The findings indicate that the platforms have serious ethical issues that stem from algorithmic manipulations and trust issues. This research advocates for increasing user knowledge of enhancement of algorithms, improvement of ethically designed AI, and ensuring compliance to such laws as GDPR. The study proposes algorithmic audits, transparency dashboards and user empowerment tools. Finally, this section concludes with future avenues for research aimed at increasing fairness and trust on digital platforms.

Authors

Institutions

Publication Details

Journal
Journal of Ecohumanism
Published
2024-09-30
DOI
https://doi.org/10.62754/joe.v4i1.6815
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trust Lost, Ethics Found? Rethinking LinkedIn’s Algorithmic Marketing Practices

Fajer Danish
Journal of Ecohumanism
Blockchain Technology Applications and Security
article

Trust Lost, Ethics Found? Rethinking LinkedIn’s Algorithmic Marketing Practices

Fajer Danish
article en

Abstract

The study conducted explains the moral responsibilities of algorithms involved in the view count notifications of LinkedIn profiles while placing emphasis on the variation caused during or after buying a premium. Utilizing the Trust model by Mayer et al. (1995) and dark patterns theory as a foundation, the study employs longitudinal surveys, qualitative interviews, and comparative analysis to assess LinkedIn’s practices against Facebook, TikTok, Netflix and Spotify. The findings indicate that the platforms have serious ethical issues that stem from algorithmic manipulations and trust issues. This research advocates for increasing user knowledge of enhancement of algorithms, improvement of ethically designed AI, and ensuring compliance to such laws as GDPR. The study proposes algorithmic audits, transparency dashboards and user empowerment tools. Finally, this section concludes with future avenues for research aimed at increasing fairness and trust on digital platforms.

Journal of EcohumanismVol. 4(1)
Royal University for Women (BH)
Openalex Percentile: Top 51%
Blockchain Technology Applications and Security
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.