LinkedIn’s dilemma: navigating stress and well-being on professional networking platforms
Bibliographic record
Abstract
Purpose Universities are increasingly encouraging students to join LinkedIn, a professional networking site (PNS), to enhance their employability prospects. Our study explores the double-edged sword of LinkedIn use among university students with a focus on its contrasting psychological impacts of stress and well-being. Design/methodology/approach Drawing on self-determination theory (SDT), conservation of resources theory (CORT), and recent social media research, this study proposes a theoretical model to explain users’ motivations for LinkedIn use, their experiences of LinkedIn-induced stress and well-being and how users deal with these experiences. Our model was tested via a survey of 221 undergraduate students and the use of structural equation modeling. Findings Results indicate that LinkedIn-induced well-being, stemming from the digital support of students’ basic psychological needs for autonomy, belongingness and competence, enhances their intrinsic motivation to engage with the platform. However, LinkedIn is also found to generate stress – driven by excessive demand and privacy threats – which undermines intrinsic motivation. Furthermore, LinkedIn well-being is found to be a personal resource that students leverage to manage this stress. Originality/value This study examines students’ experiences on LinkedIn, a PNS that has received less scholarly attention than hedonic social networking sites. Using SDT and CORT, we highlight the coexistence of stress and well-being on non-compulsory, utilitarian PNSs like LinkedIn. We further demonstrate how LinkedIn-derived well-being helps students manage LinkedIn technostress, addressing a key research gap, as few studies explore how social media users mitigate stress through positive mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".