Job embeddedness and workplace well-being: moderating role of remoteness
Bibliographic record
Abstract
Purpose This study aims to explore job embeddedness (JE) from a stuckness perspective by nesting the principles of conservation of resources (COR) theory in the context of public energy sector organizations. The study examines the impact of on-the-job embeddedness (ONTJE) and off-the-job embeddedness (OFFTJE) on the workplace well-being of employees. Remote locations are a common feature of energy sector organizations and a significant concern for both employees and employers. The study examines the moderating role of locational remoteness in the relationship between OFFTJE and workplace well-being. Design/methodology/approach The hypothesized model was evaluated using 399 data points collected from employees of energy sector organizations through an online survey. The data for independent and dependent variables were collected at various times. The partial least squares-structural equation modeling technique was used to assess the measurement and structural model. Findings The study found that both ONTJE and OFFTJE are positively associated with workplace well-being. However, OFFTJE demonstrated a slightly stronger standardized path coefficient (ß = 0.375) compared to ONTJE (ß = 0.323), indicating its higher predictive strength in this context. In addition, remoteness was found to moderate the relationship between OFFTJE and workplace well-being, contributing to the resource caravan’s discussion in the COR theory. Practical implications The results of the study indicate that, despite its many positive effects, JE is not a cure-all. According to the complementarity perspective, employees who are embedded report lower levels of well-being when assigned to remote locations. In this context, leaders of the organization and policymakers must consider the context in which they are embedded to avoid potential unintended negative effects. Originality/value To the best of the authors’ knowledge, this is one of the initial studies to examine the relationship between embeddedness and workplace well-being, which includes both the hedonic and eudemonic perspectives. The study also includes the understudied dimension of embeddedness and OFFTJE and weaves it into the context of the study to bring out its criticality for energy and allied sectors by studying it alongside remoteness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".