Is telework effective for organizations?
Bibliographic record
Abstract
Purpose Telework is an alternative work relationship with demonstrated positive benefits for individuals and society, yet it has not been implemented with enthusiasm by most organizations. This could be due to the lacking, consolidated evidence for management regarding whether or not telework is a good thing for the firm. The purpose of this paper is to integrate multidisciplinary literature that reports effects of telework on organizational outcomes with the aim of providing a clearer answer to the question: is telework effective for organizations? Design/methodology/approach Meta‐analytical methods were used, beginning with an interdisciplinary search for effect sizes in eight databases. Limited to scholarly journals and dissertations, results included 991 articles scanned for inclusion criteria. The independent variable is telework, measured as a dichotomous variable. Dependent variables are outcomes of interest to organizations: productivity, retention, turnover intention, commitment, and performance. In total, 22 studies were double coded and meta‐analyzed using Hunter and Schmidt's approach, followed by five exploratory moderator analyses: level of analysis, level of the employee, response rate, proportion of females, and country of the study. Significant results are discussed. Findings Review and meta analysis of 32 correlations from empirical studies find that there is a small but positive relationship between telework and organizational outcomes. Telework is perceived to increase productivity, secure retention, strengthen organizational commitment, and to improve performance within the organization. In other words, it is indeed beneficial for organizations. All five hypotheses are supported. H 1 (productivity), rc=0.23 (k=5, n =620), (95% CI=0.13−0.33). H 2 (retention), r =0.10 (k=6, n =1652), (95% CI=0.04−0.16). H 3 (commitment), r =0.11 (k=8, n =3144), (95% CI=0.03−0.18); moderator analysis shows sample age is significant (F(1,4)=4.715, p <0.05, R2=0.80). H 4 (performance), r =0.16 (k=10, n =2522). H 5 (organizational outcomes), r =0.17 (k=19, n =5502), (95% CI=0.1−0.20). Originality/value To the authors' knowledge, this is the first meta‐analysis of telework research at the organizational level, providing a unique contribution to the field in filling the gap between research on effects to the individual and society. Additional contributions resulted from the moderator analyses: first, in finding that the relationship between telework and performance is moderated by whether or not the sample was one individual per firm, or many individuals from one; and second, in finding that the relationship between telework and organizational commitment is moderated by age. Thus, the paper provides unique contributions with both scholarly and practical implications.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Meta-analysis using synthesis to answer whether telework benefits organizations; a management question, not a study of synthesis methods.
This uses meta-analysis to answer a substantive question about telework and organizations.
Uses meta-analysis to answer whether telework helps organizations; method-vs-object trap, domain management question.
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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".