The Impact of Cyberloafing on Employees’ Job Performance: A Review of Literature
Bibliographic record
Abstract
Objectives: A controversy exists since long, among the researchers about the impact of cyberloafing on employees’ job performance. Some researchers study that cyberloafing distracts employees from their job descriptions; while others argue that cyberloafing is quite helpful in different ways to add quality to employees’ work. The aim of this paper is to review the past literature in order to understand the impact of cyberloafing in shaping up or destructing employees’ job performance. Moreover, the paper highlights the methodological analysis based on literature review. Future recommendations for the use of the internet by employees on office computers as well as other social media devices to enhance employees’ job performance are given. Design: An organized review of the literature (1996- 2020) from information technology, business, management, and organizational behavior studies was performed. The topics studied were about internet, World Wide Web, cyberloafing, social media, employees’ job performance, employees’ engagement, employees’ productivity, and workplace environment. Data Sources: Different research platforms such as ‘Research gate’, ‘Emerald’, ‘Jstor’, ‘Google Scholar’, ‘SCOPUS’, ‘ELSEVIER’, ‘SCIENCE DATA’, ‘Core’, ‘ScienceOpen’, ‘ERIC’, ‘Paperity’ and internet were used to read up literature. Review Methods: Different articles written in English, related to employees’ behavior and performance as well as cyberloafing and social media, were studied. Results: The review of literature showed that cyberloafing and social media significantly impact employees’ job performance. However, the findings were not consistent, and both the positive and negative impacts of cyberloafing and social media on employees’ performance were found. Conclusion: The mixed findings indicate that cyberloafing can have both the positive and negative impact on employees’ job performance. In other words, a little bit cyberloafing is important for healthy communication, innovation and productivity; while the excessive unethical use of internet was found to have adverse effects on job performance. Therefore, it is recommended not to totally suppress cyberloafing but to devise cyberloafing control strategies which are equally acceptable to both the employees and employers. Hence, by the implementation of right cyberloafing control policies, positive outcomes of cyberloafing could be achieved.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".