Explaining social networking sites’ use continuance from employees’ perspectives
Bibliographic record
Abstract
Purpose This paper aims to better comprehend the psychological elements that drive the adoption of social networking sites (SNS). The paper attempts to explain the reasons why people sustainably use social networking websites in the workplace and how this happens. Design/methodology/approach Using a survey to collect data that was analyzed using structural equation modeling by applying the partial least squares technique. Findings The results indicated that SNS use continuance was due more to habit rather than established perceived and normative beliefs such as satisfaction and social norms. Research limitations/implications The authors recommend that the model in the study be tested in other technology environments to evaluate the external validity of the research study. The research was based on an unspecific platform, but each SNS may have its singularity that should merit further consideration. Practical implications Peers or coworker influences were noticeable in shaping one’s normative beliefs to continue using SNS in the organization. In this regard, it will be interesting to identify the mechanisms that raise the awareness of SNS in the employees’ social networks in the organization. Specifically, it will be an advantage to reach out to peers in promoting SNS use in the organization because they speak the same language as their fellow employees. Originality/value Despite several benefits related to SNS use in organizations, studies showed that most of these technologies are boycotted in the workplace. Although extensive studies are dedicated to understanding information and communication technology use continuance in general, this paper aims to inform both academicians interested in the use of enterprise SNS for business purposes and business actors concerned with growing SNS usage and retaining its users in their organizations. The paper will contribute to information systems continuance literature by integrating and extending two major theoretical frameworks.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".