Modelling the Impact of Perceived Connectivity on the Intention to Use Social Media: Discovering Mediating Effects and Unobserved Heterogeneity
Bibliographic record
Abstract
Early research examined the direct effect of perceived connectivity (PC) on intention to adopt information systems. In this study, we extend that research stream by examining the mediating effects of perceived enjoyment (PE) and perceived playfulness (PP) on the relationship between PC and the intention to use social media within the workplace. To test our proposed model, we collected data from 2,556 social media users from Australia, Canada, India, the UK, and the US. We applied the REBUS-PLS algorithm, a response-based method for detecting unit segments in PLS path modelling and assessing the unobserved heterogeneity in the data sample. Based on the strength of effects, the algorithm automatically detected two groups of users sharing the same intentions to use social media. A post hoc analysis of each group was done using contextual and demographic variables including geographic location, country, age, education and gender. Implications for practice and research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".