What drives millennials and zillennials continuously using instant messaging? Perspective from Indonesia
Bibliographic record
Abstract
This study proposes to explore the relationship between perceived risk, subjective norm, perceived value, and continuous usage intention via trust as a mediating variable. This research applied a survey which involved 320 IM users (160 from millennials and 160 from Zillennial) in Indonesia. The literature’s existing scales were used to operationalize the constructs proposed in this study. The analyses were conducted using partial least squares structural equation modeling (PLS-SEM) to test hypotheses. The results of the study show that there are differences in the results of the influence of perceived risk on continuous usage intention in the Millennial and Zillennial generations. The results of the study found that continuous usage intention received negative direct impact from perceived risk, positive direct effect from subjective norm, perceived value, and trust on Millennial. In addition, continuous usage intention received negative and not significant direct impact from perceived risk, positive direct effect from subjective norm, perceived value, and trust on Zillennial. The results are useful for instant messaging management into formulating strategies to retain their users in Indonesia. These findings provided theoretical and managerial contributions as well as future research directions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".