Exploring Impact Factors of Mobile Instant Messenger Users’ Continuance Intention
Bibliographic record
Abstract
Combining uses and gratifications theory and one construct, that is attitude, from the theory of planned behavior, led to the creation of the research model in this study.Structural equation modeling was applied to analyze data from 414 WeChat users.In this research, we aimed to explore the direct impacts of various gratifications on attitude regarding the use of mobile instant messenger.It was also sought to explore the direct and indirect influences of various gratifications on the continuance intention to use mobile instant messenger.According to the results, attitude is essential in deciding mobile instant messenger users' intention to continue using the service.Additionally, results show that process gratification, as represented by entertainment, and technology gratification, as represented by convenience are two main direct predictors of continued intention to use WeChat.Results also show that attitude acts as a full mediator between process gratification (entertainment), technology gratification (media appeal, social presence), and continuance intention of WeChat users.Attitude partially mediates the relationship between technology gratification (convenience) and continuance intention of WeChat users.Attitude is directly influenced by process gratification (entertainment), as well as technology gratification (convenience, media appeal, social presence).This research helps with grasping the inherent connection mechanism between various gratifications and continuance intention.There are a number of important theoretical and practical implications that emerge from this study.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".