Mediator-moderator, innovation of mobile CRM, e-service convenience, online perceived behav-ioral control and reuse online shopping intention
Bibliographic record
Abstract
Stimulating customers’ reuse of online services, such as online shopping, is integral for companies. Consequently, this study assessed the effects of mobile customer relationship management on reuse online intention and the impact of mobile customer relationship management on service convenience. Additionally, the study analyzed the effect of service convenience on reuse online intention, the mediating role of service convenience between mobile customer relationship management and reuse online intention and the moderating role of online perceived behavior control between service convenience and reuse online intention. This study utilized two theories: the technology acceptance model and planned behavior. A total of 249 responses from customers were analyzed with Smart Partial Least Squares. According to the study, mobile customer relationship management positively impacts service convenience and reuse online intention. Additionally, service convenience mediated the connection between mobile customer relationship management and reuse of online intention, and online perceived behavioral control moderated the association between service convenience and reuse online intention. The study focused on consumers’ motivations regarding reuse online services intention. The goal here is to aid organizations in the implementation of service convenience and innovative online strategies and applications that provide services to consumers.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".