The contributing factors towards e-logistic customer satisfaction: a mediating role of information technology
Bibliographic record
Abstract
In this era of industrialization, there is an increase rate of e-logistic services, which has raised the necessity to pay more attention on e-logistic customer satisfaction.E-logistic services spread so rapidly worldwide which overlook the significant segment of customer satisfaction.Therefore, the prime objective of the current research study is to develop a comprehensive framework for e-logistics customer satisfaction.Various studies highlighted the area of elogistic customer satisfaction, however, in a rare case, literature formally documented the problem of e-logistic customer satisfaction.Hence, less attention has been paid to the aspect of customer satisfaction in e-logistic.To address this gap, four hypotheses are proposed concerning the relationship of low distribution charges (LDC), low transit time (LTT), effective payment method (EPM), information technology (IT) and e-logistic customer satisfaction.An e-mail survey was preferred, and questionnaires were distributed by using simple random sampling technique.The three hundred (300) questionnaires were distributed among the elogistic users.The results of the current study found that low distribution charges, low transit time, effective payment method and information technology had a positive significant relationship with e-logistic customer satisfaction.Furthermore, information technology found main contributory element between effective payment method and e-logistic customer satisfaction.This study is contributing to the body of knowledge by developing a comprehensive framework to solve various e-logistic problems.Hence, the current study is helpful for e-logistic companies to mitigate e-logistic customer satisfaction problems.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".