INERTIA AND ONLINE TRUST LEADING FACTORS AFFECTING CONSUMER REPEAT-PURCHASE INTENTION IN E-COMMERCE (MODERATING ROLE OF ALTERNATIVE ATTRACTION)
Bibliographic record
Abstract
The explosion of E-commerce activities required academia and industry to recognize the key factors of customer online repurchase intention. The purpose of this study is to determine the primary drivers of customer repeat-purchase intention in the aspects of trust and inertia. The study proposed an integrated model of online repeat-purchase intention. With the prevalence of internet, there is an interactive relationship build between consumer and online channels and now it has been paid much attention by practitioners and researchers. In E-Commerce, Online shopping is the innovation which comes through internet and online shopper repeat purchase intention is affected by many factors like; consumer inertia and online trust. Managers and Academics know fairly little about how the collective effect of consumer inertia and online trust affects the repeat-purchase intention. Based on the perspective of Theory of Reasoned Action (TRA) and PPM Model, the aim of study is to explore the relationship between consumer inertia, online trust and repeat-purchase intention in E-Commerce world. The moderating role of alternative attraction is also analyzed in the study and for the collection of data, survey analysis is conducted. Sample of study is 4100 customers of major cities of Pakistan (Rawalpindi, Islamabad, Lahore, Peshawar, Karachi, Quetta). This study found a result which is somewhat consistent with literature. Overall, it is concluded that consumer inertia and online trust has significant impact on consumer repeat purchase intention. Along with this, it is originating that alternative attraction moderates this relationship. Finding of this research will enable to suggest the website operators regarding management of different strategies for online consumer. Limitation, practical implication and future direction are provided at the end of 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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".