Investigating the influence of omnichannel retailing on consumer decision making in the Jordanian market
Bibliographic record
Abstract
The present research investigates the use of omnichannel retailing and its influence on the Jordanian market, focusing on four important dimensions: integration, fulfilment, usability, and seamlessness. This study attempts to answer how strategies of omnichannel affect purchase-decisions and satisfaction of customers among students at private universities in Jordan as expectations for integrated purchase experiences that combine physical and digital channels continue to rise. Using Structural Equation Modelling (SEM), this study empirically examines the relationships between various dimensions of omnichannel retailing and consumer decision-making. The findings reveal that strong integration across sales channels, fast delivery options, simple interfaces, and seamless transitions significantly enhance consumer decision-making. This underscores the importance of robust infrastructure facilities, dependable information availability options, flexible fulfillment methods, user-friendly interface designs as well as a smooth customer support system which all together make for a great shopping experience. Thus, local retailers are given clear recommendations to invest in current systems by diversifying their delivery alternatives, improving mobile platforms, and using data analytics to match marketing efforts. The main focus of this paper is effective merchandising practices that can help retailers connect with consumers effectively during changing market situations. Moreover, there are several lessons from this research for businesses operating under stiff competition but with the increasing needs of modern-day consumers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".