A review of bricks-and-clicks dual-channels literature: trends and opportunities
Bibliographic record
Abstract
The proliferation of e-commerce has changed the retail market landscape significantly in most industries. Due to fiercer competition, many bricks-and-mortar retailers established an online channel and have become bricks-and-clicks. On the other hand, some e-tailers are adding a conventional channel to their portfolio by launching physical stores or forming a partnership with traditional retailers. Deciding on whether or not to adopt a dual-channel policy and how to operate it, in the presence of online sales, present multiple and unique research challenges. More than two decades of research have accumulated and there is a need for a comprehensive look at what has been achieved and where more research is required. Recent reviews in the field are limited in scope and depth: they focus on fulfillment and distribution issues only and cover a limited portion of the literature, less than 60 journal papers versus more than 260 papers in this survey. In this paper, we offer a structured literature review (SLR) on bricks-and-clicks dual-channels. We contribute to the literature in three main areas: (1) We present a comprehensive look at all operational aspects of bricks-and-clicks dual supply chains, (2) provide a systematic discussion of common demand modeling functions, and (3) analyze the reviewed literature, identify recent research trends and opportunities, and illustrate how existing research can be used to address up-to-date challenges in the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".