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 bricksand-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. Keywords Dual-channel supply chains, literature review, multiple-channel retailing, bricksand-clicks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".