Strategy for striking the omnichannel balance in Retail 4.0
Bibliographic record
Abstract
Purpose The rise of Industry 4.0 led to digitally-enabled evolutionary and radical changes in all branches of the retail industry, resulting in the emergence of the distinct term “Retail 4.0”. Within this paradigm, particular emphasis is placed on forming a balanced system of omnichannel sales and customer service, allowing reaching a synergistic effect in the face of constant changes, turbulence and uncertainty in the business environment. The main objective of this study is to offer and justify a practical strategy for optimal utilization of sales channels and customer service provision within the Retail 4.0 paradigm. Design/methodology/approach The conceptual argument of the study is based on the review of the literature and illustrative case studies Findings The decision-making model proposed in this study provides a roadmap for retailers. It underscores the need for a data-driven approach, where decisions are informed by real-time analytics and customer insights. This model also advocates for a flexible yet structured approach to managing various sales channels, ensuring that each channel complements and enhances the other. Originality/value The study offers and justifies an original five-stage process model for forming a balanced system of omnichannel sales and customer service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".