The relationship between resources and market coverage in small local internet retailing
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the association of e-channel resources with market coverage of small local retailers. Design/methodology/approach – This survey-based study collects data from 147 North American florists who use web sites as e-channels. The data are analysed through a set of multi-nomial logistic regressions, and multiple analysis of covariance. Measurement validation is conducted before data analysis. Findings – Considering two resources, e-IT competence and e-service agility − both critical for providing e-services − this study finds that e-service agility is significantly associated with market coverage. This paper also verifies that small local retailers who have wider market coverage than their competitors achieve higher performance from an e-channel. However, the retailers with wider market coverage do not have higher total retail sales. Practical implications – Practitioners should carefully consider the imbalance between the cost of resource development to enable market extension and the eventual performance return. Market extension requires a high level of e-service agility, but the corresponding performance return may be inadequate. This finding advises the owners or managers of small local retailers to have a complete resource plan for the effective use of an e-channel. Originality/value – This is the first survey-based study on the relationship between resources and market coverage for small internet retailing. The study uses cross-disciplinary perspectives to examine the practices of internet retailing and the resources required, based on insights on marketing, operations management, and management information systems.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".