A Complementary Resource Bundle as an Antecedent of E-Channel Success in Small Retail Service Providers
Bibliographic record
Abstract
This study proposes an e-service resource bundle (E-SRB) as an antecedent of electronic service channel (e-channel) success in small retail service providers. The E-SRB indicates a collection of three resources: e-market acuity, e-IT competence, and e-service agility. Given the interdependence of these three resources in delivering quality e-services, the authors hypothesize about their complementarity and its positive effect on performance. The results of this structural equation modeling using survey data show support for the proposed hypotheses, demonstrating that the E-SRB positively influences e-channel performance. The performance impact is not limited to perceived financial performance but extends to self-reported dollar-based sales and profits. These results have theoretical implications when it comes to linking e-service quality to financial performance. They also carry managerial implications for small-scale e-retailing, where limited resources can seriously impede the full use of the e-channel. One of these implications concerns what resources are necessary and how to allocate them in order to improve an e-service system. In the end, this study suggests that managers should understand the interrelationships that might exist among resources that collectively influence performance.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".