Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the B2B e‐commerce (B2BEC) usage patterns of North American small‐ and medium‐sized enterprises (SMEs) in their supply chains, the contextual factors that influence usage patterns, and the subsequent effects of these patterns on firm performance. Design/methodology/approach The authors conducted an online survey of North American SMEs and obtained 229 responses. They utilized several statistical methods, including cluster analysis and profile analysis, to test five hypotheses. Findings The TOE framework, supplemented with interorganizational factors, provides a valid theoretical guideline to study firms' B2BEC usage patterns. Three distinct types of B2BEC usage patterns – E‐Limiteds, E‐Leaders, and E‐Laggards – emerged. Different sets of contextual factors contribute to the formation of these three patterns of B2BEC adoption. Higher levels of B2BEC usage result in stronger firm performance. Research limitations/implications Future clustering variables could be more specific. The effects of other potential contextual factors should also be explored by future studies. This study can be replicated in other countries to determine whether the findings can be generalized. Practical implications In light of the potential performance improvements that B2BEC adoption offers, managers should assess the risks associated with maintaining their current speed of e‐business deployment versus the risks associated with escalating it. Organizations that have been more reactive should consider how well or ill their sluggish approach prepares them for navigating the inevitability of increasing sophistication in supply chain management. Originality/value Limited empirical research exists on the B2BEC usage patterns of North American SMEs, the contextual factors that motivate them to adopt different B2BEC technologies in their supply chains, and how each of these usage patterns affects their performance. The current study contributes to the literature by shedding light on these issues.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 it