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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".