Emerging B2B electronic marketplaces : explaining organizational participation with transaction cost perspective
Bibliographic record
Abstract
With the growing popularity of B2B e-marketplaces, increasing numbers of organizations are endeavouring to utilize this medium in their business activities. However, the present knowledge regarding B2B e-marketplace adoption processes is still limited. Therefore, the present study investigates circumstances under which organizations adopt B2B e-marketplaces. More specifically, it is primarily concerned with the factors motivating an organization's participation in B2B e-marketplaces from the purchasing manager's perspective, based on insights and observations obtained from transaction cost economics. A single unified framework was developed based on previous findings to investigate the emergence of efficiency motive within organizations, and to analyze the role of this motivation in organizational participation processes. A large-scale cross-sectional survey study targeted the membership of the Purchasing Management Association of Canada (PMAC) was used for empirical validation of this framework. Partial Least Squares (PLS) analysis was chosen to test the measurement model and the conceptual model based on 466 usable responses obtained. The results demonstrate that efficiency motive exerts a significant influence on intentions to adopt B2B e-marketplaces, and reveal three transaction characteristics that significantly contribute to the formulation of efficiency motive: frequency uncertainty of demand, asset specificity, and market fragmentation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".