Organizational Buyers' Adoption and Use of B2B Electronic Marketplaces: Efficiency- and Legitimacy-Oriented Perspectives
Bibliographic record
Abstract
Despite the significant opportunities to transform the way that organizations conduct trading activities, few studies have investigated the impetus for organizational strategic moves toward business-to-business (B2B) electronic marketplaces. Drawing on transaction cost theory and institutional theory, this paper identifies two groups of factors—efficiency- and legitimacy-oriented factors, respectively—that can influence organizational buyers' initial adoption of, and the level of participation in, B2B e-marketplaces. The effects of these factors on initial adoption of and participation level in B2B e-marketplaces are empirically tested with data collected, respectively, from 98 potential adopter and 85 current adopter organizations. The results of a partial least squares analysis of the data indicate that the two groups of factors exhibit different patterns in explaining initial adoption in the preadoption period and participation level in the postadoption period. Specifically, all three of the efficiency-oriented factors investigated in this study—product characteristics, demand uncertainty, and market volatility—and their subconstructs exhibit a significant influence on adoption intent or participation level, or both. The results demonstrate that two legitimacy-oriented factors—mimetic pressures and normative pressures—and their subconstructs have a significant impact on adoption intent, but not on participation level. Our findings also indicate that clearly different patterns exist between the two groups of factors in explaining adoption intent and participation level.
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.
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 | 0.005 | 0.020 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".