Assessing Relational E-Strategy Supporting Business Relationships
Bibliographic record
Abstract
As per the Census Bureau of the Department of Commerce, the estimate of U.S. retail e-commerce sales for the first quarter of 2009 was $31.7 billion. For the same period, e-commerce accounted for 3.5 percent of total sales with a value of $30.2 billion sales. As electronic business (e-business) has become essential in our economy, organizations have begun to demand a return on their investment in such endeavors (Damanpour and Damanpour, 2001). More recently, research indicates that webbased technologies enhance performance when the environmental pressures are high, the technical capabilities within the organization are well integrated, and the management team highly supports and sees value in e-business initiatives (Sanders, 2007). An extensive and diverse body of literature has been produced regarding e-business. One research angle that lacked over the years is the definition and assessment of an e-business strategy (e-strategy). Some efforts were made in evaluating e-strategy through an electronic simulation (Ha and Forgianne, 2006). Another recent research observed that human, technological and business capabilities and e-business implementation influence the business performance at various levels (Coltman, Devinney, and Midgley, 2007). However, both studies did not develop an e-strategy construct empirically tested with managers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".