Identifying the drivers of review generation in business-to-business e-commerce
Bibliographic record
Abstract
Despite the significant larger scale of B2B e-commerce and the importance of online reviews for buyers and sellers in B2B context, research on online review generation primarily focuses on the B2C context. The global expansion also poses a challenge for firms to understand the generation of online reviews beyond national boundaries. Yet, current research on online reviews offers little guidance in this area, as most of the extant research on online reviews has focused on the U.S. or a single country. This dissertation is an attempt to shed light on the limited knowledge of review generation in B2B and in globalization context. Using a large-scale empirical study, I highlight the importance of online reviews, identify their drivers in the B2B context, and examine the effect of national culture on online review generation. In essay 1, I investigate the effects of customer-initiated contacts, relationship duration, free-tiered pricing, and competition on online review generation. I also examine how those effects vary in different situations and investigate the consequence of online review sharing in B2B context. In essay 2, I investigate how individualism and uncertainty avoidance affect the decision to share online reviews and how these national culture's dimensions moderate the effect between customers’ interactions with the firm and online review generation. My research shows that findings from the existing literature (which have primarily been based on a B2C context and a single market) have limited applications to B2B contexts and firms operating in different countries and cultures, and further research is required in these important, yet underexplored areas.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".