Does market orientation as mediated by market turbulence, competitive intensity and technological turbulence have an impact on the organizational performance of the new web-based service industries?
Bibliographic record
Abstract
Several studies have been developed, adding considerable support to the theory of Market Orientation. Previous research has expanded the work by Jaworski and Kohli (1993) by using their market orientation framework model and applying it to a variety of industries. The present study investigated the relationship of Market Orientation to organizational performance moderated by environmental factors such as market turbulence, competitive intensity, and technological turbulence. The relationship was studied in a sample of fifty marketing and non-marketing employees of Web hosting and Internet service provider companies based in 45 cities and 19 different states located in the United States of America. One respondent was located in Canada. Results indicated business performance was significantly affected by the market orientation of the firms tested. There was no statistically significant relationship between market orientation and organizational performance when the relationship was moderated by the environmental variables---market turbulence, competitive intensity and technological turbulence. The study found correlation between organizational performance and each of the components of market orientation---collection, dissemination and response to market intelligence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".