A Study on the Influence of Consultant Capacity on Consulting Utilization and Social Network: Focused on Moderating Effect of Gender
Bibliographic record
Abstract
Background/Objectives: This study will identify social networks and consultant capacity concepts to verify that social networks are important factors and study whether consultant capacity and social networks influence consulting use.Methods/Statistical analysis: The subjects of the study can be companies that have consulted consulting services of SMBs, and the samples were analyzed by conducting a questionnaire survey on more than 240 SMBs that have consulted consulting services in Korea. The survey consisted of 30 questions including 10 demographic items, and Likert 5-point scale was used. In the empirical analysis, descriptive analysis, exploratory factor analysis, confirmatory factor analysis, structural model analysis, and adjustment effect test were analyzed by AMOS 22.0 using SPSS 22.0.Findings: Studies have shown that first, the knowledge of consultants was shown to have a positive effect on the social network. Second, the ability of consultants was found to have a positive effect on social networks. Third, the attitude of consultants was found to have a positive effect on social networks. It is analyzed that the attitude of the consultant is expressed in personal feelings and that a strong network can be formed through a sincere attitude. Fourth, social networks have been found to have a positive effect on consultancy utilization. It means that the utilization of consulting can be improved through the formation of an active social network. Fifth, analyzing the differences in the path between the gender, it was found to be affected by the Moderating effect. In the case of men, consultant knowledge and attitudes have derived positive results in social networks and consulting use. And in the case of women, the ability of consultants became more active in consulting with social networks. Therefore, the difference in the effect between male and female was confirmed statistically.Improvements/Applications: In this study, it was confirmed that there was a difference between men and women when the consultant's ability affected the consulting utilization rate. Therefore, it is necessary to conduct a detailed study of measures to supplement the gender gap in the competence of consultants in SMB consulting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".