A Study on Structural Relationships Between Consulting Service Quality, Management Human Capital, and Entrepreneurship
Bibliographic record
Abstract
Background/Objectives: The purpose of this study is to empirically verify the effect of consulting services quality on management human capital and ultimately Entrepreneurship in the rapidly changing business environment. This study attempted to empirically examine the effect of the consulting service quality perceived by corporate managers on the Business Management Human Capital and Entrepreneurship of the company when the company experienced consulting.Methods/Statistical analysis: This study conducted survey research on managers n = 255 in Korea. In this study, we used SPSS 22.0 Version Program to verify the research hypothesis. Next, we performed SEM using the AMOS 22.0 Version program to implement the Structural Equation Modeling. Specifically, Frequency Analysis, Descriptive Analysis, Pearson`s Correlation Analysis, Confirmatory Factor Analysis, Reliability Test, and Pathway Analysis were conducted.Findings: The major findings are as follows. Process- Quality was found to have a statistically significant effect on Development Ability, Leadership Ability, and Marketing Ability. Next, the Technical-Quality was found to have a statistically significant effect on Development Ability, Recruiting Ability, Leadership Ability, and Marketing Ability. Lastly, only two variables, Leadership Ability and Marketing Ability, which affect Entrepreneurship, were statistically significant. Based on this, we tried to empirically verify the importance of consulting. The results presented in this study have practical implications for empirically verifying the necessity of consulting. According to the research results, it is necessary to make efforts to improve leadership and marketing ability in order to enhance Entrepreneurship.Improvements/Applications: However, this study has some limitations. Because this study is based on a limited sample, there are some limitations to the generalization of the results. Therefore, if further research is conducted later, we expect that the study will be conducted on more samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".