UPAYA MENINGKATKAN KINERJA PEMASARAN \nMELALUI ORIENTASI PASAR DAN ORIENTASI \nKEWIRAUSAHAAN DENGAN INOVASI SEBAGAI \nVARIABEL INTERVENING \n(Studi Empiris pada Usaha Mikro Kecil dan Menengah Kota Semarang)
Bibliographic record
Abstract
Micro, Small and Medium Enterprises (MSMEs) have an important role \nfor the growth of the national economy. In the first quarter of 2014, the number of \nMSMEs in Central Java increased, reaching 53.951 business units. However, in \nline with the increasing number of MSMEs and increasing turnover generated by \nSMEs in Central Java until 2013, when analyzed the average turnover per \nMSMEs by dividing turnover per year divided by the number of SMEs, it is known \nthat the average turnover per MSMEs in Central Java in 2013 actually decreased, \nwhich decreased by 0.0102 billion from the year 2012. The purpose of this \nresearch was to determine the factors that can improve the marketing \nperformance of MSMEs. Empirical studies conducted in this research is on Micro, \nSmall and Medium Enterprises (MSMEs) of Semarang City. The reason is \nbecause Semarang is Capital City of Central Java and MSMEs of Semarang \ncontribute most to the GDP of Central Java province. \nThis study uses four variables: Market Orientation, Entrepreneurship \nOrientation, Innovation and Marketing Performance. Research hypothesis testing \nusing the data of 120 respondents MSMEs in Semarang City. The analysis \ntechnique used in this research is Structural Equation Model (SEM) of 21.0 \nAMOS program. \nThe research proves that in order to improve marketing performance on \nMSMEs can be via 4 process. However, the most influential on the increase \nmarketing performance in MSMEs is to innovate on products that are supported \nby market-oriented.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".