ENVIRONMENTAL AND ECONOMIC PERFORMANCE MEASUREMENT THROUGH GREEN SUPPLY CHAIN AND GREEN IN STORE MICRO, SMALL AND MEDIUM ENTERPRISES IN PROBOLINGGO CITY

: Micro, Small and Medium Enterprises or MSMEs are a business group with a very large number in Indonesia. According to the Central Statistics Agency (BPS) the number of Micro, Small and Medium Enterprises (MSMEs) is 64,000,000 MSMEs and the City of Probolinggo has 33,395 MSMEs. Indonesia ranks second in Asia as the country that produces the most plastic waste after China. High waste production and the majority of the MSME industry make it important to implement Green Supply Chain Management.This study aims to determine the impact of the Green in Store Processes dimension on Performance Outcomes through the mediation of the Green Supply Chain Processes dimension with the research object of MSMEs in Probolinggo City.This study uses quantitative methods with multivariate analysis methods in data processing assisted by SmartPLS. Data was collected by distributing questionnaires using a Likert scale as a measurement scale. Data processing in this research is Direct and Indirect using SmartPLS software by carrying out two Bootstrapping processes in one tailed and two-tailed to determine the direct and indirect effects between variables.Based on the results of research conclusions, it is proven that many variables do not have a significant effect. The researcher suggests that the Probolinggo City Government should directly guide MSMEs regarding the implementation of Green Supply Chain Management. Picture 3.10 Model Struktural Bootstrapping two-tailed Result


Picture 1 Framework
Resource: Green Supply Chan Manajemen in food retailing: survey-evidence in Croatia (Peltjak, Kristina, 2017) According to Peltjak, Kristina (2017) Green In-Store Processes as Independent Variables that will affect Performance Outcomes (Environmental Performance and Economic Performance) as Dependent Variables by using Green supply chain processes (Cooperation with Suppliers, Green Purchasing, and Green Logistics) as mediating. The framework as shown in Figure 1 explains the relationship between Green In-Store, Green In-Store (Water and Energy Management, and Waste Management), Green supply chain processes (Cooperation with Suppliers, Green Purchasing, and Green Logistics), on the Performance Outcomes (Environmental Performance, and Economic Performance) of an object of research, in this case the UMKM in Probolinggo City. The difference from previous research is in terms of the object of this research is MSMEs in Probolinggo. Previous researchers only tested the hypothesis with a direct effect by looking for the relationship between each variable. While the focus of this research is to find the relationship of each variable directly or indirectly. The role of researchers in this study as data collectors, data managers, and analyzers on the results of this study.

Research Methodology
The population is the entire element that will be used as inference or generalization. According to Hadari Nawawi (1983) The population in this study is MSMEs in Probolinggo City. According to Sugiyono (2013:81) Sampling is part of the population in the form of numbers and characteristics. For the sample from this study, 30 samples of MSMEs in Probolinggo were taken. In this study, the sampling procedure used a non-probability technique using the purposive sampling method. Quantitative research uses clear data analysis techniques and is directed to answer the problem formulation or test hypotheses in the proposal. The data analysis technique uses statistical methods that are already available because the data is quantitative. This study uses descriptive analysis and uses a continuum line to help analyze the results of the questionnaire. In this study, researchers also used quantitative methods obtained from respondents using a Likert scale through questionnaires distributed using google forms and then processed using the PLS program because in the framework of this research there were more than one dependent variable. There are several structural model assessment procedures that can be used to justify the results of the PLS called the rule of thumb.

Result
Based on the results of the data that has been obtained, the next step researchers process data using smartPLS 3.0 software, the option used to calculate the model is using PLS Algorithm and Bootstrapping.  Based on the results of the table above, the results of data processing show that all variables have a Conbach's Alpha value above 0.7, it can be concluded that all of these variables are reliable. Internal Consistency reliability is used to measure and test the consistency of indicators on one variable, as measured by the value of Cronbach's alpha and the value of composite reliability A construct is declared reliable if the value of Cronbach's alpha and composite reliability is above 0.7. All variables have Cronbach's alpha and CR values of more than 0.7, it can be concluded that the construct is declared reliable. Convergent Validity is used to measure the validity of indicators as a measure of variables that can be seen from the outer loading of each variable indicator. An indicator is said to have good reliability if it has an outer loading value above 0.7 The indicator is said to be valid if the AVE (Average Variance Extracted) value is above 0.50 so it can be said that the measurement has met the criteria for convergent validity. Each variable indicator has a value of more than 0.50 which means that all indicators are declared valid.
Discriminant Validity is a reflective indicator that can be seen in the cross loading value between the indicator and its construct. The results of the discriminant validity test can be seen from the cross loading correlation table which is an alternative to the AVE test and the Fornell-Larker Criterion Correlation.   amounted to 9.7% + 8.3% = 18%, because below 50%, the relationship between Variable X and Z is categorized as a weak relationship.

Q square
Q Square is used to measure how well the observed values generated by the model and parameter estimates are. If the Q Square value is less than 0 (zero) then the model lacks predictive relevance, whereas if the Q Square value is greater than 0 (zero) then the model has predictive relevance. So from the data obtained, the five variables have a value greater than 0 (zero), meaning that the model has predictive relevance. Researchers conducted two measurements of bootstrapping by differentiating the type of test into one-tailed and twotailed.

Result
Based on the calculation of R Square, it is found that all variables have a weak relationship because the R Square value is less than 50%. The value of R square for variable Y (Green Logistics, Green Purchasing, and Cooperation with Suppliers) means the effect of Variable X (Green in Store Processes) on Variable Y (Green Supply Chain Processes) of 4.9% + 8.8% + 8.3% = 22% , because it is less than 50%, the relationship between Variables X and Y is weak. Meanwhile, for Variable Z, Performance Outcomes (Environmental Performance and Economic Performance) amounted to 9.7% + 8.3% = 18%, because below 50%, the relationship between Variable X and Z is categorized as a weak relationship. Based on the Q Square calculation, all variables have results above 0, so it can be concluded that the model has predictive relevance.  (2021) Based on the results of the table above, the following conclusions can be drawn: 1. The relationship between Water and Power Management and Green Logistics is significant. 2. The relationship between Water and Power Management with Green Purchasing is not significant.

The relationship between Water and Power Management with Cooperation with
Suppliers is not significant. 4. The relationship between Disposal Management and Green Logistics is not significant. 5. The relationship between Disposal Management and Green Purchasing is significant. 6. The relationship between Disposal Management and Cooperation with Suppliers is significant. 7. The relationship between Green Purchasing and Environmental Performance is not significant. 8. The relationship between Green Purchasing and Economic Performance is not significant. 9. The relationship between Green Logistics and Environmental Performance is not significant. 10. The relationship between Green Logistics and Economic Performance is not significant.
Based on the calculation of the smartPLS software, the data is processed by Direct Effect using Bootstraping with a one-tailed test type, it can be concluded that the Water and Power Management variable for Green Logistics has a T Value of 1.897 which is greater than the direct T table of 1.65 and has a significance level of 5%. Then H0 is rejected and H1 is accepted, meaning that the green logistics variable has a direct effect or has a relationship with Water and Energy Management. Then H1a is accepted. These results are in line with previous research conducted by Peltjak, Kristina (2018), explaining that Green Logistics has a positive effect on Water and Energy Management. Disposal Management of Green Logistics has a negative effect with a T Value of 0.366 which is smaller than the direct T table of 1.65 and has a significance level of 5%. Then H0 is accepted and H1 is rejected, meaning that the Green Logistics variable has no direct effect or has a relationship with Disposal Management. Then H2a is rejected. These results are in line with previous research conducted by Peltjak, Kristina (2018), explaining that Green Logistics has a negative effect on Disposal Management. Disposal Management of Green Purchases has a positive influence with a T Value of 2,964 which is greater than the direct T table of 1.65 and has a significance level of 5%. Then H0 is rejected and H1 is accepted, meaning that the Green Purchase variable has a direct effect or has a relationship with Disposal Management. Then H2b is accepted. These results are in line with previous research conducted by Peltjak, Kristina (2018), explaining that Green Purchasing has a positive effect on Disposal Management. Disposal Management on Cooperation with suppliers has a positive influence on the T Value of 2.094 which is greater than the direct T table of 1.65 and has a significance level of 5%. Then H0 is rejected and H1 is accepted, meaning that the Cooperation with Suppliers variable has a direct effect or has a relationship with Disposal Management. Then H2c is accepted. These results are in line with previous research conducted by Peltjak, Kristina (2018), explaining that Cooperation with Suppliers has a positive effect on Disposal Management. Green Purchase on Economic Performance has a negative effect with a T Value of 0.189 which is smaller than T table indirect 1.65 and has a significance level of 5%. Then H0 is accepted and H4 is rejected, it can be concluded that the Economic Performance variable has no direct effect or has no relationship to green purchases. So H4b is rejected. This result is in line with previous research conducted by Peltjak, Kristina (2018), explaining that economic performance has a negative effect on green purchases.

Conclusions and Recommendations
From the results obtained, not all variables affect Environmental and Economic Performance, if MSME actors in the city of Probolinggo want to improve Environmental and Economic Performance then they need to pay attention to aspects of the Green In Store Processes and Green Supply Chain Processes variables. In the Green In Store Processes variable, implementing good Water and Energy Management, and Waste Management will improve performance in the Green Supply Chain Processes field. The Green Supply Chain Processes variable that affects the field of Economic Performance is the Cooperation with Suppliers variable. MSME actors must also pay attention to external factors that affect Environmental Performance and Economic Performance. The variables that have a direct significant effect are Water and Power Management with green logistics, Waste Management with Green Purchasing, Disposal Management with Cooperation with Suppliers and Cooperation with Suppliers with Economic Performance. For further research, it is possible to use the same research model in different industries with a wider scope. From the results obtained, not all variables have an effect on Environmental and Economic Performance. Probolinggo city government wants to improve Environmental and Economic Performance, so they need to pay attention to aspects of the Green In Store Processes and Green Supply Chain Processes variables. Researchers suggest that the Probolinggo city government should directly guide MSMEs regarding the implementation of Green Supply Chain. The Probolinggo city government can help fund MSMEs and suppliers to produce or use environmentally friendly bags in order to reduce the plastic waste produced by MSME actors. In addition to educating MSME actors and suppliers, the researchers also suggest that the Probolinggo city government also educates the city community to be wiser in using and disposing of plastic waste.