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The Effect Supply Chain Management Practices and Supply Chain Integration on The Performance of Malang City SMEs
Tirta Yoga1*, Djoko Koestiono2, Agustina Shinta2
1Postgraduate of Agribusiness, Faculty of Agriculture, Brawijaya University, Veteran Street (65145), Malang, Indonesia
2Departement of Socio-Economics, Faculty of Agriculture, Brawijaya University, Veteran Street (65145), Malang, Indonesia
Received: 12 January 2022; Revised: 23 May 2022; Accepted: 27 May 2022
ABSTRACT
This study aimed to know whether there is a significant effect of supply chain management practices and supply chain integration on Micro, Small, and Medium Enterprises (MSMEs) performance in the food and beverage agroindustry of Malang City. This research used a quantitative approach conducted in Malang City in 2021. The data were collected through a questionnaire from 100 MSMEs in the food and beverage agroindustry of Malang City. The hypothesis in this research was tested using the analysis method of Structural Equation Modeling (SEM) by using Partial Least Square (PLS). The study was able to prove the existence of a significant effect of Supply Chain Management and Supply Chain integration on the performance of MSMEs agroindustry. MSMEs should continue to improve and implement Supply Chain Management practices because these factors are proven to affect operational performance.
Keywords: operational; performance; SCI; SCM How to cite:
Yoga, T., Koestiono, D., & Shinta, A. (2022). The Effect Supply Chain Management Practices and Supply Chain Integration on The Performance of Malang City SMEs. HABITAT, 33(2), 101–111.
https://doi.org/10.21776/ub.habitat.2022.033.2.11 1. Introduction
The development and competition in the business world rapidly increasing in Indonesia makes the competition between companies more challenging. Micro, Small, and Medium Enterprises (MSMEs) are essential pillars in the Indonesian economy because of their contribution to establishing the National Gross Domestic Product (GDP) and expanding job opportunities and employment. The Ministry of Cooperatives and MSMEs data showed that 65,471,134 MSMEs spread around Indonesia in 2019, which increased from the previous year by about 1.98%. Its role in employment is higher than large businesses, wherein 2019 MSMEs can absorb a workforce of 123,368,672 people, or 96.92% of all businesses in Indonesia. The contribution of MSMEs to the National GDP in 2019 was 60.3% (Kementerian Koperasi dan UKM, 2019).
The rapid growth of SMEs makes the competition in the market increase. The
competition occurs not only between the SMEs but also with the large-scale company. The competition between companies is increasing in line with improving people's needs and lifestyles (Sangadji & Sopiah, 2013). The fierce competition coupled with the outbreak of the Covid-19 pandemic in Indonesia resulted in a significant decrease in GDP contribution by MSMEs in 2020, which was 37.3% (Lokadata, 2020), indicating that MSMEs are in a weak state.
One of the cities in Indonesia that is experiencing rapid development in the growth of its MSMEs is Malang City. It is proven by the International Council for Small Business (ICBS) award in collaboration with the Ministry of Cooperatives and Small and Medium Enterprises of the Republic of Indonesia called the Natamukti Pranata award. The award was given because Malang City is considered capable of encouraging the sustainability of MSMEs and creating an excellent business ecosystem (Badan Perencanaan dan Litbang, 2017). The potential of Malang City, such as having various holiday destinations for tourists, a variety of interesting culinary tours, and
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*Correspondence author.
E-mail: tirtayoga13@gmail.com
Available online at HABITAT website: http://www.habitat.ub.ac.id a dense population, opens up opportunities for the
community to establish MSMEs.
The development of MSMEs in Malang City must be in line with the ability of each MSME to survive in the competition. To survive in business competition, MSMEs need to create competitive advantages to generate better economic value than competitors (Barney, 2012).
The performance of the company or organization must continue to be improved so that the company can compete. Organizational performance is the actual output or result produced by an organization that is measured and compared with the output produced (Asghar Afshar Jahanshahi, 2012).
MSMEs in Malang City have great opportunities, as seen from the number of MSMEs developing in Malang City. However, in reality, MSMEs in Malang City still have problems in efforts to improve MSME performance. Several problems occur in the supply chain of raw materials, competitive advantage, quality of raw materials and capital, and the level of innovation produced by these SMEs. It is evidenced by the level of innovation and the use of technology that is not evenly distributed between MSMEs.
The problem is further supported by the emergence of the Covid-19 pandemic, where the government needs to issue several policies that are burdensome for the MSME sector to ease the widespread pandemic, such as the implementation of isolation, social distancing to policies so that people stay at home (Sinuraya, 2020). It requires the creative industry to create new strategies to study market changes to find out the changes in the pattern of consumer demand, fulfill the needs of raw materials, and internal patterns of MSMEs in producing and distributing goods to consumers MSMEs can survive in running their business.
One way to improve the company's performance is through Supply chain Management (SCM).
SCM practice is a practice that could improve company performance. Practice on SCM could positively impact and could be profitable for the company. The high practice of SCM in a company could improve performance and competitive capabilities (Hertz, 2007). Companies that practice good SCM will have better performance than competitors and will be able to decrease the overall cost of fulfilling and serving consumer needs. According to (Chopra & Meindl, 2006), SCM is all parties involved, either directly or indirectly, in fulfilling orders and requests from consumers. All parties involved are not only producers or suppliers but also distributors,
storage places, sellers, and consumers. SCM is a significant part of a company, so every company manager needs to be able to plan, implement, and control the SCM process. Meanwhile, according to Nyoman in Zulher & Norawati (2019). The supply chain is a network of companies that work together to create and deliver a product to the end- user.
Based on the description above, this study aims to analyze the effect of SCM practices and Supply Chain Integration (SCI) on the performance of SMEs in the food and beverage agroindustry in Malang City.
2. Theoretical Underpinning
A supply chain is a network of companies that work together to create a product and deliver it to end-users (Zulher & Norawati, 2019).
Meanwhile, according to Supply Chain Management (SCM) (Sumarsan, 2013), It is a set of activities in the form of entities/companies involved in the production and distribution of goods to end consumers, from raw materials to finished products. This process involves companies taking raw materials from nature, then factories converting semi-finished products into finished products and distributing the finished products to final consumers.
The increasingly rapid growth of MSMEs can create increasingly tight competition between MSMEs. To survive in the competition, MSMEs must adopt supply chain management and supply chain integration practices to increase business performance. It is supported by several previous studies, namely: (Zulkarnain et al., 2018); and (Paulraj et al., 2012), which explained that SCM practices could improve business performance.
Practicing SCM has a positive effect on business performance. The higher the SCM practice in a company, the higher its performance.
However, there is a difference in some studies that SCM practices alone cannot increase efficiency because efficiency can be achieved through the interaction of multiple supply chains, including integration. Singh et al. (2010) and Amalia (2018) showed that supply chain management practices significantly negatively affect business performance. It highlights points of failure such as storage time, loss of location, product variations, and high operating costs.
These factors lead to a gap between the practices used for the organization's operations.
Based on the description of the problems (phenomena) described in this study and the
Available online at HABITAT website: http://www.habitat.ub.ac.id differences in the results of previous studies
(research gaps), the supply chain management practices have a positive effect on company performance. However, some researchers show that supply chain management practices negatively affect company performance. Further research is needed based on the phenomenon of the gap and research gap above.
3. Research Methods
This type of research is classified as explanatory research. The researcher aims to analyze the relationship between variables by testing hypotheses and using a quantitative approach to discuss the causal relationship and effect between two or more variables using hypothesis testing. This study aims to examine and analyze the relationship between variables (Singarimbun & Efendi, 1989). This study highlights the effect of supply chain management practices and supply chain integration on company performance in SMEs in Malang City.
The research was conducted in food and beverage agro-industry SMEs located in Malang City, East Java. The research location was determined by purposive sampling. This sampling technique was used in studies that prioritize research objectives (Burhan). Malang City is considered a city with a large population. The development of the largest number of MSMEs is ranked number two after Surabaya City. The survey was conducted in August 2021.
This research used a stratified sampling method. This technique served to determine the strata of the MSMEs sample taken based on the type of business, food, and beverages in the Malang City Trade and Industry Office data. The sample in this study was 100 MSMEs registered at the Malang City Trade and Industry Office. The sample used in the SEM-PLS method needs to be at least 30 to 100 respondents (Zuhdi et al., 2016).
The criteria of respondents are the owner of the business or the person in charge of the food and beverage agro-industry MSMEs who understand their MSMEs and have data needed in the study.
The data used in this study are primary data and secondary data (Sekaran, 2014). Primary data in this study were obtained directly from the research object, the results of interviews with respondents.
In contrast, secondary data were data obtained from references and literature related to the research object.
The data analysis method used a descriptive statistical analysis and Structural Equation
Modeling (SEM) analysis. The descriptive analysis of respondents was used to find out the description of the respondents as measured by the number of indicators stated in the questionnaire.
This method was used to determine the average and distribution of respondents' answers. This descriptive analysis provides an overview or description of the data from the average (mean), standard deviation, variance, maximum, minimum, range, sum, and skewness (Ghozali, 2012; Hair, 2018). Furthermore, using SEM model analysis to determine the relationship and effect between variables assisted by the WarpPLS program.
The variables in this study consisted of exogenous and endogenous variables. Exogenous variables comprised of 1) supply chain management practices with four indicators, which are: strategic supplier partnerships, customer relations, information sharing, and information quality (Mbuthia & Rotich, 2014), and 2) supply chain management integration with three indicators, which are: supplier integration, internal integration, and company integration with customers (Kim, 2006). The endogenous variable is the company's performance with two indicators:
performance on customer satisfaction and operational performance (Sirait et al., 2017).
4. Result and Discussion
4.1. Descriptive Analysis of Supply Chain Management Practices, Supply Chain Integration and Company Performance Description analysis of variables will examine and understand respondents' perceptions of the answered questionnaires. The results of each statement are compiled based on a Likert scale and then described in terms of frequency, mean and standard deviation. This item was used to measure indicators in research which then act as dimensions that measure variables. The average (mean) score on the items is measured by referring to (Solimun), which recommends the following levels: (1) 1-1,5 (very low); (2) >21,5-2,5 (low);
(3)>2,5-3,5 (medium); (4) >3,5-4,5 (high; and (5)
>4,5 (very high).
a. Description of Supply Chain Management Practices
There were 12 questions in variables of supply chain management practice. The following descriptive analysis results are presented in Table 1.
Available online at HABITAT website: http://www.habitat.ub.ac.id Table 1. Descriptive Statistics of Supply Chain Management Practices
SR R AT T ST Mean Std. Deviation Desc.
X1.1.1 1 4 22 46 27 3,94 0,8625 High
1% 4% 22% 46% 27%
X1.1.2 0 7 28 50 15 3,73 0,8022 High
0% 7% 28% 50% 15%
X1.1.3 1 21 14 42 22 3,63 1,0792 High
1% 21% 14% 42% 22%
X1.2.1 0 3 20 65 12 3,86 0,6551 High
0% 3% 20% 65% 12%
X1.2.2 0 9 28 39 24 3,78 0,9165 High
0% 9% 28% 39% 24%
X1.2.3 2 8 19 58 13 3,72 0,8655 High
2% 8% 19% 58% 13%
X1.3.1 1 15 22 44 18 3,63 0,9812 High
1% 15% 22% 44% 18%
X1.3.2 4 9 19 48 20 3,71 1,0180 High
4% 9% 19% 48% 20%
X1.3.3 2 18 23 42 15 3,5 1,0200 Medium
2% 18% 23% 42% 15%
X1.4.1 1 3 28 46 22 3,85 0,8333 High
1% 3% 28% 46% 22%
X1.4.2 0 11 26 40 23 3,75 0,9361 High
0% 11% 26% 40% 23%
X1.4.3 1 8 23 50 18 3,76 0,8776 High
1% 8% 23% 50% 18%
Average 3,74 0,9039 High
Based on the results of the descriptive analysis in Table 1, the average (mean) for all respondents' assessment items on the supply chain management variable is 3.74, which means that most respondents state that the level of supply chain management practice in their company is high. The average standard deviation value of 0.9039 indicates that the value of the data
distribution for each item is narrow, which means that the majority of respondents' answers are even.
b. Description of Supply Chain Integration Variable
There were 13 questions in the supply chain integration variable. The following descriptive analysis results are shown in Table 2.
Table 2. Description of Supply Chain Integration
SR R S T ST Mean Std. Deviation Description
X2.1.1 0 7 41 47 5 3,5 0,7035 Medium
0% 7% 41% 47% 5%
X2.1.2 0 5 42 43 10 3,58 0,7410 High
0% 5% 42% 43% 10%
X2.1.3 0 37 34 29 0 2,92 0,8125 Medium
0% 37% 34% 29% 0%
X2.1.4 0 19 40 35 6 3,06 1,0032 Medium
0% 19% 40% 35% 6%
X2.1.5 0 14 42 33 11 3,06 1,0032 Medium
0% 14% 42% 33% 11%
X2.2.1 0 37 30 23 10 3,06 1,0032 Medium
0% 37% 30% 23% 10%
X2.2.2 0 41 27 22 10 3,01 1,0200 Medium
0% 41% 27% 22% 10%
X2.2.3 0 37 16 33 14 3,24 1,1021 Medium
0% 37% 16% 33% 14%
X2.2.4 0 56 7 25 12 2,93 1,1393 Medium
0% 56% 7% 25% 12%
X2.3.1 0 0 37 47 16 3,79 0,7006 High
0% 0% 37% 47% 16%
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SR R S T ST Mean Std. Deviation Description
X2.3.2 0 44 6 33 17 3,23 1,1879 Medium
0% 44% 6% 33% 17%
X2.3.3 0 0 30 53 17 3,87 0,6765 High
0% 0% 30% 53% 17%
X2.3.4 0 1 36 47 16 3,79 0,7189 High
0% 1% 36% 47% 16%
Average 3,31 0,9086 Medium
Based on the results of the descriptive analysis, as shown in Table 2, the average value (mean) for all respondents' assessment items on supply chain integration is 3.31, which means that most respondents state that the level of supply chain integration is medium. The average standard deviation value of 0.9086 indicates that the value of the distribution of data for each item is narrow,
which means that the majority of respondents' answers are even.
c. Description of Supply Chain Integration Variable
There were 13 questions in the supply chain integration variable. The following descriptive analysis results are shown in Table 3.
Table 3. Description of Supply Chain Integration
SR R S T ST Mean Std. Deviation Description
X2.1.1 0 7 41 47 5 3,5 0,7035 Medium
0% 7% 41% 47% 5%
X2.1.2 0 5 42 43 10 3,58 0,7410 High
0% 5% 42% 43% 10%
X2.1.3 0 37 34 29 0 2,92 0,8125 Medium
0% 37% 34% 29% 0%
X2.1.4 0 19 40 35 6 3,06 1,0032 Medium
0% 19% 40% 35% 6%
X2.1.5 0 14 42 33 11 3,06 1,0032 Medium
0% 14% 42% 33% 11%
X2.2.1 0 37 30 23 10 3,06 1,0032 Medium
0% 37% 30% 23% 10%
X2.2.2 0 41 27 22 10 3,01 1,0200 Medium
0% 41% 27% 22% 10%
X2.2.3 0 37 16 33 14 3,24 1,1021 Medium
0% 37% 16% 33% 14%
X2.2.4 0 56 7 25 12 2,93 1,1393 Medium
0% 56% 7% 25% 12%
X2.3.1 0 0 37 47 16 3,79 0,7006 High
0% 0% 37% 47% 16%
X2.3.2 0 44 6 33 17 3,23 1,1879 Medium
0% 44% 6% 33% 17%
X2.3.3 0 0 30 53 17 3,87 0,6765 High
0% 0% 30% 53% 17%
X2.3.4 0 1 36 47 16 3,79 0,7189 High
0% 1% 36% 47% 16%
Average 3,31 0,9086 Medium
Based on the results of the descriptive analysis, as shown in Table 3, the average value (mean) for all respondents' assessment items on supply chain integration is 3.31, which means that most respondents state that the level of supply chain integration is medium. The average standard deviation value of 0.9086 indicates that the value of the distribution of data for each item is narrow,
which means that the majority of respondents' answers are even.
d. Description of Company Performance Variables
There were 11 questions in the company's performance variable. The following descriptive analysis results are shown in Table 4.
Available online at HABITAT website: http://www.habitat.ub.ac.id Table 4. Description of Company Performance
SR R AT T ST Mean Std. Deviation Description
Y1.1.1 0 1 19 61 19 3,98 0,6510 Medium
0% 1% 19% 61% 19%
Y1.1.2 0 1 19 64 16 3,95 0,6256 Medium
0% 1% 19% 64% 16%
Y1.1.3 0 0 18 72 10 3,92 0,5257 Medium
0% 0% 18% 72% 10%
Y1.1.4 0 1 18 71 10 3,9 0,5596 Medium
0% 1% 18% 71% 10%
Y1.1.5 0 0 17 72 11 3,94 0,5284 Medium
0% 0% 17% 72% 11%
Y1.1.6 1 0 26 64 9 3,8 0,6356 Medium
1% 0% 26% 64% 9%
Y1.2.1 1 37 50 10 2 2,75 0,7300 Medium
1% 37% 50% 10% 2%
Y1.2.2 2 6 24 46 22 3,8 0,9211 High
2% 6% 24% 46% 22%
Y1.2.3 0 5 14 54 27 4,03 0,7844 High
0% 5% 14% 54% 27%
Y1.2.4 0 3 28 63 6 3,72 0,6208 Medium
0% 3% 28% 63% 6%
Y1.2.5 0 3 30 56 11 3,75 0,6872 Medium
0% 3% 30% 56% 11%
Average 3,78 0,6609 Medium
Based on the results of the descriptive analysis in, Table 3 shows the average (mean) of the overall respondents' assessments for all items of the company's performance variables with an average of 3.78, which means that most of the respondents stated that the company's performance was medium. The average standard deviation value is 0.6609, which indicates that the value of the data for each item is narrow, which means that the majority of respondents' answers are uniform.
4.2. Analysis of The Effect of Supply Chain Practices, Supply Chain Integration, and Performance of Food and Beverage Agroindustry Companies in Malang City The structural equation model of this study is a reflective first-order–second-order reflective indicator on the overall relationship between items and indicators also indicators with latent variables.
The results of evaluating the reflective model of the first-order structural equation are shown in Table 5.
Table 5. First Order Analysis Result
Indicator Item Loading Factor P Value CR CA AVE
(X1.1) X1.1.1 0,933 <0,001
0,911 0,850 0,774
X1.1.2 0,771 <0,001
X1.1.3 0,925 <0,001
(X1.2) X1.2.1 0,918 <0,001
0.900 0,829 0,751
X1.2.2 0,733 <0,001
X1.2.3 0,935 <0,001
(X1.3)
X1.3.1 0,939 <0,001
0.940 0,905 0,840
X1.3.2 0,877 <0,001
X1.3.3 0,933 <0,001
(X1.4) X1.4.1 0,760 <0,001
0,843 0,719 0,641
X1.4.2 0,795 <0,001
X1.4.3 0,845 <0,001
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Indicator Item Loading Factor P Value CR CA AVE
X2.1
X2.1.1 0,814 <0,001
0,926 0,900 0,715
X2.1.2 0,867 <0,001
X2.1.3 0,856 <0,001
X2.1.4 0,815 <0,001
X2.1.5 0,873 <0,001
X2.2
X2.2.1 0,879 <0,001
0,938 0,913 0,792
X2.2.2 0,907 <0,001
X2.2.3 0,888 <0,001
X2.2.4 0,885 <0,001
X2.3
X2.3.1 0,978 <0,001
0,964 0,949 0,870
X2.3.2 0,836 <0,001
X2.3.3 0,948 <0,001
X2.3.4 0,964 <0,001
Y1.1
Y1.1.1 0,699 <0,001
0,859 0,802 0.509
Y1.1.2 0,776 <0,001
Y1.1.3 0,731 <0,001
Y1.1.4 0,808 <0,001
Y1.1.5 0,716 <0,001
Y1.1.6 0,512 <0,001
Y1.2
Y1.2.1 0,554 <0,001
0,883 0,832 0,606
Y1.2.2 0,813 <0,001
Y1.2.3 0,850 <0,001
Y1.2.4 0,827 <0,001
Y1.2.5 0,811 <0,001
Information : CR : Composite Reliability CA : Cronbach Alpha
AVE : Averange Variannce Extracted a. Internal Consistency Reliability Test
The internal consistency reliability test was conducted by looking at the value of composite reliability and Cronbach alpha. If the composite reliability value is above 0.7 and Cronbach Alpha is above 0.6, the item fulfills the internal consistency reliability test (Solimun et al., 2017).
Based on the results of the table above, it can be seen that the value of composite reliability and the value of Cronbach's alpha on each indicator item already look good and follow the criteria.
1) Convergent Validity a) Indicator Validity
The validity of the indicator is used to test the validity of the indicator measurement as indicated by the loading factor value. A loading factor is a number that shows the relationship between indicators and variables. According to Hair (2018), if a loading factor value of less than 0.3 has met the minimum level, a loading factor of less than 0.4 is considered better. A loading factor greater than 0.5 is regarded as a significant positive value. Meanwhile, according to (Solimun et al., 2017), if the indicator is greater than 0.5, it is considered sufficient to meet convergent validity. Based on the evaluation results shown in
Table 5, it can be seen that the loading factor used is greater than 0.5, so it can be stated that the loading factor value meets the specified criteria.
b) Average Variance Exracted
Average variance Extracted (AVE) is used to test the evaluation of convergent validity, which has criteria > 0.5. Based on Table 4, it is known that all indicators have AVE results above 0.5 so that they can be stated according to the criteria.
c) Discriminant Validity
This approach compares the loading value of an indicator to its latent variable and the loading value of the indicator to other latent variables. If the latent loading value with each indicator is greater than the cross-loading on the other latent variables, then the latent variable can predict the indicator better than the other variables. Solimun et al. (2017), the loading value is valid if the item factor in an indicator is greater than the correlation value of other indicator items. Based on the discriminant validity test based on the cross- loading (first-order) approach above, the loading value on all variables follows the specified criteria, so the indicators that measure these variables are declared valid.
b. Second Order Evaluation 1) Internal Consistency Test
Available online at HABITAT website: http://www.habitat.ub.ac.id The internal consistency reliability test saw
the coefficient of composite reliability and Cronbach Alpha. If the composite reliability value is above 0.7 and Cronbach Alpha is above 0.6, the item meets the internal consistency reliability test
(Solimun et al., 2017). Based on the test results in Table 6, it can be seen that the composite reliability and Cronbach Alpha values look good because the overall value is above the specified value.
Table 6. Second Order Analysis Results
Variable Indicator Loading Factor P Value CR CA AVE
Praktek SCM
X1.1 0,797 <0,001
0,923 0,888 0,750
X1.2 0,862 <0,001
X1.3 0,761 <0,001
X1.4 0,930 <0,001
SCI X2.1 0,889 <0,001
0,901 0,833 0,752
X2.2 0,912 <0,001
X2.3 0,797 <0,001
Kinerja Perusahaan
Y1.1 0,831 <0,001
0,817 0,551 0,690
Y1.2 0,831 <0,001
Information : CR : Composite Reliability CA : Cronbach Alpha
AVE : Averange Variannce Extracted 2) Convergent Validity Test
a) Indicator Validity
The indicator validity test is used to test the validity of the indicator measurement indicated by the loading factor value. According to Solimun (2017), if the indicator is greater than 0.5, it is considered sufficient as a criterion for the fulfillment of convergent validity. In this study, the loading factor limit used was greater than 0.5 so that the loading factor value met the specified criteria.
b) Average Variance Extracted
Average Variance Extracted (AVE) is used to test the evaluation of convergent validity, which has criteria > 0.5. Based on the results in Table 5,
all indicators have good AVE results to meet the specified criteria.
c. Evaluation of The Structural Modal (Inner Model)
The evaluation of the structural model is carried out on the analysis of the path diagram modeling based on the second-order model. The collinearity test is the results of the calculations based on the full collinearity test on each latent variable (Kock). VIF values that meet the criteria are those with a value of < 3.3. In Table 7, it is known that all latent variables of the model have full collinearity VIFs values of less than 3.3. So it can be concluded that all other variables have met the collinearity assumption.
Table 7. Full Collinearity VIF Test
Variable Full Collin. VIF
Supply Chain Management Practice (X1) 1.271
Supply Chain Integration (X2) 1.314
Company Performance (Y1) 1.058
In Table 7, it is known that the company's performance variable (Y1) has an R-square value of 0.217 (21.7%) which indicates the predictive power of the variable can be explained by the predictor variable in the model of 21.7% while the
remaining 78.3% is contribution of variables not discussed in the study. Meanwhile, the Q-square value shows a good predictive value because it has a value greater than zero, which is 0.209.
Table 8. Evaluation Result of R square and Q square
Indicator Y1
R-Square 0,217
Q2predictive relevance 0,209
Available online at HABITAT website: http://www.habitat.ub.ac.id d. Evaluation of Model Fit and Quality Indices
Testing the valid criteria of fit and quality indices model refer to Solimun (2017). Based on
the results in Table 9, it is known that out of ten criteria have been met. It can be said that the model has met the model fit requirements.
Table 9. Model Fit and Quality Indices Result
Model fit and quality indices Criteria Result Information Avarage path coefficient (APC) Accepted if p < 0,05 APC = 0.307 P <
0.001
Significant Fit Average R-square (ARS) Accepted if p < 0,05 ARS = 0.173 P=
0.018
Significant Fit Average adjusted R-squared
(AARS)
Accepted if p < 0,05 AARS = 0.160 P
= 0.025
Significant Fit Average block VIF (AVIF) Accepted if≤ 5, idealif
≤ 3.3
1,049 Ideal
Average full collinearity VIF (AFVIF)
Accepted if≤ 5, idealif
≤ 3.3
1,215 Ideal
Tenenhaus GoF (GoF) Small≥ 0.1,Medium≥ 0,25, Large≥ 0.36
0,355 Large
Sympson's paradox ratio (SPR) Accepted if≥ 0.7, ideal if = 1
1.000 Ideal
R-squared contribution ratio (RSCR)
Accepted if≥ 0,9, ideal if = 1
1.000 Ideal
Statsitical supperession ratio (SSR) Accepted if≥ 0.7 1,000 Accepted Nonlinear bivariate causality
direction ratio (NBLBCDR)
Accepted if≥ 0.7 1,000 Accepted
e. Hypothesis Test
Hypothesis testing is done by looking at the path coefficient and p-value. If the p-value is
<0.10, then the hypothesis is accepted, and if the p-value is >0.10, the hypothesis is rejected.
Table 10. Path Coefficient Evaluation Result Path
coefficient
Coefficient t value p value Confidence intervals
Conslusion
X1→ X2* 0.466 5.293 <0.001 [0.294; 0.005] Supported
X1→ Y1** 0.191 2.012 0.023 [-0.639;0,377] Supported
X2→ Y1* 0.263 2.830 0.003 [ 0.081;0,446] Supported
Based on Table 10, it can be seen that: (1) supply chain management practices towards supply chain integration show a positive path coefficient with a significantly high level. These results mean that the value of supply chain management practices on supply chain integration has a significant effect so that the hypothesis can be accepted. (2) the practice of supply chain management on company performance shows a positive path coefficient with a moderate level of significance. These results mean that the value of supply chain management practices significantly affects the company's performance so this hypothesis can be accepted. (3) supply chain integration on company performance shows a positive path coefficient with a high significance level. These results mean that the value of supply chain integration significantly affects the company's performance, so that this hypothesis can be accepted.
5. Conclusion
Supply chain management practices positively and significantly impact supply chain integration. Supply chain management practices
have a positive and significant impact on company performance. Supply chain integration has a positive and significant impact on company performance. The considerable role of supply chain management practices on the company's performance makes the MSMEs of the food and beverage agro-industry SMEs in Malang City need to be improved. Improving coordination and cooperation among supply chain members in MSMEs could improve the company's performance through good supply chain management.
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