KnE Social Sciences | International Conference on Economics, Business and Economic Education 2018 (ICE-BEES 2018) | pages: 309–320

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1. Introduction

Micro, Small and Medium Enterprises (MSMEs) have an important role in developing the national economy through the growth of their businesses. The contribution of MSMEs year by year shows good performance in terms of the number of business units, employment, Gross Domestic Product (GDP), total non-oil and gas exports, and investment value (Ministry of Cooperatives and Small and Medium Enterprises RI, 2012). MSMEs have an important and strategic role in national economic development. The contribution of MSMEs to the Indonesian economy is no doubt. MSMEs have a labor absorption rate of around 97% of all national workers and have a contribution to Gross Domestic Product (GDP) of around 57%. In addition to contributing to economic growth and employment, MSMEs also play a role in distributing development results. MSMEs have also proven to be unaffected by the crisis. When the crisis hit in the period of 1997-1998, only MSMEs were able to stand firm. Data from the Central Bureau of Statistics show that after the economic crisis of 1997-1998, the number of MSMEs was not reduced, it actually increased, even able to absorb 85 million to 107 million workers until 2012. In that year, the number of entrepreneurs in Indonesia was 56,539,560 units. Of this amount, MSMEs accounted for 56,534,592 units or 99.99%. The remaining, around 0.01% or 4,968 units are large businesses (LPPI, BI 2015). In an effort to manage the MSME business in order to be developed properly, it cannot be separated from the role of adequate human resources (labor).

These factors must be a concern and be encouraged continuously to be able to support MSME business development to the maximum [10]. The fact is that the current competition requires MSME to have human resources that have creativity and innovation capabilities. Assegaf and Wasitowati (2016) show that the problems faced by MSMEs today are due to the weak capacity of human resources, which has an impact on the weak capability of innovation and performance. Especially in creative MSMEs that must be able to compete globally will need human resources that have innovation, business orientation and competitors [10].

Various literatures show that SMEs that apply knowledge management get the same benefits as large companies that apply knowledge management. Knowledge management can be seen as a strategy that creates, obtains, transfers, leads to the use of knowledge in order to improve organizational performance, supports organizational adaptation, sustainability and competence, gains competitive advantage and customer commitment, improves understanding of human resources, protects intellectual assets, improves the quality of decisions, services and products, and reflects new knowledge and insights [13]. In MSME, the importance of applying knowledge management in enhancing new insights, innovations, and business performance has been studied by several researchers.

Byukusenge, et al., (2016) found that the application of knowledge management in an MSME will have an impact on increasing innovation and business performance. Setyanti and Farida (2016) also found that knowledge sharing which is one of the dimensions of knowledge management has proven to be able to drive the business performance of MSMEs. Other research also shows that the implementation of knowledge management has a positive impact on the innovation ability and business performance of MSMEs [5,2,11].

Even so, research that tries to explain the clear mechanism for how knowledge management influences the business performance of MSMEs is still unexplained. Previous studies have not tried to explain how the process or mechanism of Knowledge Management Success Factors can be a reference for the application of knowledge management in MSMEs, then the implementation process becomes a key in the success of increasing innovation and business performance [6,15,8,3]. In other words, previous studies were carried out separately. Therefore, this study tries to explain comprehensively on how the mechanism of applying knowledge management will improve the innovation and business performance of MSMEs.

2. Literature Review

Business performance

Organizational performance is the totality of work achieved by an organization. The performance of an organization can be seen from the extent to which the organization can achieve goals based on the goals that have been set before [13]. In this study, organizational (business) performance will be measured by using 3 approaches from Byukusenge, et al., (2016) which include: profit, sales growth, and market share.

Innovation

Innovation is a process to make changes, large and small, instantly and gradually, gradually, to produce, process, and serve, the results of which will introduce something new to the organization and will add value to customers and contribute to new knowledge in organization [7].

Knowledge management

Knowledge Management is the processes of creating knowledge (Create Knowledge), capturing knowledge (Capture Knowledge), organizing knowledge (Organize Knowledge), accessing knowledge (Access Knowledge) and then using knowledge (Use Knowledge). There are 3 levels of KM, namely individual level, team level and organizational level. Individual level focuses on exchanging knowledge between individual employees on the same or different teams. Team level KM focuses on the interaction of team members collaboratively in collaboration to evaluate information and knowledge management. Organizational level KM is centered on mechanism that can support and facilitate the distribution of knowledge across individuals in the organization. The factors proposed as the success key of the implementation of knowledge management are adopted from Yusuf and Wanjau's research (2014), which proposes 4 dimensions, but considering the compatibility in the field, the authors use only 3 dimensions: Organizational Culture, Information and Technology, and HR Capacity.

3. Hypotheses

The survival and competency excellence of an MSME is greatly influenced by how the overall business performance of the MSME is. In creating competitive business performance in the current era of competition, it is necessary for a business to pay attention to the skills that must be possessed by its HR. One of the skills that must be possessed by MSMEs especially in the creative processing industry is high innovation ability. However, at present MSME players still have low awareness and innovation capabilities. The solution that can be offered for this problem is the application of knowledge management in MSMEs. Knowledge management is a management system that has been proven to be a key factor in increasing innovation in large companies. Even some researchers have shown that the application of knowledge management in MSMEs also has the same impact, namely on improving the positive outcomes of these MSMEs.

Thus, with the implementation of knowledge management, it is expected to be able to improve MSME innovation, which in turn will improve the business performance of these MSMEs. However, the impact of the application of knowledge management to innovation and business performance is strongly influenced by how knowledge management is applied. If it is applied effectively, it will be highly successful in influencing the outcomes. Therefore, it is important for MSMEs to pay attention to the factors that can be used as a key to the success of MSME implementation. Some of the factors that can be the key to the successful implementation of MSMEs are organizational culture, information technology, and human resource capacity. Thus, the research model will be formulated as follows:

Figure 1

The Research Model.

fig-1.jpg

According to the framework of thought, the hypotheses are formulated as follows:

  • Knowledge management has positive influence on the innovation of MSME in Semarang City

  • Knowledge management has positive influence on the business performance of MSME in Semarang City

  • Innovation has positive influence on the business performance of MSME in Semarang

  • Innovation mediates the relationship of knowledge management towards the business performance of MSME in Semarang City

4. Research Method

Research design and sample

The research design used is a quantitative approach. Data sources on this quantitative study are obtained from primary data sources. The aim of this approach is to understand better and deeper about the influence of MSME understanding on the concept of knowledge management and the importance of the habit sharing to be applied. The sampling technique used is purposive sampling, that is, the sample is chosen because it has the criteria required in this study. The samples in the study are 100 MSMEs which are the members of the creative community of Semarang.

Analysis of structural equation modeling (SEM) with PLS warp programs is used to test the hypotheses. This statistical analysis tool is chosen because it has several advantages. First, SEM-PLS is suitable for research models that use latent variables and have calculated measurement error. Second, SEM analysis can simultaneously test multiple dependences as in this research model. Third, component-based SEM (PLS) can estimate a fairly complex model with a small sample size.

5. Result and Discussion

Instrument test (Validity test and reliability test)

This study uses convergent validity and discriminant validity. The following is an explanation of convergent validity and discriminant validity as follows:

Convergent validity, measured by using a factor load loading for the reflective indicator model or component loading for the formative indicator model,. If the factor loading is 0.30 or the factor loading and the component loading of the indicator are significant, the indicator meets the convergent validity. Based on calculation by using WarpPLS 6.0, it shows that all of these statements are stated to meet convergent validity because the factor loading 0.30 so that the statement item is used in this study. The discriminant validity of the questionnaire can be seen from the comparison of the root value of AVE (Average Variance Extracted) with correlation coefficient. If the root AVE is greater than the correlation coefficient with other variables, then the questionnaire is said to be valid discriminant.

Table 1

The Root AVE and Correlation Coefficient.


KM BP INOV
KM 0.741 0.073 0.315
BP 0.073 0.732 0.389
INOV 0.315 0.389 0.678
Source: Processed Primary Data (2018)

Based on Table 1, the results of the AVE root and the Correlation Coefficient tests show that all statement items are greater than the correlation of the relevant variables so that discriminant validity is fulfilled and all statements can represent problems in the research and in accordance with the actual conditions in the object of the research.

Result of reliability test

The following is the reliability test result of the instrument.

Table 2

Composite Reliability and Cronbach's Alpha.


No. Variable Composite Reliability Coefficient Cronbach's Alpha Coefficient
1 KM 0.905 0.879
2 BP 0.912 0.891
3 INOV 0.854 0.799
Source: Processed Primary Data (2018)

Based on Table 2, the results of the reliability test for the variables in this study indicate that all variables meet composite reliability because the composite reliability coefficients > 0.70 and all variables also meet internal reliability consistency because of cronbach's alpha coefficients > 0.60 so that all variables meet composite reliability and internal consistency. That is, all of these statements are able to measure a problem with a constant so that it can be regarded as a reliable measuring tool.

Model fit and quality indices

The criteria listed in the goodness of fit model of Table 3 are as the rule of thumb, so that the test results should not be applied rigidly and absolutely. When there are one or two models of fit and quality indices, the model can still be used. The following is the test result as shown in Table 3.

Table 3

Model Fit and Quality Indices.


No. Model Fit and Quality Indices Fit Criteria Analysis Result Description
1. Average path coefficient (APC) p < 0.05 0.263 (P < 0.001) Good
2. Average R-squared (ARS) p < 0.05 0.147 (P < 0.001) Good
3. Average adjusted R-squared (AARS) p < 0.05 0.140 (P < 0.001) Good
4. Average block VIF (AVIF) Accepted if < = 5, Ideally < = 3.2 1.139 Ideal
5. Average full colpatharity VIF (AFVIF) Accepted if < = 5, Ideally < = 3.2 Ideal
6. Tenenhaus GoF (GoF) Small > = 0.1, medium > = 0.25, large > = 0.36 1.200 Ideal
7. Sympson's paradox ratio (SPR) Accepted if > = 0.7, Ideally 1 Ideal
8. R-squared contribution ratio (RSCR) Accepted if > = 0.9, Ideally 1 0.275 Ideal
9. Statistical suppression ratio (SSR) Accepted if > = 0.7 1.000 Ideal
10. Nonpathar bivariate causallity direction ratio (NLBCDR) Accepted if > = 0.7 Ideal
Source: Processed Primary Data (2018)

It can be seen from Table 3 that the goodness of fit model has good results to explain the relationship between latent variables and their assumptions.

The test result of direct influence hypotheses

Hypothesis testing uses resampling method and is done by t-test. The rules of decision for testing hypotheses are as follows, when p-value is obtained 0.10 (alpha 10%), it is said to be significantly weak, if p-value is 0.05 (alpha 5%) then it is said to be significant and if p-value is 0.01 (alpha 1%) it is said to be significantly high in Table 4.

Table 4

Test Results of Direct Influence Hypotheses.


No. <Relationship among Variables Path Coefficient P-Value Description
1. KM INOV 0.021** < 0.001 Significantly high
2. KM BP 0.366 ns 0.388 Insignificant
3. INOV BP 0.402** < 0.001 Significantly high
Source: Processed Primary Data (2018)

Test results of mediating hypotheses

Table 5

Test Result of Mediating Hypotheses.


Mediating Variable Testing
X M Y Path Coefficient of Indirect Influence P-Value Description
KM INOV BP 0.147 0.002 Sig
Source: Processed Primary Data (2018)

The influence of knowledge management (KM) on innovation (Inov)

Based on Table 4, the results of testing the direct influence hypothesis indicate that there is an influence of Knowledge Management (KM) on Innovation (Inov) with a path coefficient of 0.021 and p < 0.001. Given that p < 0.01, it is said to be significantly high, so H1 is supported. The positive path coefficient (0.021) indicates that the higher the influence of Knowledge Management (KM), the Innovation (Inov) is increasing.

The results of this study are in line with Byukusenge, et al., (2016), Hakim and Hassan (2012) who found that the application of knowledge management in an MSME will have an impact on increasing business innovation. Based on this, the better the implementation of knowledge management, the better the business innovation of MSMEs will be.

The influence of knowledge management (KM) on business performance (BP)

Based on Table 4, the results of testing the direct influence hypothesis shows that there is no influence of Knowledge Management (KM) on Business Performance (BP) with path coefficients of 0.366 and p = 0.388. Given that p 0.10, it is said to be insignificant, H2 is not supported. This indicates that high or low Knowledge Management (KM) will not affect Business Performance (BP).

The results of this study are not in line with Setyanti and Farida (2016), Evardsoon and Durst, (2013), Asegaff and Agung, (2016) which show that knowledge management has a positive influence on the performance of a business. However, the results of this study are in line with Hakim and Hassan (2012) which shows that knowledge management has no significant influence on performance. According to Hakim and Hassan (2012), the gap in research results regarding the influence of knowledge management on performance is already common. This is because knowledge management itself has several dimensions that may have different influences and roles in each context of the research.

The influence of innovation (Inov) on business performance (BP)

Based on Table 4, the results of testing the direct influence hypothesis indicate that there is an influence of Innovation on Business Performance (BP) with a path coefficient of 0.402 and p < 0.001. Given that p < 0.01, it is said to be significantly high, so that H3 is supported. The positive path coefficient (0.021) indicates that the higher the influence of Innovation (Inov) then Business Performance (BP) is increasing. The results of this study are in line with Saraswati and Widiartanto, (2016), Setyanti and Farida (2016), Hakim and Hassan (2012) who found that innovation influences company performance. The better a company innovates, the better the performance of the company will be.

The influence of knowledge management (KM) on business performance (BP) through innovation (Inov)

Based on Table 5 the results of testing the hypothesis of the indirect influence shows that there is an influence of Knowledge Management (KM) on Business Performance (BP) through Innovation (Inov) with a path coefficient of 0.147 and p < 0.002. Given that p < 0.01, it is said to be significantly high, so H4 is supported. This means that Innovation is a mediating variable because it is able to mediate the relationship between Knowledge Management (KM) and Business Performance (BP). The results of this study are in line with Hakim and Hassan (2012), Saraswati and Widiartanto (2016) who argue that the influence of knowledge management on business performance can be mediated by innovation. Although knowledge management cannot influence the company's performance directly, knowledge management remains an important factor in increasing innovation. Then, increasing business innovation will further improve the company's performance.

6. Conclusions and Implications

Based on the results, they show that the influence of Knowledge Management on Innovation, the better the implementation of Knowledge Management, the more Innovation increases. Innovation also affects business performance, the better the MSME innovation, the better the business performance. Directly, knowledge management does not have a significant influence on MSME business performance. Even so, knowledge management still plays an important role in improving business performance through innovation. This means that innovation is a mediating variable because it can connect between knowledge management and business performance. The better the application of knowledge management to MSMEs, the more they will improve their ability to innovate. Then, the high level of innovation will improve the performance of MSMEs.

The results of this study provide an understanding that knowledge management is a very important practice to be applied not only to large companies but also to MSMEs. Companies in general and MSMEs in particular should keep paying attention and improve the implementation of knowledge management to get superior business performance.

This research still has limitations, as well as samples that are limited to certain types of MSMEs (creative MSMEs). Therefore, further research is recommended to increase the number of samples used. In addition, this study only uses a quantitative approach, so it has not been able to answer the problems that need in-depth study. Furthermore, it is recommended to use the mix method approach to obtain a deeper picture of the phenomenon.

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