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Jurnal_analisis Faktor-faktor Yang Mempengaruhi Pertumbuhan Klaster Industri Mebel Klaten

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Analysis of Factors Influencing the Growth of the Furniture Industrial Clusters in Klaten (Case Study: Furniture Industrial Clusters in Serenan and Mireng) Citra Anggityas Industrial Engineering Department, Diponegoro University Jl. Prof. Sudharto, SH Semarang, INDONESIA [email protected] Abstract Since the implementation of regional autonomy in Indonesia, development of potential local or regional is a new orientation for regional development. Through the cluster approach, it is expected to provide services to SMEs in order to more focused, collaborative and efficient business activites. Klaten is chosen as research target because of Klaten is the second largest cluster furniture after furniture industrial cluster in Jepara that have the potential export - oriented furniture clusters and still need to be given attention in the development process. Furniture industrial cluster in Serenan and Mireng are taken as samples because both of clusters has a large number of business units, a large amount of labors, and a volume of sales, and so that can represent 33 furniture clusters in Klaten district. Generalized, viewed from a variety of indicators of industry, furniture business in Klaten Regency in 2000-2004 has been decreasing. The decline in growth rates show that industial cluster furniture in Klaten is still inability to develop their business in dealing the problems that hamper the performance of the furniture business. This also shows that the industrial cluster furniture in Klaten less able to compete with the industrial cluster furniture in other areas. The purpose of this study is to analyze the condition of the furniture industry cluster Klaten and the factors that influence the growth of the furniture industry cluster Klaten. Thus, this research can provide solutions to strengthen the growth of the furniture industry cluster in Klaten and for continuing for grow and survive the threats and challenges that continue through increased innovation, creativity, both in terms of product diversification and marketing in the global competition. This Research Refers to field studies and develop with literature studies with reference to previous studies about the factors that influence the growth of SMEs. Researchers will be built a new research model that will be used to analyze empirically about the growth of the furniture industry cluster in Klaten. This model will analyze the influence factor of networking, capital, marketing, role of government, and entrepreneurship to the growth of the furniture industry cluster Klaten. This research used analysis method Partial Least Square (PLS) through a second order approach. 1
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Page 1: Jurnal_analisis Faktor-faktor Yang Mempengaruhi Pertumbuhan Klaster Industri Mebel Klaten

Analysis of Factors Influencing the Growth of the Furniture Industrial Clusters in Klaten

(Case Study: Furniture Industrial Clusters in Serenan and Mireng)

Citra Anggityas Industrial Engineering Department, Diponegoro University

Jl. Prof. Sudharto, SH Semarang, INDONESIA

[email protected]

AbstractSince the implementation of regional autonomy in Indonesia, development of potential local or regional is a new

orientation for regional development. Through the cluster approach, it is expected to provide services to SMEs in order to more focused, collaborative and efficient business activites. Klaten is chosen as research target because of Klaten is the second largest cluster furniture after furniture industrial cluster in Jepara that have the potential export-oriented furniture clusters and still need to be given attention in the development process. Furniture industrial cluster in Serenan and Mireng are taken as samples because both of clusters has a large number of business units, a large amount of labors, and a volume of sales, and so that can represent 33 furniture clusters in Klaten district. Generalized, viewed from a variety of indicators of industry, furniture business in Klaten Regency in 2000-2004 has been decreasing. The decline in growth rates show that industial cluster furniture in Klaten is still inability to develop their business in dealing the problems that hamper the performance of the furniture business. This also shows that the industrial cluster furniture in Klaten less able to compete with the industrial cluster furniture in other areas.

The purpose of this study is to analyze the condition of the furniture industry cluster Klaten and the factors that influence the growth of the furniture industry cluster Klaten. Thus, this research can provide solutions to strengthen the growth of the furniture industry cluster in Klaten and for continuing for grow and survive the threats and challenges that continue through increased innovation, creativity, both in terms of product diversification and marketing in the global competition.

This Research Refers to field studies and develop with literature studies with reference to previous studies about the factors that influence the growth of SMEs. Researchers will be built a new research model that will be used to analyze empirically about the growth of the furniture industry cluster in Klaten. This model will analyze the influence factor of networking, capital, marketing, role of government, and entrepreneurship to the growth of the furniture industry cluster Klaten. This research used analysis method Partial Least Square (PLS) through a second order approach.

Data processing showed that the factors that most influence the growth of the furniture cluster in Serenan are networking, capital, marketing, and entrepreneurship. While the factors that affect growth in the Furniture Cluster Mireng, among other networking, capital, marketing, role of government, and entrepreneurship.

Kata Kunci : Factor Analysis, Cluster Growth, Industrial Cluster Furniture Klaten, Partial Least Square (PLS)

1

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I. INTRODUCTIONDuring 1997-2006, the number of SMEs reached 99% of all

business units in Indonesia. SMEs contribution to Gross Domestic Product (GDP) have reached 54% -57%. Contribution of SMEs to the employment is about 96% and about 91% of SMEs did export activities through third-party exporters/ brokers. Only 8.8% of which deal directly with buyers/importers in foreign countries (Afiah, 2009). In the era of regional autonomy, each region is encouraged to take advantage of local resources to enhance the competitiveness of products produced by the region, both of domestic and international markets with the paradigm of “locally but think globally action” (Oka, 2000). Industrial cluster is one of the way for enhance the competitiveness of products. Porter (2003) explains cluster is a geographic concentration of companies and institutions are interconnected in a particular area.

During ten years up to 2004, the contribution of manufacturing sector to the economics of Indonesian is average of 26.9%, of which the non-oil processing industries contribute 86.5%, and the rest is oil and gas processing industries.Wood Industry (including rattan and bamboo) is one of the non-oil processing industry that have contribution 5.55% (2005), 5.82% (2006), and 6.19% (2007) to GDP. This sector also has a poor growth, which is only -0.92% (2005), -0.66% (2006), and -1.74% (2007) (Deperindag, 2008).

Indonesia has several potential areas of industrial products. Central Java Province has the largest village in the wood processing industry, as many as 5,125 villages (BPS, 2007). In this region, the wood furniture industry is a major commodity exports of non-oil, after the plywood and textiles (Susila, 2007). In 2003, the Jepara wood furniture industry accounted for 60-70% of national exports of furniture and have been able to break through the market share in 71 countries. However, it has actually grown Sentra other wooden furniture industry with a smaller scale, such as, Klaten, Sukoharjo, and Semarang (Effendi, 2006).

Fig.1 The Growth of Wood Furniture Export Value of Indonesia in 2000-2005

Source: Asmindo (2006) dalam Tambunan 2006

Klaten Regency is a center for wooden furniture industry the second largest after the district of Jepara. Wood furniture is a commodity Klaten district, in addition to tile and ceramics,

garments, metal casting, iron Pande, and tobacco (Susila, 2007). Klaten furniture products have characteristics mostly plain or without engraving (Effendi, 2006).

From the tabel 1., cluster furniture in Serenan and Mireng are important industry that needs attention, it’s meaning that this location has good prospects and should be developed but this industry requires attention in the development process

Table 1. Comparison of Serenan and Mireng Output

Variable Serenan MirengVolume of production (unit) 262.136 unit 59.681 unitVolume of sales (Rp) Rp 112.368.000.000 Rp 16.464.100.000Income/month Rp 976.000.000 Rp 509.020.000Turnover Rp 39.320.400.000 Rp 8.952.150.000Investment Rp 18.420.000.000 Rp 3.571.200.000Unit Business 200 unit usaha 155 unit usahaEmployement 1200 orang 1000 orangLQ (2007 to 2009) 5,93 (potential) 2,59 (potential)Growth Rate (2007 to 2009) -12,24 (low) -18,28 (low)GDP 2009 0,86% (low) 1,96% (low)Gini Index of Klaten District 0,196 (High Equality)

Serenan furniture industry is the largest furniture industry center in Klaten district, which 75% of the population working as a craftsman. The rest work as civil servants, farmers, and businesses trading or self-employed. Although he has had major work, they still do the odd job as a furniture craftsman (Susila, 2007)

Recent data from WTO showed that in the year of 2005, China has became the largest furniture export country in the world surpassed Italy with an export value of US$ 14 billion or accounted of 18% from total furniture export. While Indonesia’s furniture export value during the same period was recorded at US$ 1.79 billion or covered only 2 % of world furniture market (Figure 4). There is a great possibility that China’s export value of furniture will continue to progress rapidly in the following years, judging from the country’s aggressive effort to boost its export performance both furniture and other products as well. And Indonesia has to be very cautious since the expansion of China’s furniture in the world market may be a great disadvantage for Indonesia’s furniture, even loosing its external market (Tambunan, 2006).

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Fig. 2 The Export Value of Indonesia’s Furniture and Competitor Countries in Asia, 2005 (in billion US$)

Source: Asmindo (2006) dalam Tambunan 2006

In general, viewed from various indicators of industry, furniture business in Serenan Klaten Regency Years 2000-2004decreased. Growth number of units of furniture business in Serenan year 2002-2004 is negative, in a row -23.96%, -14.53% And -2.87%. From the workforce growth side in 2000-2004 is fluctuated, while the year 2003-2004, employment growthachieve a negative number, respectively of -56.38% and -5.34%. Likewise investment value growth in 2000-2004, which also fluctuate, and in 2004 negative investment value growth for-28.09. The same thing happened condition of the furniture production value. In 2002-2004, growth in production valueNegative furniture at -0.59, -47.35 and -6.79 (Susila, 2004)

This study is used analysis technique Partial Least Square (PLS) through a second order approach using software SmartPLS that will analyze the relationship between networking, capital, marketing, role of government, and entrepreneurship to the growth of the furniture industrial cluster in Klaten.

Some previous studies about the furniture industry is very diverse, drawing on different models. Junaidi (2002) explains the factors that hamper the competitiveness of the furniture industry among others, low capital, low quality of raw materials due to lack of good drying, high levels of strict quality control of industrial partners, and the weakness of the craftsmen in attracting buyers from abroad. Fereshti, et al (2008) explains the Furniture Cluster Serenan still requires strengthening in business process both in terms of product quality, capital, marketing, and ability to innovate. Susila (2007) explains that the furniture business Serenan require guidance from government, universities, and other appropriate institutions to achieve technical efficiency. Effendi (2006) explains the furniture industry in Central Java is still constrained in terms of raw materials, production processes are less efficient, low-quality products, competitive market, and barriers in the furniture trade.

Based on the observed study and previous study, it can be resulted that many factors that are inhibiting the growth of Klaten furniture industrial cluster. Therefore, this research contributes empirically study to examine the factors that affect growth in Furniture Cluster Klaten to provide a view of condition of clusters and solutions to strengthen growth in the furniture industrial cluster in Klaten for continuing the grow and survive the threats and challenges that continue to grow through increased innovation, creativity, both in terms of product diversification and marketing in the global competition. Through this study, the goals of the research are:

1. To identify condition of the furniture industrial cluster in Klaten.

2. To analyze factors that influence the growth of the furniture industrial clusters in Klaten.

3. To give recommendation for increasing the growth of furniture industrial clusters in Klaten.

II. RELEVANT LITERATURE A. Industrial Cluster

Porter (2003), Clusters are geographically integrated companies and associated organizations that share together technological know-how, knowledge, skills, competencies, and resources.

Navickas (2009), SME cluster is the centralization of SMEs in their location. Most researchers agree that one of the main powers to promote the economic development of a small territory (town, village) is a large number of SME clusters, based on the township enterprises and the private enterprises, called ‘lump economy’, such as ‘one village – one product’, ‘one town – one industry’. The lump economy is constituted of several professional towns and villages, that are concentrated on producing one product.

Navickas (2009), Clusters (clusterization) can be seen as a productivity and innovativeness improvement tool, while both innovativeness and productivity are greatly associated with growing competitiveness in national and global markets. Cluster policies can lead to economic and social development, generating new jobs and bringing people out of poverty.

Navickas (2009), Clusterization can stimulate the development and growth of SME sector, as SMEs that participate in clusters can get advantage from advanced and specialized infrastructure, qualifi ed workforce, increased possibilities to penetrate new markets, increased ability to meet the needs of clients, cost reduction in manufacturing operations.

B. Wood Furniture Industry

Currently in Indonesia there are about 950 wood furniture industry business units with a capacity of 3.41 million m3/year (not including small-scale furniture industry and home industry) and absorbted the direct labor as much as 435,112 people. The location of the furniture industry is spread almost all over the Archipelago, but a fairly large concentration in several provinces, such as: Central Java (Jepara, Solo, Klaten, Semarang), In Yogyakarta, DKI Jakarta, East Java (Surabaya, Gresik, Pasuruan, Mojokerto), North Sumatra, East Kalimantan, Central Kalimantan, South Kalimantan, South Sulawesi, North Sulawesi and Central Sulawesi (Deperindag, 2008).

BPS data in 2005, further compiled by ASMINDO, stated that USA and Erope are the primary market for wood furniture export from Indonesia with wood and rattan made materials. During that year, US market covered around 36.6% from the total export of Indonesia’s furniture, while largest export destination countries in EU market include Dutch, England, Germany, France and Italy. In Asia, Japan is most important market which covered 12% shares from total export of Indonesia’s wood furniture (Tambunan, 2006).

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Fig. 3 Primary Destination Countries of Indonesia’s Wood Furniture Exports in 2005

Source: Tambunan 2006

Junaidi (2002) explains that large furniture industry usually has its own factory mainly to work on finishing the product, export-oriented, have a good relationship with the craftsmen and mastering the raw materials. Characteristics of small and medium-sized furniture industry besides being a major subcontractor company also markets its products aimed at meeting domestic and foreign. While a small industry has a local market orientation. Only medium and large companies involving hundreds of . This can be explained, when small and medium-sized companies received orders from big companies.

C. Growth of Cluster Industrial FurnitureAnderson (1994) in Rachel (2004) explains, the relationship

between actors in the cluster is very important. The success of clusters is determined also by the quality of relations between actors. Interaction of business actors in the cluster will generate benefits in the form of the creation of specialty products in a region, is formed and the development of cooperation and a strong network of cooperation between SMEs and other actors, like governments, supporting industry, research institutions, and financial institutions (Anderson, 2004 in TBR economics). Tambunan (2006) measure of economic growth in a cluster as seen from the Gross Domestic Product (GDP), production volume, sales volume, export volume and market share and competitiveness clusters of the RCA.

David Beeton (2007), explains difficult to measure the benefits of the cluster. However, standard procedures to determine the cluster growth is usually seen from the product output, sales volume, employment, business unit growth, and growth in the number of jobs.

Rachel (2004) using employment growth and LQ to see the growth of clusters. Armen Zulman (2006) explains, the good investments that can effective and able to encourage local and national economic growth in maksimal. Piet Rietveld (1993) also explains that the growth of labor as an indicator of the growth of SMEs.

However, many believes that Indonesia’s wood industry may survived the competition due to the ability to fit in the market condition by improving quality control, superior design, ability to meet the international environment standard, and enhancing marketing effort in international market (Tambunan, 2006).

D. Partial Least Square (PLS)Partial Least Square (PLS) is a method of analysis that is not

based on many assumptions that aims to make predictions (Ghozali, 2008). PLS is one branch of the Structural Equation Model (SEM) based Variance or called Variance Base often called Structural Equation Model (VBSEM) which is the development of the covariance-Based Structural Equation Models (CBSEM). In VBSEM allowed a reflective model. This is because the Partial Least Square analyzing constructs can be formed with reflective indicators and formative indicator. The purpose of VBSEM is to make predictions. In PLS, the data do not have normal distribution and does not need big sample. Data unnecessary have normal distribution, means that the scale used to measure the indicator can use a variety of scales ranging from the interval until the ratio. Analysis of the Partial Least Square model can be based on inner and outer models.

Partial Least Square (PLS) Second Order(Ghozali, 2008), research sometimes is have a multidimensional construct with which each dimension is measured by indicators. Dimensional first order or any other name would directly related to the indicator. There are 4 types of second order constructs, namely:a. Type I : Reflexive first order, second order Reflexiveb. Type II : Reflexive first order, second order Formativec. Type III : Formative first order, second order Reflexived. Type IV : Formative first order, second order Formative

This study uses the second order type I.

Analysis of Outer ModelAnalysis of outer model is used to determine the relationship

between variables with the indicators. Analysis of outer model can be done through convergent validity, discriminant validity, and composite reliability (Ghozali, 2008).

Analysis of Inner ModelAnalysis of inner model is used to determine the relationship

between variables. Analysis of inner models can be done by the R-Square and through the structural path coefficients. R-Square is the coefficient of determination used to see the ability of independent variables to explain the dependent variable. While the structural path coefficient was used to determine how big the influence of independent variables to the dependent variable (Ghozali, 2008).

Table 2. PLS Assessment Criteria (Ghozali, 2008)

Criteria Parameter ExplanationOuter Model

Convergent Validity

The value of loading factor should be above 0.7 (for new research factor loading values permissible > 0.5)

Discriminant Validity (Cross Loading)

Each block indicator has a higher loading value for each of the latent variables measured compared with other indicators to latent variables.

Composite Measuring Internal consistency.

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Reliability The value must be above 0.6

Inner Model

R2 Results R2 of 0.67, 0.33 and 0.19 for endogenous latent variables in structural model indicates that the model of "good", "moderate", and "bad"

Estimation Path Coefficient

The estimated value for the connection point in the structural model should be significant. The value of this significance can be obtained from the bootstrapping procedure.

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Multigroup AnalysisMultigroup in PLS analysis is used to empirically examine

differences between groups. Until recently, multigrup analysis used to compare the PLS model for several groups. Often, the researchers only examine and discuss different models of the structural paths for two or more data-sets. When assessing the significance of the differences, the t-test values are based on standard errors obtained were collected through a procedure such as bootstrap resampling of the sample respectively (Cin, 2010).

III. RESEARCH METHODOLOGY

Types of ResearchThis research is the deductive-inductive research that starting

with observing the research object and developed with reference previous studies about the factors that influence the development of SMEs.

SamplesClusters in Mireng and Serenan in Klaten are taken as a

samples of object research because both of clusters have a large number of business units, a large amount of labors, and a large turnover, and have similar characteristics so that they can represent 33 furniture industrial cluster in Klaten district.

Data CollectionThis research is observing the condition of furniture industrial

and also used disseminating questionaire and interviewed the actors who direct involved the activity of furniture industry as an owner of SMEs, chief of cooperative, the chief of society of the clustersfor each cluster, the government, and Association of furniture.

Description of Research VariablesResearchers will be building a new conceptual model in this

research based on previous study.

Fig. 4 Conseptual ModelBased on the above conceptual model, the hypothesis can be

arranged as follows:1. H1: Networking effecting the growth of clusters

Cluster networking is the relationship and interaction between actors in cluster (Lyon & Atherton in TBR economics 2007). Tambunan (2009) explained that a good network of partnerships is necessary for export-oriented SMEs. Through linkages, there are companies that do not just compete (competition) between the one with (Kacung Marijan, 2005). Anderson (1994) in Rachel (2004) explains, the relationship between actors in the cluster is very important. The success of clusters is determined by the quality of the relationship. Anderson (2004) in TBR Economics (2007) explains that interaction of business actors in the cluster will generate benefits in the form of the creation of specialty products in a region, is formed and the development of cooperation and a strong network of cooperation between SMEs and other actors, like governments, supporting industry, research institutions, and financial institutions. Dr. Djamhari (2006), network of partnerships are the factors that influence survival cluster. Cooperation among SMEs in the national level and at global scale is very important as tools in developing economies. The principle of partnership, namely: are complementary, mutually reinforcing, interdependence, and mutual benefit, is really a solid foundation. Valentinas and Asta (2009), Cooperation between SMEs at national level as well as on a global scale is very important as tools in developing economies. Research in the North East of England by Lyon and Atherton in TBR economics (2007) showed that clustering involves multiple linkages, which are Vertical supplier links, Horizontal informal links, Horizontal formal collaboration, Formal association, and Gaining access to common assets and resources.

2. H2: Capital effecting the growth of clustersNguyen, et al (2009), capital is one of the factors that influence efforts to enhance the growth of small and medium enterprises. In addition to credit requirements not easily met, and the lack of information provided by financial institutions to entrepreneurs. The difficulty of capital for SMEs is constrained in access difficulty in obtaining capital, and difficulties in borrowing on the relevant parties. SMEs have almost no access to long-term credit, or even for short-term credit in the domestic formal credit fund, they also have almost no access to formal credit internationally. Dr. Djamhari (2006), capitals are the factors that influence for survival cluster. Tambunan (2005) explains that constraints in small and medium industry are the lack of capital and difficulty in marketing. Nguyen, et al (2008) Cooperation between SMEs at national level as well as on a global scale is very important as tools in developing economies.

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3. H3: Marketing effecting the growth of clustersMarketing according to Kotler (2002) in Suwarni (2009) is a social process in which individuals get what they need and want by creating, offering, and freely exchanging products of value with others. Nguyen, et al (2009), to enhance the growth of SMEs, it takes good marketing and also by interference of government in the conduct mentoring. DR. Choirul Djamhari (2006) explains the existence of large corporations, access to markets, and market information effect on cluster growth. Almasdi Syahza (2003) describes, in terms of marketing, the difficulties faced by small businesses primarily due to the limitations of the various important things, such as information about changes and market opportunities, fund marketing / promotion, knowledge about business and marketing strategies (especially local and regional levels).Suwarni (2009), dimensions of marketing includes a strategy, often called the strategy mix that defines the marketing mix is a combination of four dimensions that form the core of the company's marketing system namely: product strategy, pricing strategy, promotional strategy, and distribution strategy that can increasing volume of sales.

4. H4: Role of Government effecting the growth of clustersRaines (2001) dalam TBR economics (2007), menjelaskan kebijakan klaster adalah intervensi dari pemerintah atau actor masyarakat lain dalam memperhatikan perkembangan klaster. Jin (2006) explains that the role of government is needed in boosting the competitiveness of SMEs. Porter (2006) The role of government influence to enhance product competitiveness. Wickam (2005), there are three key government roles positively influenced cluster’s development. The first was the state government’s initial non-committal stance towards the specific development of the state’s burgeoning shipbuilding industry. The second role surrounded the enhancement of the state’s reputation within the domestic market as a centre research. The third role was the government’s support for the entrepreneurial activities, when it became apparent that the company was a potential source of significant economic growth for the regional economy Wickam (2005), Government bodies establish local product standards or regulations that mandate or influence buyer needs. Government policy also influences firm strategy, structure and rivalry, through such devices as capital market regulations, tax policy and antitrust laws.

5. H5: Entrepreneurship effecting the growth of clustersSeonarto, et al (2006), required an effort to strengthen entrepreneurship in order to increase cluster performance through the strengthening of capital, management organization, capital, and marketing. According to the Iin Mayasari Hisrich (2009), entrepreneurship is the process of creating something new by devoting the necessary time and

effort, assuming the financial risk, psychological, and social, and receiving the resulting rewards of monetary and personal satisfaction and independence. Tambunan (2005), explains that the representation of women entrepreneurs in Indonesia is still low. This is caused by low levels of education and lack of opportunity to get training. Nguyen (2009), emphasized the entrepreneurial culture must be maintained to support SMEs in areas such as values, beliefs, attitudes and norms of behavior and performance of SMEs. William, et al (2003) explains that entrepreneurship is influenced by the courage in taking risks, proactive attitude, innovation, and culture. SME owners typically are not willing to take risky business decisions and the attitude of prominent risk aversion among entrepreneurs.The research variables, dimensions, and indicators in

Cluster Growth can be seen on table 3.1 below.Table 3. Research Variables, Dimensions, and Indicators

Variables Dimensions IndicatorsNetworking Vertical supplier links The effectiveness of joint purchase

of raw materialsThe effectiveness of collective-selling

Horizontal informal links

The effectiveness of information sharing in a clusterSatisfaction order fulfillment cooperation in cluster The effectiveness of order fulfillment cooperation in the cluster Satisfaction order fulfillment cooperation with similar clustersThe effectiveness of order fulfillment cooperation with similar cluster

Horizontal formal collaboration

The effectiveness of cooperation with Partners

Formal association Involvement with the AssociationThe effectiveness of cooperation with the Association

Gaining access to common assets and resources

The effectiveness of cooperation with the governmentThe effectiveness of cooperation with big companies

Capital Sources of capital Sources of capitalFluency of capital turnover

Access of Capital Quantity break-out in the submission of capital

Rules of borrowing Ease of administrationLevel of ability eligible loan

Marketing Product Strategy The effect of using brandEase of serviceImplementation of quality managementApplication of Standard products

Price Strategy Price flexibilityDisconEase of payment

Promotion Strategy Level of use of brochures

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Level of online advertisingQuantity exhibition participationThe quantity of direct salesLevel of publicity

Distribution Strategy Convenient transportationConvenient storageEase of distribution

Role of Government

National Industrial Policy

The role of competition policyThe role of capital policyThe role of taxation policyThe role of employment policyThe role of trade policyThe role of eco-labeling policies

Government Program Quantity of trainingQuantity assistance

Condition of government

Effects of social and political stability the countryEffect economiesEffect of state security

Entrepreneurship

risk-taking The courage fulfill ordersThe courage stocking goodsThe courage to innovate

Proactive Business OrientationUtilization order opportunitiesCompetition LevelUtilization of market opportunitiesUtilization of opportunities to innovate

Innovation The quantity of product design innovationThe quantity of material diversificationQuantity of product diversificationThe quantity of waste production innovationThe quantity of production process innovationQuantity finishing innovation

Culture Level of welfareLevel of survivalLocationHistory OrganizationKinshipSubculture adopted levelsLevel of employment opportunitiesLong-Term Orientation

Cluster Growth

Growth of volume of salesGrowth of employmentGrowth of market

IV.RESULTA. Condition Of Industrial Furniture Cluster

Based on observing the condition of furniture industrial cluster, here are the result of issues raised in the Cluster furniture Klaten:

Table 4. Condition of Furniture Industrial Cluster in Klaten

Factors Problems

Networking Majorities are depending on exporters orders Relationships with partners are very prioritized

Capital The capital majority is own capital and tend to be small,

and the capital of the others come from banks, but very small too.

Do not have a long-term planning and planning that is tend to be weak because of limited capital. Consequence there is no business expansion.

Marketing Traditional, Relying "getok tular" (from mouth to mouth).

Lack of innovation in marketing such as lack of internet marketing, or following exhibitions.

Aspects of locations that are still in the countryside is also become a limit for the marketing process.

Lack of showrooms which have good management

Role of Government

Lack of socialization of industry policy There have been training from the government, but no

action continuity in coaching and mentoringEntrepre-neurship

The type of product requested by the buyer, so that rare product innovation.

There is no radical innovation of the production process Majorities are very dependent with collectors or

exporters and there is no attempt to penetrate directly into the export market.

Majorities are still less educated, it making difficult to be invited to develop

B. Analysis ModelOuter Model Serenan and Mireng

In outer model, we testing the relationship between indicators to the first order constructs is shown by constructs are shown by convergent validity, discriminant validity, and composite reliability and testing the relationship between indicators to the second order construct are shown by t-value indicators to the second order construct. The finish result of the running are shown in appendix 1. that represented the indicators can meet the criteria outer model and indicators have been significant for measuring the second order constructs networking, capital, marketing, role of government, entrepreneurship, and growth of cluster. Based on the outer model result, we can analysis the inner model.

Inner Model SerenanIn inner model, we testing the relationship between first order

and second order that can shown in appendix 2. and testing the hypothesis (second order construct with construct growth of cluster) is shown in the table below.

Table 5. Hypothesis Testing Result Serenan

H: RelationshipPath

CoefficientT

StatisticsR

SquareDecision

H1 GROWTH OF CLUSTER -> NETWORKING

0,454 3,571 0,206 accepted

H2 GROWTH OF CLUSTER -> CAPITAL

0,238 1,708 0,242 accepted

H3 GROWTH OF CLUSTER -> MARKETING

0,492 3,821 0,057 accepted

H4 GROWTH OF CLUSTER -> ROLE OF GOVERNMENT

0,034 0,240 0,001 rejected

H5 GROWTH OF CLUSTER -> ENTREPRENEURSHIP

0,415 4,063 0,172 accepted

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R Square Model 0,530

Based on the table 4, it can be seen the R-square of model is 0.530. These results indicate that the model has a good feasibility of being able to explain the growth of clusters with variable network, capital, marketing, the role of government, and entrepreneurship by 53%. The 47% remaining can be explained by other variables outside of the five variables. The value of determination coefficient model including "moderate", is above 33% (Imam Ghozali, 2008).

Testing the hypothesis on PLS analysis, basicallywas to test the significance of the existing path coefficient on the model. Hypothesis is accepted if the statistical t-value greater than t-table (1.659) for significance of α = 0.1 and df = 49.

Figure 4. Big and Small Effect Factors of The Cluster Growth Model in Serenan (Blue : Big Effect, Red : Small Effect)

H1: Networking effecting the growth of clustersHypothesis 1 is shown that t statistics in this relationship is

3.571. Value of t statistics is in an acceptence area, which is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H1 is accepted. That is, networking partnerships statistically affect the growth of clusters with the value of the parameter coefficient of 0.454.This hypothesis is supported by the theory presented by Tambunan (2005) which argue that a good network of partnerships are needed SMEs mainly export-oriented SMEs and also supports the theory Djamhari (2006) which argue networking partnerships affect the growth of cluster. Based on the value of R square of 0.206 which means that the construct of growth can be explained by the construct of networking partnership for 20.6% by five second order constructs which are vertical supplier links, horizontal links informal, formal horizontal collaboration, formal association, and Gaining access while 89,4% remaining influenced by other factors outside the five second order construct it. R square construct networking relatively weak,

Networking in Serenan especially horizontally informal networking links that relationship in an effective order

fulfillment is necessary important for supporting the growth of the cluster Serenan. This can be enhanced through the formation of small groups are better organized as a partnership in order fulfillment, better information sharing with member in the cluster and outside the cluster as the cluster Serenan.While vertical supplier links that are still less effective could be improved small groups organized and mutually neutral in terms of financial and process. This can be initiated through the sale of products through to include members of the cluster to the exhibition. The program can be run more effectively through assistance from the government. Serenan as export-based SMEs have also would be better if building a good network of groups with buyers, suppliers, and investors in order to expand their market share.

H2: Capital effecting the growth of clustersHypothesis 2 is shown that t statistics in this relationship is

1.708. Value of t statistics is in the acceptence area is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H2 is accepted. That is, capital is statistically affect cluster growth with the value of the parameter coefficient of 0.238. The hypothesis is supported by the theory presented Nguyen (2009) that clusters affect the growth of capital. R2 value is 0.005 which means that the construct of growth can be explained by the construct of capital amounting to 0.5% by three second order constructs which are sources of capital, access of capital, and borrowing requirements.

Capital especially the sources of capital, is very affecting growth in the cluster Serenan. This is because there are still many employers who still rely on private capital and they are very scared in borrowed capital that has them particularly requirement of capital from large institutions. Differently, if the existing capital is form of loan assistance from the government that is sometimes provided free of charge, the craftmens will seek to fulfill the capital requirements. This can be improved by doing a credit in cooperative. While access to capital that has been very difficult, can be improved by strengthening networks of cooperation, especially on the part of investors.

H3: Marketing effecting the growth of clustersHypothesis 3 is shown that t statistics in this relationship is

3.821. Value of t statistics is in an acceptence area, which is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H3 is accepted. That is, marketing is statistically affect the growth of clusters with the parameter value of 0.492. This hypothesis is strengthened by the theory of Nguyen (2009) which argue that marketing affects the growth of clusters. R2 value of 0.230 which means that the construct of growth can be explained by the characteristics of the source of 23% by four second order constructs which are product strategy, pricing strategy, promotional strategy and distribution strategy, while the other 77% is influenced by other factors beyond the four second order these constructs. R square value of marketing constructs relatively weak.

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Marketing especially pricing strategy that the price flexibility, discounts, and ease of payment are things to consider for fixed gain potential market. This can also be strengthened with the implementation of product quality management, compliance with product standards, ease of service, and use is necessary in supporting the smooth growth of the cluster Serenan. With the improvement in terms of quality, It can be improved in addition to the development of private enterprise, sharing information with fellow entrepreneurs furniture, and participation in such associations can be helpful such as ASMINDO because ASMINDO is an organization that tries to facilitate the entrepreneurs of furniture to be able to compete well in terms of standard products through meetings or seminars on product standards in the growing global market. While the strategy was less effective promotion can be enhanced also through personal development, through sharing with small groups that have been established in terms of marketing, trying to innovate in marketing, such as making of furniture catalogs, brochures of interest or sales through online in stages. Participation in ASMINDO can also help employers to be able to find information about exhibitions. All of this should also be constituted with the desire to expand, a proactive attitude and courageous attitude of the risk.

H4: Role of Government effecting the growth of clustersHypothesis 4 is shown that t statistics in this relationship is

0.186. Value of t statistics are within the rejection area is located in the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H4 is rejected. This means that the government's role is not significantly affect the growth of clusters with the value of the parameter coefficient of 0.034. This hypothesis is contrary to Jin, et al (2006) and Mark Wickham who claim the role of government affect the growth of clusters. Based on the value of R2 is 0,001 which means that the construct of growth can be explained by the characteristic of 0.1% by three second order constructs, government policies, government programs, and state government. R square value of capital construct relatively weak.

The hypothesis was rejected. This could be because the information of industrial policy not too clear and not so touching for entrepreneurs in managing the furniture business in Serenan. So did the problems of government programs that they are not effective in increasing growth in Serenan. Government programs that often there is no continuation of this process was not effective in supporting the growth of clusters Serenan. The assistance that is still less touching business processes in Serenan. It can also be caused by differences in "time" the effective role of government. That in Serenan, cooperation with the government is not new, cooperation with the government have on several occasions carried out. So in Serenan cooperation with the government is seen as something familiar and the role of governments are now considered not met the expectations of cluster Serenan. That Serenan today needed, government can be able to facilitate both financial and accommodation in the

exhibition, especially for export scale as do local governments Sukoharjo. But this turns out not to be well facilitated by the government.

H5: Entrepreneurship effecting the growth of clustersHypothesis 5 is shown that t statistics in this relationship is

4.063. Value of t statistics is in the acceptence area is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H5 is received. That is, entrepreneurship is statistically affect the growth of clusters with the value of the parameter coefficient of 0.415. This hypothesis is supported by the theory of Nguyen (2009) and Tambunan (2009) and Soenarto (2006) which states entrepreneurial influence the growth of clusters. Based on the value of R2 of 0.172, which means growth of clusters can be explained by the construct by 17% by four second order constructs, which are risk-taking, proactive, innovation, and culture, while the other 77% is influenced by other factors beyond the four second order construct. R square value of entrepreneurship constructs relatively weak.Entrepreneurship especially culture dimension that the level of welfare, kinship relationships, the level of chance, get a job, the level of subcultures adopted, and long-term orientation is needed in supporting the smooth growth of the cluster Serenan. This can be improved in addition to the development of private enterprise, sharing information, initiating management system and a good division of labor such as authorizing a woman to help administration activities so that the entrepreneurs are able to see the development of their respective businesses so that entrepreneurs can create and run vision and mission is clear about his company for several years to come. Meanwhile, proactive attitude is still less effective as a lack of ability to innovate and explotion the market opportunities can be enhanced also through personal development, through sharing with small groups that have been established in the business process. All of this should also be constituted with the desire to expand.

Inner Model Mireng

Table 5. Hypothesis Testing Result MirengH: Relationship

Path Coefficient

T Statistics

R Square

Decision

H1 GROWTH OF CLUSTER -> NETWORKING

0,494 4,395 0,244 accepted

H2 GROWTH OF CLUSTER -> CAPITAL

0,336 2,306 0,113 accepted

H3 GROWTH OF CLUSTER -> MARKETING

0,534 5,670 0,286 accepted

H4 GROWTH OF CLUSTER -> ROLE OF GOVERNMENT

0,488 3,568 0,238 accepted

H5 GROWTH OF CLUSTER -> ENTREPRENEURSHIP

0,255 1,695 0,065 accepted

RSquare Model 0,658

Based on the table 5, it can be seen the R-square of model is 0.658. These results indicate that the model has a good feasibility

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of being able to explain the growth of clusters with variable networing, capital, marketing, the role of government, and entrepreneurship at 65.8%. While the rest of 34.2% explained by other variables outside of the five variables. The value of determination coefficient model including "moderate", ie above 33% (Imam Ghozali, 2008).

Testing the hypothesis on PLS analysis, basicallywas to test the significance of the existing path coefficient on the model. Hypothesis is accepted if the statistical t-value greater than t-table (1.659) for significance of α = 0.1 and df = 49.

Figure 5. Big and Small Effect Factors of The Cluster Growth Model in Serenan (Blue : Big Effect, Red : Small Effect)

H1: Networking effecting the growth of clustersHypothesis 1 is shown that t statistics in this relationship is

4.395. Value of t statistics is in an acceptence area, which is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H1 is accepted. That is, networking partnerships statistically affect the growth of clusters with the value of the parameter coefficient of 0.494.This hypothesis is supported by the theory presented by Tambunan (2005) which argue that a good network of partnerships are needed SMEs mainly export-oriented SMEs and also supports the theory Djamhari (2006) which argue networking partnerships affect the growth of cluster. R square value of 0.244 which means that the construct of growth can be explained by the construct of networking partnership of 24.4% by five second order constructs, namely vertical supplier links, horizontal links informal, formal horizontal collaboration, formal association, and Gaining access while at 76.6 % are influenced by other factors outside the five second order construct it. R square value construct networking relatively weak.

Networking in Mireng especially horizontally informal networking links that the relationship in an effective order fulfillment is necessary important for supporting the growth of the cluster Mireng. This can be enhanced through the formation of small groups are better organized as a partnership in order fulfillment, better information sharing with member in the cluster and outside the cluster as the cluster Mireng.While Gaining access is still less effective can be increased through assistance from the government on an ongoing basis or make a small group to gather network in reaching a large company that will provide a continuous order Mireng entrepreneurs.H2: Capital effecting the growth of clusters

Hypothesis 2 is shown that t statistics in this relationship is 2.339. Value of t statistics is in the acceptence area is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H2 is accepted. That is, capital is statistically affect cluster growth with the value of the parameter coefficient of 0.336. The hypothesis is supported by the theory presented Nguyen (2009) that clusters affect the growth of capital. Based on the value of R2 of 0.113 which means that the construct of growth can be explained by the construct of capital of 11.3% by three second order constructs, sources of capital, access of capital, and the borrowing requirement amounted to 88.7% while others are influenced by other factors outside the four second order construct them. R square value of capital construct relatively weak.

Capital, especially the problem of borrowing capital requirements, are things that affect growth in the cluster Mireng. This is because there are still many employers who still rely on private capital and they are very scared in borrowed capital that has them particularly requirement of capital from large institutions. Differently, if the existing capital is form of loan assistance from the government that is sometimes provided free of charge, the craftmens will seek to fulfill the capital requirements.

H3: Marketing effecting the growth of clustersHypothesis 3 is shown that t statistics in this relationship is

3.568. Value of t statistics is in an acceptence area, which is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H3 is accepted. That is, marketing is statistically affect the growth of clusters with the parameter value of 0.488. This hypothesis is strengthened by the theory of Nguyen (2009) which states that marketing affects the growth of clusters. R2 value of 0.286 which means that the construct of growth can be explained by the characteristic of 28.6% by four

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second order constructs which are product strategy, pricing strategy, promotional strategy and distribution strategy, amounting to 71.4% while others are influenced by other factors outside the four second order construct it. R square value of marketing constructs relatively weak.

Marketing promotion strategy mainly influences the growth in Mireng. It can also be enhanced through personal development, through sharing with small groups that have been established in terms of marketing, trying to innovate in marketing, such as making of furniture catalogs, brochures of interest or sales through online in stages. Participation in ASMINDO can also help employers to be able to find information about exhibitions. All of this should also be constituted with the desire to expand, a proactive attitude and courageous attitude of the risk. While the product strategy, which are product quality management application, fulfillment of product standards, ease of service, and the use of which is indispensable in supporting the smooth growth of the cluster Mireng still less effective. This can be improved in addition to the development of private enterprise, sharing information with fellow entrepreneurs furniture, and participation in such associations can be helpful such as ASMINDO because ASMINDO is an organization that tries to facilitate the furniture business to be competitive both in terms of product standards through meetings or seminars about growing product standards in the global market.H4: Role of Government effecting the growth of clusters

Hypothesis 4 is shown that t statistics in this relationship is 3.568. Value of t statistics is in the acceptence area is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H4 is accepted. That is, the role of government were statistically affect the growth of clusters with the value of the parameter coefficient of 0.488. This hypothesis is supported by the theory of Byoung Ho and Chang Jin Moon (2006) and Mark Wickham who claim the role of government affect the growth of clusters. R2 value of 0.238 which means that the construct of growth can be explained by the characteristics of the source of 23.8% by three second order constructs, which are government policies, government programs, and state governments amounted to 76.2% while others are influenced by other factors beyond the four second order these constructs. R square value role of government.construct relatively weak.

This suggests that the role of government industrial policy in particular, which are competition policy, capital, taxation, employment, trade, and eco-labeling affect less smooth growth in the cluster Mireng. This is influence the growth of cluster in Mireng although national industrial policy not very clear for entrepreneurs in Mireng. So did the problem the government condition that they are not effective for increasing growth in Mireng. This is due to political circumstances, economic, and

security of Indonesia, which remains unstable greatly affect the sustainability of business processes in Mireng furniture.

H5: Entrepreneurship effecting the growth of clustersHypothesis 5 is shown that t statistics in this relationship is 1.695. Value of t statistics are within the acceptence area is located outside the area of ± 1.659 for significance of α = 0.1 with df = 49. Thus, hypothesis H5 is received. That is, entrepreneurship statistically affect the growth of clusters with the value of the parameter coefficient of 0.255. This hypothesis is supported by the theory of Nguyen (2009) and Tambunan (2009) and Soenarto (2006) which states entrepreneurial influence the growth of clusters. R2 value of 0.080 which means that the construct of growth can be explained by the characteristics of the source of 8% by four second order constructs, which are risk-taking, proactive, innovation, and culture, while the other 92% is influenced by other factors beyond the four second order constructs are . R square value of marketing constructs relatively weak.

This shows that the culture in particular which are the level of welfare, kinship relationships, the level of chance, get a job, the level of subcultures adopted, and long-term orientation affect the smooth growth in the cluster Mireng. This can be improved in addition to the development of private enterprise, sharing information, initiating management system and a good division of labor such as authorizing a woman to help an administration activities so that the entrepreneurs are able to see the development of their respective businesses so that entrepreneurs can create and run vision and mission is clear about his company for several years to come. Meanwhile, proactive attitude that is still less effective as a lack of ability to innovate and explotion market opportunities can be enhanced also through personal development through sharing with small groups that have been established in the business process. All of this should also be constituted with the desire to expand.

Multi-group AnalysisBased on multigroup analysis, we can represented the

different between Serenan and Mireng form the table below:

Table 6. Multi-Group Analysis Serenan And Mireng

H: RelationshipT-Statistics Multigrup

Explanation

H1 GROWTH OF CLUSTER -> NETWORKING -0,251 Not significant

H2 GROWTH OF CLUSTER -> CAPITAL -2,072 significant

H3 GROWTH OF CLUSTER -> MARKETING -0,400 Not significant

H4 GROWTH OF CLUSTER -> ROLE OF GOVERNMENT -2,593 significant

H5 GROWTH OF CLUSTER -> 0,887 Not significant

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ENTREPRENEURSHIP

From table 6., it can be seen that the relationships that occur in Serenan and Mireng. There is has no difference between relationships about networking to the growth of cluster, relationships marketing to the growth of cluster, and relationships entrepreneurship to the growth of cluster. But for relationships between capital and role of government to the growth of cluster are difference. If the analysis multigrup produce a significant relationship, it indicates there has difference between the two samples. For the relationship that "significant", the value of the parameter coefficient is greater in one group, it can be mean those variables has a greater effect in that group than other groups. Whereas if the relationship "not significant", we can not conclude that the larger the coefficient parameters have more influence as well, there is relatively.

After a crosscheck on the field, was actually Cluster Mireng still do not feel the government's role effectively. The result of PLS analysis does not match the reality on the ground, the business actors in Mireng still a bit of a concern for training and counseling, but basically the government was already doing the activities in Mireng. This difference can also caused by differences in "time" effective cooperation with the government. Where in Serenan, cooperation with the government for a long time and runs smoothly so that the longer expectation Serenan cluster can not be fulfilled by the government especially in terms of facilitating the exhibition. While in Mireng, which is quite effective cooperation from the government with an average cluster Mireng still not too long, so that the desired expectations Mireng more can be met by the government.

As for the capital variables, the field indeed shows the difference between these two clusters. This is evident from the access to capital and sources of capital more fluent that exist in Serenan. There are 3 state that provide capital assistance for cluster Serenan. While in Mireng, such as state-owned financial institutions have not provided relief, whereas 25% profit policy of SOEs should be used for the development of SMEs. Financial institutions until now only offer credit loans commercial nature.

As for variables networking, marketing, and entrepreneurship, although seen the value of the coefficient parameters in the networking and marketing larger in Mireng, but this can not be interpreted marketing and networking in Mireng better than Serenan. This is relatively, because the coefficient parameters can be of high value because it is influenced by other variables that influence is smaller. This can be seen from the value of the parameter coefficient in Serenan for networking and marketing variables are smaller than in Mireng. But for the entrepreneurship variable has a value which is much higher than in Mireng entrepreneurship. (Dr. Sandra in www.smartpls.de, 2008)

As an illustration, the networking in Serenan effect on growth of 45.4%. While in Mireng of 49.4%. Although not much different but this value as if indicates the network in Mireng relatively better than Serenan. It can also be caused by

differences in "time" effective cooperation. Where in Serenan, cooperation with fellow members of the cluster, the government, large corporations, ASMINDO, BDS, JICA and financial institutions such as cooperatives had a long and runs smoothly. While in Mireng, cooperation in Mireng average is still not too long, such as cooperation with big companies or the establishment of cooperation that has been running 2 years through cooperatives. This is probably what makes the craftsmen in Mireng feel quite prominent on the effectiveness of the cooperation process this 2 years compared to previous years which has been running in Mireng. As for Serenan cooperation that had long been running with the stackholder which has been running continuously is viewed as something that is unusual because it has long been doing Serenan cooperation.

For the marketing variables, the results of running SmartPLS, data showed that sales at Mireng relatively better at marketing than Serenan. But in reality on the cluster, Serenan and Mireng has different about segmentation, because Serenan already export-oriented. It can also due to the coefficient parameters in entrepreneurship in Serenan better than in Mireng.For entrepreneurship variables, Relatively field conditions are suitable PLS results that showed growth in Serenan influenced entrepreneurship cluster with a higher value than in Mireng. This is suitable with the condition of cluster Serenan that the craftmens have been able to perform export independently.

Research Recommendation SerenanFrom the results of PLS and interviews about cluster conditions with representative of cluster, the government, ASMINDO, then the following is the proposed recommendation that appropriate safeguards are in cluster furniture Serenan: Propose furniture market in urban areas to the government Established the showroom with its partners with a

professional administration, where the craftsmen partners to deposit their products for sale in showroom togetherness

Making the furniture cluster as tourist attractions Facilitating professional exhibition Making furniture catalogs for each existing showroom E- Marketing Analyzing and Designing an oven that can reduce the failure

drying. Exchange an information with another furniture business or

other businesses to fill in Serenan Training innovation design Waste Management Training Using brand furniture for every output production (eg

Serenan-1, 2, and so, based on home number) Training administration system Entering new markets to expand the network for example,

works with the contractor / owner of both housing and offices of government agencies and private sector in supplying the interior of the new building.

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Conduct training programmed applied in the long term and sustainable cooperation with partners who are experts in their sector (example: JICA)

Mireng Propose furniture market in urban areas to the government Established the showroom with its partners with a

professional administration, where the craftsmen partners to deposit their products for sale in the showroom togetherness

Forming small groups and began to strengthen networks within the cluster in cooperation.

Making furniture catalog E-Marketing Analyzing and designing oven drying can reduce the failure

of drying Exchange of information with the furniture business or other

business Training design innovation Waste Management Training Training Administration System Government in collaboration with one of the nearest

University LPPM Klaten to build clusters Mireng

V. CONCLUSION AND FUTURE RESEARCHThe condition of the furniture cluster in Klaten in this case

Serenan and Mireng still need to be considered where the furniture industry is an industry that have prospect because this industry is capable for producing goods oriented export. However, there are still many things such as networking partnerships, capital, marketing, the role of government, and entrepreneurial spirit that still needs improvement.

Data processing showed that the factors that most influence the growth of the furniture cluster in Serenan are networking, capital, marketing, and entrepreneurship. While the factors that affect growth in the Furniture Cluster Mireng, among other networking, capital, marketing, role of government, and entrepreneurship.

From the analysis result cluster growth model furniture in Klaten, the researchers provide recommendations for improvement, such as for training that appropriate and sustainable from the government, so that the training being carried out on target and to improve the performance of SMEs furniture. Cluster need to innovative production to minimize production costs. Cluster need a forum or cooperation for the strengthening of strong networks between business, investors, buyers, government, and supporting industries as well as related services. Governments should facilitate in building a furniture market in the city center because the location of the majority of the furniture cluster in the corners of County survival is still less effective in marketing. Therefore, the furniture market needs to be built to enhance the brand image of Klaten furniture so it can compete with other furniture cluster with furniture cluster in like Jepara, Sukoharjo or Semarang.

For the next future research, we can adding other variables such as technology, standardization, etc. We recommend further research must be done on an ongoing basis, eg for early stage research conducted to determine the supply chain management that occurs in those clusters. Furthermore, for the second stage, can be analyzed factors that affect growth, productivity, or specific things such as standardization or entrepreneurship. Then on the final stage can be improvements in the form of strategies to strengthen the cluster in improving its competitiveness.

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William, F., Than, Ha, T., Entrepreneurial Orientation, Uncertainty Avoidance and Firm Performance, Bangkok: Asian Institute of Technology, 2003.

http://www.smartpls.de/forum/viewtopic.php?t=152&postdays=0&postorder=asc&start=0 (21 Oktober 2010)

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Appendix 1.

Variables Dimensions Indicators Code

Serenan MirengConvergent

Validity (Factor

Loading)

Discriminant Validity (Cross

Loading)

CRT-

value

Significantly first order to second

order

Convergent Validity (Factor

Loading)

Discriminant Validity (Cross

Loading)

CRT-

value

Significantly first order to second

orderNetworking Vertical

supplier links

The effectiveness of joint purchase of raw materials

Jej-VS1 0,972 Valid0,966

4,638 Significant 0,948 Valid 0,950 6,966 Significant

The effectiveness of collective-selling Jej-VS2 0,961 Valid 3,219 Significant 0,954 Valid 7,976 SignificantHorizontal informal links

The effectiveness of information sharing in a cluster

Jej-HI1 0,597 Valid

0,884

4,862 Significant Not Valid Not Valid

0,826

- -

Satisfaction order fulfillment cooperation in cluster

Jej-HI2 0,644 Valid 4,197 Significant 0,748 Valid 5,003 Significant

The effectiveness of order fulfillment cooperation in the cluster

Jej-HI3 0,841 Valid 7,133 Significant 0,594 Valid 3,100 Significant

Satisfaction order fulfillment cooperation with similar clusters

Jej-HI4 0,886 Valid 8,779 Significant 0,790 Valid 8,169 Significant

The effectiveness of order fulfillment cooperation with similar cluster

Jej-HI5 0,883 Valid 8,660 Significant 0,804 Valid 7,793 Significant

Horizontal formal collaboration

The effectiveness of cooperation with Partners Jej-HF1 1,000 Valid 1,000 3,551 Significant 1,000 Valid

1,0006,435 Significant

Formal association

Involvement with the Association Jej-FA1 0,896 Valid0,901

5,593 Significant 0,879 Valid

0,823

9,857 SignificantThe effectiveness of cooperation with the Association

Jej-FA1 0,915 Valid 8,614 Significant 0,792 Valid 4,016 Significant

Gaining access to common assets and resources

The effectiveness of cooperation with the government

Jej-GA1 0,922 Valid

0,905

4,501 Significant 0,913 Valid

0,860

4,406 Significant

The effectiveness of cooperation with big companies Jej-GA2 0,895 Valid 4,104 Significant 0,822 Valid 2,967 Significant

Capital Sources of capital

Sources of capital Mod-SM1 0,960 Valid0,961

2,446 Significant 0,866 Valid0,824

6,512 Significant

Fluency of capital turnover Mod-SM2 0,963 Valid 3,293 Significant 0,807 Valid 6,680 Significant

Access of Capital

Quantity break-out in the submission of capital Mod-AP1 1,000

Valid1,000 5,374 Significant 1,000 Valid 1,000 13,116 Significant

Rules of borrowing

Ease of administrationMod-SP1 0,988

Valid0,988 20,427 Significant 0,943 Valid 0,945 15,699 Significant

Level of ability eligible loan Mod-SP1 0,988 Valid 18,154 Significant 0,950 Valid 38,964 SignificantMarketing Product

StrategyThe effect of using brand Pas-SPR1 0,624 Valid 0,818 2,818 Significant 0,543 Valid 0,770 2,375 SignificantEase of service Pas-SPR2 0,853 Valid 5,060 Significant 0,710 Valid 3,950 SignificantImplementation of quality management

Pas-SPR3 0,648 Valid 2,885 Significant 0,638 Valid 1,930 Significant

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Application of Standard products Pas-SPR4 0,770 Valid 3,348 Significant 0,797 Valid 3,811 SignificantPrice Strategy

Price flexibility Pas-SH1 0,754 Valid

0,830

3,984 Significant 0,746 Valid

0,786

2,144 SignificantDiscon Pas-SH2 0,830 Valid 5,356 Significant 0,589 Valid 1,730 SignificantEase of payment

Pas-SH3 0,778 Valid 4,006 Significant 0,875 Valid 10,117 Significant

Promotion Strategy

Level of use of brochures Pas-SPS1 0,586 Valid

0,758

2,000 Significant 0,940 Valid

0,919

15,395 SignificantLevel of online advertising Pas-SPS2 0,590 Valid 1,886 Significant 0,936 Valid 12,677 SignificantQuantity exhibition participation Pas-SPS3 0,647 Valid 1,886 Significant 0,946 Valid 11,903 SignificantThe quantity of direct sales Pas-SPS4 0,764 Valid 3,157 Significant 0,754 Valid 3,228 SignificantLevel of publicity Pas-SPS5 0,507 Valid 1,754 Significant 0,538 Valid 1,979 Significant

Distribution Strategy

Convenient transportation Pas-SD1 0,898 Valid0,760

2,790 Significant 0,701 Valid0,769

4,003 SignificantConvenient storage Pas-SD2 0,715 Valid 1,689 Significant 0,913 Valid 10,509 SignificantEase of distribution Pas-SD3 0,510 Valid 1,993 Significant 0,535 Valid 2,430 Significant

Role of Government

National Industrial Policy

The role of competition policy Keb-KI1 0,795 Valid

0,867

5,846 Significant 0,680 Valid

0,860

2,027 SignificantThe role of capital policy Keb-KI2 0,658 Valid 5,152 Significant 0,586 Valid 2,415 SignificantThe role of taxation policy Keb-KI3 0,676 Valid 3,161 Significant 0,797 Valid 6,889 SignificantThe role of employment policy Keb-KI4 0,717 Valid 3,308 Significant 0,738 Valid 3,876 SignificantThe role of trade policy Keb-KI5 0,818 Valid 7,667 Significant 0,743 Valid 6,164 SignificantThe role of eco-labeling policies Keb-KI6 0,660 Valid 6,588 Significant 0,713 Valid 4,164 Significant

Government Program

Quantity of training Keb-PP1 0,944 Valid0,939

3,749 Significant 0,969 Valid 0,968 8,195 SignificantQuantity assistance Keb-PP2 0,938 Valid 3,545 Significant 0,969 Valid 8,235 Significant

Condition of government

Effects of social and political stability the country

Keb-KP1 0,876 Valid0,934

6,786 Significant 0,878 Valid 0,904 2,885 Significant

Effect economies Keb-KP2 0,935 Valid 10,602 Significant 0,854 Valid 2,688 SignificantEffect of state security Keb-KP3 0,912 Valid 13,911 Significant 0,882 Valid 2,919 Significant

Entrepreneurship risk-taking The courage fulfill orders entre-RT1 0,846 Valid0,840

17,889 Significant 0,799 Valid0,928

7.497 SignificantThe courage stocking goods entre-RT2 0,735 Valid 5,158 Significant 0,959 Valid 10.634 SignificantThe courage to innovate entre-RT3 0,809 Valid 7,946 Significant 0,937 Valid 15.870 Significant

Proactive Business Orientation entre-PR1 0,730 Valid

0,771

3,918 Significant 0,697 Valid

0,832

2.846 SignificantUtilization order opportunities entre-PR2 Not Valid Not Valid - - 0,804 Valid 3.876 SignificantCompetition Level entre-PR3 Not Valid Not Valid - - 0,728 Valid 1.894 SignificantUtilization of market opportunities entre-PR4 0,827 Valid 5,955 Significant 0,742 Valid 8.188 SignificantUtilization of opportunities to innovate entre-PR5 0,617 Valid 3,284 Significant Not Valid Not Valid - -

Innovation The quantity of product design innovation

entre-IV1 0,786 Valid

0,901

8,848 Significant 0,907 Valid

0,941

18.038 Significant

The quantity of material diversification

entre-IV2 0,849 Valid 13,799 Significant 0,935 Valid 12.208 Significant

Quantity of product diversification entre-IV3 0,818 Valid 6,122 Significant 0,845 Valid 6.818 SignificantThe quantity of waste production innovation

entre-IV4 0,877 Valid 7,064 Significant 0,889 Valid 17.413 Significant

The quantity of production process innovation

entre-IV5 Not Valid Not Valid - - Not Valid Not Valid - -

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Quantity finishing innovation entre-IV6 Not Valid Not Valid - - Not Valid Not Valid - -Culture Level of welfare entre-BD1 0,608 Valid

0,800

4,655 Significant 0,760 Valid

0,788

16.331 SignificantLevel of survival entre-BD2 Not Valid Not Valid - - Not Valid Not Valid - -Location entre-BD3 Not Valid Not Valid - - Not Valid Not Valid - -History Organization entre-BD4 Not Valid Not Valid - - 0,560 Valid 4.185 SignificantKinship entre-BD5 0,646 Valid 4,783 Significant Not Valid Not Valid - -Subculture adopted levels entre-BD6 0,747 Valid 4,903 Significant 0,711 Valid 3.205 SignificantLevel of employment opportunities entre-BD7 0,569 Valid 1,896 Significant 0,737 Valid 3.652 SignificantLong-Term Orientation entre-BD8 0,751 Valid 14,180 Significant Not Valid Not Valid - -

Cluster Growth Growth of Volume of Sales tum-OM 0,896 Valid0,939

0,844 Valid0,869Growth of employment tum-PS 0,912 Valid 0,863 Valid

Growth of market tum-TK 0,935 Valid 0,780 Valid

Page 19: Jurnal_analisis Faktor-faktor Yang Mempengaruhi Pertumbuhan Klaster Industri Mebel Klaten

Appendix 2. Relationship of First Order to Second Order

Second order First orderSerenan Mireng

Outer Loading T Statistics Explanation R square Outer Loading T Statistics Explanation R square

Networking

vertikal supplier links 0,575 4,455 Significant 0,331 0,741 7,961 Significant 0.549

horizontal informal links 0,879 21,932 Significant 0,772 0,815 18,616 Significant 0.664

horizontal formal collaboration 0,610 3,551 Significant 0,372 0,635 6,435 Significant 0.403

formal association 0,768 8,448 Significant 0,590 0,801 10,782 Significant 0.641

gaining access 0,624 5,078 Significant 0,389 0,610 4,729 Significant 0.373

Capital

Sources of capital 0,903 36,794 Significant 0,816 0,819 11,189 Significant 0.670

Access of Capital 0,835 10,252 Significant 0,698 0,833 13,116 Significant 0.694

Rules of borrowing 0,880 25,502 Significant 0,774 0,926 42,611 Significant 0.858

Marketing

Product Strategy 0,880 22,762 Significant 0,774 0,601 8,514 Significant 0.361

Price Strategy 0,902 17,703 Significant 0,814 0,740 10,082 Significant 0.547

Promotion Strategy 0,624 6,765 Significant 0,389 0,848 17,337 Significant 0.720

Distribution Strategy 0,566 3,899 Significant 0,320 0,780 12,226 Significant 0.608

Role of Government

National Industrial Policy 0,898 24,736 Significant 0,806 0,857 14,380 Significant 0.734

Government Program 0,541 3,930 Significant 0,293 0,773 8,482 Significant 0.598

Condition of government 0,841 19,542 Significant 0,708 0,587 3,942 Significant 0.345

Entrepreneurship

risk-taking 0,908 34,367 Significant 0,825 0,806 16,654 Significant 0.650

Proactive 0,751 10,875 Significant 0,564 0,669 9,501 Significant 0.448

Innovation 0,862 17,809 Significant 0,744 0,855 25,147 Significant 0.732

Culture 0,922 38,442 Significant 0,850 0,859 30,621 Significant 0.739

Page 20: Jurnal_analisis Faktor-faktor Yang Mempengaruhi Pertumbuhan Klaster Industri Mebel Klaten

Appendix 3. Result of Running

Serenan

Page 21: Jurnal_analisis Faktor-faktor Yang Mempengaruhi Pertumbuhan Klaster Industri Mebel Klaten

Mireng


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