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Open Access
Research article

Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia

ratna roostika*,
tri wahyuningsih,
sigit haryono
Department of Management, UPN ‘Veteran’ Yogyakarta
Journal of Corporate Governance, Insurance, and Risk Management
|
Volume 2, Issue 3, 2015
|
Pages 16-33
Received: 04-13-2015,
Revised: 06-07-2015,
Accepted: 06-19-2015,
Available online: 07-19-2015
View Full Article|Download PDF

Abstract:

Escalating competition among destinations has become more obvious. Shopping for handicrafts is one of important activities in tourism. Handicraft industry is a low technology, labor intensive, and run dominantly by small medium enterprises (SMEs). Handicraft industry is one of creative industries sector in Indonesia that promises to accelerate the growth of the Indonesian economy. To remain competitive, understanding on the competitive nature of handicrafts market need to be continuously monitored and adjusted to SMEs strategies. Using partial least squares path modeling on a cross-sectional sample of 54 SMEs owners in Bantul Yogyakarta, this study examines relationships among factors of destination competitiveness with clusters competitiveness and socio-economic welfare. The predictors assessed include given resources, created resources, related-supporting factors, demand conditions and strategy-structure-rivalry. Results indicate that supporting factors and strategy-structure-rivalry are not significantly impact on cluster competitiveness. The new final model was found that clusters competitiveness mediates the relationships between three destination competitiveness factors and socio-economic welfare. This study enriches theories on destination competitiveness, particularly in assessing clusters as the object of the study. The results are also important for policymakers in strengthening destination competitiveness strategy.

Keywords: SMEs, Creative industry, Destination competitiveness, Economic prosperity

1. Introduction

Since oil and gas exports have decreased in 90th era, Indonesian Government expects to boost exports from non-oil and gas industry including creative industries. Creative industry is now an important sector due to its growth rate, capability to generate significant income, and in providing job. Creative industry in Indonesia has grown at an average rate of GDP up to 9% or amounting US$ 58.5 per year (thejakartapost 2014). There are 14 sub-sectors in Indonesian creative industries and some of them include handicraft, arts and antique markets, performing arts, movie, fashion, game, culinary, and designs. The growth of creative industry is highly influenced by the current advanced in technology, globalization as well as the increase in global income. More people are entering into middle level of income thus they have better access to education, technology as well as they are more mobile. The increase of global welfare gives engine to the growth of creative industries.

The growth of creative industries in Indonesia cannot be separated from the growth of tourism sector as a sector which provides the biggest market for selling creative products and services. When tourism sector is leading, tourism shopping as part of tourism activities will also flourish, generating multiplier effects to other sectors including creative industries. Tourism competitiveness or destination competitiveness refers to not only economic competitiveness, but also to social and cultural aspects of the nations (Kim 2012). Destination competitiveness can also lead to long-term sustainability and sustain standard of living to the designated area (Crouch & Ritchie 1999). Handicrafts industry as part of the creative industries is one among important activities that tourists always spend during their visits. The competitiveness of the handicrafts sectors is vital for sustainability of the destination competitiveness. To maintain the level of competitiveness, handicrafts industries which commonly operate by Small Medium Enterprises (SMEs) should always aware of the level of global competition and aware of their internal capability to compete. In this very dynamic era and globalization, market changes rapidly. Thus appropriate competitive strategies are necessary to stay in the unpredictable market.

Since Porter’s introduction to the diamond model in his book The Competitive Advantage of Nations (Porter, 1998), studies on destination competitiveness (e.g. Dwyer & Kim 2003; Gursoy, Baloglu & Chi 2009; Lee & King 2009) have been flourishing to explain how tourism industry can stay competitive. Porter points out that abundant natural resources or cultural resources are not guarantee for winning tourism industry. In reality, countries like Switzerland and Singapore with less natural resources are rank high as recorded in Travel and Tourism Competitiveness Index (TTCI) compared to developing or less-developed countries with plenty cultural and natural beauties.

The ultimate goal of the nations’ economic development is the increase of their people welfare. By studying the SMEs handicraft industry in Bantul Yogyakarta, The purpose of this research is to build a destination competitiveness model to help local policy makers to understand more on their relevant competitive factors given their unique local resources. This study also aims to answer the question by investigating the impact of competitive factors as identified by Porter namely given resources, created resources, related-supporting resources, strategy-structure-rivalry, and demand conditions to clusters competitiveness. Further, this study also aims at answering how clusters competitiveness impacts on socio-economic welfare.

2. Creative Industry in Bantul Yogyakarta

The growth of creative industries provides optimism to accelerate the growth of the Indonesian economy. This sector has 14 sub-sectors that have been able to contribute significantly to the Indonesian economy. In 2004, creative industries achieved the highest growth with record 8.17 percent. This achievement was noted exceeded the average national economic growth which was only 5.03 % (Pangestu 2008). The significant performance of creative industries in Indonesia was shown by the value of exports in 2006 amounted Rp 81.4 trillion and this number accounted for 9.13% of the total national export value (Nurani 2014). In terms of work force market, this sector is able to absorb the labor force on average of 5.8% from 2002 to 2006. Handicrafts as a sub-sector of creative industries ranks the second largest value of GDP contribution (25.51%), employment (31.07%), number of exporters (33.02%,) and export (32.44%) (Viva 2010).

Yogyakarta is one of major tourist destinations in Indonesia. This province known as special region of Yogyakarta (DIY) has many ancient cultural heritages, beautiful beaches, and a spectacular active volcano. Local way of life and culture are of unique potentials for regional development, especially in the tourism industry. In addition, the city of Yogyakarta carries a number of unique attributes such as quality of education, quality of crafts makers, and quality of artists. Not only Yogyakarta is important destination for international visitors, but also Yogyakarta has a strong magnet for Indonesians to learn knowledge and culture. Lead by Sultan of Yogyakarta, he creates an atmosphere where creativities can be openly flourished. This special atmosphere motivates the birth of many artists and many cultural products that attract many Indonesians and foreigners to come to Yogyakarta. Similarly, creativities in handicrafts productions are also receives positive atmosphere to develop. It is therefore, when thinking of finding special handicrafts that represents Indonesia, many have suggested to find them in Yogyakarta.

More specifically, Bantul is one of Yogyakarta’s Regencies located in southern part of Yogyakarta Province. Bantul has benefited from the success of Yogyakarta tourism sector. The local government noted a very successful category for the number of local tourists visit realization (see table 1). However the realization for overseas tourists’ visit was not considered successful as in year 2013. Regardless the number of overseas visitors, Bantul contributes to 80% of Yogyakarta total crafts export (Sujatmiko 2013). The majority of Bantul residents work as artistic craftsmen including pottery, bamboo hand fan, batik material, wooden batik, leather, natural fiber accessories, etc. Many of them are lack of knowledge for new technological usage in handicrafts industry and they are likely to maintain the traditional productions. On the other hand, the younger generations have quite responsive to the globalization and have adapted to the new demand and technology. But they do not have much interest working as handicrafts entrepreneurs as their future career. Handicrafts producers are dominantly SMEs and thus very vulnerable to global competition. Proper policies and strategies are vital for handicrafts SMEs to stay competitive.

Table 1. Tourism Performance in Bantul

Performance

Indicator

Realization

2011

Realization

2012

Target

2013

Realization

2013

Achievement

Value (%)

Category

1.

local/national Visitors

1.738.808

Visitors

2.340.081

Visitors

1.649.462

Visitors

2.153.404

Visitors

130,55

Very successful

2.

Overseas

Visitors

17.654

Visitors

16.497

Visitors

16.661

Visitors

2.153

Visitors

12,92

Not

sucessful

Source: Lakip 2013 Bantul

3. Destination Competitiveness

Any industry that does not maintain its competitiveness will be difficult to survive. Competitiveness issue has becoming more pronounced after Porters book of Nation Competitive Advantage. Porter in his book focuses competitiveness at the macro level. His concept is also applicable to regional, industrial and cluster level. Competitiveness itself according to Hughes (1993) is about efficiency and trade performance (market shares). The Organization for Economic Co-operation and Development (OECD) defines competitiveness as “the degree to which a country can, under open market conditions, produce goods and services that meet the test of international markets, while simultaneously maintaining and expanding the domestic real incomes of its people over the long term” (OECD 1992, p. 237). Competitiveness is capability in integrating productivity, efficiency, and profitability, for the purpose of higher standards of living and social welfare (Kim 2012).

The concept of destination competitiveness has evolved from competitiveness in the tourism sector. Destination competitiveness is the ability of a destination to deliver goods and services that perform better than other destinations (Dwyer & Kim 2003). Starting in around 1990s, in tourism context, a growing number of tourism researchers has put attention on destination competitiveness (e.g. Crouch & Ritchie 1999; Dwyer & Kim 2003; Gooroochurn & Sugiyarto 2005, Gomezelj & Mihalic 2008). The most comprehensive work on Destination Competitiveness (DC) has been conducted by Crouch and Ritchie (1999). Crouch and Ritchie (1999) conceptual model was built based on Porter’s “diamond of national competitiveness”. Crouch and Ritchie DC model was then known as The Competitive Destination: A sustainable tourism perspective (RC’s model) (Kim 2012). Crouch and Ritchie contribution in their model was in the comprehensive and multidimensional way of DC model that includes societal prosperity in a global world. Dwyer and Kim (2003) additionally also suggest an “integrated model” which represents determinants and indicators of destination competitiveness. Dwyer and Kim (2003) model (DK’s model) combine the main elements of Porter’s national competitiveness model and the main elements of destination competitiveness from RC’s model. DK’s model includes inherited resources, created resources, supporting –related resources, destination management, situational conditions and demand conditions. Further, DK’s model also involves socioeconomic prosperity as output of destination competitiveness.

Since the work of RC’s model and DK’s model, a number of studies on DC have increased significantly (Kim 2012). This research is a combination of competitiveness theory model (Porter, 1990) and DK’s model. More specifically, this study adjusts the model of DC in Indonesian tourism sector into handicrafts sector. Handicrafts clusters are used as competitive destinations since the clusters are intensively visited by both tourists and traders. The theory of destination competitiveness should logically applicable for handicrafts clusters competitiveness.

The focus of this study is similar to common study in DC model that is building a model to explain how a handicrafts village can improve cluster competitiveness by examining the factors as identified by Porter and DK’s model. The predictors that are built from Porter and DK’s model are as follow:

Given Resources: The given resources represent more on the main factor conditions that attract visitors/buyers in the clusters. As the main raw material resources, it is a critical factor in creating handicrafts products. This consists of natural and cultural resources, such as Human resources (qualification level, cost of labor, commitment etc.) and material resources (natural resources, vegetation, space etc).

Created Resources: The created resources are more dealing with endowed resources such as government facilities, infrastructure, transportation, telecommunication, training, special events or festivals, entertainment, shopping and marketplace. This resources are manmade, thus competitiveness cannot rely merely from comparative advantage. A competitive move by actively building manmade facilities to support the abundant natural and cultural resources should improve the performance of the clusters.

Supporting/Related Industries: Is the industries that add value to given resources and created resources. Supporting/related resources cover industry that complement the handicrafts industries such as hotel and accommodation, culinary, travel transportation, entertainment, training and education, and other industries. The growth of supporting/related industries may influence the demand on the handicrafts industries even though supporting industries are not the main value in the handicrafts value chain.

Demand Conditions: Demand conditions describe the attractiveness of home demand for products and services produced in the regions/destinations/nations. Home demand may influence the quality, innovation and competition on the industry. Porter (1990) argued that, home demand is determined by three following characteristics namely: the mixture (the mix of customers needs and wants), the scope and growth rate, and the mechanisms that transmit domestic preferences to foreign markets (Kim 2012). A country can achieve competitive advantages in an industry when clearer and earlier signals of home demand trends are shown to domestic suppliers than to foreign competitors (Porter 1990, Kim 2012). Normally, the influence of home demand is higher than overseas demand in affecting organization's ability to compete.

Strategy-Structure-Rivalry: The activities by government, industries, and communities that can enhance the appeal of the main resources, strengthen the related and supporting industries, and demand conditions. This factor includes policy, safety, protection, and degree of overcoming rivalry. Porter (1990) defined strategy-structure-rivalry as the conditions that determine the ability of the companies to establish, organize, manage, and to determine the characteristics of domestic competition. Relating to strategy-structure-rivalry, cultural aspects play an important role. Different places have different cultures and thus management style. Factors like management structures, working morale, decision making, and people interactions are shaped differently in different places. Culture can cause advantages and disadvantages for industries’ competitiveness. The nature of industrial ownership and control, including the family-business based industries that are commonly associated with SMEs determine the specific nature of strategy-structure-rivalry.

Clusters Competitiveness: Ketels (2015) defines that clusters is regional concentrations of economic activities with a set of industries related linked by different types of networks. Clusters as define by Porter (1990) is ‘geographic concentration of inter-connected companies and institutions working in a common industry’. Within clusters, there are arrays of collaboration and competition of services and providers that create unique infrastructures. Clusters might also be associated with a specific type of competitive behaviors (Ketels 2015). Regions with a strong presence of clusters are more likely to success in achieving economic growth or GDP benefits as compared to region without clusters. Competition based on quality and unique values in clusters triggers production processes that are more focused on efficiency (Ketels 2015). Clusters and competitiveness are conceptual frameworks used to analyze the differences in economic performance across locations. Dynamic clusters contribute to a location’s competitiveness. Clusters thus provide important information to understand and diagnose the drivers of a location’s economic performance. Similar to destination competitiveness, clusters competitiveness can be defined as the ability of a cluster to deliver goods and services that perform better than other destinations.

Socio-economic prosperity: Socio-economic prosperity is defined as the social ‘welfare’ or ‘well being’. The Socio-economic prosperity includes economic prosperity as well as the quality of life of residents in the regions/clusters. The success of clusters should provide more job and activities that enable to increase local income and buying power. The community around will be more dynamic and the investors will be more attracted for building better infrastructures and public facilities.

By applying the combination of Porter’s and DK’s model of Destination Competitiveness, this study will assess the relationships between Determinant of destination competitiveness, clusters competitiveness and socio-economic welfare. The above discussions form the basis for the following hypotheses:

Hypothesis 1: A significant positive relationship exists between given resources and clusters competitiveness.

Hypothesis 2: A significant positive relationship exists between created resources and clusters competitiveness.

Hypothesis 3: A significant positive relationship exists between related-supporting conditions and clusters competitiveness.

Hypothesis 4: A significant positive relationship exists between demand conditions and clusters competitiveness.

Hypothesis 5: A significant positive relationship exists between strategy-structure-rivalry and cluster competitiveness.

Hypothesis 6: clusters competitiveness mediates the relationships between destination competitiveness factors and socio-economic welfare.

4. Research Methodology

This study tests the structural model explaining the relationship between destination competitiveness, clusters competitiveness, and socio-economic welfare. The main determinants of destination competitiveness were built based on Porter’s diamond framework. To assess the structural relationships, Structural Equation Modeling (SEM) with latent variables, specifically Partial Least Square (PLS) path modeling, is employed.

4.1 Data Collection and Analysis Tools

In order to be able to analyze the structural model, quantitative research is applied. Data were collected by survey to SMEs owners in Jipangan, Bantul Yogyakarta. The questionnaire was developed consisting of a total of 32 questions. Five questions were designed to evaluate given resources, five questions were for created resources, five questions for related- supporting industries, six questions were for demand factors, and five questions were for strategy-structure-rivalry. All questions to measure given resources, created resources, demand conditions, supporting-related industries and strategy-structure-rivalry were taken from Crouch & Ritchie (2003), Dwyer & Kim (2003), and Kim (2012). Five questions for clusters competitiveness were developed from Mena (2006). Six questions on socio-economic welfare were developed from Kim (2012). A five-point Likert scale was used as scaling method.

4.2 Statistical Population and Statistical Samples

Statistical population of the study consists of all SMEs producing handicrafts residing in Jipangan Bantul Yogyakarta. Out of 75 questionnaires distributed, 54 questionnaires were valid and reliable to use as valid data for further multiple regression analysis. The 75 questionnaires were distributed in within 2 months period using convenient sampling method.

4.3 Descriptive Data Analysis

According to the profile of the respondents collected, it can be described that 64.8% of respondents are male SMEs owners and 35.28% are female SMEs owners. The respondents’ age range from 5.6% between 15 - 25 years old, 33.3% between 26 - 35 years old, 35.2% between 36 - 45 years old, and 25.9% older than 45 years old. Monthly earnings are mostly under Rp. 20 million per month and only five respondents with income per month exceeding Rp. 20 million per month. The respondent personal data also reveal that 60.7% respondents went to high school as their highest formal education achieved. Most SMEs also have run their business over 4 years long. The target market is still focused on national market. Problems in running business are mostly in the forms of funding and hiring talented labors.

4.4 Assessment for the Measurement Model

PLS allows the measurement and structural models to be analyzed at the same time (Chin 1998). Analysis using PLS are usually conducted in two stages: 1) the assessment of the measurement model, which focuses more on the reliability and validity of the measures; and 2) the assessment of the structural model which is more concerned with the path coefficients, model adequacy and selecting the best final model (Hulland 1999). These two-step approaches were taken for good psychometric properties before further conclusions can be drawn. This study will follow the statistical analysis according to the two-step approaches.

The measurement model in PLS is evaluated by examining: (1) the individual loading of each item; (2) Internal Composite Reliability (ICR); (3) Average Variance Extracted (AVE); and (4) discriminant validity (Chin 1998). The measurement model focuses on ensuring the validity and reliability of the measures. Firstly, the individual loading of each item can be seen from the following table 2. Each of the loading scores determines the correlation between indicators and their respective constructs. The loading scores can be used to determine the contribution of each indicator to the relevance of its respective construct. The higher the loadings indicate the stronger the relationships in terms of shared variance with the construct. Item loading is also known as item reliability. The higher loading the higher reliability. The loading of 0.5 or 0.6 may still be acceptable in the early stage of scale development (Chin 1998). After running the PLS analysis, there are some indicators that have not met the threshold of the standard minimum loading (< 0.5). These indicators were then dropped and not included in further analysis. Some indicators which were dropped include: One indicator from given resources, two indicators from created resources, four indicators from demand conditions, and four indicators from socio-economic prosperity indicators. In total there were eleven indicators dropped or not reliable as measures in the variables being investigated.

Table 2. Individual loading after filtering

Original Sample

(O)

Sample Mean (M)

Standard Deviation

(STDEV)

Standard Error

(STERR)

T Statistics (|O/STERR|)

A1 <- GivenRes

0.6639

0.623

0.162

0.162

4.0981

A2 <- GivenRes

0.8346

0.7936

0.1395

0.1395

5.9842

A3 <- GivenRes

0.8245

0.8297

0.1157

0.1157

7.1258

A4 <- GivenRes

0.7864

0.7487

0.1409

0.1409

5.5807

B3 <- CreatedRes

0.8433

0.8348

0.0682

0.0682

12.3615

B4 <- CreatedRes

0.8875

0.8748

0.1102

0.1102

8.0526

B5 <- CreatedRes

0.7947

0.7773

0.0823

0.0823

9.6575

E3 <- Fac Demand

0.7629

0.7586

0.0692

0.0692

11.0243

E4 <- Fac Demand

0.5188

0.5105

0.1416

0.1416

3.6637

E5 <- Fac Demand

0.5536

0.5432

0.1519

0.1519

3.6446

E8 <- Fac Demand

0.7124

0.682

0.1666

0.1666

4.2759

H1 <- Competitiveness

0.8234

0.8208

0.0455

0.0455

18.1005

H2 <- Competitiveness

0.7073

0.7149

0.0944

0.0944

7.4895

H3 <- Competitiveness

0.5514

0.5094

0.1498

0.1498

3.6801

H4 <- Competitiveness

0.563

0.5553

0.1619

0.1619

3.4773

H5 <- Competitiveness

0.5498

0.5256

0.1911

0.1911

2.8775

I1 <- Welfare

0.623

0.6221

0.0945

0.0945

6.5899

I10 <- Welfare

0.7028

0.7112

0.0721

0.0721

9.7536

I6 <- Welfare

0.6349

0.6028

0.1169

0.1169

5.4326

I7 <- Welfare

0.6587

0.6494

0.0907

0.0907

7.2659

I8 <- Welfare

0.7856

0.7734

0.081

0.081

9.6947

I9 <- Welfare

0.7248

0.7147

0.0688

0.0688

10.5269

Table 3. AVE, ICR, and Cronbach’s Alpha

AVE

Composite

Reliability

R Square

Cronbachs

Alpha

Communality

Redundancy

Competitiveness

0.4203

0.7789

0.3607

0.6611

0.4203

0.0453

CreatedRes

0.7101

0.88

0

0.7969

0.7101

0

Fac Demand

0.4163

0.7354

0

0.5293

0.4163

0

GivenRes

0.6089

0.8607

0

0.7918

0.6089

0

Welfare

0.4769

0.8446

0.265

0.7961

0.4769

0.0933

Secondly, using the data from table 3, Internal Composite Reliability (ICR) can be analyzed. According to Chin (1998) and Fornell and Larcker (1981), ICR should be higher than 0.7. Based on table 3 ICR ranges from the lowest 0.7355 (factor demand) to 0.88 (created resources). This means that ICR values in this study fulfils the requirement as reliable measures. The third step is testing the AVE. As suggested by Chin (1998) and Fornell and Larcker (1981), AVE should be higher than 0.5.

This study has four factors having AVE below 0.5. All of these factors are maintained since the content validity has been assessed by experts in the competitiveness study and the measures have been well developed and tested from previous studies. In addition, AVE is not the only identification for valid measure. The discriminant validity will be further tested using cross loadings and square root AVE.

Table 4. Crossloadings

Competitiveness

CreatedRes

Fac Demand

GivenRes

Prosperity

A1

0.2155

0.3009

0.027

0.6639

0.0931

A2

0.298

0.1627

0.0902

0.8346

0.0693

A3

0.3986

0.2988

0.1381

0.8245

0.1891

A4

0.2041

0.1996

0.0171

0.7864

0.0466

B3

0.2904

0.8433

0.1183

0.3304

0.338

B4

0.3667

0.8875

0.1934

0.2149

0.4952

B5

0.2704

0.7947

0.042

0.2542

0.4532

E3

0.3363

0.2102

0.7629

0.0578

0.3208

E4

0.1898

0.2405

0.5188

0.0551

0.3925

E5

0.2538

0.0177

0.5536

0.1375

0.3272

E8

0.3418

-0.0323

0.7124

0.0344

0.2224

H1

0.8234

0.3176

0.4515

0.2374

0.4602

H2

0.7073

0.4062

0.292

0.3406

0.3776

H3

0.5514

0.1942

0.2415

0.1709

0.2696

H4

0.563

0.1245

0.1449

0.2421

0.3278

H5

0.5498

0.0135

0.2535

0.2543

0.1427

I1

0.5316

0.3211

0.3774

0.2479

0.623

I10

0.3587

0.4601

0.2428

0.2034

0.7028

I6

0.2665

0.286

0.2312

0.0239

0.6349

I7

0.2222

0.2503

0.3435

-0.1028

0.6587

I8

0.2586

0.4096

0.4259

-0.0692

0.7856

I9

0.2459

0.3349

0.2262

0.0493

0.7248

Table 5. Correlation and AVE square roots

Competitiveness

CreatedRes

Fac Demand

GivenRes

Prosperity

Competitiveness

0.6483

0

0

0

0

CreatedRes

0.3718

0.8430

0

0

0

Fac Demand

0.447

0.1486

0.6452

0

0

GivenRes

0.3812

0.3101

0.1036

0.7803

0

Prosperity

0.5148

0.5123

0.462

0.1426

0.69058

In order to test the discriminant validity, crossloading and AVE square roots will be analyzed. The discriminant validity is shown when the indicators are better associated with their respective construct than they are with other constructs. When checking the cross-loadings, researchers must ensure whether each group of indicators should load higher for its respective construct than indicators of other constructs (Cunningham 2008). The crossloading matrix of the measures showing the correlations between all items and constructs are displayed in table 4. Since all indicators that have not satisfied the item loadings have been dropped, there remained indicators that as shown in table 4 were well associated with their respective construct. The indicators that associated with their respective construct load higher than association to other constructs.

In addition, as indicated in Table 5, the square root of the AVE was tested against the intercorrelations of the construct with the other constructs in the model to ensure discriminant validity (Chin, 2003, Fornell & Larcker, 1981). All the square root of the AVE exceeded the correlations with other variables. Thus, the measurement model was considered satisfactory with the evidence of adequate reliability, convergent validity, and discriminant validity.

4.5 Assessment for the Structural Model

The use of R-squared (R2) is important in determining the predictive ability of the model. PLS produces R2 for each of dependent construct in the model. The bigger the R2, the more predictive power the model implies. As seen in Figure 2, R-squared (R2) of cluster competitiveness is 37.2% and socio-economic prosperity is 26.5%. The rule-of-thumb for the significance of R2 of the predicted variables should be greater than 0.10 (Falk & Miller 1992). Even though they both cluster competitiveness and socio-economic do not show a strong R2, they are higher than the threshold 0.10.

This study proposes an indirect relationship between destination competitiveness factors and socio-economic welfare via clusters competitiveness. In assessing indirect relationships, this study follows the approach suggested by Baron and Kenny’s (1986). Baron and Kenny’s (1986) method is widely accepted in marketing studies (e.g. Agarwal et al. 2003; Matear et al. 2002). There are three requirements highlighted to test the mediation effect: 1) the independent variable (X) must affect the mediating variable (Y); 2) the independent variable (X) must affect the dependent variable (Z); and 3) the mediating variable (Y) must affect the dependent variable (Z). As can be seen in Figure 1 below, the impacts of related-supporting industries on clusters competitiveness is not significant with the coefficient of 0.049. Similarly, the impact of strategy-structure-rivalry to clusters competitiveness is also not significant with coefficient of 0.093. This means that both factors do not satisfy the requirements as suggested by Baron and Kenny (1986) for indirect model. These two factors were then dropped and are not use for further analyses.

Figure 2 shows the final model after related-supporting factors as well as strategy-structure-rivalry were dropped. Using the result from the final model, it can be concluded that hypothesis three and hypothesis five were not supported. On the other hand, hypotheses 1, 2, and 4 were supported, meaning that positive relationship exists between given resources, created resources, and demand conditions to clusters competitiveness. Hypothesis six is also accepted when dropping related-supporting industries and strategy-structure-rivalry. This means that clusters competition mediates the relationships of three destination competitiveness factors and socio-economic welfare. Table 6 shows the significance of the final structural model of this study.

Figure 1. Proposed Research Model
Figure 2. Final Research Model
Table 6. Total effect

Original Sample (O)

Sample Mean (M)

Standard Deviation

(STDEV)

Standard Error

(STERR)

T Statistics (|O/STERR|)

Competitiveness -> Prosperity

0.5148

0.5458

0.0552

0.0552

9.3316

CreatedRes -> Competitiveness

0.231

0.2318

0.0841

0.0841

2.7476

CreatedRes -> Prosperity

0.1189

0.1282

0.051

0.051

2.3301

Fac Demand -> Competitiveness

0.3848

0.4049

0.0656

0.0656

5.8695

Fac Demand -> Prosperity

0.1981

0.2205

0.0403

0.0403

4.9207

GivenRes -> Competitiveness

0.2697

0.2797

0.0931

0.0931

2.8965

GivenRes -> Prosperity

0.1388

0.1516

0.0496

0.0496

2.8008

5. Discussions

Based on respondents’ profile, majority of the handicrafts producers are small and medium business owners. They are in the middle age with dominantly less than 45 years old. The level of education background is majority high school graduates. These data imply that handicrafts owners in Bantul have limitations and thus getting supports from government, education institutions, and industries are of significant important. Due to limitations in funding and education, handicrafts SMEs in Bantul lack crafts skill and managerial competences. Limitation in education could effect on the ability to handle global competition as well as adapting global market. Management skills and leaderships are vital to SMEs owners in that they can motivate their employee better. Most owners stated that the most challenges were related to lack of funding and labor talents. Difficulties to find young generations to work as craftsmen can be caused by the low appreciation for craftsmen product by the locals and this job is perceived as not giving a good career. Another reason is many younger generations prefer to work in the manufacture setting as compared to become entrepreneurs. The problem related to funding should be overcame by a more collaborative approaches facilitated by government, industries, education institutions and SMEs.

Bantul is a small town located in southern part of Yogyakarta. Bantul has huge natural resources. It is well known as exporters of bags and other accessories made from natural fibers. Kotagede is also parts of Bantul which is very famous with silver craft. Pottery from Kasongan is another well known product made by Bantul residents. Wooden batik is a new innovation of batik crafts as alternative to traditional batik materials. According to the factors analyzed as the sources of destination competitiveness, three factors (given resources, created resources and demand conditions) are positively significant in predicting clusters competitiveness. Cluster competitiveness also significantly mediates the relationship between destination competitiveness factors and socio-economic factors. The following discussions will start from direct relationships of factors that have significant impacts.

Given resources and created resources are positively impacted on clusters competitiveness. This finding supports the finding from previous study (eg. Dwyer & Kim (2003); Jackson (2006); Eickelpasch, et al. (2010); Kim (2012)). These resources as in Porter Diamond is known as factor conditions. The handicrafts SMEs in Bantul are commonly a small-medium size and they have not applied advanced managerial practices such as branding, promotion or financial planning. Many are depending on product order. The specific characteristics of the craftsmen are the indigenous skills that they have. These indigenous skills provide uniqueness that is not easily copied. This indigenous skills gives specific areas /clusters a distinct skill in the making of product supported by the availability of raw materials and created resources. Having clusters with distinct product talents gives Bantul attractiveness particularly for artistic product hunters. On the other side, Bantul has ample of natural resources providing steady stock for production process. The availability and ease of access on raw materials help SMEs to work efficiently. Steady stock of raw materials and human talents are seen by SMEs owners as directly contribute to their competitiveness. However, challenges on the future stocks of human talent must be addressed since younger generations are reluctant to work as handicrafts entrepreneurs and prefer to work in the more formal institutions.

Given resources as sources of comparative advantage will not perfectly work if created resources are not well provided. The provision of infrastructure, transportation, telecommunication, and access support to market network should complement given resources. Porter argued that today’s winning competition is no longer coming from comparative but shifting to competitive advantage. Both given and created resources are seen by respondents as directly impact on clusters competitiveness since the capability to produce must be assisted by attractive market place, policy and regulations, as well as public infrastructures. Government interference in this factor is important. The provision of marketplace for handicrafts SMEs is not only done by physically providing the market. But more importantly, the program, the content, and the network fulfilled to the market will be more useful for SMEs participating in the market. Government should actively encourage SMEs participations for trade event, organize festivals and assist network building with potential buyers.

This study supports the proposition that demand factors have impact on clusters competitiveness. Demand conditions represent the attractiveness of home demand and may influence the quality, innovation and competition on the industry. The significant influence of demand conditions to clusters competitiveness implies that SMEs respondents admit that handicrafts market becomes more attractive and competitive when local demand and varieties of demand increase. Varieties of buyers background whether local or international buyers increase the craftsmen talents since they have to be able to translate the products required by many different buyers. As tourism and education centre, Yogyakarta is very open to visitors not only as for tourist but also as for students to learn new skills/knowledge and as for residents to make a living. The openness and the blend among people from all over the world makes the interaction on needs, wants, experiences, skills and education high. The higher sophistication on demand influences the level of competition and ultimately the level of products’ competitiveness made in Bantul.

The fact that related-supporting industries are not significantly impact on clusters competitiveness is quite surprising and does not align with previous research findings. However, this finding can be explained by these following arguments. The handicrafts SMEs are mainly focus on production activities as compared to doing commercial or marketing activities. They also see that tourism sectors such as entertainment, cultural performances, culinary, education, hotel, travel agency as not directly relating to profit in their production processes. This might explain why respondents who are SMEs owners do not see that supporting-related industries directly impact on clusters competitiveness. In reality, SMEs handicrafts industry is highly depending on the success of tourism industry. However, any activities that do not relate to success on production process is not considered competitive effort as seen by SMEs.

Research on handicrafts SMEs in Bantul has found that strategy-structure-rivalry does not have impact on clusters competitiveness. This means that this finding is not in align with previous findings on destination/tourism sectors when using Porter Diamond as predictors of competitiveness. This factor represents how the industries are established, organized and managed. The different findings can be explained similar to finding as with supporting-related industries factors. Simply, respondents are not considering anything that is not directly within internal production processes as determining their competitiveness. Due to lack of managerial skills and network interactions, SMEs owners are too focus on internal capability and lack response to external environment. The family business orientation could be the reason why change is not common. SMEs also majority satisfied with managing business as far as by depending on order (made to order). They do not proactively expand and introduce their business proactively. This type of traditional nature of running business will not survive in facing the global competitors. Government, education institutions, and bigger industries should take into action in protecting the sustainability of handicrafts SMEs.

Clusters competitiveness was found to influence socio-economic welfare. Clusters competitiveness also mediates the relationships between created resources, given resources, and demand conditions to socio-economic welfare. This finding is in align with previous studies by Dwyer & Kim (2003), Jackson (2006), Eickelpasch, et al. (2010), and Kim (2012). This finding implies that success in clusters will increase the level of competitiveness. When the cluster is competitive, more jobs will be created, more supplies will be needed, and the residents will have better buying power. In this situation, the economy will be more attractive. Simply, the quality of life of the residents within competitive cluster will increase. As previously stated, regions with a strong presence of clusters are more likely to success in achieving economic growth or GDP benefits as compared to region without active clusters. Thus, it is important that cluster approach needs to be adopted in the handicrafts SMEs since the final goal of all development is residents welfare and the quality of life. Additionally, the role of clusters competitiveness also vital since it mediates the relationships between three factors of destination competitiveness to socio-economic welfare. This again confirms that cluster approaches for handicrafts SMEs need to be adopted so that better collaboration among clusters or among SMEs are even better and stronger.

6. Conclusion and Recommendations

The goal of national development is the increase in the quality of residents’ life. In order to achieve the quality of life, a nation must be competitive. The purpose of this research is to analyze the application of destination competitiveness in the handicrafts SMEs to see whether the factors of competitiveness do influence the clusters competitiveness and socioeconomic welfare. After several steps of statistical analyses, the final model reveals that only three hypotheses supported meaning that only given resources, created resources, and demand conditions that directly and indirectly effect socio-economic welfare. Clusters competitiveness also significantly supported as mediating variable. Hypothesis three and hypothesis five are not supported. Thus, these factors (related/supporting industries and strategy-structure-rivalry) were not used as analysis in the final model.

This study offers some contributions to the literature and the managerial practices. The factors determining destination competitiveness studied in the handicrafts SMEs in Bantul Yogyakarta provides more evidences on the application of Ported Diamond as a base for developing destination competitiveness strategy. Clusters competitiveness is also important in determining the socio-economic welfare of the region/area being investigated. For the managers, a broader aspect of competitive moves should be considered. Not only that SMEs owners focus merely on internal capabilities, but also external factors that do not seems to directly effect on competitiveness should be seriously taken into consideration. The understanding of the external competitive factors will help SMEs owners/managers and policy makers to decide a more sustainable strategy for longer competitiveness.

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Roostika, R., Wahyuningsih, T., & Haryono, S. (2015). Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia. J. Corp. Gov. Insur. Risk Manag., 2(3), 16-33. https://doi.org/10.56578/jcgirm020302
R. Roostika, T. Wahyuningsih, and S. Haryono, "Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia," J. Corp. Gov. Insur. Risk Manag., vol. 2, no. 3, pp. 16-33, 2015. https://doi.org/10.56578/jcgirm020302
@research-article{Roostika2015DestinationCA,
title={Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia},
author={Ratna Roostika and Tri Wahyuningsih and Sigit Haryono},
journal={Journal of Corporate Governance, Insurance, and Risk Management},
year={2015},
page={16-33},
doi={https://doi.org/10.56578/jcgirm020302}
}
Ratna Roostika, et al. "Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia." Journal of Corporate Governance, Insurance, and Risk Management, v 2, pp 16-33. doi: https://doi.org/10.56578/jcgirm020302
Ratna Roostika, Tri Wahyuningsih and Sigit Haryono. "Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia." Journal of Corporate Governance, Insurance, and Risk Management, 2, (2015): 16-33. doi: https://doi.org/10.56578/jcgirm020302
Roostika R.Wahyuningsih T., Haryono S.. Destination Competitiveness Analysis for Creative Crafts Industries in Bantul Yogyakarta Indonesia[J]. Journal of Corporate Governance, Insurance, and Risk Management, 2015, 2(3): 16-33. https://doi.org/10.56578/jcgirm020302
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