Keywords

Creative Industries, Market Orientation, Learning Orientation, Organizational Innovativeness, Organizational Performance, SME's, SEM-Smartpls.

Introduction

The contribution of the creative industries in the global economy spurred numerous empirical studies which have paid attention in many countries in the last decade (UNCTAD, 2016). Indonesia as one of the developing countries is also challenged to improve national competitiveness in the global marketplace. According to the Ministry of Trade Republic of Indonesia in the book of creative industry development towards the vision of creative economy 2025, Indonesia’s creative industries can be grouped into 14 sub sectors, including:

• Advertising

• Architecture

• The art and antiques market

• Craft

• Design

• Fashion

• Video, film and photography

• Interactive games

• Music

• Performing arts; music, dance and theatre

• Publishing, printed and new media

• Computer services and software

• Television and broadcasting

• Research and development

Deputy of Research, Education and Development of Creative Economy Agency (BEKRAF) stated in (Tempo, 2016), creative industry contribution in the last year reached 642 trillion IDR or 7.05 percent of Indonesia's GDP. The biggest contribution comes from culinary as much as 32.4 percent, fashion 27.9 percent and craft 14.88 percent. In addition, in terms of human resources, the creative industry is the fourth largest sector with a contribution of 10.7 percent or 11.8 million people dominated by fashion, culinary and handicraft businesses. Given the fact shows that the quality of human resources is the mainstay that distinguishes creative industries from other industries. Creative industry needs human input in the form of knowledge-how and high skill (Booyens, 2012). Thus, the creative industry is more oriented to scale production in small quantities but requires a higher level of exclusivity and creativity.

The ability of human resources in this sector to produce creative and innovative products can increase the value and competitiveness of products in the global market as well as handicraft products such as embroidery and weaves. This research was conducted in West Sumatera as one of famous area with creative industry of embroidery and weaves in Indonesia. Previous research on the performance of SMEs in both developed and developing countries has been done (e.g. Beneke et al., 2016; Wang, 2016; Herath & Mahmood, 2013; Pandya, 2012). However, there are only few empirical study efforts dedicated to discuss the performance of SMEs in the creative industries’ sector supporting tourism, especially embroidery and weaves, needs for further investigation.

The development of creative industries in addition to having an impact on business life and national economy, also impact on the image of the tourist destination. The empowerment of the local economy sector that is full of uniqueness and creativity can be a tourist attraction for visitors who want to see, know, feel or even have tourism support products in the area (Oskam & Boswijk, 2016); (Sutawa, 2012). This suggests that although both types of handicraft products need to preserve local Minangkabau cultural identity, product innovation through product modification such as motif designs tailored to the flexibility of the tastes of the target market can also increase the performance level of SMEs. Despite the number of empirical work concerning the drivers and outcomes of organizational innovativeness and performance, research that encompasses all the relevant constructs in comprehensive manner remains rather limited. The emergence of this phenomenon is encouraging authors to study the variables of market orientation, learning orientation and organizational innovativeness as an antecedent performance of SMEs in the creative industries supporting tourism in West Sumatra, Indonesia.

Literature Review and Hypotheses Development

Market Orientation and Learning Orientation

Throughout the 1990s, the concept of market orientation is conceived as the main attention in the marketing literature, for instance, (Narver & Slater, 1990; Kohli & Jaworski, 1990; Slater & Narver, 1994). Nevertheless, few empirical researches on market orientation in SMEs have been published (Reijonen, Párdányi, Tuominen, Laukkanen & Komppula, 2014), especially creative industries. Many research streams are replete with studies promulgating the linkage between market orientation and business performance in manufacturing (e.g. Buli & Buli, 2017; Rhee, Park & Lee, 2010; Hussain, Cholette & Castaldi, 2007; Jordan, Zidda & Lockshin, 2007, service (e.g. Avlonitis & Giannopoulos, 2012; Nasution, Mavondo, Matanda & Ndubisi, 2011) and non-profit organization (e.g. Singh, 2009; Camarero Izquierdo & José Garrido Samaniego, 2007; Oplatka & Hemsley-Brown, 2007).

Nowadays, the implementation of marketing concept through market orientation is crucial for the existence of fierce competition, turbulent and uncertainty environment both large and small firms. Theoretically, market orientation is a foundation of long term collaboration among partners in a value chain and in turn being a sustainable source of competitive advantage (Liao, Chen, Hu, Chung & Liu, 2017). Narver & Slater (1990) defined market orientation as an organization culture which aims to identify the target market’s needs and wants and satisfied them more effectively and efficiently rather than competitor. It formed by three elements, consists of customer orientation, competitor orientation and resource orientation.

Despite the concept of market orientation focused on customers, competitors and resources, learning orientation on the other hand offer an organizational capability to adopt a basic learning process (Rhee et al., 2010). Baba (2015) suggested that learning orientation is a collective capability which derived from the process of cognitive and experiential and involving the acquisition, sharing and utilization of knowledge. Liao et al., (2017) defined learning orientation as an organizational process to improve individual knowledge by transform it into part of the organization’s knowledge system in an organized way and comprised to commitment to learning, open-mindedness and shared vision.

In order to enhance organizational learning, Anderson & Kodate (2015) proposed four elements as important factors: organizational legitimacy, training, supportive administration and tools for incident analysis. In other words, it considered that leadership style by providing well-trained will empower the employee and which in turn, influence on organizational learning and innovation (e.g. Kim, 2015; Vargas, 2015; Froehlich, Segers & Van den Bossche, 2014; Khalifa & Ayoubi, 2015; García-Morales, Jiménez-Barrionuevo & Gutiérrez-Gutiérrez, 2012).

In terms of the relationship between market orientation and learning orientation, many scholars argued that once a firm becomes market oriented, it begins to practice learning (Nasution et al., 2011). Without the competency to adopt new knowledge and insight, firms are less likely to maintain sensitivity with market changes (Fang, Chang, Ou & Chou, 2014). Hence, a higher learning orientation tends to lead to a higher market orientation. Many previous studies (e.g. Pandya, 2012; Keskin, 2006; Baker & Sinkula, 2002; Bell, Whitwell & Lukas, 2002; Narver & Slater, 1990) also revealed that learning orientation may rely on market orientation.

Thus, we hypothesis that:

H1 Market orientation significantly influences on learning orientation

Organizational Innovativeness

Market orientation refers to an organizational capability to identify customer needs and disseminate information from obtained customers in order to respond quickly and timely ahead of competitors (Narver & Slater, 1990; Kohli & Jaworski, 1990), while innovativeness is considered as one of the most important strategic means to gain competitive advantage (Tajeddini, 2011). Market oriented firms concern to customer needs and wants as well as competitors. It implied that a firm have to adopt innovation by creating, launching and commercializing new products over competitors (Gledson & Phoenix, 2017). Most of these literatures have founded the effect of market orientation on innovativeness (e.g. Choi, 2014; Widiartanto & Suhadak, 2013; Hassim, Asmat-Nizam & Bakar, 2011).

Innovativeness could be conceptualized as an action based capacity to compose or develop the ‘newness’ of ideas, product and process within organization (Rhee et al., 2010). Staniewski, Nowacki & Awruk (2016) suggested that innovations produce solutions to overcome problems and represent the benefit to enhance a higher quality services for customers. A firm with strong market orientation may focuses on learning and innovation from external market environment (Huang & Wang, 2011). Boso, Cadogan & Story (2013) have acknowledged that market orientation act to drive the product innovation success from 164 exporters in Ghana. Despite organizational characteristics, innovation was also influenced by market orientation as the key antecedent (Beck, Janssens, Debruyne & Lommelen, 2011). Accordingly, a firm which stressed on innovation culture will have a tendency to pay more attention to market orientation. Therefore, it is proposed that:

H2 Market orientation significantly influences on organizational innovativeness

Organizational Performance

The effect of orientations as predictors of SMEs performance has been investigated in single or mixed orientations coupled with other factors (Hakala & Kohtamäki, 2010);

(Chandrakumara, De Zoysa & Manawaduge, 2011). According to Fang et al. (2014). Market orientation facilitates the development of internal and external market capabilities which subsequently improve organizational performance. The degree of market orientation indicates a firms’ reaction to respond customer and market demand. Jiménez-Jiménez & Cegarra-Navarro (2007) also have pointed out that market orientation is positively associated with firm-level performance, including financial and business performance.

Many researchers have focused their study on investigating whether market orientation influence the organizational performance, while others have discussion on learning orientation. In these regards, this study highlights the simultaneous influence of both kinds of orientation. Building the learning capability of the firm is one approach that allows firms to face a tight competition in uncertainty environment (Huang & Wang, 2011). The adoption of learning orientation in organization can lead the organizational performance to be achieved. SMEs which apply the learning orientation for the purpose of organization will able to learn about the organizational environment. Real, Roldán & Leal (2014) proposed a comprehensive model that evidence the learning process might be considered as the main determinant on business performance.

Market orientation and learning orientation are inputs of the firm’s innovation process (Lin, Peng & Kao, 2008). Although these three strategic behaviours were mainly impact on firm performance on the dynamics of the market (Mahmoud, Blankson, Owusu-Frimpong, Nwankwo & Trang, 2016), innovation capability is the most determinant of business performance (Tajeddini, 2010). It is important for companies to pay more attention to market orientation as it proves to be able to drive innovation. Hence, it seems particularly crucial to SMEs with limited resources to carry out innovativeness as a key driver of organizational performance (Damanpour, Walker & Avellaneda, 2009; Vrande, Jong, Vanhaverbeke & Rochemont, 2009). Building on the line above arguments, the following hypotheses are formulated:

H3 Market orientation significantly influences on organizational performance

H4 Learning orientation significantly influences on organizational performance

H5 Organizational innovativeness significantly influences on organizational performance

Framework

The study proposed an integrated model as shown in Figure 1 to investigate the influence of market orientation, learning orientation and organizational innovativeness as determinants of SME’s performance, especially creative industries like embroidery and weaves.

Research Method

A quantitative research method with explanatory and cross-sectional research design was undertaken to empirically evaluate the proposed framework. Therefore, the study tests five hypotheses developed from existing theory. It exploits a non-probability sampling technique, namely purposive sampling. The sample selection was done by identified these following criteria:

• Respondent is an owner or SME’s manager,

• A firm that belongs to creative industries in West Sumatera, Indonesia, especially embroidery and weaves sector,

• A firm that doing production and marketing activities. Hence, a sample of 131 SME’s of creative industries were obtained to participate in order to collect data through a field survey using questionnaires.

Data and information for the study are gathered from questionnaires which developed by a thorough literature review. Each variables were measured by indicators along a 5-point scale, where 1=strongly disagree and 5=strongly agree. Questionnaires were further analysed by Structural Equation Modelling (SEM) using statistical tools Partial Least Square (PLS), one of the Variance-Based SEM (VB-SEM) software.

PLS was chosen because it is a powerful analytical tool, does not require many assumptions and able to analyse formative construct. Formative construct was measured by three dimensions on each variable of market orientation and learning orientation. Market orientation derived from customer orientation, resource orientation and competitor orientation. Learning orientation formed by commitment to learn, shared vision and open-mindedness. All these orientation dimensions were adapted from Sinkula, Baker & Noordewier (1997) cited in Choi (2014).

Prior to primary data collection, validity and reliability test were conducted. The test of validity was executed with PLS algorithm process to generate the convergent and discriminant validity through outer loading value. Reliability test was executed by calculate the value of composite reliability. After doing instrument testing, structural measurement model or inner model evaluation was conducted to analyse the research model through a bootstrapping process. Therefore, the purpose of this evaluation can predict the linkage between latent variables.

Results

Assessing the Measurement Model

In the measurement model assessment, this study was evaluated by validity and reliability testing. Due to multidimensional construct of first-order reflective and second-order formative model, the SEM-PLS algorithm process should be conducted through convergent validity and discriminant validity testing. Firstly, this study performed convergent validity test to measure the AVE and outer loading value of the reflective measurement model. According to Ghozali (2006), convergent validity is accepted if the AVE and outer loading value above 0.70. Nevertheless, the outer loading of 0.50-0.70 is still accepted if the value of AVE above 0.50 (Jogiyanto & Abdillah, 2009). The initial test of convergent validity until re-estimation 2 process was exhibited on Table 1.

Table 1: Convergent Validity Testing
	Initial outer loading	Initial AVE	Outer loading Re-estimation 1	AVE Re-estimation 1	Outer loading Re-estimation 2	AVE Re-estimation 2
COMP1	0.745924	0.526380	0.775814	0.600706	0.791606	0.708458
COMP2	0.849208		0.884897		0.914522
COMP3	0.859247		0.889083		0.891878
COMP4	0.544579		0.458734		Deleted
COMP5	0.482957		Deleted		Deleted
COMP6	0.808077		0.833118		0.868777
COMP7	0.697134		0.724096		0.727378
COMT1	0.852851	0.758303	0.856270	0.758399	0.856312	0.758401
COMT2	0.913325		0.915140		0.915172
COMT3	0.851345		0.848667		0.848653
COMT4	0.864237		0.861788		0.861731
CUST1	0.658901	0.535317	0.656691	0.595907	0.661157	0.596491
CUST10	0.071042		Deleted		Deleted
CUST2	0.743712		0.743955		0.746528
CUST3	0.858531		0.854548		0.856184
CUST4	0.827972		0.827588		0.829411
CUST5	0.830282		0.838248		0.839007
CUST6	0.804400		0.808677		0.809345
CUST7	0.798787		0.801214		0.802088
CUST8	0.744851		0.744509		0.740442
CUST9	0.639502		0.640654		0.634385
MIND1	0.666497	0.429188	0.708544	0.613963	0.708391	0.613952
MIND2	0.835517		0.851993		0.852106
MIND3	0.381127		Deleted		Deleted
OI1	0.543640	0.471958	0.526607	0.554252	0.525948	0.554854
OI2	0.564853		0.548712		0.543063
OI3	0.828588		0.845988		0.846252
OI4	0.864414		0.876516		0.878901
OI5	0.823809		0.841954		0.845057
OI6	0.323598		Deleted		Deleted
PERF1	0.319298	0.476416	Deleted	0.617512	Deleted	0.617599
PERF2	0.639175		0.622281		0.616626
PERF3	0.866926		0.887778		0.888521
PERF4	0.802251		0.822893		0.826499
RESC1	0.892105	0.594309	0.886134	0.609990	0.889943	0.611018
RESC2	0.873017		0.872518		0.876729
RESC3	0.576755		0.607234		0.610546
RESC4	0.797669		0.804012		0.798777
RESC5	0.741565		0.757242		0.753906
RESC6	0.699289		0.724454		0.725446
VIS1	0.764235	0.579906	0.765160	0.580094	0.765164	0.580094
VIS2	0.787004		0.789198		0.789113
VIS3	0.745917		0.745387		0.745520
VIS4	0.748199		0.745969		0.745923

As exhibited in Table 1, there are five indicators with loading less than 0.70. Consequently, these indicators have to drop and re-run the model. The initial AVE also showed that open mindedness (MIND), organizational innovativeness (OI) and organizational performance (PERF) have an AVE value less than 0.50. To meet the requirements, five invalid indicators (COMP5, CUST10, MIND3, OI6 and PERF1) are removed for later re-run (re-estimation 1).

The result of AVE in re-estimation 1 denoted that all of variables are accepted. However, one indicator (COMP4) has an outer loading value which still rejected the rule of thumb. Then, this indicator was deleted and re-run (re-estimation 2). The final estimation highlighted that all of the indicators obtained an appropriate value of AVE and outer loading. After test the convergent validity, the discriminant validity is evaluated by comparing the root of AVE with latent variable correlations score (Hair & Hult, 2016). Due to the AVEs score in the diagonal line higher than in the corresponding raw and column, the discriminant validity of the study is accepted. The discriminant validity of reflective construct in this study exhibited in Table 2.

Table 2: Latent Variable Correlations
	COMP	COMT	CUST	MIND	ORG INNOVT	ORG PERFORM	RESC	VIS
COMP	0.841699
COMT	0.325065	0.870862
CUST	0.234180	0.333110	0.772328
MIND	0.142006	0.389035	0.285033	0.783551
ORG INNOVT	0.350723	0.639422	0.239585	0.378585	0.744885
ORG PERFORM	0.144520	0.292281	0.396332	0.299138	0.291147	0.785875
RESC	0.565711	0.089471	0.173568	0.020463	0.092539	0.159805	0.781676
VIS	0.249052	0.173571	0.239350	0.450175	0.136039	0.367544	0.219529	0.761639

Beside the convergent validity, the measurement model is also conducted the reliability testing in order to analyse the consistency and the stability of the instrument. Composite reliability with the rule of thumb above 0.70 was used to test the reliability of construct (Hair & Hult, 2016). As presented in Table 3, all constructs have the accepted values of composite reliability.

Table 3: Composite Reliability
	Composite Reliability
COMP	0.923474
COMT	0.926172
CUST	0.929481
MIND	0.759265
ORG INNOVT	0.856123
ORG PERFORM	0.825753
RESC	0.902780
VIS	0.846694

Furthermore, the empirical SEM-PLS result with formative measurement model was examined by the bootstrapping process through assessing the collinearity among formative indicators. The present study uses SPSS software to provide the VIF and tolerance value in order to confirm the non-presence of multicollinearity. If a tolerance value is 0.20 or lower and VIF is 5.00 or higher, it indicated that the model has a collinearity problem (Hair & Hult, 2016). The result of collinearity testing was displayed in Table 4.

Table 4: Collinearity Testing
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	1.519	0.421		3.604	0.000
	MO	-0.003	0.070	-0.003	-0.036	0.971	0.904	1.107
	LO	0.659	0.135	0.504	4.867	0.000	0.571	1.751
	OI	-0.052	0.100	-0.053	-0.521	0.603	0.588	1.701

Another relevance parameter to assess the validity of formative indicators is outer weight. It estimates the weight’s significance of each indicator using T-values (Hair & Hult, 2016). Table 5 present the outer weight of formative indicators.

Table 5: Outer Weight (Mean, Stdev, T-Values)
	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	Standard Error (STERR)	T Statistics (|O/STERR|)
COMP1 -> MARKET ORIENT	0.066927	0.052885	0.100047	0.100047	0.668949
COMP2 -> MARKET ORIENT	0.081471	0.042628	0.119576	0.119576	0.681332
COMP3 -> MARKET ORIENT	0.301387	0.292083	0.147942	0.147942	2.037.196
COMP4 -> MARKET ORIENT	0.075938	0.071282	0.112920	0.112920	0.672492
COMP5 -> MARKET ORIENT	0.047510	0.053618	0.113317	0.113317	0.419268
COMP6 -> MARKET ORIENT	0.041340	0.020292	0.111487	0.111487	0.370806
COMP7 -> MARKET ORIENT	0.028383	0.033080	0.085577	0.085577	0.331661
COMT1 -> LEARN ORIENT	0.128874	0.121443	0.092817	0.092817	1.388.478
COMT2 -> LEARN ORIENT	0.187635	0.199815	0.105498	0.105498	1.778.569
COMT3 -> LEARN ORIENT	0.032438	0.026121	0.089652	0.089652	0.361825
COMT4 -> LEARN ORIENT	0.172865	0.170022	0.105193	0.105193	1.643.307
CUST1 -> MARKET ORIENT	0.018901	0.008589	0.076616	0.076616	0.246698
CUST10 -> MARKET ORIENT	0.078928	0.072487	0.072374	0.072374	1.090.559
CUST2 -> MARKET ORIENT	0.154728	0.156917	0.093080	0.093080	1.662.306
CUST3 -> MARKET ORIENT	0.130873	0.119748	0.160704	0.160704	0.814376
CUST4 -> MARKET ORIENT	0.034074	0.022866	0.160030	0.160030	0.212921
CUST5 -> MARKET ORIENT	0.081841	0.058538	0.123781	0.123781	0.661177
CUST6 -> MARKET ORIENT	0.104030	0.109737	0.129088	0.129088	0.805884
CUST7 -> MARKET ORIENT	-0.021510	-0.034896	0.117667	0.117667	0.182807
CUST8 -> MARKET ORIENT	0.161290	0.161498	0.140617	0.140617	1.147.022
CUST9 -> MARKET ORIENT	0.188988	0.190123	0.131923	0.131923	1.432.571
MIND1 -> LEARN ORIENT	0.107410	0.087237	0.145155	0.145155	0.739972
MIND2 -> LEARN ORIENT	0.148416	0.136817	0.181487	0.181487	0.817779
MIND3 -> LEARN ORIENT	0.051277	0.054097	0.041686	0.041686	1.230.081
RESC1 -> MARKET ORIENT	0.007897	-0.006915	0.203666	0.203666	0.038774
RESC2 -> MARKET ORIENT	0.005385	0.050696	0.200711	0.200711	0.026831
RESC3 -> MARKET ORIENT	-0.094529	-0.103241	0.155504	0.155504	0.607886
RESC4 -> MARKET ORIENT	0.085341	0.082882	0.136629	0.136629	0.624617
RESC5 -> MARKET ORIENT	0.149373	0.173006	0.212022	0.212022	0.704515
RESC6 -> MARKET ORIENT	-0.220299	-0.224946	0.239175	0.239175	0.921077
VIS1 -> LEARN ORIENT	0.171092	0.145160	0.093004	0.093004	1.839.630
VIS2 -> LEARN ORIENT	0.195562	0.170454	0.101384	0.101384	1.928.922
VIS3 -> LEARN ORIENT	0.232467	0.217486	0.106759	0.106759	2.177.490
VIS4 -> LEARN ORIENT	0.262928	0.228628	0.104476	0.104476	2.516.642

Table 5a showed that many formative indicators in this study have a non-significant value of outer weight. As suggested by Jogiyanto & Abdillah (2009), authors decide to keep these invalid indicators for further testing of structural model due to the strength of theory-driven conceptualization of this construct.

Evaluating the Structural Model

R-Square

The study analyse the coefficient determination or R² values to express the percentage of variance of the dependent variable which is explained by independent variables. Hair, Ringle & Sarstedt (2011) proposed that R² values ranging from 0.01 to 0.09 are considered low, while those ranging from 0.09 to 0.25 are viewed moderate and those ranging from 0.25 to 1 are regarded high. Table 6 exhibited the result of the R² value.

Table 6: R-Square
	R Square
LEARN ORIENT	0.993375
MARKET ORIENT
ORG INNOVT	0.168786
ORG PERFORM	0.275827

As presented in Table 6, the R² value of learning orientation was found to be 0.993375. The result posited that 99.34% of learning orientation is explained by market orientation. It indicated that R² value of learning orientation is considered high. Moreover, the R² value of organizational innovativeness and organizational performance are 0.168786 and 0.275827 respectively. It suggested that 16.88% of the innovativeness variance was explained by market orientation and 27.58% of the performance variance was explained by market orientation, learning orientation and organizational innovativeness. Thus, it denotes that R² value of organizational innovativeness and organizational performance are both viewed moderate.

Hypotheses Testing

All of the hypotheses were tested using SEM-PLS through a bootstrapping process, which also known as the inner model assessment. In this regard, statistical analysis can be done by considering the significant level of path coefficient among the latent variables. Hair & Hult (2016) stated that the hypothesis will be accepted if the t-value (theoretical t-value) higher than t-table, whereas a significant level of t-table is 1.64 (α=5%). Acceptance or rejection of hypotheses based on arrow direction of the relationship and significance of the model is exhibited by Table 7.

Table 7: The Overall Result Of Hypotheses Testing
Hypotheses	Original Sample (O)	T Statistics (|O/STERR|)	Result (Significant, if T-Stat>1,64)
H1: MARKET ORIENT -> LEARN ORIENT	0.013623	0.410462	Not Supported
H2: MARKET ORIENT -> ORG INNOVT	0.410836	5.093915	Supported
H3: MARKET ORIENT -> ORG PERFORM	0.298676	1.846794	Supported
H4: LEARN ORIENT -> ORG PERFORM	0.273908	2.076193	Supported
H5: ORG INNOVT -> ORG PERFORM	0.040246	0.390375	Not Supported

In brief, Table 7 summarizes the overall result of the hypotheses proposed. From five hypotheses proposed in the research framework, two of them are not supported. The influence of market orientation toward learning orientation has an original value=0.013623, t-statistics=0.410462. It shows that t-value<t-table, which means that hypothesis 1, is not supported. This result denotes that market orientation is not significantly influence on learning orientation.

The influence of market orientation on organizational innovativeness and organizational performance have the original value=0.410836 and 0.298676 with t-statistics=5.093915 and 1.846794, respectively. It shows that t-value>t-table, which means that hypothesis 2 and 3 are supported. These results indicate that market orientation is significantly influence on organizational innovativeness and organizational performance.

The influence of learning orientation toward organizational performance has an original value=0.273908, t-statistics=2.076193. It shows that t-value>t-table, which means that hypothesis 4, is not supported. This result indicates that learning orientation is also significantly influence on organizational performance.

In contrast, the influence of organizational innovativeness on organizational performance has an original value=0.04026, t-statistics=0.390375. It presents that t-value<t-table, which means that hypothesis 5 is not supported. This result denotes that organizational innovativeness is not significantly influence on organizational performance.

Discussion

Regarding the contribution of the creative industries in the global economy, scholar’s attention is considered as a necessity for SMEs in the last decade. The study aimed to fill a gap in the literature by investigating empirically the linkage of market orientation, learning orientation and organizational innovativeness as antecedents of SME’s performance. Market orientation, learning orientation and organizational innovativeness, known as the three strategic firm’s behaviour have been examined in an enormous variety of industries. This study focuses on creative industries supporting tourism in Indonesia due to the innovation is thus imperative for this sector. Additionally, Indonesia is one of the world’s leading tourism with beautiful islands and beaches as tourist destination, leading to the profitable business industry.

The result of the study will answer the aim of this research. Path coefficient value was obtained to show the linkage among variables. A hypothesis is supported if the T-value is greater than T-table. This study focused on how the effect of market orientation, learning orientation and organizational innovativeness on organizational performance. The first hypothesis considered market orientation significantly influences on learning orientation was not supported. Learning orientation appears insignificant effect toward market orientation. This study not supported the research of Rahab (2012) which showed that firm market-orientation positively impacts firm learning orientation. Lack of government support both in terms of funding and information on grants to support the SME sector financially has no effect on the intention of SME owners to make a learning orientation as a key commodity for the firm.

Regard to this study, a firm’s competencies to disseminate new knowledge or information are more likely become outdated. Thus, owners further develop the utilization of corporate resources to improve firm’s performance without external support. While the market orientation theory suggests that it has a significant effect on other strategic orientations, Gabriel Cegarra-Navarro & Rodrigo-Moya (2007) have also pointed to contrast findings related to these linkages. They found that learning culture was not being adequately utilized by market orientation.

The findings of hypothesis 2 and 3 showed that market orientation has a positive effect on organizational innovativeness and performance respectively was proven. Market orientation consists of three categories: customer orientation, resource orientation and competitor orientation (Choi, 2014). It indicates that SMEs orientation to treat customer as priority, the government support to provide information about funding and monitor the competitor’s strategy are crucial to encourage ideas or products innovation as well as enhance firm’s productivity. This result is in line with the opinion of several researchers (e.g. Otache & Mahmood, 2015; Shehu & Mahmood, 2014; Rojas-Méndez & Rod, 2013; Pandya, 2012; Jyoti & Sharma, 2012).

According to hypothesis 4, learning orientation significantly influence the organizational performance was supported. Learning orientation derived from three dimensions including a commitment to learning, shared vision and open-mindedness (Choi, 2014). It indicates that SMEs creative industries supporting tourism accepts transparently the criticism and suggestions provided by customers though the learning process has become a common value for the firms. Besides the employee’s commitment to the goals of firm, an owner’s ability to learning better would enable to escalate the performance of the firm. Relating to this result, Mahmoud et al., (2016) also stated that the component of learning orientation, including commitment to learning, shared vision and inter-organizational knowledge sharing collectively have a significant impact on business performance.

Interestingly, innovation by SMEs does not affect the company’s performance (hypothesis 5 was not supported). The research is conducted on creative industries that make innovation as a mandatory. Hence, modifying products, developing new ideas, creating new products, training employees regularly and supporting employee initiatives have become a common value for SMEs. Embroidery and weaves industry studied is Minangkabau traditional handicraft. This is due to SME’s creative industries should be market oriented to understand what consumers want. Consumers who prefer unique products from Minangkabau do not require high innovation. With its uniqueness, traditional motives have been able to make consumers interested. This finding is consistent with several studies (e.g. Cabral, Coelho & Costa, 2015; Santos, Basso, Kimura & Kayo, 2014; Yalcinkaya, Calantone & Griffith, 2007), while it is incompatible to results obtained by Widiartanto & Suhadak (2013), García-Morales et al. (2012), Hassim et al. (2011).

Implications

The research significantly had both theoretical and managerial implications. The theoretical contribution was yield from a comprehensive perspective in understanding the SME’s performance in creative industries, especially embroidery and weaves sector in developing countries like Indonesia. Theoretically, results of the study indicate that the SMEs should adapt the market orientation to achieve and enhance the level of innovativeness and organizational performance. Practically, for customer orientation as a part of market orientation, SMEs should be able to increase the customer satisfaction by provide excellent services, fulfil customer needs and respond quickly to customer complaints. Consequently, SMEs will design many programs that support activities to boost the customer satisfaction.

Regarding the resource orientation, SMEs should aware with the information about the changes in government policies and how to get the grants from private foundations. SMEs need to create some agreement with the government and private foundations to develop the organizational performance. Moreover, SMEs should be able to monitor the competitor’s action and strategies. These activities enable the organization to pay closer attention to motivating employees and give some enhancement in organizational innovativeness and performance.

In terms of learning orientation, managers play important roles to build the learning orientation environment. Managers and employees should view the commitment to learning as the value of the firm’s competitive advantages. Shared vision also indicates that managers concern about learning orientation in their organization. Employees view themselves as partners that can communicate freely and managers know how to handle it as the element of the open-mindedness culture in organization.

Acknowledgement

The authors are grateful to Indonesian Minister of Research Technology and Higher Education (KemenristekDikti-DP2M) giving the financial support of this research under the grant of Excellence Higher Education Institution Research (Penelitian Unggulan Perguruan Tinggi/PUPT) in the year of 2017.

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