INDUSTRIAL CLUSTERING AND OPERATIONAL PERFORMANCE: THE ROLE OF INNOVATION CAPABILITY

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1 INDUSTRIAL CLUSTERING AND OPERATIONAL PERFORMANCE: THE ROLE OF INNOVATION CAPABILITY UNTUNG SETIYO PURWANTO 1 AND RAIHAN 2 1 Faculty of Industrial Engineering, Jakarta Islamic University, usp.usm@gmail.com, raihan17@gmail.com ABSTRACT This study aims to examine the effects of industrial clustering (four factors) on operational performance (three aspects) and the mediating role of innovation capability (four types) in this relationship. This study considers the variables being investigated as latent variables. A cross-sectional survey design was applied involving 238 Indonesian manufacturing SMEs. The data were analyzed using structural equation modeling. The finding confirms that industrial clustering is critical for enhancing SMEs operational performance and innovation capability as well. This study highlights the importance of the mediating role of innovation capability when examining the relationship between industrial clustering and SMEs operational performance. The results imply that SMEs need to search for external resources to enhance their innovation capability and operational performance. Keywords: Industrial clustering; innovation capability; operational performance; manufacturing SMEs INTRODUCTION Many researchers have attempted to link various factors of industrial clustering to firm performance, providing a common proposition concerning the positive effects of industrial clustering towards the performance enhancement of the clustered companies ([1]; [2]; [3]). Yet, previous empirical studies have provided mixed results. Researchers such as Hervas-Oliver and Albors-Garrigos [4] and Lin and Sun [5] noted that there are still certain unanswered questions which have not been addressed in the previous studies on industrial clustering effect, in particular its direct and indirect effect on the operational performance of manufacturing SMEs. Ali and Perlings [6] highlighted, previous studies have yet disclosed fully how industrial clustering mechanism can improve the performance of manufacturing SMEs in developing countries. Furthermore, literature on industrial clustering also suggests that industrial clustering and its mechanism is an effective approach in fostering innovation capability ([1]; [7]; [3]). The premise is that both formal and informal relationships among cluster entities are very likely to generate a knowledge transfer and innovation processes within an industrial cluster. However, as Casanueva et al. [8] underlined, the literature have yet provided a comprehensive understanding regarding different types of innovation residing in an industrial cluster and the resources required to develop innovation as well. Therefore, additional research is needed to provide more insight pertaining to the effects of industrial clustering on each types of innovation capability of the clustered companies. Defined as the geographical concentration of similar or interrelated industries, industrial clustering has been widely proposed as a strategic option for SMEs to improve their performance [3]. Despite many researchers asserted industrial clustering as one of important sources in enhancing firm performance, little is so far empirically known pertaining to the mediating effect of innovation capability between these two constructs. There are certain unanswered questions which have not been addressed in the previous studies on industrial clustering in term of its direct and indirect effect on firm performance [5]. The aim of this study is to advance the current knowledge by examining the relationships between industrial clustering, innovation capability, and operational performance within the context of the Indonesian manufacturing SMEs. In particular, a major 27

2 purpose of this study is to examine the effect of industrial clustering on operational performance and the mediating effect of innovation capability between these two constructs. The findings aim at contributing towards understanding the direct and indirect effects of industrial clustering on operational performance. The findings can help the SMEs management to obtain more insight regarding industrial clustering as instrument for developing the operational performance of the clustered manufacturing SMEs. The remainder of this paper is organized as follows. Section 2 presents a review of the relevant literature that underpins the theoretical conceptualizations and the development of the research hypotheses that are put forward. This is followed by Section 3 with a description of the research methodology employed to carry out the empirical work. Section 4 comprises the results and discussions, and finally, the conclusions of the study are presented in Section 5. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT Industrial clustering and operational performance In the last decade, there is a growing attention on the economics of industrial location and, particularly, in the issue of industrial clusters. In this perspective, industrial clusters could be referenced as geographic concentration of interrelated companies, including specialized suppliers, service providers, companies in related industries, and supporting institutions [3]; a group of companies from the same or related industries geographically located near to each other [9]; or a group of production activities located in a certain region consisting of a few industrial sectors [10]. Porter [3] asserts that cluster potential is based on several interacting factors, which are grouped into four headings, and arranged in a four-dimensional diamond metaphor: i.e. firm strategy, structure and rivalry; demand conditions; related and supporting industries; and factor conditions. Scholars such as Baptista and Swann [1], Lin et al. [11] cited geographical concentrations of industries as one important instrument for developing SMEs performance, as well as one approach to overcome the size limitations of SMEs. Recent studies have investigated the performance implication of industrial clusters, providing empirical evidence regarding the positive effects of the clusters on the performance. Hervas-Oliver and Albors-Garrigos [4] demonstrated that the presence of related industries and supporting institutions in an industrial cluster significantly impact on the improvement in productivity and financial-based performance of the clustered companies in Spain and Italy. Hendry and Brown [12] confirmed that the local network is positively associated with financial-based performance of the clustered companies in UK. Bertolini and Giovannetti [13] found that geographical proximity of related industries stimulates the existence of a local network. This in turn allows the clustered Italian SMEs to improve their innovation performance. The study of Chiu [14] reveals evidence that the local network is positively associated with product and process innovation, as well as financial-based performance in the clustered Taiwan companies. Furthermore, Chiaroni and Chiesa [15] verified that geographical proximity enables the clustered companies to take advantage in term of productivity, innovation, and new business. Kesidou and Szirmai [16] revealed that the knowledge spillover in an industrial cluster positively impact on the improvement of innovation performance of the clustered companies. Liu [17] provides evidence that the local advantage positively impact on the improvement of product and process innovation and labor productivity as well. Meanwhile, Muscio [18] showed that the presence of related industries in an industrial cluster positively impacts on the improvement of product innovation capability. Therefore, the following working hypothesis can be formulated. Hypothesis 1: Industrial clustering is positively associated with operational performance in manufacturing SMEs. Industrial clustering and innovation capability Literature on industrial cluster also suggested could serve as sources for the development of innovation capability of companies located in the cluster. The premise is that mechanism between industrial cluster elements (e.g. government agencies, professional associations, and academic research and training institutes) in nature facilitate knowledge sharing and spillover, create both formal and informal interactions, and build technological infrastructure among companies consisting in the cluster ([1]; [19]; [3]). Knowledge spillover residing in an industrial cluster, as Morosini [20] 28

3 notes, play an important role as antecedent to the enhancement of innovation capability. As knowledge sharing and spillover tends to be geographically localized, accordingly, the companies locating in an industrial cluster are likely to gain a better access to knowledge residing in the cluster. This in turn, would enrich the collective knowledge that is required to perform innovation [21]. Therefore, the following working hypothesis can be formulated. Hypothesis 2: Industrial clustering is positively associated with operational performance in manufacturing SMEs Innovation capability and operational performance Many definitions of innovation have been proposed in the literature. For example, innovation could be referenced as a process of development and implementation of a new product, process, service, organizational structure, and business model [22]; the introduction and implementation of new idea or knowledge in an organization [23]; or a process to find out and implement a new product, process, organizational form, and market [24]. Meanwhile, the OECD [25] define innovation as the implementation of a new and significantly improved product (good or service), or a process, a new marketing method, or a new organisational method in business practice, workplace organisations or external relations. Despite the difference in its definition, researchers mostly agreed that manufacturing organizations need to perform innovation to obtain a high performance, create new values for the customer, and obtain financial benefit for the company. Furthermore, literature on innovation has introduced several typologies of innovations. For instance, innovation could be classified into two different types: technological and nontechnological innovations. Technological innovation refers to product and process innovations, while non-technological innovation concern with service and organizational innovations [26]. Innovation is also could be separated between administrative and technical innovations, product and process innovations, technological and architectural innovations, and radical and incremental innovations [27]. In the meantime, the OECD [25] differentiates four types of innovation: product innovation, process innovation, organizational innovation, and marketing innovation. In spite of the different attention on innovation dimensions, researchers commonly agree regarding the important role of innovation capability in supporting a company s performance. Following a survey methodology, a number of studies have investigated the relationship between innovation capability and performance. Sher and Yang [28] confirmed innovation capability positively associated with performance as measured by returns on assets of Taiwanese integrated circuit industry. Calantone et al. [29] also found that innovation capability positively impact on the improvement of financial based performance of both manufacturing and service companies in the USA. Keskin [30] verified innovation capability positively related to the financial based performance improvement of Turkish manufacturing SMEs. Furthermore, Jimenez- Jimenez et al. [31] confirmed three types of innovation capability, namely product, process, and organizational innovations positively impact on the improvement in productivity and financial-based performance. Raymond and St- Pierre [32] suggest that manufacturing SMEs need to encourage their innovation capability. They found that innovation capability was positively related to manufacturing cost reduction, product quality improvement, and service level enhancement. Likewise, Sen [33] confirmed that manufacturing SMEs with higher capability on product innovation perform better in term of total sales and market share than those with lower capability. Therefore, the following working hypothesis can be formulated. Hypothesis 3: Innovation capability is positively and significantly associated with the operational performance in manufacturing SMEs Mediating effect of innovation capability As have been stated above, many researchers emphasized the role of industrial clustering as a driver for effective innovation capability which contributes to firm performance ([1], [19], [3]). In this perspective, Porter [3] highlights that interaction between industrial cluster factors facilitate knowledge sharing and build technological infrastructure among companies consisting in clusters. Knowledge spillover residing in an industrial cluster plays an important role as antecedent to the enhancement of innovation capability, as Morosini [20] note. This innovation capability, in turn, will support in obtaining high performance [28]. Raymond and St-Pierre [32] assert 29

4 innovation capability was positively related to manufacturing cost reduction, product quality improvement, and service level enhancement. To examine the role that innovation capability may play in the relationship between industrial clustering and operational performance, this study propose pose the following hypothesis. Hypothesis 4: Innovation capability mediates the relationship between industrial clustering and operational performance in manufacturing SMEs RESEARCH METHODOLOGY Sample and data collection This study followed a survey method to collect data by using a single respondent design. The survey method was chosen because, as Panayides [34] noted, it allow the researchers to obtain a large number of respondents efficiently, in addition that the data required could be obtained by the use of a mail-administered or direct-distributed questionnaire. Meanwhile, the use a single respondent design is reasonable and has been widely applied in an operational and management research, particularly when the study is dealing with investigating several phenomena across different industries [32]. Sampling frame is the listing of manufacturing companies as listed in Indonesian Manufacturing Directory 2013 provided by the Indonesia Statistical Board. A purposive sampling method was employed to select the sample. A total of 428 structured questionnaires were directly distributed to the targeted sample with 552 questionnaires among of them were received. After checking their completeness, 14 questionnaires were not utilized in the analysis due to data missing and ambiguity answers. Thus the sampled companies are 238 firms. It consisted of 32.3% of firms operating within the electrical parts, 28.2% in machining jobs, 21.0% in automotive parts, and 18.5% in plastic/paper products sectors. Variables measurement This study adopted Porter s [3] definition of industrial cluster to develop a specific measurement scale for industrial clustering construct. This study also considers the work of other researchers ([16], [5], [18]) to capture the industrial clustering factors. Four dimensions were selected to be included in the industrial clustering construct, i.e. local network (Clust1), presence of related industries (Clust2), government support (Clust3), and local demand (Clust4). A total of 16 items were utilized to assess these four dimensions. As applied in Guerrieri and Pietrobelli [35], this study focus on assessing the industrial clustering effects, namely the extent to which the industrial clustering is perceived as important factor by manufacturing SMEs in improving their performance. Fivepoint Likert scale, anchored with 1 = (not at all important) to 5 (very important), were used to measure the scales of industrial clustering. With regard to innovation capability, this study consider four dimensions to capture innovation capability construct, i.e. product innovation capability (Inov1), process innovation capability (Inov2), organizational innovation capability (Inov3), and marketing innovation capability (Inov4). On the basis of OECD s [25] definition of innovation, this study developed a specific measurement scale for the four types of innovation capability. This study also takes into account the measures developed for the four types of innovation capability in Camison and Lopez [36] and Guan and Ma [37]. A total of 20 items were employed to capture four dimensions of innovation capability. Five-point Likert scales utilized. The endpoints of the scales were 1 (Not at all important) and 5 (Very important). In relation to operational performance, this study considers product quality (Perfo1), manufacturing cost (Perfo2), and delivery (Perfo3) as three dimensional measures of operational performance. A total of nine items, adopted from Abdel-Maksoud [38] and Alegre- Vidal, et al. [39] were employed to capture the three dimensions of operational performance. Perceptual measures were applied to measure these three operational performance dimensions. In particular, operational performance was measured by having respondents rate of their companies performance relative to relative to that of their principal competitor. Five-point Likert scale was applied, ranging from 1 (Much worse) to 5 (Much better). Data analysis method The variables being investigated in study were treated as latent variables consisting of a distinct set of reflective indicators. Accordingly, a structural equation modeling (SEM) was applied to assess the structural model representing the relationship among the three. This study applied the two-stage approach [40], in analyzing the proposed model using SEM. The first stage was concerned with assessing the adequacy of the measurement model in relation 30

5 the reliability, validity, and dimensionality of scales to measure industrial clustering, innovation capability, and operational performance. In hence, confirmatory factor analysis was applied. The second stage addressed examining the proposed hypotheses on the structural relationships among industrial clustering, innovation capability, and operational performance. This procedure was run by using AMOS 5 with maximum likelihood estimation techniques. Five statistics indices were applied to assess the model fit, i.e., the Chi-square (X 2 ), goodness-of-fit index (GFI), comparative fit index (CFI), Tucker Lewis index (TLI), and root mean square error of approximation (RMSA). Research model This study primarily concerns with investigating the simultaneous relationship involving industrial clustering, innovation capability, and operational performance of manufacturing SMEs. Figure 1 depicts the conceptual model proposed in this study. Table 1 Validity and reliability of the scales (industrial clustering) Table 2 Validity and reliability of the scales (innovation capability) Figure 1 The proposed model RESULTS AND DISCUSSIONS Measurement model analysis In first step, this study applied an unrotated principal component analysis (PCA) to assess to the dimensionality of each measuring scale of the latent variables being investigated. Next, this study calculated Cronbach alpha to assess internal consistency for each measuring scales. Table 1, Table 2, and Table 3 summarize the results of the test. As it emerged, the PCA generated factor loadings of 0.50 for all measuring scales; while the reliability analysis provided Cronbach alpha of 0.70 for all constructs. Results lead to confirm the scales validity and reliability ([41], [42]). Table 3 Validity and reliability of the scales (operational performance) 31

6 Structural model analysis This study performed a structural equation modeling (SEM) to assess causal relationships between industrial clustering, innovation capability, and operational performance in manufacturing SMEs. In particular, this study assesses mediating effect of innovation capability through Baron and Kenny [43] method. Following these authors, this study needs to verify [1] the significant relationship between industrial clustering and innovation capability and [2] also the significant relationship between innovation capability and operational performance. In this perspective, mediation is established if the effect of industrial clustering variable on operational performance variable is reduced by innovation capability variable. In this study, several fit indices were applied to verify the full structural models fit. In this perspective, this study needs to ensured that [1] the Chi-Square value per degrees of freedom did not exceeded 3, [2] the Goodness-of-fit index (GFI) value is greater than 0.90, [3] the Tucker Lewis Index (TLI) value exceeded 0.95, and that [4] the Root Mean Square Error of Approximation (RMSEA) value did not exceeded Table 4 summarizes the results of the full structural models fit indices. As seen in Table 4, it was found that the model has an excellent adjustment to the data. Table 4 Fit indices of model tested Next, this study applied three parameters to assess the proposed hypothesis: standard regression coefficients (β), critical ratio (C.R.) and level of significance (p). In this perspective, a relationship between two variables is significant if the C.R. value is greater than 1.96 and p value is lesser than Figure 2 depicts the results of CFA for the simultaneous relationship between variables being investigated. As seen in Figure 2, there is a positive relationship between industrial clustering and operational performance (β =0.231; p < 0.01). Therefore, H 1 is supported. Results support the notion that industrial clustering and operational performance are indeed positively related. A positive relationship between industrial clustering and innovation capability is also established (β = 0.781; p < 0.001). Therefore, H 2 is supported. As hypotesized in this study, there is a significantly positive relationship between innovation capability and operational performance (β = 0.754; p < 0.001). Therefore, H 3 is supported. Figure 2 The results of CFA To test mediating effect of innovation capability in the relationship between industrial clustering and operational performance, this study examined conditions suggested by Baron and Kenny [43]. Following the authors, this study examined the relationship between industrial clustering and innovation capability to determine if these two construct had significant relationship. Second, this study examined the relationship between industrial clustering and operational performance to determine if these two construct had significant relationship. Third, this study examined the relationship between innovation capability and operational performance to determine if these two construct had significant relationship. Table 5 summarized the results of CFA for testing the mediating effect of innovation capability. Table 5 The results of CFA (before and after inclusion of innovation capability) 32

7 Three insights could be derived from Table 5. First, industrial clustering has significantly positive relationship with innovation capability. Therefore, the first condition for mediating effect of innovation capability is supported. Second, industrial clustering has significantly positive relationship with operational performance. Therefore, the second condition for mediating effect of innovation capability is supported. Third, innovation capability has significantly positive relationship with operational performance. To test the third condition for mediating effect, this study examined the change in chi-square value for industrial clustering variable between before the inclusion of innovation capability variable and after the inclusion of innovation capability variable in the model. The significance of industrial clustering on operational performance is reduced when innovation capability is included in the model. The results of CFA show the mediating effect of innovation capability in the relationship of industrial clustering and operational performance. Therefore, H 4 is supported. As it emerged, industrial clustering was found to have a positive effect on operational performance (β = 0.231; p < 0.01). The findings underline the important role of industrial clustering in enhancing operational performance of manufacturing SMEs. This phenomenon might be explained by the fact that the SMEs operating in an industrial cluster tend to build a production network to improve their collective performance: enabling the SMEs to overcome the problems associated with the production processes, marketing, and raw materials procurement. Meanwhile, the presence of related industries, such as raw materials and components suppliers, tend to reduce the production and operational cost. Such a condition would provide several benefit such as the availability of production resources, improving product quality, and reducing manufacturing costs [15], [11]. As hypothesized, results provided evidence that industrial clustering is positively and significantly associated with innovation capability of manufacturing SMEs (β = 0.783; p < 0.001). The findings support the notion pertaining to the positive effects of industrial clustering in fostering innovation capability of companies operating in an industrial cluster [5]. Furthermore, results indicate that innovation capability was positively and significantly associated with operational performance of manufacturing SMEs (β = 0.754; p < 0.01). The results confirm that manufacturing SMEs with higher capability to perform product, process, and marketing innovations are expected to have higher operational performance as measured by manufacturing cost, product quality, and delivery. The finding supported the notion regarding the positive effects of innovation capability on the firm performance [31]. CONCLUSIONS This study develops a conceptual model to examine the mediating role of innovation capability in the relationship between industrial clustering and operational performance in manufacturing SMEs. The results show that industrial engineering can positively enhance operational performance. However, if we include innovation capability as a mediator, the directly positive relationship between industrial clustering and operational performance will attenuate. The results implies that industrial clustering indirectly influences operational performance by influencing innovation capability. In other words, innovation capability plays a mediating role through which industrial clustering benefits operational performance. This study contributes to literature on industrial clustering in several ways. First, this research examines the effects of industrial clustering on operational performance by utilizing four factors in industrial clustering and three dimensions in operational performance. While the importance of industrial clustering as important source for developing performance of the clustered companies has been recognized, little is so far known as regards its effect on operational performance [16]. The results of this research contribute towards understanding about the simultaneous effects of local network, the presence of related industries, government support, and local advantage on manufacturing cost, product quality, and delivery performance based on empirical data. Second, this research evaluates the inclusion of innovation capability in the relationship of industrial clustering and 33

8 operational performance. The results of this study provide more insights regarding how industrial clustering affects operational performance. The findings make a contribution to the industrial clustering literature by clarifying the role that innovation capability plays in the relationship involving industrial clustering and operational performance. Ali and Perlings [6] underlined that previous studies have yet disclosed fully how industrial clustering mechanism can improve performance in the clustered companies. Third, this research explores industrial clustering factors and their effects toward product, process, and organizational and marketing innovation capability. Therefore, this study provides more comprehensive analysis on the relationship between industrial clustering and innovation capability based on empirical data. Casanueva et al. [8] underlined that literature on industrial clustering have yet provided a comprehensive understanding regarding the effects of industrial clustering on innovation types and activities residing in an industrial cluster. From a practical point of view, the results of this study suggests that SMEs owner and managers should be aware of the importance of innovation capability in the relationship of industrial clustering and firm performance. SMEs owner and managers have to facilitate continuosly of innovation by taking a leading role in managing the innovation process. SMEs can intensify and enhance innovation through the generation new ideas and converse it into innovative products and services. SMEs owner and managers need to foster an enabling environment that allows employees to share and exchange new ideas to create new products and services. This study has some inherent limitations that could be noted to point to lines for future study. First, this study was conducted by using cross-sectional sample design. The design does not allow in concluding the causality among the three constructs being investigated in this research [30]. Future studies might apply longitudinal design to investigate the causal nature regarding the relationship among the three constructs being investigated in this study. Second, constructs being investigated in this research are multi-dimensional in nature. Different study might operationalize the constructs by using different measures. Future studies might take into account other measures to provide more insights regarding the relationships among the three construct being examined in this study. For example, one might add the knowledge spillover factor in industrial clustering construct or flexibility dimension in operational performance construct. Lastly, the sample involved in this study is derived from a single developing country, namely Indonesia. This in turn, limits the generalisability of the findings. Manufacturing SMEs in different country might have different characteristics and have different performance measures [30]. This study suggests future studies might use data obtained from different countries. References 1. Baptista, R. and Swann, G. M. P. (1998). Do firms in clusters innovate more? Research Policy, 27, Krugman, P.R. (1991). Geography and Trade. MIT Press, Cambridge, MA 3. Porter, M.E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76 (6), Hervas-Oliver, L.J., Albors-Garrigos, J. (2007): Do clusters capabilities matter; an empirical application of the resourcebased view in clusters. Entrepreneurship & Regional Development, 19 (2), Lin, G.T.R. and Sun, C.C. (2010). Driving industrial clusters to be nationally competitive. Technology Analysis & Strategic Management, 22 (1), Ali, M. and Perlings, J. (2011). Value Added of Cluster Membership for Micro Enterprises of the Handloom Sector in Ethiopia, World Development, 39(3), Beaudry, C. and Breschi, S. (2003). Are firms in clusters really more innovative?. Econ. Innov. New Techn. 12(4), Casanueva, C., Castro, I., Galan, J.L. (2012). Informational networks and innovation in mature industrial clusters. Journal of Business Research. In Press, Available online 14 March Bell, G.G. (2005). Clusters, networks, and firm innovativeness. Strategic Management Journal, 26 (1),

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