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A Product Quality-Supply Chain Integration Framework



Zahra Lotfi, Shahnorbanun Sahran and Muriati Mukhtar
 
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ABSTRACT

Product quality is one of the key competitive factors that will enable firms to survive and succeed in the global market place. Product quality is an important dimension of operational performance in supply chain management that has not received sufficient attention from the research community. Previous researches in this field have neglected to consider the relationship between supply chain integration and dimensions of product quality. Hence, the effect of supply chain integration on quality performance has received less attention. Therefore consideration must be given to the development of collaborative activities between manufacturer, supplier and costumer which enables firms to work together and improve product quality. Accordingly, the primary focus of this study is to investigate the relationship between dimensions of product quality and supply chain integration. The dimensions considered for product quality are design quality and conformance quality, whereas the dimensions for supply chain integration are customer integration, supplier integration and internal integration. The relationships between these dimensions are then embodied in a framework which will be validated. This research adopts both a qualitative conceptual approach and a quantitative approach in the development of the framework. The literature is consulted in identifying the dimensions of supply chain integration and product quality. These dimensions are then modeled into a questionnaire survey and are administered to identify manufacturing companies. Validity and reliability of the scales for the construct of interest were assessed through a factor analysis and Cronbach-alpha test. The results provide considerable support for our product quality-supply chain integration framework that can be used in the manufacturing sector.

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Zahra Lotfi, Shahnorbanun Sahran and Muriati Mukhtar, 2013. A Product Quality-Supply Chain Integration Framework. Journal of Applied Sciences, 13: 36-48.

DOI: 10.3923/jas.2013.36.48

URL: https://scialert.net/abstract/?doi=jas.2013.36.48
 
Received: September 26, 2012; Accepted: December 24, 2012; Published: February 01, 2013



INTRODUCTION

Researchers have demonstrated that firms which collaborate and cooperate with other firms or create inter-firm relationship with others will have better competitive advantages than those which do not (Kotcharin et al., 2012; Flynn et al., 2010; Koufteros et al., 2005; Lau, 2011; Kim, 2009). Hence, there are an increasing number of empirical studies and investigations devoted to the direct and indirect impacts of Supply Chain Integration (SCI) on product quality and firm performance (Kotcharin et al., 2012; Zhang et al., 2011; Zhu and You, 2011; Musheng et al., 2008; Tsung, 2000).

With recent advances in Information Technology (IT), the research and the practice of collaboration between firms is now having a significant impact on many aspects of supply chains (Chae et al., 2005; Wang et al., 2011; Zapp et al., 2012). There are some researches focusing on the interaction between the various dimensions of supply chain integration and performance (Cao and Zhang, 2011; Narasimhan and Kim, 2002; Vickery et al., 2003; Pagell, 2004; Hosseini et al., 2012). There are some researches focusing on the impact of product quality management practices (Boon-Itt, 2011; Kotcharin et al., 2012; Koufteros et al., 2005; Rosenzweig et al., 2003; Swink et al., 2007; Wong et al., 2011; Devaraj et al., 2007; Das et al., 2006; Cousins and Menguc, 2006; Fynes and de Burca, 2005). However, there is a lack of attention given to the relationship between supply chain integration and dimensions of product quality.

The existing researches on SCI, however, include definitions and dimensions (Van Der Vaart and Van Donk, 2008). While some researches concentrate on the individual dimensions of SCI (Cousins and Menguc, 2006; Homburg and Stock, 2004; Koufteros et al., 2007), especially customer and supplier integration, others use a variety of definitions to examine SCI as a single construct (Armistead and Mapes, 1993; Rosenzweig et al., 2003). Furthermore, many conceptualizations of SCI do not include the central link such as, internal integration and hence are incomplete (Flynn et al., 2010). External integration establishes interactive and collaborative relationships with customers and suppliers, whereas internal integration recognizes the significance of interactions between departments within a manufacturer. Both of these integrations are necessary to enhance the performance of a supply chain and improve customers’ satisfaction (Flynn et al., 2010). Supply Chain Integration can be classified into three dimensions: (1) Customer Integration, (2) Supplier Integration and (3) Internal Integration. Customer and supplier integration are also known as external integration.

Improvements in the quality of products and services that enterprises offer may add value for customers. Clark et al. (1992) classified total product quality into two dimensions: firstly, conformance quality which is defined by how well the actual product conforms to the design once it has been manufactured and secondly, design quality which is defined as the extent to which quality is designed into the product (Fynes et al., 2005). Hence, studies which test the effect of supply chain integration on dimensions of product quality remains a research opportunity. The two main criteria for choosing Supply Chain Integration (SCI) are the degree to which a manufacturer strategically integrates with its supply chain partners and collaboratively manages intra- and inter-organizational processes. These will in turn lead to effective and efficient flows of information and hence, provide products with better qualities to the customer. However, the effect of supply chain integration on quality performance has received less attention.

The aim of the present study is (1) to review the impact of supply chain integration on performance particularly product quality, (2) to propose a product quality- supply chain integration framework and (3) to report on the validation of the framework using empirical methods.

PROPOSED FRAMEWORK

The impact of supply chain integration on performance: Many researches have been conducted to show the relationship between supply chain integration and some factors of supply chain performance. Some researchers believe SCI is one-dimensional (Marquez et al., 2004; Rosenzweig et al., 2003) while others have divided it into external and internal integration (Campbell and Sankaran, 2005; Petersen et al., 2005; Zailani and Rajagopal, 2005). There are also some researchers that have represented multiple dimensions (Droge et al., 2004; Gimenez and Ventura, 2005; Koufteros et al., 2005).

This study investigates some supporting literature on dimensions of supply chain integration and performance and then classifies all performance into two categories which include: strategic performance (long term critical success factors and key performance) and operational performance (short term critical success factors and key performance). Table 1 shows some related literature on the relationship between SCI and dimensions of strategic performance and Table 2 demonstrates a summary of prior literature on the relationship between each dimension of Supply Chain Integration (SCI) and operational performance which are described in the following paragraphs.

Within the context of manufacturing strategy, the importance of supply chain integration for competitive capabilities was investigated in studies of world-class manufacturers. Many researchers, including Swink et al. (2007), Kim (2009) , Rosenzweig et al. (2003) and Kotcharin et al., (2012) investigated the effect of supply chain integration on some competitive capabilities such as; Product Quality, Delivery, Cost, Customer Service, Marketing Technology, Differentiation, reliability, Process Flexibility and New product flexibility.

Some researchers have shown the relationship between supply chain integration and some dimensions of business performance such as market/product diversification, product responsiveness, time to market, market share index, customer satisfaction, financial performance, Return on Assets (ROA), sales growth, the percentage of revenues from new products, growth in sales, return on sales, growth in return on sales, growth in profit, growth in market share, Return on Investment (ROI) and growth in ROI, market performance and customer satisfaction (Flynn et al., 2010; Swink et al., 2007; Narasimhan and Kim, 2002; Droge et al., 2004; Kim, 2009; Fynes et al., 2005; Rosenzweig et al., 2003; Germain and Iyer, 2006).

On the other hand some researchers focus on effects of SCI on operational performance factors such as time-to-product (Droge et al., 2004; Das et al., 2006), New product introduction time, manufacturing cycle time reduction (Das et al., 2006), lead time, conformance to specifications, quality improvement, (Cousins and Menguc, 2006), product innovation (Koufteros et al., 2005, 2007), product modularity, process modularity (Droge et al., 2012), project team effectiveness, design performance (Petersen et al., 2005), profitability (Koufteros et al., 2005; Das et al., 2006), Delivery (Droge et al., 2012; Swink et al., 2007; Kotcharin et al., 2012; Wong et al., 2011; Rosenzweig et al., 2003; Cousins and Menguc, 2006; Das et al., 2006; Devaraj et al., 2007), Cost (Droge et al., 2012; Swink et al., 2007; Kotcharin et al., 2012; Wong et al., 2011; Rosenzweig et al., 2003; Cousins and Menguc, 2006; Devaraj et al., 2007), process flexibility and Process improvement (Kotcharin et al., 2012; Swink et al., 2007; Rosenzweig et al., 2003; Cousins and Menguc, 2006), production flexibility (Devaraj et al., 2007; Wong et al., 2011) and new product flexibility (Swink et al., 2007).

Table 1: Summary of some prior literature on relationship between supply chain integration and strategic performance

Table 2: Summary of some prior literature on relationship between supply chain integration and operational performance

Germain and Iyer (2006) investigate internal integration and customer integration on logistics performance and Cao and Zhang (2011), Vickery et al. (2003) and Narasimhan and Kim (2002) focus on firm performance and also Pagell (2004) indicates the relationship between firm performances with only supplier integration. Some dimensions of Supply Chain Practical Capability such as technological factor , logistical factor and structural factor have been researched by Kim (2009) and Vickery et al. (2003). Narasimhan and Kim (2002) show the positive effects of internal integration on both external integration: supplier and customer integration and vice versa.

We categorize performance into two categories which are strategic performance and operational performance as shown in Table 1 and 2. There are some indicators for strategic performance in some prior literature on relationship between supply chain integration and strategic performance such as: trust, marketing technology, product technology, information technology, process technology, commitment and co-operation, adaptation, interdependence, logistics performance, profitability, quality improvement and customer service.

There are also some indicators for operational performance that indicate in some prior literature on relationship between supply chain integration and operational performance such as: Customer satisfaction, Cost, Delivery, Financial Performance, Market performance, Firm Performance, Time Performance, Product Performance, Conformance to specifications and Product Quality. Table 2 shows these performance indicators and related sources of previous researches.

From Table 2, we can conclude that lack of attention has been granted to dimensions of supply chain integration and product quality which contains design quality and conformance quality.

Supply chain integration and product quality: There are other researches that look at quality in supply chain management (Devaraj et al., 2007; Swink et al., 2007; Das et al., 2006; Koufteros et al., 2005). Cousins and Menguc (2006) work on quality improvement and supply chain integration. Kotcharin et al. (2012), Wong et al. (2011), Boon-Itt (2011) and Rosenzweig et al. (2003) investigate the significant effect of SCI on overall product quality but only Fynes et al. (2005) present the relationship of supply chain relationship quality on two important dimensions of product quality that are design quality and conformance quality.

Kotcharin et al. (2012) investigated the interaction of internal and external integration and noted the corresponding impact on competitive capabilities (Product Quality, Delivery, Low Cost and Process Flexibility). They used the Structural Equation Modeling (SEM) to analyze the empirical data collected from the survey conducted with the cooperation of 130 automotive suppliers in Thailand. Their findings include a positive relationship between internal integration and external integration; and also a high level of product quality attained through internal and external integration leading to an enhanced delivery capability.

Wong et al. (2011) investigated the contingency effects of Environmental Uncertainty (EU) on the relationships between three dimensions of supply chain integration specifically internal integration, supplier integration and customer integration and four dimensions of operational performance, namely, delivery, production cost, product quality and production flexibility. They gathered data from 151 automotive manufacturing companies in Thailand and found that under a high EU, the relationship between supplier/customer integration and delivery and flexibility performance and those between internal integration and product quality and production cost, will be enhanced.

Boon-Itt (2011) examined the effect of communication and collaboration as types of information technology on the relationship between supply chain integration and product quality performance among 111 Thai suppliers and automakers in the automotive industry. They found that under framework linking supply chain integration strategies and product quality performance, different information technology types can be set up to enhance product quality.

Devaraj et al. (2007) investigated the relationships between eBusiness capabilities (customers, purchasing and collaboration), production information integration (supplier integration and customer integration) and operational performance (cost, quality, flexibility and delivery). Data from 120 automotive and computers/ electronics industries were collected and analyzed. Their findings include the positive effect of supplier integration on all dimensions of operational performance and demonstrate its greatest impact on delivery time, costs and quality.

Swink et al. (2007) researched about four different types of strategic integration at the manufacturing plant level. They investigated the roles that manufacturing-based competitive capabilities (cost efficiency, quality, delivery, process flexibility and new product flexibility) play in adjusting the relationships between strategic integration (corporate strategy integration, product-process technology integration and strategic supplier integration) and business performance (market performance and customer satisfaction). A survey was conducted among 224 manufacturing firms in North America and the results illustrated that customer and supplier integration activities provide benefits for business performance and also that strategic supplier integration has a significant relationship with market performance, but not with customer satisfaction.

Koufteros et al. (2005) examined the effect of uncertainty, equivocality and platform development strategy on the relationships among internal integration, external integration and competitive capabilities (product innovation, quality, profitability). They gathered the data from 244 manufacturing companies of several industries. They found that both internal and external integration have a positive effect on product innovation and quality and eventually profitability. Their findings also include internal integration as an important factor of external integration leading to higher levels of competitive capabilities.

Rosenzweig et al. (2003) examined the ways that manufacturing-based competitive capabilities (product quality, delivery reliability, process flexibility and cost leadership) mediate the association between supply chain integration and business performance (Return on Assets (ROA), customer satisfaction, sales growth and the percentage of revenues from new products). They gathered and analyzed data obtained from 238 manufacturers in the top quartile of sales revenues in 35 countries and found that supply chain integration intensity leads directly to an enhanced business performance, hence, approving the conventional wisdom concerning the growing importance of supply chain integration in the consumer products sector. They also concluded that the benefits of integration should first be transformed into operational capabilities credited by customers, such as product quality, process flexibility, delivery reliability, or cost leadership.

Fynes et al. (2005) focused on the interaction between different dimensions of Supply Chain (SC) relationships (such as trust, commitment, adaptation, communication and collaboration) and quality performance. They examined two dimensions of product quality including design quality and conformance quality but they did not search on the direct effect of supply chain integration. They propose that some dimensions of supply chain relationship quality such as communication, trust, adaptation, commitment, interdependence and co-operation are strong indicators of a higher order construct. They also found the relationship between these indicators and two dimensions of product quality (design quality and conformance quality) in one hand and on the other; they showed the positive effect of product quality dimensions on customer satisfaction. They developed a framework, incorporating dimensions of SC relationships and quality performance. The model was validated using data collected from 200 suppliers in the electronics sector in the Republic of Ireland by using AMOS 4. Their findings asserted that by focusing on the management of SC relationships organizations, product quality can be enhanced.

From the literature review, we can conclude that very little attention has been granted to the dimensions of supply chain integration and product quality which is composed of design quality and conformance quality. Even though Fynes et al. (2005) investigated on these two dimensions of product quality, but they did not research into their direct effect on supply chain integration. Therefore, the relationship between supply chain integration with the complete dimensions of product quality has not received sufficient attention from the research community.

Proposed product quality-supply chain integration framework: The review of the literature illustrates that a supply chain consists of multiple firms, both upstream (supply), downstream (distribution) and the ultimate customer (Christopher, 2011). We argued about product quality based on Clark et al. (1992). Here total product quality is composed of two dimensions. Firstly, conformance quality which is defined by how well the actual product conforms to the design once it has been manufactured and secondly, design quality which is defined as the extent to which quality is designed into the product (Fynes et al., 2005). Based on this classification for product quality and Flynn et al. (2010) research for integrated relationship between manufacturers and their supply chain partners, our proposed framework is shown in Fig. 1.

This framework is derived from the literature review as given in this paper. The proposed framework is divided in two main categories: Supply Chain Integration and Product Quality. We consider three types of supply chain integration: customer integration, internal integration and supplier integration as independent variables and two types of product quality: conformance quality and design quality as dependent variables.

Fig. 1: Research proposed framework

Our analysis focuses on the relationship between dimensions of supply chain integration and dimensions of product quality of the entire supply chain.

MATERIALS AND METHODS

To illustrate the relationship of supply chain integration and product quality in the manufacturing sector, we consider two important dimensions of product quality in supply chains specifically design quality and conformance quality.

The proposed methodology for conducting this study is as follows:

Construct a literature review on the relationship between dimensions of product quality and supply chain integration
Construct a framework between dimensions of supply chain integration and dimensions of product quality based on literature review
Construct a draft questionnaire to validate the proposed framework
Modify the questionnaire
Design and implement the survey
Analyze the validity and reliability of the survey

This study was conducted to provide a deep understanding and a set of theoretical and empirical findings. Quantitative method was applied to the data gathered from manufacturing sector in the year 2012. In this method, all the relevant secondary data on general information and demographic, customer integration, supplier integration, internal integration, design quality and conformance quality were used for analysis.

The data required for the quantitative analysis was collected with the aid of a questionnaire that was devised to address the proposed framework. According to Creswell (2009), investigators use a questionnaire to a sample or to an entire population of people to describe the attitudes, opinions, behaviors, or characteristics of a population as it is a common and accepted method to provide data for social science research.

The sampling method which was applied in this study was the simple random sampling method. A simple random sample is a sampling method in which every member of the population has an equal and independent chance of being chosen. Data collection procedure in this study was a cross-sectional survey design. Cross-sectional studies involve observations of the whole population, or a representative subset, at a specific period of time. The target respondent was the manufacturing sector.

There are many ideas for an adequate sample size in Structural Estimating Modeling (SEM). Hatcher (1994) mentions in his book that the minimum size of the sample should be five times the number of variables. Therefore, since there are five variables in this study, 25 respondents or more should be adequate. From 100 surveys just 32 surveys were returned, 2 of them were ignored because they showed the same grading for all the questions. The method to submit the survey was sent via email from the 32 returned surveys. The response rate was 30% for 30 usable surveys. The empirical analysis used data from a total of 30 manufacturing companies. Validity and reliability of the scales for the constructs of interest were assessed through a factor analysis and Cronbach-alpha test. For the purpose of this research, respondents were chosen randomly in the period of two months in the manufacturing sector.

INSTRUMENT DEVELOPMENT

The data in this study was mainly drawn from the survey questionnaire. The questionnaire had six sections, namely (1) general information, (2) customer integration, (3) supplier integration (4) internal integration (5) design quality and (6) conformance quality.

Table 3 demonstrates the details of the questionnaire which had 42 questions; 19 on general information, 5 on customer integration, 5 on supplier integration, 5 on internal integration, 4 on design quality and 4 questions on the conformance quality.

The questionnaire was distributed randomly to the target sample via email. The questionnaire items are included in Appendix. This study used SPSS v.20 to perform descriptive statistics analysis, variable reliability and validity analysis. These analyses are described in the following sub-sections.

Reliability: In this research, a primary sampling with the size of 30 samples in the manufacturing sector had been done by using the internal consistency method. In fact, the measurement tool was considered by using statistical methods. In other words, variable’s reliability of this research has been determined based on the Cronbach alpha to be between 0 and 1. If the calculated coefficient is near to zero, it indicates a lack of reliability of question and if this value is close to one, it indicates a high reliability. Some fields accept alpha levels higher than 0.45. In social sciences, the alpha level of 0.70 is considered good and its reliability is accepted.

According to the Table 4 the values of the alpha coefficient for all independent and dependent variables are more than 0.70 and the total Cronbach alpha is 0.855 (Table 5). This means that the items have relatively high internal consistency.

Table 3: Summary of research variables

Table 4: Cronbach’s alpha for research variables

Validity: The questionnaire was tested for two main types of validity: content validity and construct validity. Content validity is a judgement, by experts, of the extent to which a question truly measures the concept it was supposed to measure. The instrument developed in this study demonstrates the content validity as the selection of measurement items was based on both, an exhaustive review of the literature and detailed evaluations by ten academicians and five manufacturers during pre-testing. The questionnaire was finalized after some small modifications. The feedback obtained during a pilot study helped to ensure that the items were representative of the construct. Therefore, it is argued that the measures satisfy the conditions for content validity.

To measure the validity of the instruments, construct validity with the aid of factor analysis was conducted. The construct validity of a test is calculated over a period of time on the basis of an accumulation of evidence.

To make the decision about which variables are involved in each factor, according to a rule of thumb, for a sample with capacity of 30, variables with modulus coefficient greater than 0.4 and eigenvalue greater than 1 are considered. (Hair et al., 1998) The eigenvalues are then revealed in a scree plot diagram. Measure of Sampling Adequacy (MSA) is a criterion used to determine the validity of factor analysis. According to (Hair et al., 1998), if MSA is greater than 0.50, the result for factor analysis is valid.

Table 5: Summary items analysis from SPSS

There are 3 independent and 2 dependent variables in this research. According to the factor analysis results, the items have high validity in constructing the variables. The details of factor analysis for each variable are presented as follows.

Customer integration: The first factor analysis is related to customer integration. The statistic value of KMO for this analysis is 0.735 that is presented in Table 7 and shows that the result of factor analysis is valid.

Fig. 2: Scree plot for customer integration

Table 6: Total variance explained
Extraction method: Principal component analysis

According to factor analysis, all items are provided for customer integration factor. As it is mentioned before, only questions that have variables with modulus coefficient greater than 0.4 and eigenvalue greater than 1 will be selected as the question which forms the analysis. Results from Table 6 show the eigenvalue for the first factor to be larger than the eigenvalue for the next factor (2.921 vs. 0.813). Additionally, the first factor accounts for 58.427% of the total variance. This suggests that the scale items are uni-dimensional and Fig. 2 shows the scree plot for customer integration.

Supplier integration: The second factor analysis is about supplier integration. The statistic value of KMO for this analysis is 0.682 as presented in Table 7 proving that the result of factor analysis is valid. According to factor analysis, all items are provided for the supplier integration factor. Results from Table 6 illustrate that the eigenvalue for the first factor is larger than the eigenvalue for the next factor (3.038 vs. 0.816). Additionally, the first factor accounts for 60.751% of the total variance. This suggests that the scale items are uni-dimensional and Fig. 3 shows the scree plot for supplier integration.

Internal integration: The third factor analysis is related to internal integration. The statistic value of KMO for this analysis is 0.770 as shown in Table 7 proving that the result of factor analysis is valid. Hence, all items are provided for the internal integration factor. Results from Table 6 demonstrate that the eigenvalue for the first factor is larger than the eigenvalue for the next factor (2.840 vs. 0.787). In addition, the first factor accounts for 56.793% of the total variance. This suggests that the scale items are uni-dimensional and Fig. 4 shows the Scree plot for internal integration.

Design quality: The fourth factor analysis is related to design quality. The statistic value of KMO for this analysis is 0.655 as presented in Table 7 which shows that the result of factor analysis is valid. Therefore all items are provided for design quality factor. Results from Table 6 show the eigenvalue for the first factor is larger than the eigenvalue for the next factor (2.407 vs. 0.907).

Table 7: KMO and Bartlett’s test of the variables

Fig. 3: Scree plot for supplier integration

Fig. 4: Scree plot for internal integration

Fig. 5: Scree plot for design quality

Moreover, the first factor accounts for 60.177% of the total variance. This suggests that the scale items are uni-dimensional and Fig. 5 shows the scree plot for this variable.

Fig. 6: Scree plot for conformance quality

Table 8: Component matrix
Extraction method: Principal component analysis

Conformance quality: The last factor analysis is related to conformance quality. The statistic value of KMO for this analysis is 0.682 that is presented in Table 7 and shows that the result of factor analysis is valid. Hence, all items are provided for design quality factor. Results from Table 6 show the eigenvalue for the first factor to be larger than the eigenvalue for the next factor (2.378 vs. 0.734). Additionally, the first factor accounts for 59.446% of the total variance. This suggests that the scale items are uni-dimensional and Fig. 6 shows the scree plot for conformance quality.

Table 8 shows factor loading for all components ranging between 0.650 and 0.889. We found that most of the factor loading for each component is above 0.7 and all factor loading were over 0.650 and highly significant which supported the proposed framework and confirmed the structure of the constructs.

CONCLUSION

This research will supply empirical support for the argument that an understanding of quality product performance requires considering factors beyond quality practices. Focusing on the management of supply chain integration can also lead to an enhanced product quality in organizations. This study indicates product quality-supply chain integration framework in supply chains to be reliable and valid for the evaluation of the relationship between dimensions of product quality and dimensions of supply chain integration. The dimensions considered for product quality are design quality and conformance quality, whereas the dimensions for supply chain integration are customer integration, supplier integration and internal integration.

The objective of this research was to develop a valid and reliable instrument for the relationship between dimensions of product quality and dimensions of supply chain integration which were identified based on the literature review. The validity of the proposed framework was evaluated by the analysis conducted on the data gathered from the questionnaire. The analysis included, content validity, construct validity and reliability which were applied to 23 items from 5 constructs. Based on the obtained results, all 5 constructs namely, design quality, conformance quality, customer integration, supplier integration and internal integration are shown to be valid. The values of the Cronbach’s alpha, correlation coefficients and composite reliability prove the reliability of product quality-supply chain integration instrument. Factor loading demonstrated that all 5 constructs are uni-dimensional. Therefore, the overall results in this pilot study indicate that the proposed framework can be confidently adopted for use in future study.

Our study has two main limitations relating to its scope and method. Firstly, we only focused on effects of supply chain integration on product quality but there are several other important dimensions of competitive capabilities which may also lead to improvements of firm performance. Secondly, our model is constructed particularly for the manufacturing sector. Further studies should include relationships between more constructs and also involve firms in other sectors besides manufacturing.

This framework can be used effectively in any manufacturing firm. In the future work, research will be extended by applying the analysis on the population via., Structural Estimation Modeling (SEM) which is a statistical technique for testing and estimating the relationship of data by factor analysis, path analysis and regression which may result in refining and expanding the framework. SEM tests the framework for its usefulness and applicability. Future work should consider more competitive dimensions also expand the framework to include other industrial sectors besides manufacturing.

APPENDIX

Questionnaire items: For three variables includes: Customer Integration, Supplier Integration and Internal Integration scales were 5-point Likert with anchors “not at all” and “extensive”. For two variables includes: Design Quality and Conformance Quality scales were 5-point Likert with anchors “not important” and “absolutely critical”.

Customer integration (CI): As studied by Wong et al. (2011), Narasimhan and Kim (2002) and Flynn et al. (2010):

CI1: Share information to major customers through information technologies
CI2: Have a high degree of joint planning and forecasting with major customers to anticipate demand visibility
CI3: Have a high level of information sharing with major customers about market information
CI4: Our customers provide information to us in the procurement and production processes
CI5: Our customers are involved in our product development processes

Supplier integration (SI): As studied by Wong et al. (2011), Narasimhan and Kim (2002) and Flynn et al. (2010) for SI1-3 and Flynn et al. (2010) for SI4, 5:

SI1: Share information to our major suppliers through information technologies
SI2: Have a high degree of strategic partnership with suppliers
SI3: Have a high degree of joint planning to obtain rapid response ordering process (inbound) with suppliers
SI4: Our major supplier shares available inventory with us
SI5: We share our demand forecasts with our major supplier

Internal integration (II): As studied by Wong et al. (2011), Narasimhan and Kim (2002) and Flynn et al. (2010) for SI1-4 and Flynn et al. (2010) for SI5:

II1: Have an integrated system across functional areas under plant control
II2: Have a high level of responsiveness within our plant to meet other department’s needs
II3: Within our plant, we emphasize on information flows among purchasing, inventory management, sales and distribution departments
II4: Within our plant, we emphasize on physical flows among production, packing, warehousing and transportation departments
II5: The utilization of periodic interdepartmental meetings among internal functions

Design quality (DQ): As studied by Fleischer and Liker (1992), Fynes et al. (2005) and Fynes and de Burca (2005):

DQ1: Average number of engineering change orders in first year after product introduction due to production problems
DQ2: Technical performance
DQ3: Meets the criteria for ease of production or assembly
DQ4: Matches the requirements of the customer’s production process

Conformance quality (CQ): As studied by Voss and Blackmon (1994), Fynes and de Burca (2005) and Fynes et al. (2005) for CQ1-3 and Maani et al. (1994) for (CQ4):

CQ1: Internal scrap and rework costs as a percent of product cost
CQ2: Internal yield on new product introduction
CQ3: Defect rate for this product at final inspection
CQ4: Return product and customer complaint during the warranty period
REFERENCES
Armistead, C. and J. Mapes, 1993. The impact of supply chain integration on operating performance. Logistics Inform. Manage., 6: 9-14.
CrossRef  |  

Boon-Itt, S., 2011. Achieving product quality performance: The roles of supply chain integration and information technology. Int. J. Innov. Manage. Technol., 2: 373-376.
Direct Link  |  

Breite, R. and S. Maenpaa, 2011. Viewing supply chain integration through information-related elements. Proceedings of the IMP Conference in Glasgow, UK, August 30-September 3, 2011, Glasgow, UK -.

Campbell, J. and J. Sankaran, 2005. An inductive framework for enhancing supply chain integration. Int. J. Prod. Res., 43: 3321-3351.
CrossRef  |  Direct Link  |  

Cao, M. and Q. Zhang, 2011. Supply chain collaboration: Impact on collaborative advantage and firm performance. J. Oper. Manage., 29: 163-180.
CrossRef  |  Direct Link  |  

Chae, B., H.J.R. Yen and C. Sheu, 2005. Information technology and supply chain collaboration: Moderating effects of existing relationships between partners. Eng. Manage. IEEE Trans., 52: 440-448.
CrossRef  |  

Christopher, M., 2011. Logistics and Supply Chain Management Harlow. Financial Times Prentice Hall, England, New York.

Clark, K.B., W.B. Chew and T. Fujimoto, 1992. Manufacturing for Design: Beyond the Production/RandD Dichotomy. In: Integrating Design And Manufacturing For Competitive Advantage, Susman, G.I. (Eds.). Oxford University Press, New York, USA., pp: 178-204.

Cousins, P.D. and B. Menguc, 2006. The implications of socialization and integration in supply chain management. J. Operat. Manage., 24: 604-620.
CrossRef  |  Direct Link  |  

Creswell, J.W., 2009. Research Design: Qualitative, Quantitative and Mixed Methods Approaches. 3rd Edn., Sage Publications Inc., New York.

Das, A., R. Narasimhan and S. Talluri, 2006. Supplier integration-finding an optimal configuration. J. Operat. Manage., 24: 563-582.
CrossRef  |  Direct Link  |  

Devaraj, S., L. Krajewski and J.C. Wei, 2007. Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. J. Oper. Manag., 25: 1199-1216.
CrossRef  |  

Droge, C., J. Jayaram and S.K. Vickery, 2004. The effects of internal integration practices on time-based performance and overall firm performance. J. Operat. Manage., 22: 557-573.
CrossRef  |  Direct Link  |  

Droge, C., S.K. Vickery and M.A. Jacobs, 2012. Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study. Int. J. Prod. Econom., 137: 250-262.
CrossRef  |  

Fleischer, M. and J.K. Liker, 1992. The hidden professionals: Product designers and their impact on design quality. IEEE Trans. Eng. Manage., 39: 254-264.
CrossRef  |  Direct Link  |  

Flynn, B.B., B. Huo and X. Zhao, 2010. The impact of supply chain integration on performance: A contingency and configuration approach. J. Operat. Manage., 28: 58-71.
CrossRef  |  Direct Link  |  

Fynes, B. and S. De Burca, 2005. The effects of design quality on quality performance. Int. J. Prod. Econ., 96: 1-14.
CrossRef  |  Direct Link  |  

Fynes, B., C. Voss and S. de Burca, 2005. The impact of supply chain relationship quality on quality performance. Int. J. Prod. Econ., 96: 339-354.
CrossRef  |  Direct Link  |  

Germain, R. and K.N.S. Iyer, 2006. The interaction of internal and downstream integration and its association with performance. J. Bus. Logist., 27: 29-52.
CrossRef  |  

Gimenez, C. and E. Ventura, 2005. Logistics‐production, logistics‐marketing and external integration: Their impact on performance. Int. J. Operat. Prod. Manage., 25: 20-38.
CrossRef  |  Direct Link  |  

Golicic, S.L. and J.T. Mentzer, 2006. An empirical examination of relationship magnitude. J. Bus. Logist., 27: 81-108.
CrossRef  |  

Hair, J.F., R.E. Anderson, R.L. Tataham and W.C. Black, 1998. Multivariate Data Analysis. Prentice Hall, New Jersey, USA.

Hatcher, L., 1994. A Step-by-Step Approach to Using the SAS System Factor Analysis and Structural Equation Modeling. SAS Publishing, Cary, USA., ISBN: 10-1555446434.

Homburg, C. and R.M. Stock, 2004. The link between salespeople's job satisfaction and customer satisfaction in a business-to-business context: A dyadic analysis. J. Acad. Marketing Sci., 32: 144-158.
CrossRef  |  

Hosseini, S.M., S. Azizi and N. Sheikhi, 2012. An investigation on the effect of supply chain integration on competitive capability: An empirical analysis of Iranian food industry. Int. J. Bus. Manage., Vol. 7. 10.5539/ijbm.v7n5p73

Huo, B., X. Zhao and J.H.Y. Yeung, 2004. Trust, relationship commitment and supply chain integration in China. Proceedings of the 4th International Conference on Electronic Business, (ICEE'04), Beijing, China, pp: 223-228.

Kim, S.W., 2009. An investigation on the direct and indirect effect of supply chain integration on firm performance. Int. J. Prod. Econ., 119: 328-346.
CrossRef  |  Direct Link  |  

Kotcharin, S., S. Eldridge and J. Freeman, 2012. Investigating the relationships between internal integration and external integration and their impact on combinative competitive capabilities. Proceedings of the 17th International Working Seminar on Production Economics, February 20-24, 2012, Innsbruck, Austria, pp: 1-12.

Koufteros, X., M. Vonderembse and J. Jayaram, 2005. Internal and external integration for product development: The contingency effects of uncertainty, equivocality and platform strategy. Decis. Sci., 36: 97-133.
CrossRef  |  Direct Link  |  

Koufteros, X.A., T.C.E. Cheng and K.H. Lai, 2007. Black-box and gray-box supplier integration in product development: Antecedents, consequences and the moderating role of firm size. J. Operat. Manage., 25: 847-870.
CrossRef  |  Direct Link  |  

Lau, A.K.W., 2011. Supplier and customer involvement on new product performance: Contextual factors and an empirical test from manufacturer perspective. Ind. Manage. Data Syst., 111: 910-942.
CrossRef  |  

Maani, K.E., M.S. Putterill and D.G. Sluti, 1994. Empirical analysis of quality improvement in manufacturing. Int. J. Qual. Reliab. Manage., 11: 19-37.
CrossRef  |  Direct Link  |  

Marquez, A.C., C. Bianchi and J.N.D. Gupta, 2004. Operational and financial effectiveness of e-collaboration tools in supply chain integration. Eur. J. Operat. Res., 159: 348-363.
CrossRef  |  Direct Link  |  

Musheng, Y., Z. Yu and N. Jiarui, 2008. Product quality collaborative control technology in SCM. Proceedings of the IEEE International Conference on Automation and Logistics, September 1-3, 2008, Qingdao, China, pp: 180-185.

Narasimhan, R. and S.W. Kim, 2002. Effect of supply chain integration on the relationship between diversification and performance: Evidence from Japanese and Korean firms. J. Operat. Manage., 20: 303-323.
CrossRef  |  Direct Link  |  

Pagell, M., 2004. Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics. J. Operat. Manage., 22: 459-487.
CrossRef  |  Direct Link  |  

Petersen, K.J., R.B. Handfield and G.L. Ragatz, 2005. Supplier integration into new product development: Coordinating product, process and supply chain design. J. Operat. Manage., 23: 371-388.
CrossRef  |  Direct Link  |  

Rosenzweig, E.D., A.V. Roth and J.W. Dean Jr., 2003. The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers. J. Operat. Manage., 21: 437-456.
CrossRef  |  Direct Link  |  

Swink, M., R. Narasimhan and C. Wang, 2007. Managing beyond the factory walls: Effects of four types of strategic integration on manufacturing plant performance. J. Operat. Manage., 25: 148-164.
CrossRef  |  Direct Link  |  

Tsung, F., 2000. Impact of information sharing on statistical quality control. IEEE Trans. Syst. Man Cyber., 30: 211-216.

Van Der Vaart, T. and D.P. Van Donk, 2008. A critical review of survey-based research in supply chain integration. Int. J. Prod. Econ., 111: 42-55.

Vickery, S.K., J. Jayaram, C. Droge and R. Calantone, 2003. The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. J. Operat. Manag., 21: 523-539.

Voss, C. and K. Blackmon, 1994. Total Quality Management and ISO 9000: A European Study. Centre for Operations Management, London Business School, London.

Wang, Y., A. Potter, M. Naim and D. Beevor, 2011. A case study exploring drivers and implications of collaborative electronic logistics marketplaces. Ind. Marketing Manage., 40: 612-623.
CrossRef  |  Direct Link  |  

Wong, C.Y., S. Boon-Itt and C.W.Y. Wong, 2011. The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. J. Operat. Manage., 29: 604-615.
CrossRef  |  Direct Link  |  

Zailani, S. and P. Rajagopal, 2005. Supply chain integration and performance: US versus East Asian companies. Supply Chain Manage.: Int. J., 10: 379-393.
CrossRef  |  Direct Link  |  

Zapp, M., C. Forster, A. Verl and T. Bauernhansl, 2012. A reference model for collaborative capacity planning between automotive and semiconductor industry. Procedia CIRP, 3: 155-160.
CrossRef  |  Direct Link  |  

Zhang, G.B., Y. Ran and X.L. Ren, 2011. Study on product quality tracing technology in supply chain. Comput. Ind. Eng., 60: 863-871.
CrossRef  |  Direct Link  |  

Zhu, L.L. and J.X. You, 2011. Moral hazard strategy and quality contract design in a two-echelon supply chain. J. Syst. Sci. Syst. Eng., 20: 70-86.
CrossRef  |  

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