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Cooperation in the supply chain and lean production adoption

Evidence from the Spanish automotive industry

José Moyano-Fuentes Department of Business Organization, Marketing and Sociology,

University of Jaén, Linares, Spain

Macarena Sacristán-Dı́az Department of Financial Economics and Operations Management,

University of Seville, Sevilla, Spain, and

Pedro José Martı́nez-Jurado Department of Business Organization, Marketing and Sociology,

University of Jaén, Linares, Spain

Abstract

Purpose – The purpose of this paper is to empirically examine the impact of the level of cooperation in the supply chain on lean production (LP) adoption. The effect of the level of cooperation with both suppliers and customers with regards to the intensity of LP adoption is examined, as is the joint effect of cooperation and information integration with customers.

Design/methodology/approach – Analysis is carried out on a sample of 84 manufacturing plants that are first tier suppliers to original equipment manufacturers in the Spanish automotive industry. Data were gathered from plant CEOs via a combination of regular mail, e-mail and internet-based survey methods. Data are analyzed using exploratory factor analysis and hierarchical regression.

Findings – The results show that while greater levels of cooperation with suppliers do not impact on the intensity of LP adoption, greater levels of cooperation with customers do have a significant effect. Also, the greater the cooperation with customers and the more information integrated with them, the higher the intensity of LP adoption.

Research limitations/implications – The findings stress the importance of having a holistic vision of the supply chain to explain the degree of LP adoption. Further developments include simultaneously studying the role of both information and physical flows along the supply chain in LP adoption, and analyzing the time lag that occurs between a company increasing cooperation with suppliers and customers, and progress being made in the intensity of LP adoption.

Practical implications – Practitioners must be mindful that a greater level of cooperation with customers creates a favourable environment for increasing the intensity of adoption of LP.

Originality/value – This paper contributes to research on the explanatory factors of LP adoption by adding new factors to explain it, including the level of cooperation between supply chain agents and the interaction of information integration and cooperation with customers.

Keywords Spain, Automotive industry, Lean production, Cooperation with suppliers, Cooperation with customers, Information integration

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0144-3577.htm

This study was funded by the research projects P08-SEJ3607 and ECO2010-22105-C03-02 of the Andalusian Regional Government and the Spanish Ministry of Economy and Competitiveness.

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International Journal of Operations & Production Management

Vol. 32 No. 9, 2012 pp. 1075-1096

q Emerald Group Publishing Limited 0144-3577

DOI 10.1108/01443571211265701

1. Introduction Lean production (LP) is an integrated socio-technical system whose main objective is to eliminate waste by concurrently reducing or minimizing supplier, customer, and internal variability (Shah and Ward, 2007). However, it is not enough to focus on manufacturing operations to achieve these objectives; LP must be extended to product development and aspects of distribution and supply chain management (Holweg, 2007).

Companies that want to move forward in adopting LP must therefore manage variable supply, processing times and demand (Hopp and Spearman, 2004; de Treville and Antonakis, 2006), and also manage relationships in the value chain (Shah and Ward, 2007). In fact, LP recognizes the importance of the vertical links between a company’s value chain and its suppliers’ and channel members’ value chains (Mason-Jones et al., 2000). These links are extremely important since, as Panizzolo (1998) states, the most critical factor for LP adoption is the management of external relationships, rather than the internal operations of the company. Indeed, high performance in LP is indicative of a well-structured, well-developed and highly involved supply relationship between suppliers and customers; that is, the success of LP adoption depends on managing the relationships with the partners in the chain. However, there has been scant research conducted on analyzing the impact of supplier-customer relationships (Simpson and Power, 2005; Jayaram et al., 2008).

In this respect, Simpson and Power (2005) found that relational supplier-customer links have a positive influence on LP adoption by the supplier, and proposed a line for future research regarding the fact that these links would also have a positive influence on the implementation of LP. However, as far as the authors of the present paper are concerned, little empirical evidence exists regarding the influence of coordination with supply partners on the extent to which a company implements lean practices. Some evidence exists, for example, regarding the importance of coordination mechanisms for suppliers and customers in terms of the improvement of manufacturing lead time and delivery speed and reliability (Da Silveira and Arkader, 2007). These are the same objectives as those sought by LP, and consequently these findings pave the way for investigating whether more advanced coordination with customers and suppliers might impact on advances in LP adoption. For their part, Jayaram et al. (2008) emphasize that a company’s commitment to LP should be preceded by the building of close relationships with supply chain agents. Their results show that building these relationships with suppliers and customers drives lean strategy. However, their research analyzes LP from a strategic perspective that is focused on product and process design, and uses a rather biased concept of LP in which indicators of some of the practices widely accepted to be connected with LP adoption, such as total productive maintenance (TPM), total quality management (TQM) and human resource management (HRM), are not included.

Nonetheless, the impact that building close relationships with supply chain agents has on the development or progress of LP at the operational level has not been studied to date. As a result, this article tries to fill this gap in the literature, and deals with the effects that developing relationships with chain agents might have on the intensity of LP adoption.

In an attempt to add some empirical evidence to this as-yet insufficiently supported issue, a holistic concept of LP is used, which includes its commonly accepted practices: just in time ( JIT), TQM, TPM and HRM. Using the exploratory findings of Simpson and Power (2005) and Jayaram et al. (2008) as a starting point, this paper goes one step further and analyzes the relationship between the development of cooperative links

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with suppliers and customers and the intensity of LP adoption – that is, the authors try to explain how LP adoption can be improved by developing coordination with chain partners. This paper argues that institutional theory (DiMaggio and Powell, 1983) is a useful approach in explaining the intensity of LP adoption in organizations. Previously, this theory was used to help to explain the adoption of TQM (Westphal et al., 1997; Ford, 2011), inter-organizational linkages (Teo et al., 2003) and Six Sigma (Braunscheidel et al., 2011). In this paper, a research is carried out into the mechanism of institutional isomorphic change by which firms that collaborate with their supply chain agents would be more likely to improve LP adoption. As a result, the first research question is formulated as follows:

RQ1. Does the level of cooperation in the supply chain have an impact on the intensity of LP adoption?

On the other hand, Jayaram et al. (2004) find that structural factors, such as the building of a relationship with trading partners, facilitate value creation along the supply chain through the use of coordination mechanisms, such as internal and external integration. In line with this, Stank et al. (1999) and Gunasekaran and Ngai (2004) point out that efficient and effective information sharing between chain agents is essential in order to achieve supply chain integration, and the improved results that arise from this. Devaraj et al. (2007) indicate the importance of information flows in relation to demand-oriented production information exchange, which are underpinned by collaboration efforts that result in the increased accuracy of information about production. For this reason, it is interesting to examine the integration practices adopted by companies that include information transfers or develop production activities and synchronization between suppliers and customers. Companies use management systems to improve supply chain integration, however there is no research devoted to directly analyzing the relationship between supply chain information integration and LP adoption. This paper focuses on how information integration with customers, specifically between first tier suppliers and OEMs, affects the degree or intensity of LP adoption of the former. Thus, the second research question is established as follows:

RQ2. What effect does interaction between cooperation and information integration with customers have on LP adoption?

In order to address both research questions, we developed an empirical study based on a survey given to first tier auto component supplier managerial staff. The article is organized into five parts starting with this introduction. The second part is devoted to the theoretical background of this research, and to formulating the hypotheses. The methodology used is subsequently described. Next, the results obtained are discussed and, finally, conclusions are drawn and the challenges that can be tackled by future research are set out.

2. Theoretical background and research hypotheses 2.1 Lean production Since LP became widespread (Womack et al., 1990), there have been clear moves towards adopting the principles on which this management system is based. These affect companies not only internally, but also with regard to their external organization (Shah and Ward, 2007).

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Broadly speaking, LP has been described from two perspectives: the first is more philosophical and is linked to the guiding principles and general objectives, while the second is more practical, and is related to the management tools and techniques of which it is comprised (Hopp and Spearman, 2004). The goal pursued within this management system is maximum efficiency, with operations conducted at the minimum cost and with zero wastage. For this, the variability of the system as a whole needs to be minimized (de Treville and Antonakis, 2006; Shah and Ward, 2007), and LP companies need to adopt a management philosophy based on continual improvement that allows results to be improved, and involves all levels of the organization (Womack et al., 1990; Womack and Jones, 1996).

The basic principles of LP translate into a series of bundles of practices. There is a broad consensus that the most characteristic bundles of practices connected with LP adoption are:

. JIT;

. TPM;

. TQM; and

. HRM (Shah and Ward, 2003; Cagliano et al., 2006; Narasimhan et al., 2006; Shah et al., 2008).

As a result, and in line with the greater detail provided in the section below devoted to measures, the “intensity of LP adoption” construct used in this research as a dependent variable will be considered as the degree or extent to which some indicators of these practices are implemented in the companies analyzed.

2.2 Level of cooperation in the supply chain, and LP adoption LP aims to integrate all the activities that affect goods and services delivered to customers, including those delivered by both the company itself and external suppliers (Womack and Jones, 1996). LP practices have come to be very important aspects of effective supply chain management in terms of cost saving and responding to customers’ needs (Mason-Jones and Towill, 1999; Handfield and Nichols, 1999, Chapter 4; Li et al., 2005). Nevertheless, the success of LP depends more on managing supply chain relationships than on internal operations (Panizzolo, 1998).

With regards to relationships with suppliers, there is ample empirical evidence that recognizes the importance of developing close, long-term cooperative relationships (Lamming, 1993, Chapter 6; Jones et al., 1997; MacDuffie and Helper, 1997; Sako and Helper, 1998). To be precise, adopting LP has sparked a change in companies’ purchasing philosophies and policies, and these are now based on a greater degree of confidence in supplier relationships (Helper and Sako, 1995; Sako and Helper, 1998). Similarly, LP adoption has led to a new concept of relationships with customers, which is also based on long-term cooperation agreements ( Jones et al., 1997). Companies must adopt a proactive approach to customer service and be prepared to exceed customer expectations.

In short, to move forward in LP adoption, companies have to establish relationships with suppliers and customers based on confidence and a high level of motivation to learn, and which allow knowledge to be shared freely (MacDuffie and Helper, 1997). By this means, companies can operate via what authors call the “lean supply chain” – a system that enables costs to be reduced, flexibility to be increased and substantial

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improvements in products to be made (Vonderembse et al., 2006). However, there is still little empirical research on the subject (Simpson and Power, 2005; Jayaram et al., 2008).

Jayaram et al. (2008) find that a lean strategy should be preceded by the forging of relationships with the main supply chain partners. Simpson and Power (2005) state that the results obtained with LP depend on the extent to which knowledge is shared in supplier-customer relationships. On the basis of these results, it is fair to consider the influence that the strengthening or development of relationships with trading partners might have on the intensity of LP adoption. More specifically, the impact that the degree of cooperation with trading partners has on the intensity of LP adoption needs to be investigated in greater depth.

On the other hand, the pressure of supply chain members has a positive relationship with the supply chain technology adoption, and with the adoption of inter-organizational information systems (Iacovou et al., 1995; Kamaruddin and Udin, 2009). Pressure from members, also known as channel power, is defined as “the influence one channel member has over another channel member” (Williams, 1994). Supply chain member pressure can be categorized as an inter-organizational factor. Inter-organizational means the cooperation and commitment of all participating members. These participants may have complex economic and business relationships with each other, which result in a number of social, political and economic factors influencing the adoption of inter-organizational systems (IOS) (Premkumar et al., 1997). An inter-organizational factor that may influence the intensity of LP adoption is coercive pressure from supply chain members.

According to institutional theory, organizations faced with the same environmental conditions adopt the same structures and ways of working, which forces one unit in a population to resemble other units that face the same set of environmental conditions (DiMaggio and Powell, 1983). One of the mechanisms that explains these similarities is coercive isomorphism, which implies that powerful organizations demand the adoption of certain innovations (DiMaggio and Powell, 1983). As Braunscheidel et al. (2011) indicate with regard to the adoption of Six Sigma, an example of coercive isomorphism would be the customer requirement that a supplier adopts and implements LP – , i.e. the customer insists on the adoption of LP as a condition for doing business. Moreover, the customer’s perception of the success of LP may also then motivate that customer to pressure the suppliers with whom it cooperates to adopt LP.

In addition, with regards to the major changes that come along with the adoption of LP, collaboration with other agents in the chain could help to increase the intensity of adoption, thanks to the availability of technical, human and financial resources and to the mutual benefits that could be derived from such collaboration.

Combining all this reasoning, the following two hypotheses relating to the level of cooperation among the agents in the supply chain and the intensity of LP adoption are put forward:

H1. Greater cooperation with suppliers is positively related to the intensity of LP adoption.

H2. Greater cooperation with customers is positively related to the intensity of LP adoption.

Figure 1 shows the hypothesized links between aspects relating to cooperation with suppliers and customers, and the intensity of LP adoption.

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2.3 Cooperation and information integration with customers, and LP adoption Companies are improving their competitiveness not only through the integration and strategic alignment of internal processes, but also through process integration and alignment between companies (Cagliano et al., 2006). Supply chain integration therefore plays a key role in competitiveness, and is related to the coordination mechanisms that exist both inside and outside company boundaries (Frohlich and Westbrook, 2001; Romano, 2003).

In order to achieve supply chain integration, a great management effort is required to link and coordinate chain processes related to information flows and physical flows (Frohlich and Westbrook, 2001; Romano, 2003). Jayaram et al. (2004) find that building relationships with trading partners facilitates the creation of value through the use of coordination mechanisms like supply chain integration. One basic step for improving results from supply chain integration is effective and efficient information sharing among supply chain agents (Stank et al., 1999; Gunasekaran and Ngai, 2004). Specifically, the transfer of production-related information from the customer to the supplier means that the latter’s operations can be better adapted to the customers’ needs. Suppliers can put policies in place to contend with variability in demand and balance production and demand, and so reduce inventories along the chain (Harrison and van Hoek, 2002, Chapter 8; Disney and Towill, 2003).

Variability in demand can be attributed to a lack of trust between the agents in the supply chain, which creates an information shortage. This triggers management problems, such as excess inventories, unsatisfied demand, high delivery times, etc. that impact negatively on the ultimate aim of achieving the maximum satisfaction of the end customer. New chain structures based on cooperation strategies between chain members can be used to reduce this variability (e.g. VMI or vendor management inventory). Information sharing and trust between chain members play an important role in the adoption of these strategies (Barratt, 2004; Petersen et al., 2005). In fact, inter-organizational trust between customers and suppliers has a determining effect on cooperation between them (Kim et al., 2010). More specifically, it is important that downstream chain members share their information effectively and efficiently with upstream members (Chu and Lee, 2006).

Sharing information on demand with suppliers enables companies to establish their production or purchasing programme, whilst also calculating automatic inventory restocking for the customer. This gives customers a better balance between excess inventory and missed sales, whilst reducing supplier uncertainty and giving the supplier greater flexibility (Disney and Towill, 2003; Holweg et al., 2005). Moreover, when information is shared about demand and inventory, the time

Figure 1. Hypothesized relationships between cooperation/LP adoption

Cooperation with suppliers

Cooperation with customers

Intensity of LP adoption

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between the order being placed and the delivery being made is reduced (Cachon and Fisher, 2000; Lee et al., 2000). This translates into the ability to respond more quickly to customer needs; the company is therefore more customer-oriented (Fisher et al., 2000), and consequently more predisposed to cooperating with the customer.

Information sharing between companies is, precisely, the first step in chain integration. However, what distinguishes successful companies from the “also-rans” is their capacity for cooperation (Devaraj et al., 2007). A high degree of cooperation between customer and company will provide the basis for effective collaboration in production planning and inventory management along the chain, thus allowing for “pull” production and the adoption of joint decisions.

Rosenzweig et al. (2003), meanwhile, find that manufacturing capabilities mediate the relationship between supply chain integration and business performance, which supports coherency between external and internal processes. This means that there is a link between external and internal integration practices. If these two lines of thought are combined, the joint effect of cooperation and production information integration could be expected to have a positive influence on the intensity of LP adoption. Therefore, the third hypothesis is formulated as follows:

H3. The effect of interaction between cooperation with customers and information integration with customers has a positive influence on the intensity of LP adoption.

Figure 2 shows the hypothesized relationship between these constructs.

3. Methodology 3.1 Sample and data collection There have been increases in the number and importance of relationships with suppliers and customers in the automotive industry. These relationships are not confined to the pursuit of short-term economic imperatives or cost reduction, but rather encompass innovation in technology and creative – and joint – research and development, as well as improvements in quality. The influences of globalization, the adoption of LP and the development of modularization have all had profound influences on the relationship between automobile assemblers and their suppliers – in particular those in the first tier (Morris et al., 2004). First tier suppliers are in a strategic position in the supply chain, since they are usually heavily involved in the strategy of automakers, and at the same time are familiar with the fabric of second tier suppliers and the way in which the requirements of automakers are passed on to them (Aláez-Aller and Longás-Garcı́a, 2010).

Figure 2. Hypothesized relationship

of cooperation with customers and

information integration with LP adoption

Cooperation with customers

Information integration

with customers

Intensity of LP adoption

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For these reasons, the cooperation of these suppliers with their customers and suppliers is crucial; without it, production sequence and programming bottlenecks could paralyze the delivery programme.

This is why the hypotheses have been tested using data from a sample of manufacturing plants which are first tier suppliers to original equipment manufacturers (OEMs) in the Spanish automotive industry. The initial sample frame is from the database of the Spanish Automotive Equipment and Components Manufacturers Association (SERNAUTO). SERNAUTO is the only Spanish association in the automotive sector which includes all the equipment and component manufacturers in Spain. The association’s staff draws up periodic reports and statistics about the situation regarding the equipment and component manufacturing industry. To do this, SERNAUTO has built up a database with information about the structural and organizational features of each of the manufacturing plants in the industry. This database can be used for research purposes with SERNAUTO’s authorization, and has frequently been used in prior literature (Martı́nez-Sánchez and Pérez-Pérez, 2003; Lapiedra et al., 2004). SERNAUTO’s database is made up (as of 31 December 2007) of a total of 216 manufacturing plants belonging to 74 different companies (first tier suppliers).

Before sending out the questionnaire, a draft version was tested with a panel of five renowned researchers, along with three directors working for one of the most important automotive components and equipment suppliers in the world with a presence in 22 countries. The questionnaires were sent to the aforementioned researchers and directors by e-mail. A form was attached to the e-mail so that the directors and researchers could include any relevant comments about item wording, content and order within the questionnaire. All the forms were returned using the same means (e-mail) by the researchers and directors after a phone follow-up and within the expected deadline. A pilot study was then carried out in three different manufacturing plants to ensure that the definitions of items were meaningful and comprehensive for the sample. This ensured content validity.

Since, according to Phillips (1981), high-ranking informants tend to be more reliable sources of information than their lower-ranking counterparts, we targeted Chief Executive Officers (CEOs) in order to ensure that the respondents were knowledgeable of the intensity of LP adoption. In addition, in the letter that accompanied the questionnaire, suggestions were made for the collaboration of other managers, where necessary, for certain parts of the questionnaire. Finally, the director of operations took part in ten cases and the director of human resources in 27 cases. This, and the fact that anonymous answers from respondents were acceptable, were the procedures used to reduce common method bias (CMB), as suggested by Podsakoff et al. (2003).

However, the use of self-reported data could constitute an important limitation. Should this occur, CMB could augment relationships between the variables. In such a case, conducting a Harman’s test would obtain a single general factor to account for most of the covariance for the dependent and independent variables (Podsakoff and Organ, 1986). Consequently, a Harman’s single factor test was performed to detect possible CMB. The results of the unrotated factor analysis of 20 items indicated the presence of four factors – the same number of factors included in our regression models. Consequently, the results of the Harman’s single factor test indicate that the sample lacked a significant presence of CMB.

The questionnaire was sent to the CEOs in January and February 2008 via a combination of regular mail, e-mail and internet-based survey methods. Attached to the

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questionnaire was an explanatory letter highlighting the purpose and aims of the study and asking the CEO to take part. The letter also mentioned that other directors were participating in the survey.

After a telephone follow-up process, 84 fully completed questionnaires were obtained, producing a response rate of 38.89 percent, almost doubling the 20 percent established by some authors as an acceptable answer rate in OM research (Malhotra and Grover, 1998; O’Leary-Kelly and Vokurka, 1998), and equal to that of similar studies on the subject in the automotive sector ( Jayaram et al., 2008).

The regional distribution of the plants in the sample is similar to the distribution of the population as a whole. Most plants are located in the north of Spain (60.7 percent of the sample compared to 64.9 percent of the population). The two regions with the greatest presence of manufacturing plants, both in terms of population and in the sample, are Catalonia (38 percent) and Castile and Leon (13 percent). The sample covers different manufacturing activities related to the manufacturing and assembly of components supplied to OEMs in the automotive industry. Table I shows the sample and population distributions in terms of the most representative industrial activities. The sample distribution corresponds broadly with that existing in the population.

With regard to distribution by sample size, small plants (up to 249 employees) account for 41.7 percent of the sample, medium-sized plants (from 250 to 499 employees) account for 34.5 percent, while large plants (from 500 employees up) account for 23.8 percent.

Finally, random telephone calls were made to plants that did not return the questionnaire, with no evidence of any specific pattern explaining why companies failed to respond or reasons for not doing so. In general, there does not seem to be a particular kind of plant that was more likely to respond, thus reducing the non-response bias that can occur in mailed surveys. Also, early versus late respondents were compared (Armstrong and Overton, 1977), and no statistically significant differences were found in any study variables (a ¼ 0.05).

3.2 Measures A number of variables were used to measure the intensity of LP adoption, the degree of cooperation with suppliers, the degree of cooperation with customers, and production information integration with customers. A number of control variables were also included. To identify the level of cooperation with trading partners, the survey respondents were asked about the degree of cooperation with suppliers and customers compared to the

Sample Population ISIC Industry n % n %

343 Manufacture of parts and accessories for motor vehicles and their engines 49 58.3 107 49.5

252 Manufacture of plastics products 11 13.1 26 12 319 Manufacture of other electrical equipment 6 7.1 13 6.1 289 Manufacture of other fabricated metal products,

metalworking service activities 6 7.1 10 4.6 Other industries (22 industries) 12 14.4 60 27.8

Total 84 100 216 100

Table I. Industry distribution of sample and population

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average for the industry using a seven-point Likert scale (1 – much less, 4 – about the same, 7 – to a much greater extent). In addition, Heide and Miner’s (1992) idea of measuring cooperation through cooperative behaviours was used. The answers were subsequently grouped through exploratory factor analysis with varimax rotation in order to identify the main underlying constructs. Two factors resulted; initial eigenvalues were all higher than one, and the total variance explained was 67.9 percent. The results of this factor analysis are shown in Table II.

The first and second factors measure the level of cooperation with customers and suppliers, respectively, based on the degree to which they are involved in new product development, the extent to which long-term relationships with them have been established, and their commitment to reducing costs on a yearly basis. Both constructs comprise the elements that are normally used in the literature to define cooperation with trading partners (Bensaou, 1997; Li et al., 2005; Kim et al., 2010).

For information integration, the respondents were asked about the degree to which they agreed with a series of statements relating to production information integration between their plants and customers using a seven-point Likert scale (where 1 – totally disagree, 4 – neither agree nor disagree, 7 – totally agree). The factor analysis of these items confirmed that only one factor existed (Table III). The percentage of variance explained was 43.6 percent. This construct was measured on the basis of the information that the customer gives the plant on production-related aspects (Devaraj et al., 2007).

These three factors have been used as independent variables in regression models in order to examine their influence on the intensity of LP adoption.

To this end, the respondents were asked about the degree to which a given LP practice was implemented in the plant compared to the average for the industry using a seven-point Likert scale (where 1 – much less than the competition, 4 – the same as the competition, 7 – much more than the competition). The factor analysis of these items confirmed that only one factor existed (Table IV). The percentage of variance explained was 68.5 percent. This factor measures the adoption of LP practices through the introduction of JIT, TPM and TQM, and some HRM practices, such as the development of employees’ skills and work team incentives. This construct consists of the elements

Factor Variable Variable average

Factor loading

Factor average

Cronbach’s a

Cooperation with customers

We establish long-term relationships with our customers

4.7 0.87

The customers are directly involved in the development of new products

4.5 0.58 4.7 0.7

Customers are committed by contract to annual reductions of costs

5.0 0.90

Cooperation with suppliers

We establish long-term relationships with our suppliers

4.8 0.63

The suppliers are directly involved in the development of new products

4.7 0.88 4.7 0.8

The suppliers are committed by contract to annual reductions of costs

4.7 0.84

Table II. Factor analysis of cooperation with customers and suppliers

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commonly used to define the concept of LP (Womack and Jones, 1996; Hines et al., 1997; Shah and Ward, 2003; Cagliano et al., 2006; Narasimhan et al., 2006; Shah et al., 2008).

The validity of each of the constructs was tested. Convergent validity was demonstrated by each factor having loadings in excess of 0.5 (Bagozzi and Yi, 1988), and discriminant validity was also supported, since none of the variables had loadings of higher than 0.4 on more than one factor (Fullerton and McWatters, 2001). Construct validity was also supported by the existence of similar measures in the literature (Spina and Zotteri, 2001). Internal consistency was tested using Cronbach’s a. All the constructs had satisfactory alphas (a $ 0.7).

Control variables were also included in the regression models in order to control for competitive, cost and structural factors used in the literature, with similar goals to those of this research (Shah and Ward, 2003; Johnston and Wright, 2004; Cagliano et al., 2006). The following specific control variables were used: number of employees (as a measure

Factor Variable Variable average

Factor loading

Factor average

Cronbach’s a

Information integration with customers

Our plant receives sales forecast information from its customers

5.4 0.51

Our plant receives information from its customers about their Master Production Programmes

5.3 0.72 4.8 0.7

Our plant receives sales forecast information from its customers about the state of their inventories

4.0 0.59

The customer collaborates with us to jointly develop the net component needs that we should deliver

5.0 0.69

Customers authorise our plant to replenish components automatically

4.1 0.74

Table III. Factor analysis of

production information integration between

company and customers

Factor Variable Variable average

Factor loading

Factor average

Cronbach’s a

LP Close location of machines and processes in plant

5.7 0.90

Manufacturing cells 5.4 0.85 Layout permits low stocks and quick manufacturing

5.2 0.73

TQM 5.1 0.57 Some time devoted to planned equipment maintenance related activities everyday

5.1 0.89 4.9 0.7

Regular maintenance of all equipment 5.2 0.89 We use JIT 4.6 0.62 We are actively responsible for developing our employees’ skills

4.7 0.74

Not just individual incentives are established but for the work team as a whole

3.3 0.88 Table IV.

Factor analysis of intensity of LP adoption

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of plant size), purchasing costs as a percentage of total costs (as an approximate measure of the degree of vertical integration in the plant), number of different products (as a measure of plant diversification), number of suppliers (as a measure of the degree of complexity of the company’s supply) and number of customers (as a measure of customers’ bargaining power). In addition, we considered the suitability of including the age of the plant (as a measure of years in operation) given the influence it could have on the intensity of LP adoption, as the older a plant is, the greater the opportunity for close long-term supplier/customer relationships and, as a result, for greater cooperation, which is one of the independent variables in our research. The influence of plant age on LP adoption was analyzed in the study by Shah and Ward (2003), which was subsequently referred to by Sousa and Voss (2008).

Table V presents the descriptive statistics and correlations between the variables used in the analysis. The table shows that there are some significant correlations between the dependent variables and the independent and control variables; as a result, the latter will be taken into account in the subsequent analysis.

3.3 Model evaluation Hierarchical regression analysis was used to test the hypotheses. This analysis allows the variance percentage explained by each of the independent variables to be identified separately (Pedhazur and Schmelkin, 1991, Chapter 18; Cagliano et al., 2006). Dividing the variance through hierarchical regression analysis is the most appropriate methodology when there are correlations between the independent variables, as seen previously in Table V. To test H1 and H2, the independent variables were included separately, one by one (Table VI), beginning with the control variables (Model 1). Next, the variable relating to cooperation with suppliers was included on its own (Model 2), followed by the variable regarding cooperation with customers (Model 3). Finally, the two independent variables were included together (Model 4). The same method of including the independent variables individually was used to test the H3 (Table VII). The first variable included was information integration with customers (Model 5), followed by the two independent variables – information integration and cooperation with customers (Model 6). Tables VI and VII score the contributions made by each set of variables by determining the change in R 2 after each set was included.

4. Results Tables VI and VII show the results of the hierarchical regression analysis. Table VI, Model 1, which only includes control variables, does not show any significant relationship between these variables and the intensity of LP adoption. Model 2 shows that the level of cooperation with suppliers does not have any significant influence on the intensity of LP adoption. However, cooperation with customers does have a significant influence on the intensity of LP adoption (Model 3). This model explains 47 percent of the total variance, with cooperation with customers representing only 27 percent of the total variance. When all the independent variables are taken into consideration in a single model (Model 4), it can be seen that the only independent variable that continues to have a significant positive influence on the degree of LP adoption is cooperation with customers (b ¼ 0.68, p , 0.01). This variable is shown to have a robust impact. It is with this last model that the highest R 2 level is achieved. It can also be seen in Models 3 and 4 that the “number of customers” control variable also has a significant

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p la

n t

2 3 .9

1 6 .1

2 0 .2

0 3 .

N u

m b

er o f

su p

p li

er s

1 5 9 .7

2 4 3 .2

2 0 .2

0 0 .2

5 4 .

N u

m b

er o f

cu st

o m

er s

3 7

9 8 .8

2 0 .2

3 0 .0

3 3

* 0 .5

9 *

5 .

N u

m b

er o f

em p

lo y

ee s

3 7 7 .6

2 7 6 .2

0 .2

0 0 .3

2 *

* 0 .4

7 *

* 0 .1

7 6 .

N u

m b

er o f

d if

fe re

n t

p ro

d u

ct s

4 7 0 .7

8 4 7 .1

2 0 .0

0 0 .1

2 2

0 .1

1 2

0 .1

6 0 .0

0 7 .

P er

ce n

ta g

e o f

p u

rc h

a se

co st

s 5 8 .4

1 3 .3

0 .0

6 2

0 .0

9 0 .0

7 2

0 .0

5 0 .0

5 2

0 .3

7 *

8 .

C o o p

er a ti

o n

w it

h cu

st o m

er s

4 .9

1 .3

0 .2

0 2

0 .0

6 2

0 .0

2 0 .2

6 2

0 .0

2 0 .1

6 0 .1

5 9 .

C o o p

er a ti

o n

w it

h su

p p

li er

s 5 .9

1 .5

0 .2

1 2

0 .1

3 2

0 .0

8 0 .0

7 0 .0

2 2

0 .1

3 0 .0

4 0 .3

7 *

1 0 .

In fo

. in

te g

ra ti

o n

w it

h cu

st o m

er s

4 .8

1 .0

0 .3

6 *

* 2

0 .2

7 *

2 0 .2

4 2

0 .0

0 0 .0

1 0 .1

5 0 .1

7 0 .3

5 *

* 0 .3

6 *

N o te s :

C o rr

el a ti

o n

s a re

si g

n ifi

ca n

t a t:

* p ,

0 .0

5 a n

d *

* p ,

0 .0

1 ; N ¼

8 4

Table V. Means, standard

deviations and Pearson correlation coefficients

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relationship with the intensity of LP adoption, which in this case is negative. This is not surprising if we take into account that the fewer the number of customers, the more critical it becomes to maintain quality and promised delivery time and, as a result, the more necessary LP becomes.

Table VII analyzes the impact of cooperation and information integration with customers on the intensity of LP adoption. Model 5 includes the control variables plus these two independent variables. Once more, the significant positive influence of cooperation with customers on the intensity of LP adoption is apparent. However, information integration with customers does not have a significant impact on the intensity of LP adoption. Model 6, which includes the joint effect of the two independent variables, shows how the interaction between cooperation and information integration with customers has a significant impact on the intensity of LP adoption.

Independent variables Model 1 Model 2 Model 3 Model 4

Age of plant 20.27 20.18 20.10 20.07 Number of employees 0.29 0.26 0.17 0.018 Percentage of purchase costs 20.02 20.00 20.15 20.15 Number of different products offered 20.06 20.05 20.21 20.26 Number of suppliers 20.34 20.30 20.07 20.09 Number of customers 20.11 20.14 20.48 * 20.50 *

Cooperation with suppliers 0.14 20.12 Cooperation with customers 0.60 * * 0.68 * *

F 1.21 1.10 3.27 * 2.83 *

R 2

0.20 0.22 0.47 0.48 Adjusted R 2 0.03 0.02 0.32 0.31 DR

2 0.02 0.27 0.28

Notes: Significant at: *p , 0.05 and * *p , 0.01; the values are standardised regression coefficients (bs)

Table VI. Impact of cooperation with customers and suppliers on the intensity of LP adoption

Independent variables Model 5 Model 6

Age of plant 20.12 0.14 Number of employees 0.18 0.23 Percentage of purchase costs 20.08 0.03 Number of different products offered 20.18 20.10 Number of suppliers 20.04 20.24 Number of customers 20.48 * 20.33 Cooperation with customers 0.57 * * 20.75 Information integration with customers 0.03 21.38 Cooperation with customers £ Information integration with customers 2.21 *

F 2.72 * 3.22 *

R 2 0.48 0.56

Adjusted R 2 0.30 0.38 DR

2 0.08

Notes: Siginificant at: *p , 0.05 and * *p , 0.01; the values are standardised regression coefficients (bs)

Table VII. Impact of information integration and cooperation with customers on the intensity of LP adoption

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Moreover, this interaction is the only significant variable in this model – , i.e. cooperation with customers no longer has a significant impact when the joint effect of cooperation and information integration with customers is examined. Furthermore, this model explains 56 percent of the total variance, with the joint effect representing only 8 percent of the total variance. In Tables VI and VII it can also be seen that the only control variable that has a significant influence on the intensity of LP adoption is, again, the number of customers, and then only in Model 5 and not when the joint effect of two independent variables is considered in Model 6.

5. Discussion The research hypotheses are discussed below on the basis of the results of the regression analysis presented in the previous section.

5.1 Hypothesis H1 The results of the analysis do not support H1: greater levels of cooperation with suppliers are not positively related to the intensity of LP adoption. This result is not consistent with prior empirical evidence by Jayaram et al. (2008), which seems to assume a positive and significant influence, at least with regard to earlier stages of LP adoption. These results could be explained with reference to the position that first tier suppliers occupy in the automobile industry supply chain and the power relationships that exist between the different tiers and the focal company in the supply chain. Considering the fact that the chains in our research are producer-driven, and assuming that OEMs act as chain directors, these negotiate with the first tier suppliers; they, in turn, negotiate with second tier partners, and so on. Cooperation with suppliers might be influenced by proximity to the end customer. In other words, the further upstream we move, the more companies we find, the smaller their size, and the lower the value of the products obtained, which also makes cooperation less important and affects the establishment of trust relationships (Stuart and McCutcheon, 1996). In the automotive industry, OEMs are usually the leaders that can influence the management practices of the first tier suppliers (Martı́nez-Sánchez and Pérez-Pérez, 2003) and, in this way, the intensity of LP adoption. This influence can be observed below, in the analysis of the results obtained with regards to H2. However, when cooperation between first and second tier suppliers is analyzed, that is, with regard to the hypothesized relationship in H1, more intense adoption of LP is not perceived.

Second tier suppliers are small companies that work for a range of different industries (apart from the automotive industry), which means that they have less bargaining power compared to first tier suppliers. This has led to first tier suppliers not feeling the need to link cooperation with suppliers with the adoption of strategic management systems like LP.

5.2 Hypothesis H2 The results allow H2 to be accepted: there is a direct relationship between the level of cooperation with customers and the intensity of LP adoption. These results are consistent with the assumption that coercive pressures from customers to reach the high efficiency level required in the automotive sector lead firms which collaborate with their customers to adopt LP more intensely. In addition, given the major changes related to adopting LP, firms that adopt it must receive support and consultancy during the implementation process. Many OEMs have successfully adopted LP

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(Holweg, 2007) and have an adequate knowledge of it. As a consequence, they can easily share their expertise with those first tier suppliers they cooperate with, helping them to overcome problems arising during the implementation process. At the same time, first tier suppliers are interested in advancing in terms of their adoption of LP so that it might act as a safeguard to their cooperation with customers and, therefore, allow them to continue to have cordial and long-term relationships with them.

5.3 Hypothesis H3 The results also allow H3 to be accepted: the greater the interaction between cooperation and information integration with customers, the greater the intensity of LP adoption. These results broaden those obtained in the previous hypothesis by assuming that cooperation with customers must therefore be complemented by manufacturers’ production information integration, in order for the company to opt for moving forward in LP adoption and, thus, continue to have warm and long-term relationships with manufacturers. The transfer of production-related information from the customer to the supplier means that the latter’s operations can be better adapted to the customers’ needs. Suppliers can put policies in place to contend with variability in demand and balance production and demand, and so reduce inventories along the chain, thus allowing for “pull” production and, in general, more intense adoption of LP practices (Devaraj et al., 2007). The combined effect of cooperation and integration with customers would similarly indicate a high degree of inter-organizational trust (Kim et al., 2010) and to maintain this, companies may decide to reduce their internal variability once they have made some advances in implementing LP.

Finally, it should be indicated that none of the competitive forces or the cost and structural factors contemplated have a significant impact on LP adoption.

6. Conclusions This study’s findings build upon prior results on explanatory factors for LP adoption in the literature, especially those of Simpson and Power (2005) and Jayaram et al. (2008), while simultaneously trying to add some empirical evidence to this as-yet insufficiently supported issue. To do this, a level of operational analysis is used that shows the degree to which a company has incorporated lean practices. The main novelty of this is that we go one step further and analyze the relationship between the development of cooperative links with suppliers and customers and the intensity of LP adoption; that is, we try to explain how LP adoption can improve through the development of coordination with chain partners. In addition, as far as we are aware this is the first time that an analysis has been conducted into the effect that information integration between chain agents, specifically between first tier suppliers and OEMs, has on the degree of intensity of LP adoption.

The results stress the importance of having a holistic vision of the supply chain when explaining the degree of LP adoption. They highlight the findings of Panizzolo (1998) by underscoring the importance of external relationships in LP adoption. They also build upon the results found by Jayaram et al. (2008) by going further into relationships with chain agents when explaining LP adoption. The study also complements the results of research by Simpson and Power (2005), by finding that information integration with customers on its own does not explain the intensity of LP adoption.

Our results also stress the importance of using institutional theory to assess motivation for the intensity of LP adoption. The customer’s perception of the success of LP may

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motivate that customer to pressure the suppliers with whom it cooperates to adopt similar improvement mechanisms. At the same time, the level of cooperation makes customers predisposed to offer appropriate support to their suppliers in order to overcome any problems that might arise during the LP implementation process. This is in line with contributions made by Womack and Jones (1996), who state that a company’s efforts to implement LP will only be rewarded if it extends throughout the whole product delivery process, with changes being initiated at the customer end. That cooperation with customers combined with information integration with them is what triggers their proactive attitude towards stepping up LP adoption is precisely what this study demonstrates.

In other respects, the degree of cooperation with suppliers has been seen not to influence LP adoption. This might indicate that first tier suppliers do not consider their suppliers to be strategic partners, and consequently that collaboration with them has no influence on the intensity of LP adoption of the analyzed companies.

A number of managerial implications can be drawn from the research presented in this paper. Managers should be aware that collaboration with customers creates an environment that favours the adoption of LP. What is more, as far as relationships with customers are concerned, cooperation efforts must be backed up by information integration with customers, while simultaneously making advances in LP adoption with a view to maintaining customer cooperation and integration.

This study is not free of limitations. The aim of the analysis is exploratory and the use of a relatively small sample (84 plants) has prevented us from providing some measures of internal consistency and composite reliability, such as AVE, CFI and SRMR.

Other limitations could be the basis for future research. To be specific, in this study only information integration on production has been examined as a relevant factor in downstream supply chain integration. Thus, we suggest that future studies simultaneously examine the role of both information and physical flows along the supply chain in LP adoption. In addition, the results have only been obtained from a study of the Spanish automobile industry, and so the findings cannot be inferred to be universally applicable to other countries and industries. Therefore, a logical extension of this research would be its empirical replication in other contexts. Similarly, future studies could expand the analysis to include all of the agents in the chain. Although from theoretical reasoning we infer that cooperation and information integration lead to a certain degree of LP adoption, it would also be beneficial in the future to use longitudinal methodology to study causality in the relationships that were observed, as the cross-sectional nature of the data handled and the hierarchical regression carried out do not allow any indications of causality to be statistically inferred. Similarly, further developments should focus on an investigation of the time lag that occurs between a company increasing cooperation with suppliers and customers, and progress being made with regards to the degree to which LP is implemented.

References

Aláez-Aller, R. and Longás-Garcı́a, J.C. (2010), “Dynamic supplier management in the automotive industry”, International Journal of Operations & Production Management, Vol. 30 No. 3, pp. 312-35.

Armstrong, J.S. and Overton, T.S. (1977), “Estimating nonresponse bias in mail surveys”, Journal of Marketing Research, Vol. 14 No. 3, pp. 396-402.

Cooperation in the supply

chain

1091

Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94.

Barratt, M. (2004), “Unveiling enablers and inhibitors of collaborative planning”, International Journal of Logistics Management, Vol. 15 No. 1, pp. 73-90.

Bensaou, M. (1997), “Interorganizational cooperation: the role of information technology and empirical comparison of US and Japanese supplier relations”, Information Systems Research, Vol. 8 No. 2, pp. 107-24.

Braunscheidel, M.J., Hamister, J.W., Suresh, N.C. and Star, H. (2011), “An institutional theory perspective on Six Sigma adoption”, International Journal of Operations & Production Management, Vol. 31 No. 4, pp. 423-51.

Cachon, G.P. and Fisher, M. (2000), “Supply chain inventory management and the value of shared information”, Management Science, Vol. 46 No. 8, pp. 1032-48.

Cagliano, R., Caniato, F. and Spina, G. (2006), “The linkage between supply chain integration and manufacturing improvement programmes”, International Journal of Operations & Production Management, Vol. 26 No. 3, pp. 282-99.

Chu, W.H.J. and Lee, C.C. (2006), “Strategic information sharing in a supply chain”, European Journal of Operational Research, Vol. 174 No. 3, pp. 1567-79.

Da Silveira, G.J.C. and Arkader, R. (2007), “The direct and mediated relationships between supply chain coordination investments and delivery performance”, International Journal of Operations & Production Management, Vol. 27 No. 2, pp. 140-58.

Devaraj, S., Krajewski, L. and Wei, J.C. (2007), “Impact of e-business technologies on operational performance: the role of production information integration in the supply chain”, Journal of Operations Management, Vol. 25 No. 6, pp. 1199-216.

de Treville, S. and Antonakis, J. (2006), “Could lean production job design be intrinsically motivating? Contextual, configurational and levels-of-analysis issues”, Journal of Operations Management, Vol. 24 No. 2, pp. 99-123.

DiMaggio, P.J. and Powell, W.W. (1983), “The iron cage revisited: institutional isomorphism and collective rationality in organizational fields”, American Sociological Review, Vol. 48 No. 2, pp. 147-60.

Disney, S.M. and Towill, D.R. (2003), “The effect of vendor managed inventory (VMI) dynamics on the bullwhip effect in supply chains”, International Journal of Production Economics, Vol. 85 No. 2, pp. 199-215.

Fisher, M.L., Raman, A. and McClelland, A. (2000), “Rocket-science retailing is almost here: are you ready?”, Harvard Business Review, Vol. 78 No. 4, pp. 115-24.

Ford, M.W. (2011), “Customized compliance: exploring patterns of quality practice adoption in a multidivisional context”, The Quality Management Journal, Vol. 18 No. 3, pp. 21-32.

Frohlich, M.T. and Westbrook, R. (2001), “Arcs of integration: an international study of supply chain strategies”, Journal of Operations Management, Vol. 19 No. 2, pp. 185-200.

Fullerton, R.R. and McWatters, C.S. (2001), “The production performance benefits from JIT adoption”, Journal of Operations Management, Vol. 19 No. 1, pp. 81-96.

Gunasekaran, A. and Ngai, E.W.T. (2004), “Information systems in supply chain integration and management”, European Journal of Operational Research, Vol. 159 No. 2, pp. 269-95.

Handfield, R.B. and Nichols, E.L. Jr (Eds) (1999), Introduction to Supply Chain Management, Prentice-Hall, Upper Saddler River, NJ.

IJOPM 32,9

1092

Harrison, A. and van Hoek, R. (2002), Logistics Management and Strategy, Prentice-Hall, Harlow.

Heide, J.B. and Miner, A.S. (1992), “The shadow of the future: effects of anticipated interaction and frequency of contact in buyer-seller cooperation”, Academy of Management Journal, Vol. 35 No. 2, pp. 265-91.

Helper, S.R. and Sako, M. (1995), “Supplier relations in Japan and the United States: are they converging?”, Sloan Management Review, Vol. 36 No. 3, pp. 77-84.

Hines, P., Dimancescu, D. and Rich, N. (1997), The Lean Enterprise, Amacon, New York, NY.

Holweg, M. (2007), “The genealogy of lean production”, Journal of Operations Management, Vol. 25 No. 2, pp. 420-37.

Holweg, M., Disney, S., Holström, J. and Småros, J. (2005), “Supply chain collaboration: making sense of the strategy continuum”, European Management Journal, Vol. 23 No. 2, pp. 170-81.

Hopp, W.J. and Spearman, M.L. (2004), “To pull or not to pull: what is the question?”, Manufacturing and Service Operations Management, Vol. 6 No. 2, pp. 133-48.

Iacovou, C.L., Benbasat, I. and Dexter, A.S. (1995), “Electronic data interchange and small organizations: adoption and impact of technology”, MIS Quarterly, Vol. 19 No. 4, pp. 465-85.

Jayaram, J., Kannan, V.R. and Tan, K.C. (2004), “Influence of initiators on supply chain value creation”, International Journal of Production Research, Vol. 42 No. 20, pp. 4377-99.

Jayaram, J., Vickery, S. and Droge, C. (2008), “Relationship building, lean strategy and firm performance: an exploratory study in the automotive supplier industry”, International Journal of Production Research, Vol. 46 No. 20, pp. 5633-49.

Johnston, D.A. and Wright, L. (2004), “The e-business capability of small and medium sized firms in international supply chains”, Information Systems and e-Business Management, Vol. 2 Nos 2/3, pp. 223-40.

Jones, D.T., Hines, P. and Rich, N. (1997), “Lean logistics”, International Journal of Physical Distribution & Logistics Management, Vol. 27 Nos 3/4, pp. 153-73.

Kamaruddin, N.K. and Udin, Z.M. (2009), “Supply chain technology adoption in Malaysian automotive suppliers”, Journal of Manufacturing Technology Management, Vol. 20 No. 3, pp. 385-403.

Kim, K.K., Park, S.H., Ryoo, S.Y. and Park, S.K. (2010), “Inter-organizational cooperation in buyer-supplier relationships: both perspectives”, Journal of Business Research, Vol. 63 No. 8, pp. 863-9.

Lamming, R. (1993), Beyond Partnership: Strategies for Innovation and Lean Supply, Prentice-Hall, Hemel Hempstead.

Lapiedra, R., Smithson, S., Alegre, J. and Chiva, R. (2004), “Role of information systems on the business network formation process: an empirical analysis of the automotive sector”, Journal of Enterprise Information Management, Vol. 17 No. 3, pp. 219-28.

Lee, H.L., So, K.C. and Tang, C.S. (2000), “The value of information sharing in a two-level supply chain”, Management Science, Vol. 46 No. 5, pp. 626-43.

Li, S., Rao, S.S., Ragu-Nathan, T.S. and Ragu-Nathan, B. (2005), “Development and validation of a measurement instrument for studying supply chain management practices”, Journal of Operations Management, Vol. 23 No. 6, pp. 618-41.

MacDuffie, J.P. and Helper, S. (1997), “Creating lean suppliers: diffusing lean production throughout the supply chain”, California Management Review, Vol. 39 No. 4, pp. 118-51.

Malhotra, M.K. and Grover, V. (1998), “An assessment of survey research in POM: from constructs to theory”, Journal of Operations Management, Vol. 16 No. 4, pp. 407-25.

Cooperation in the supply

chain

1093

Martı́nez-Sánchez, A. and Pérez-Pérez, M. (2003), “The use of EDI for interorganisational co-operation and co-ordination in the supply chain”, Integrated Manufacturing Systems, Vol. 14 No. 8, pp. 642-51.

Mason-Jones, R. and Towill, D.R. (1999), “Total cycle time compression and the agile supply chain”, International Journal of Production Economics, Vol. 62 Nos 1/2, pp. 61-73.

Mason-Jones, R., Naylor, B. and Towill, D.R. (2000), “Lean, agile or leagile? Matching your supply chain to the marketplace”, International Journal of Production Research, Vol. 38 No. 17, pp. 4061-70.

Morris, D., Donnelly, T. and Donnelly, T. (2004), “Suppliers parks in the automotive industry”, Supply Chain Management, Vol. 9 No. 2, pp. 129-33.

Narasimhan, R., Swink, M. and Kim, S.W. (2006), “Disentangling leanness and agility: an empirical investigation”, Journal of Operations Management, Vol. 24 No. 5, pp. 440-57.

O’Leary-Kelly, S.W. and Vokurka, R.J. (1998), “The empirical assessment of construct validity”, Journal of Operations Management, Vol. 16 No. 4, pp. 387-405.

Panizzolo, R. (1998), “Applying the lessons learned from 27 lean manufacturers: the relevance of relationships management”, International Journal of Production Economics, Vol. 55 No. 3, pp. 223-40.

Pedhazur, E.J. and Schmelkin, L. (1991), Measurement, Design and Analysis: An Integrated Approach, Lawrence Erlbaum Associates, Hillsdale, NJ.

Petersen, K.J., Ragatz, G.L. and Monczka, R.M. (2005), “An examination of collaborative planning effectiveness and supply chain performance”, Journal of Supply Chain Management, Vol. 41 No. 2, pp. 14-25.

Phillips, L.W. (1981), “Assessing measurement error in key informant reports: a methodological note on organization analysis in marketing”, Journal of Marketing Research, Vol. 18 No. 4, pp. 395-415.

Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports in organizational research: problems and prospects”, Journal of Management, Vol. 12 No. 4, pp. 531-44.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in behavioural research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Premkumar, G., Ramamurthy, K. and Crum, M.R. (1997), “Determinats of EDI adoption in the transportation industry”, European Journal of Information Systems, Vol. 6 No. 2, pp. 107-21.

Romano, P. (2003), “Co-ordination and integration mechanisms to manage logistics processes across supply networks”, Journal of Purchasing & Supply Management, Vol. 9 No. 3, pp. 119-34.

Rosenzweig, E.D., Roth, A.V. and Dean, J.W. Jr (2003), “The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers”, Journal of Operations Management, Vol. 21 No. 4, pp. 437-56.

Shah, R. and Ward, P.T. (2003), “Lean manufacturing: context, practice bundles and performance”, Journal of Operations Management, Vol. 21 No. 2, pp. 129-49.

Shah, R. and Ward, P.T. (2007), “Defining and developing measures of lean production”, Journal of Operations Management, Vol. 25 No. 4, pp. 785-805.

Shah, R., Chandrasekaran, A. and Linderman, K. (2008), “In pursuit of implementation patterns: the context of lean and Six Sigma”, International Journal of Production Research, Vol. 46 No. 23, pp. 6679-99.

IJOPM 32,9

1094

Simpson, D.F. and Power, D.J. (2005), “Use the supply relationship to develop lean and green suppliers”, Supply Chain Management, Vol. 10 No. 1, pp. 60-8.

Sousa, R. and Voss, C.A. (2008), “Contingency research in operations management practices”, Journal of Operations Management, Vol. 26 No. 6, pp. 697-713.

Spina, G. and Zotteri, G. (2001), “The strategic context of customer-supplier partnerships: evidence from a global survey”, Integrated Manufacturing Systems, Vol. 12 No. 7, pp. 483-92.

Stank, T.P., Crum, M. and Arango, M. (1999), “Benefits of inter-firm co-ordination in food industry supply chains”, Journal of Business Logistics, Vol. 20 No. 2, pp. 21-41.

Stuart, F.I. and McCutcheon, D. (1996), “Sustaining strategic supplier alliances: profiling the dynamic requirements for continued development”, International Journal of Operations & Production Management, Vol. 16 No. 10, pp. 5-22.

Teo, H.H., Wei, K.K. and Benbasat, I. (2003), “Predicting intention to adopt interorganizational linkages: an institutional perspective”, MIS Quarterly, Vol. 27, pp. 19-49.

Vonderembse, M.A., Uppal, M., Huang, S.H. and Dismukes, J.P. (2006), “Designing supply chains: towards theory development”, International Journal of Production Economics, Vol. 100 No. 2, pp. 223-38.

Westphal, J.D., Gulati, R. and Shortell, S.M. (1997), “Customization or conformity? An institutional and network perspective on the content and consequences of TQM adoption”, Administrative Science Quarterly, Vol. 42, pp. 366-94.

Williams, L.R. (1994), “Understanding distribution channels: an interorganizational study of EDI adoption”, Journal of Business Logistics, Vol. 15 No. 2, pp. 173-203.

Womack, J.P., Jones, D.T. and Ross, D. (1990), The Machine That Changed the World, MacMillan, New York, NY.

Womack, J.P. and Jones, D.T. (1996), Lean Thinking, Simon and Schuster, New York, NY.