The role of organizational contextand infrastructure practices

in JIT implementationAlberto Bayo-Moriones, Alejandro Bello-Pintado and

Javier Merino-Dıaz-de-CerioDepartment of Business Administration, Public University of Navarre,

Pamplona, Spain

Abstract

Purpose – The purpose of this paper is to analyze which factors determine the use of just-in-time(JIT) in companies. More precisely, the paper aims to study the role played by two variables oforganizational context (size and age) and three infrastructure practices (advanced manufacturingtechnologies – AMT, quality management, and work organization).

Design/methodology/approach – The hypotheses were tested using data collected from 203manufacturing plants with at least 20 employees. Data were collected by means of personal interviewswith plant managers. Regression analyses have been performed to test the hypotheses.

Findings – The results reveal the existence of diversity in the factors that affect the use of thedifferent components of JIT. Infrastructure practices are shown to be more determining thancontextual factors. This happens in particular with AMT, basic quality tools and the management ofthe relationships with suppliers and customers.

Research limitations/implications – The main limitations of the research are those derived fromthe cross-sectional character of the data and from information coming from surveys, especially whenthe measures are subjective.

Practical implications – The paper stresses the need to develop adequate infrastructures intechnology management, quality management and work organization to obtain all the benefits of JITimplementation.

Originality/value – The paper highlights the role of organizational context and, especially,infrastructure practices in the incidence of JIT in the manufacturing industry. Moreover, theidentification of different dimensions of JIT systems makes it possible to conclude that the influence ofthe different factors considered is not uniform across all JIT elements.

Keywords Just in time, Advanced manufacturing technologies, Quality management,Working practices, Spain

Paper type Research paper

IntroductionJust-in-time (JIT) manufacturing systems were developed initially in the Japanesemanufacturing industry. More precisely, they come from the improvement of Toyota’sproduction system. These modifications were soon adopted by other Japanesecompanies in the automotive sector and, as early as the 1980s, by American andEuropean companies. Until then companies used mass production systems designed to

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The authors acknowledge financial support from the Department of Education of theGovernment of Navarre and Spanish Ministry of Education and Science project SEJ2007-66511.

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Received June 2007Revised May 2008Accepted July 2008

International Journal of Operations &Production ManagementVol. 28 No. 11, 2008pp. 1042-1066q Emerald Group Publishing Limited0144-3577DOI 10.1108/01443570810910188

protect them from market fluctuations. The Toyota manufacturing system (JITsystem), on the contrary, considers these fluctuations as inevitable, and tries tosynchronize the production process with demand. Therefore, the main target is toeliminate waste and reduce inventories as far as possible. The JIT system requires thatall finished and semi-finished products be delivered in the right amount, theappropriate place and at the precise moment they are needed (Monden, 1983;Sakakibara et al., 2001).

Whereas a great deal of research has analyzed the effects of JIT on firmperformance, there is barely any empirical evidence about the factors that affect theirimplementation. Deepening the knowledge of the variables associated with the use ofJIT practices can contribute enormously to the recognition of what kind of companyhas difficulties in adopting such practices as well as to the identification of theobstacles that prevent their wider diffusion.

The objective of this paper is to analyze the factors that determine the use of JIT infirms from a sample of 203 Spanish manufacturing plants. Although for years JIT hasbeen the subject of much research, there is no consensus about its constituent practices.In general, two approaches to JIT are observed. According to some authors with a moreoperative approach, JIT is a defined set of practices aimed at reducing inventories or toplan production. For example, Flynn et al. (1995, p. 1327) define JIT practices as:

[. . .] based on the notion of eliminating waste through the simplification of manufacturingprocesses. Such simplification includes elimination of excess inventories and overly large lotsizes, which cause unnecessarily long customer cycle times.

On the other hand, there are authors that consider JIT from a broader point of view,turning it into a philosophy of manufacturing oriented towards continuousimprovement through the reduction of waste in all the stages of the productionprocess (Sakakibara et al., 2001)[1]. In this paper, we have adopted the first approachand have considered as JIT practices those that are most directly related to themanagement of materials flow in the plant.

JIT adjusts quite well to the definition of both process and organizational innovationprovided by the 3rd edition of the Oslo Manual (OECD and Eurostat, 2005), since itsintroduction can be considered “the implementation of a new or significantly improvedproduction or delivery method” (point 163) and “the implementation of a neworganisational method in the firm’s business practices, workplace organisation orexternal relations” (point 177). Moreover, it should also be noted that point 148 of theOslo Manual states that “the minimum requirement for an innovation is that theproduct, process, marketing method or organisational method must be new (orsignificantly improved) to the firm[2].” For these reasons, the organizational context,understood as the structural characteristics of the firm, is to play a remarkable role inthe implementation of JIT, since it is associated with very important variables in allprocesses of adoption of innovations, such as resource availability, risk tolerance andthe existence of inertia. This paper analyzes the influence of two of the variables thatbest serve to define a company: size and age.

For JIT to be beneficial to the company, other activities that support it should be carriedout beforehand (Sakakibara et al., 1997, 2001). The development of an adequateinfrastructure in other areas of the firm constitutes a fundamental element for theimplementation of JIT to contribute to an improvement inmanufacturing performance and

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the competitiveness of the plant. In this paper, we focus on three particular infrastructurepractices related to: technology, quality management and work organization.

The present paper attempts to make a number of contributions to the existingtheoretical and empirical literature on JIT. First of all, unlike most of the researchconducted on JIT, this paper does not examine the relationship between JIT andperformance. Rather, its objective is to analyze the determinants of JIT use, consideringsimultaneously several explanatory variables, some of which, in spite of theirtheoretical relevance, have been hardly subjected to empirical scrutiny. A secondcontribution has to do with the empirical treatment of the practices that belong to JIT.As opposed to the papers that analyze JIT as a homogenous system or those whichtake individual practices as units of study, the present work performs rigorous factoranalysis to identify different dimensions of JIT configured by similar groups ofpractices. This allows greater depth of analysis of the relationships between thevariables and helps to explain better the influence of the different explanatoryvariables on the implementation process of JIT. A third contribution has to do with thesample of plants considered in this paper. All manufacturing industries are included.Moreover, the information relates to Spanish firms, Spain being a context where JITimplementation has been hardly studied.

The paper is structured as follows. First, there is a review of the literature about therelationship between the different explanatory variables and the use of JIT practices.This review leads to the establishment of five hypotheses. The following sectionexplains the empirical methodology, describing the data collection process and themeasurement of the variables. Then follows the presentation of the results obtainedwhen testing the theoretical hypotheses. Finally, the research findings and conclusionsare discussed.

Theory and hypothesesSizeAlthough the benefits of JIT practices for small companies have been widelyrecognized (Manoochehri, 1988; Gunasekaran, 1997; Gunasekaran et al., 2000; Bonaviaand Marin, 2006; Aghazadeh, 2008), it is also generally admitted that there is a series offactors that lead to their greater incidence in large companies (White et al., 1999).A commonly mentioned obstacle to the introduction of JIT in small companies is theirlower availability of resources. Large companies usually enjoy more financial andhuman resources to innovate, and at the same time have better access to the knowledgenecessary for the implementation of JIT (Doolen and Hacker, 2005). These greaterresources, in addition, allow them to be better prepared to face the possible risksderived from innovation (Osterman, 1994). Moreover, the presence of economies ofscale in implementation makes the adoption of JIT more feasible in large companies.

In spite of this majority support for the positive relationship between firm size andJIT adoption, reasons for a greater presence of JIT in small firms have also been given.For example, it is argued that, due to greater inertia in large firms, management tasksin such cases are usually more complex and bureaucratic, which makes it morecomplicated to carry out innovations (Hannan and Freeman, 1984). Similarly, thegreater difficulties of coordination in large companies, together with the greaterinterdependences generated by a JIT system, may lead to greater profits in smallerfirms.

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Although the above-mentioned arguments are applicable to all JIT practices, not allof them are expected to be identically affected in their diffusion by firm size. This isconnected to the fact that firm size can be related to the presence of the conditions inwhich JIT is feasible. One of these conditions is the need for stable demand. Smallercompanies usually face a less uniform demand, since it usually comes from largecompanies, against which they have low-bargaining power (Finch and Cox, 1986).Something similar happens in relation to the ability to receive the materials at the exactmoment and in the right amount. Although there are new transport services thatfacilitate this, it is certainly complicated for a small firm to be in a position to make thiskind of demand on suppliers (Manoochehri, 1988).

Nevertheless, smaller companies are not at a disadvantage compared to the largerones as far as the possibility of producing in small lots is concerned (Manoochehri,1988). For that reason, JIT production focused more on internal aspects, such as thereduction of set-up times, might be more common in smaller companies than JITdeliveries, related to suppliers (Gilbert, 1990; Lee, 1997).

Regarding the empirical evidence, most of the research so far has established thatthe degree of use of JIT practices is greater in larger companies (Im and Lee, 1989;Ahmed et al., 1991; Hum and Ng, 1995; White et al., 1999; Shah and Ward, 2003),although some authors have not found this positive relationship (Lee, 1997;Amoako-Gyampah and Gargeya, 2001).

Even though there are opposing arguments about the relationship between size andJIT, on the whole previous theoretical and empirical research tends to show that thereis a positive correlation between them:

H1. Plant size has a positive effect on the use of JIT practices.

AgeFirm age may have several kinds of effect on the probability of a company introducingan organizational innovation such as JIT. This multiplicity of influences precludes auniversal theoretical conclusion on the relationship between age and JIT use. Theempirical literature on innovations in manufacturing does not present clear resultsabout this question either, as there are studies with quite different results (Becheikhet al., 2006).

In favour of a positive impact, it is possible to argue that firm age encouragesthe introduction of new methods of production organization, since it could reflectthe amount of resources available for innovation (Galende and de la Fuente, 2003).Older firms may also be more efficient in the implementation of innovations, sincethey have been able to accumulate the necessary experience and knowledgethroughout their history to improve the ability to identify and incorporate newideas (Cohen and Levinthal, 1990). Another point which supports this positiveinfluence is that time has provided older firms with opportunities to enterprofessional networks and to establish stronger links within the value chain, whichmakes the transmission of new management and organization techniques mucheasier (Uzzi, 1997).

In spite of the above-mentioned arguments, there are also reasons that can beadduced to support the idea of a negative relationship between the age of the companyand the introduction of innovations. For instance, the organizational sociologyliterature points out that the routines of a firm throughout its life tend to reflect the

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decisions taken at the moment of its foundation (Stinchcombe, 1965). In the case of JIT,this negative influence of firm’s age may have to do with an older workforce that isused to a certain way of doing things and with greater physical barriers to theintroduction of practices such as the reduction of machine set-up times (Shah andWard, 2003).

From another perspective, the theories of organizational learning also support theexistence of a negative relationship. Given that the greater the use of a given practice,the greater its efficiency, older companies will have fewer incentives to adoptinnovations, since they will come from a more satisfactory initial situation in terms ofperformance (Levitt and March, 1988).

Although there are opposing arguments for the effect of a firm’s age, as in otherresearch on innovation in operations management (Ketokivi and Schroeder, 2004a), weare inclined to propose that younger companies have a higher probability of using JITpractices, thus:

H2. The age of the plant has a negative effect on the use of JIT practices.

Advanced manufacturing technologiesIn response to more intense and globalized competition, manufacturers areincorporating more flexible technologies into their production processes. One of themost outstanding is advanced manufacturing technologies (AMT), a set of toolsintended to automate and integrate the different stages of design, manufacturing,planning and control of the product. AMT results from the application of informationand manufacturing technologies with the aim of increasing the response ability of theplant and to improve the results of the production process.

These technologies have found wide acceptance since, in general, they can beapplied, with more or less difficulty, to most manufacturing processes. For example,numerical control is applied to systems of mechanizing, cutting and moulding. In thecase of robots, they have an extended use in welding, painting, materials treatment andmany other unique assembly applications.

In some research the relationship between AMT and JIT has been considered soclose that JIT is even included as part of AMT (Swamidass and Winch, 2002). For thatreason, it is not surprising that other papers highlight the convenience of applyingAMT and JIT jointly. For instance, Manoochehri (1988) considers that the layoutassociated with JIT – manufacturing cells – is very well adapted to automation. In atypical cell of JIT systems, the proximity of machines favours the work of robots totransfer pieces from one machine to another. The machines and robots in the cell can becontrolled by a computer, constituting a flexible manufacturing system. However, itshould also be underlined that, although JIT facilitates the application of AMT,adoption of the latter is not a necessity.

The literature on organizational innovation also offers arguments supporting apositive relationship between the use of AMT and the use of JIT. Firms that behaveinnovatively do so in different management areas. In this context, Cagliano and Spina(2000), after reviewing the literature on the issue, conclude that firms that want toobtain the best improvement in performance from AMT need them and the neworganizational forms to be mutually adapted.

In contrast to the above-mentioned reasons, it is also possible to find arguments thatJIT works better in simple technological contexts. Gunn (1987) points out that working

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with JIT in a factory that uses AMT is much more complex than in a traditional plant,since JIT must cope with the reduced lead times of AMT, as well as more complexinter-dependencies. Moreover, it is also possible that firms may use AMT as analternative to JIT because they require less organizational change and discipline.

There is hardly any empirical research into the relationship between AMT and theuse of JIT practices. Challis et al. (2002), using a sample of companies fromNew Zealand, find that the effect of JIT on firm performance is greater at higher levelsof AMT. On the other hand, Snell et al. (2000) detect a positive and significantcorrelation between the implementation of AMT and JIT.

Finally, Zhang et al. (2006) discuss the inter-relationship between AMT andoperations improvement practices (OIP), which includes both JIT and other supportivepractices, as well as their effects on flexible manufacturing competence (FMC). In theirstudy they find a positive correlation between AMT and OIP adoption and find that therelationship between AMT and FMC depends on the adoption level of OIP. That is tosay, firms that have implemented AMT, in order to achieve good performance, shouldhave a high degree of OIP use.

In view of these findings, it is possible to formulate the next hypothesis:

H3. AMT have a positive effect on the use of JIT practices.

Quality managementAlmost from their beginning, the developments of JIT and quality management havegone hand in hand. In fact, the difficulties of defining and delimiting both conceptshave sometimes caused certain confusion. Flynn et al. (1995) suggest a separationbetween JIT practices, total quality management (TQM) practices and common orinfrastructure practices, analyzing the relationships among them. The practices thatthey identify as typical of JIT are kanban controls, JIT planning activities, reduction ofset-up times and reduction of lot size. On the other hand, statistical control of processes,product design and focus on customers would constitute TQM practices, whereasinformation feedback, plant environment, management support and the relationshipwith suppliers would make up the infrastructure practices.

The literature provides many references to the importance of quality managementfor an appropriate implementation of JIT. Generally, TQM and JIT appear as relatedsystems, due to their common objectives of continuous improvement. Although TQMcan be implemented without introducing JIT, it is difficult to succeed in introducing JITpractices if the basic principles of TQM have not been previously incorporated(Fullerton and McWatters, 2001).

Quality management practices give support to JIT, since they help to establish anecessary control of the production process. If the appearance of defective products isminimized, the product flow becomes smoother and inventory in progress is reduced.Flynn et al. (1995, 1997) indicate that quality management practices favour the use ofsmall lots, since they diminish the need to use safety inventories. In addition, theyreduce the number of items that require reprocessing. This shortens lead time, speedsup the response to the demands of the market and, therefore, improves the performanceof JIT.

Although several techniques and practices are usually included within the frame ofquality management (Dean and Bowen, 1994; Hackman andWageman, 1995), there are

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two dimensions that contribute to distinguish those companies that give quality astrategic priority. These two dimensions are:

(1) the use of tools or practices for improvement; and

(2) collaboration with suppliers and external customers.

The use of tools and methodologies to improve quality, such as six sigma andstatistical control of processes, contributes to reduce the defective product ratio in theproduction process. On the other hand, maintaining close collaboration with suppliersguarantees the quality of supplies (raw materials and components) to a greater extent,which will give rise to a smaller incidence of problems of product quality, somethingthat favours the introduction of JIT (Ramarapu et al., 1995). Tan and Wisner (2003)consider the role of suppliers to be very important in the introduction of JIT, to such anextent that they state that firms should previously have a programme for theassessment of suppliers. Close relationships with customers will also result in bettercommunication and understanding. This will help to reduce the defective productindicators, so JIT will cause fewer problems in practice.

The empirical articles which analyze the relationship between the implementationof JIT and TQM show a positive relationship between the two and a synergistic effectregarding their impact on operational performance (Flynn et al., 1995; Sriparavastuand Gupta, 1997; Lau, 2000; Cua et al., 2001). As far as the studies of implementationare concerned, Dreyfus et al. (2004) find that JIT plants introduce TQM morerigorously than traditional firms. Kannan and Tan (2005) also discuss evidence froma positive association between JIT and TQM, both at the strategic and operationallevels.

In summary, both the theory and the empirical evidence available lead us toformulate the following hypothesis:

H4. Quality management practices, such as the use of quality improvement toolsand close collaboration with suppliers and customers, have a positive effect onthe use of JIT practices.

Work organizationIn order for the implementation of JIT practices to achieve all its potential benefits, thecompany needs first to modify work organization in the right direction. Otherwise, it isprobable that the firm will face multiple difficulties that will render ineffective theinvestments aimed to introduce changes in production organization (Desphande et al.,1994). Empirical evidence shows that companies that have devoted a great deal ofeffort to modifying the organization of human resources have reached greateroperational efficiency, performance effectiveness and competitiveness as a result of theintroduction of JIT (Ahmad et al., 2003).

The change in the role of human resources that the adoption of JIT involves takesplace largely because the production system becomes highly interdependent, due to thereduction of slack resources to a minimum in the different stages of the productionprocess (Ahmad et al., 2003). Human resources must become more predictable andreliable, which demands a greater degree of coordination between the different unitsand jobs (Forza, 1996). Several modifications must take place in work organization sothat the firm is prepared to undertake the adoption of a JIT system with guarantees.

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The most outstanding ones are the search for greater worker flexibility, empowermentand the use of teams.

The lack of worker flexibility has been widely discussed as one of the main barriersto the implementation of JIT (Ahmed et al., 1991; Power and Sohal, 2000a; Salaheldin,2005). The perfect delimitation of jobs in traditional manufacturing is inadequate forthe correct operation of JIT. The main target of JIT is production according to demand,which implies that workers carry out the jobs for which they are needed at any momentin time (Niepce and Molleman, 1996). As happens with other resources, the introductionof JIT involves working without slack work times (Forza, 1996). Losses of time areavoided if workers are trained in a great variety of skills, so that they are prepared toperform different jobs and to handle different machines. The greater separationbetween the stages of the production process also demands that workers perform jobspreviously reserved for other functions, such as quality control or data analysis (Deanand Snell, 1991). For that reason, workers must be multi-skilled and trained in a variedrange of technical knowledge (Spencer and Guide, 1995).

Although JIT can be put into practice with very different approaches and styles,other aspects that favour its successful introduction are a higher provision ofautonomy to the employee in his job and greater participation in decision making(Ramarapu et al., 1995; Fullerton and McWatters, 2001). This empowerment may affectnot only those issues that are closely linked to the job, but also other aspects such aspurchasing, inventory control and cost control (Power and Sohal, 2000a). The fact thatdecisions are taken directly by workers and not by supervisors improves the system’sability to react to unforeseen circumstances, since the time dedicated to thetransmission of information between both levels in the hierarchy is eliminated.

Finally, the correct operation of JIT demands teamwork (Im and Lee, 1989;Desphande et al., 1994). In a JIT system, the output achieved by a worker is completelylinked to the performance of their co-workers, which makes it necessary to behave as ateam and not individually (Forza, 1996). Teamwork and problem solving in groupsdecentralize decision making and allow better management of uncertainty. In thissense, the implementation of improvement groups as a methodology for problemsolving is advisable (Yasin et al., 2003), since the existence of teamwork opens newlines of communication, intensifies the exchange of information and facilitates thecoordination between the different members of the firm (Power and Sohal, 2000b).In addition, the use of teams reinforces the empowerment and involvement of workers(Power and Sohal, 1997).

These arguments allow us to formulate the final hypothesis:

H5. Work organization based on greater worker flexibility and empowerment andon the use of teams has a positive effect on the use of JIT practices.

MethodologySample and data collectionThe data used in the empirical section of the paper were obtained from a surveyconducted in 2006 through personal interviews with managers of 203 manufacturingplants in Navarre (Spain) with at least 20 workers. The survey was restricted toestablishments of this size because smaller plants often show less formal and morevariable production organization andwork practices (Cappelli and Neumark, 2001). Theplant was chosen as the unit of analysis instead of the firm because the practices studied

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are used and implemented at the plant level. Moreover, it is in the plant where there is agreater knowledge of the management practices applied on the shop floor. Once theplants fulfilling the above-mentioned requirements were identified, the sample wasdefined in such way as to guarantee representativeness in size and activity sector.

In order to achieve the objectives of the research, a questionnaire was drawn upaccording to the methodological recommendations offered by Nunnally (1978). A detailedreview of the relevant theoretical and empirical literature was carried out prior to thedevelopment of the various survey questions. An initial version of the questionnaire waspre-tested in several plants. Based on the results of the pre-test, some modifications wereintroduced, which shaped the final version of the questionnaire. The data collectionprocess consisted of personal interviews in the plant. The average length of the interviewswas 40minutes. The intervieweeswere plantmanagers, whowere inmost cases either thegeneral manager or the operations manager. The response rate was 47 per cent, anacceptable rate if compared with other recent survey-based research (see, for example,Carr andKaynak, 2007;Urgal-Gonzalez andGarcıa-Vazquez, 2007). Thedistribution of thesample by industry appears in Table I.

MeasuresSeveral types of measure were used, according to the nature of the variable. We havedistinguished between reflective indicators and formative indicators (Diamantopoulosand Winklhofer, 2001; MacKenzie et al., 2005). It is important to note here that, for bothtypes of indicator, reliability and validity analyses differ considerably. Latent constructmodelswith reflective indicators posit that co-variation amongmeasures is explained byvariation in an underlying common latent factor (Boolen, 1989; Bollen and Lennox,1991). On the other hand, if the indicators are causing rather than being caused by thelatent variable measured, the indicators are known as formative (MacCallum andBrowne, 1993). The latent construct model with formative measurement posits that themeasures jointly influence the composite latent construct, and meaning emanates fromthe measures of the construct, in the sense that the full meaning of the composite latentconstruct is derived from its measures (MacKenzie et al., 2005).

The latent constructwith reflective indicators is themost common type ofmeasurementmodel found in the behavioural and organizational literature. As a consequence, there are

Industry Per cent

Food, drinks and tobacco 15.3Textiles, clothing, leather and footwear 4.9Wood and cork 3.0Paper, publishing and graphic arts 7.9Chemical industry 3.0Rubber and plastics 6.9Non-metallic mineral products 7.9Primary metal industries and fabricated metal products 21.7Machinery and mechanical equipment 8.4Electrical material and equipment, electronics and optics 6.4Transport material 9.4Other manufacturing industries 5.4Total 100

Table I.Distribution of thesample by industry(percentages)

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detailed step-by-step guides for construct specification, item selection and purification, andscale validation (DeVellis, 1991; Spector, 1992). In addition, several exploratory factoranalyses (EFA) and confirmatory factor analyses (CFA) have been carried out in order toverify internal consistency reliability, content construct validity (uni-dimensionality ofscales), convergent validity and discriminant validity.

First, an EFA was performed on the multi-item measures. That is, for the estimationof the internal consistency reliability, the questions of the questionnaire measuring thesame concept were grouped together, using Cronbach’s a to compute correlation valuesamong the questions. The minimum a coefficient required was 0.60, as recommendedby Nunnally (1978). Moreover, a minimum correlation between items of each dimensionof 0.30 was chosen (Norusis, 1993). Finally, the specific identity of the differentdimensions was checked through the unidimensionality of the constructs, afterperforming the convergent confirmation tests through exploratory factor analysis.

A CFA was performed to validate the constructs. The significance of the factorregression coefficients between each item and its latent construct was verified(Steenkamp and van Trijp, 1991). The fit indices of the model were analyzed. Theglobal fit was assessed using the goodness of fit index and the root mean square errorof approximation (RMSEA). The incremental fit indices of measurement usingnon-normality robust estimation procedures were non-normed fit index (NNFI),comparative fit index (CFI) and incremental fit index (IFI). Parsimonious fit indexeswere evaluated using the parsimonious goodness of fit index. Finally, discriminantvalidity was verified by comparing the square root of the average variance extracted(AVE) for each construct with its correlation with the other constructs.

In the case of the latent formative constructs, their nature renders an internalconsistency perspective inappropriate for assessing the suitability of these indicators(Bagozzi, 1994), since under formative measurements the latent variable is determinedby its indicators rather than the opposite and content specification is inextricablylinked with indicator specification. Consequently, “breadth of definition is extremelyimportant to causal indicators” (Nunnally and Bernstein, 1994), among other things,because failure to consider all facets of the construct will lead to the exclusion ofrelevant indicators (Diamantopoulos and Winklhofer, 2001). The multi-collinearityamong the indicators was studied to validate the formative constructs (Podsakoff et al.,2006). In this context, a variance inflation factor (VIF) below five is a good indicator ofthe absence of multi-collinearity (Judge et al., 1988).

Several variables in the paper were captured through subjective measures, whichmight cast some doubts on their reliability. However, Ketokivi and Schroeder (2004b)show that in operations management, perceptual measures correlate well withobjective measures. Similarly we would also like to note that when dealing with asample composed of companies involved in non-homogeneous activities, the use ofsubjective measures becomes advisable, since objective ones are not easily comparable(Bayo-Moriones and Merino, 2004).

To control for the potential effects of common method variance, we took intoaccount several recommendations mentioned in the literature (Podsakoff et al., 2003).For example, we used different response formats for the measurement of the variables.Moreover, we based our items on tested and widely used scales. The pre-test alsoserved as reassurance that the items were not ambiguous and were clearly understood.In order to reduce respondent’s evaluation apprehension and make them less likely to

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edit their responses to be more socially desirable, anonymity was fully guaranteed. Wealso conducted Harman’s one-factor test. Given that the un-rotated factor analysis ofthe variables used in the study resulted in 14 factors, with the first factor explainingonly 14 per cent of the common variance, we could say that our findings are not muchaffected by the problem of common method variance.

JIT practicesThe questionnaire included 15 items which referred to the implementation of JITpractices. These items were taken from several references on JIT, such as Flynn et al.(1995), Sakakibara et al., 2001, Cua et al. (2001) and Ahmad et al. (2003). The intervieweewas asked to show agreement or disagreement with several statements about theimplementation of these practices in the plant. The assessment was based on afive-point Likert scale ranging from 1 – strongly disagree to 5 – strongly agree.Although the use of a greater number of items is always desirable, the 15 items usedare representative of JIT practices and acceptable, given the usual restrictions insurvey research.

An EFAwith varimax rotation on themulti-itemmeasureswas performed in order toprove construct reliability. Using internal consistency and factor uni-dimensionality asselection criteria, six itemswere eliminated and four factors emergedwith an eigenvaluegreater than one (Table II). These four factors can be identified with the followingdimensions of JIT: plant layout, lot size reduction, set-up time reduction, and the use of akanban system. Also from a theoretical perspective they are adequate since they reflectdistinct conceptual constructs (Sakakibara et al., 1997). The difference between lot sizeand set-up time reduction may not be clear, but it should be noted that not all lotsize reductions require set-up time reductions such as with purchased inputs and whereset-up time may be inconsequential, for example, outside repetitive manufacturing.

The CFA, performed to verify whether the constructs were distinct from each otherand from single-item measures, indicated that the measurement model with the fourfactors has a good global, parsimonious and incremental fit for all the indices(NNFI ¼ 0.92; CFI ¼ 0.95; IFI ¼ 0.96; RMSEA ¼ 0.033). In all cases the varianceexplained was higher than 50 per cent.

Discriminant validity analysis was performed for each factor by comparing the rootsquare of the AVE shared between the constructs and its measures and the correlationwith the rest of constructs (Table III). As can be seen, discriminant validity isconfirmed, since the root square of AVE for the four constructs is larger than thecorrelation with the other constructs.

Finally, a global indicator of JIT production practices using the former fourconstructs was also created. It is a formative indicator, since it includes the fourdifferent dimensions of JIT production mentioned above: layout, lot size reduction,set-up time reduction and kanban. It is defined as the mean of their values. The absenceof multi-collinearity was seen to validate this indicator, as the VIF was lower than five.

Explanatory variablesThe size of the plant was measured by the logarithm of the number of workers, and ageby the logarithm of the number of years since the plant was founded. Age and size arevariables that are frequently effectively log-transformed to linearise relationships(Cohen et al., 2003).

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Index

mean

Index

SD

Factor

loading

Cronbach’sa

AVE

Layout

Wehavelaid

outtheshop

floorso

that

processes

and

machines

arein

closeproxim

ityto

each

other

3.66

0.725

0.773

0.621

0.564

Ourmachines

aregrouped

accordingto

theproduct

familyto

whichthey

arededicated

0.789

Ourplantem

phasizes

puttingalltoolsandfixturesin

theirplace

0.777

Lot

size

reduction

Weareworkingto

lower

lotsizesin

ourplant

2.90

1.034

0.699

0.719

0.598

Ourplantproducesmanydifferentproducts

0.872

Frequentlywechangethemodelsproducedin

ourplant

0.847

Set-uptimereduction

Wehavelow

set-uptimes

ofequipmentin

ourplant

2.74

0.974

0.894

0.750

0.820

Plantmanagem

entem

phasizes

reducingset-uptimes

0.894

Kanban

system

Weuse

akanban

pullsystem

forproductioncontrol

1.93

1.048

11

1

Note:Alltheitem

saremeasuredon

a1to

5Likertscale

Table II.Dimensions of the JIT

production system,descriptives and

confirmatory factoranalysis results

Role oforganizational

context

1053

The presence of AMT in the plant was captured by an index that reflects the degree ofutilization of several technologies identified by the literature (Boyer and Pagell, 2000;Jonsson, 2000; Beaumont et al., 2002; Ward et al., 2007). The interviewee had to assessthe level of implementation of these technologies in the plant on a zero-to-ten scale.Therefore, the index used to measure AMT is a formative indicator. It is computed asthe average of the degree of use of the technologies considered. Table IV displays thesetechnologies, as well as the results of the validity analysis.

In order to capture the implementation of quality management, we consideredseparately the use of advanced and basic improvement tools as well as themanagement of relationships with suppliers and customers. The first two variableswere constructed as formative indicators from the degree of implementation of twoadvanced and four basic methodologies and techniques commonly recognized aseffective for continuous improvement in manufacturing (Dale et al., 1999). The use ofeach one of them was evaluated by the manager on a zero-to-ten scale (Table IV). Thesetwo variables are calculated as the average of the degree of use of the two advancedand four basic techniques for quality improvement considered.

The vertical relationship management variable attempts to reflect to what extentthere is closeness, collaboration and information exchange with suppliers andcustomers. With this aim in mind, we considered several practices widely identified inthe quality management literature (Saraph et al., 1989) (Table IV). The manager had toassess the degree of implementation of such practices on a one-to-five Likert scale. Thevariable used, as in the previous case, is formative and is defined as the average of thenine items examined.

Finally, in order to capture the degree to which work organization is focused onworker flexibility, empowerment and teamwork, the percentage of workers involved infour related practices was assessed. They are multi-skilling, job rotation, improvementgroups and autonomous teams. All these practices have been included in many articlesdiscussing new flexible work practices and high-performance work systems (Blackand Lynch, 2004; Handel and Levine, 2004). The variable used is a formative indicatordefined as the average of the percentage of production workers involved in these fourpractices (Table IV).

All these formative indicators were validated, once the absence of multi-collinearitywas determined. The VIF for all these measures was lower than five.

Estimation methodsThe method used to test the hypotheses was ordinary least squares multipleregression. Five regression models were estimated. In the first, the global JIT index

Layout Lot size reduction Set-up time reduction Kanban

Layout (0.750)Lot size reduction 0.295 (0.773)Set-up time reduction 0.332 0.592 (0.905)Kanban 0.336 0.182 0.247 (1)

Notes: The diagonal elements indicate the root square of the average variance explained sharedbetween the constructs and its measures. The outside diagonal elements indicate the correlationbetween the constructs

Table III.Discriminant validityanalysis of JITproduction dimensions

IJOPM28,11

1054

Variable

Definition

Mean

SD

VIF

Size

Logarithm

ofthenumber

ofworkersin

theplant

4.024

0.778

Age

Logarithm

ofthenumber

ofyears

since

theplantwas

founded

3.174

0.719

Advancedmanufacturing

Formativeindex

form

theuse

ofthefollow

ingtechnologies(zeroto

tenscale)

2.864

1.731

technologies(AMT)

Shop

floordatacapture

1.381

Enterprise

resourceplanning

1.383

Preventivemaintenance

software

1.286

Bar

coding

1.198

Artificial

visiontechnology

1.320

Automated-guided

vehicles

1.163

Automated

warehousing

1.350

Com

puterizednumerical

control

machines

1.182

Robotics

1.490

Flexiblemanufacturingcells

1.428

CAD/CAM

system

s1.225

Laser

technology

1.389

Advancedqualitytools

Formativeindex

from

theuse

ofthefollow

ingadvancedtoolsforcontinuous

improvem

ent(zeroto

tenscale)

1.078

2.385

Failure

modeandeffectsanalysis

1.454

Designof

experim

ents

1.454

Basicqualitytools

Formativeindex

from

theuse

ofthefollow

ingbasictoolsforcontinuous

improvem

ent(zeroto

tenscale)

1.639

2.314

Six

sigma

1.298

Statistical

control

process

1.371

5S

1.477

Formal

methodologiesforsolvingproblems(8D,etc.)

1.544

Verticalrelationship

Formativeindex

from

thefollow

ingitem

s(oneto

fivescale)

3.835

0.514

(continued)

Table IV.Explanatory variables,

descriptives andconstruct validity

Role oforganizational

context

1055

Variable

Definition

Mean

SD

VIF

Managem

ent

Westriveto

establish

long-term

relationshipswithsuppliers

1.355

Oursuppliersareactivelyinvolved

inournew

product

developmentprocess

1.174

Werely

onasm

allnumber

ofhigh-qualitysuppliers

1.325

Oursuppliersarecertified

forquality

1.291

Wearefrequentlyin

closecontact

withourcustom

ers

1.600

Ourcustom

ersgiveusfeedbackon

qualityanddeliveryperform

ance

1.780

Westriveto

behighly

responsiveto

ourcustom

ers’needs

1.832

Weregularlysurvey

ourcustom

ers’requirem

ents

2.207

Weregularlymeasure

custom

ersatisfaction

withus

2.323

Workorganization

Formativeindex

from

thefollow

ingvariables

28.361

20.739

Percentageof

productionworkersthat

rotate

jobsin

differentsections

2.269

Percentageof

productionworkersskilledto

perform

differentjobsin

different

sections

2.116

Percentageof

productionworkersthat

belongto

autonom

ousworkteam

s1.460

Percentageof

productionworkersthat

takepartin

problem

solvinggroups

1.504

Table IV.

IJOPM28,11

1056

was used as the dependent variable. In the other four models, the dependent variableswere the four JIT dimensions identified individually: layout, lot size reduction, set-uptime reduction and kanban. Eleven industry dummy variables were included in all themodels.

ResultsThe first column inTable V shows the results of the estimation of themodel that identifiesthe determinants of JIT practices in the plant as a whole. As can be observed, thecoefficients of the variables AMT, basic quality tools, vertical relationship managementand work organization are positive and significant. As far as structural variables areconcerned, it should be noted that plant size – measured by the logarithmof the number ofworkers – has a negative and significant effect. On the other hand, plant age also presentsa negative coefficient, but it fails to reach significance at the 10 per cent level.

The second column of the table gathers the results relating to the impact of theexplanatory variables on JIT layout. In this case, only the coefficients of two of thevariables capturing quality management – that is, the management of relationshipswith suppliers and customers and the use of basic quality tools – are statisticallysignificant, both with a positive sign. Neither of the two variables on the structuralcharacteristics of the plant have significant coefficients. The same applies to AMT,advanced quality tools and work organization.

The third column displays the results of the estimation of the explanatory model forlot size reduction. As shown, size has a negative impact on the implementation of theseJIT practices. On the other hand, AMT and work organization have a positive andsignificant effect on the dependent variable. No significant influence has been detectedfor either advanced and basic quality tools, or for vertical relationship managementand plant age.

In the case of the third JIT dimension considered, set-up time reduction, the modelestimations indicate that AMT have a significant and positive effect on its adoption,with similar results for vertical relationship management. For the rest of theexplanatory variables included in the model no significant impact was found.

The last column in Table V presents the results of the estimation model for the useof a kanban system. As shown, AMT have significant and positive effects. The same istrue for basic quality tools and vertical relationship management, but in these caseswith lower statistical significance. On the other hand, plant age has a negativeinfluence on kanban implementation. No effect was detected as far as advanced qualitymanagement, work organization and plant size are concerned.

Although not reported here for space reasons, our findings for sector dummies showthat there are hardly any differences among industries in the adoption of JIT and itsfour components. The exceptions are the non-metallic mineral products industry forJIT as a whole and paper and textiles industries for kanban. In these cases these sectorsshow a lower degree of adoption.

Discussion and conclusionsThe results obtained when carrying out the tests of the empirical model confirm threeof the five hypotheses formulated about the implementation of JIT practices in theplant. Only in two cases, those of H1 (plant size) and H2 (age), have the hypothesesbeen rejected.

Role oforganizational

context

1057

JITin

theplant

Layout

Lot

size

reduction

Set-uptimereduction

Kanban

Constant

2.100***(4.490)

2.281***(4.250)

1.919**(2.435)

2.167**(2.444)

2.048**(2.543)

Size

20.142**(2

2.273)

20.096(2

1.334)

20.243**(2

2.300)

20.161(2

1.358)

20.059(2

0.545)

Age

20.073(2

1.213)

20.074(2

1.072)

0.141(1.389)

20.014(2

0.126)

20.350***(2

3.344)

AMT

0.120***(4.146)

0.007(0.218)

0.164***(3.358)

0.155***(2.794)

0.157***(3.141)

Advancedqualitytools

20.039(2

1.393)

20.040(2

1.233)

20.010(2

0.203)

20.077(2

1.448)

20.032(2

0.670)

Basicqualitytools

0.086***(3.052)

0.097***(2.988)

0.075(1.584)

0.081(1.517)

0.083*(1.706)

Verticalrelationship

managem

ent

0.339***(3.686)

0.470***(4.456)

0.248(1.603)

0.357**(2.026)

0.272*(1.713)

Workorganization

0.005**(2.207)

0.001(0.143)

0.008**(2.252)

0.006(1.390)

0.005(1.265)

R2

36.9

27.3

23.4

1723.6

F5.717***

3.667***

2.983***

1.963**

3.012***

Notes:t-statistics

inbrackets,industry

dummyvariablesincluded.* p

,0.10;** p

,0.05;*** p

,0.01

Table V.OLS estimations for thedeterminants of JIT in theplant and its dimensions(n ¼ 203)

IJOPM28,11

1058

As was already discussed in the argumentation for the hypotheses, the effect of the ageof the plant on any type of organizational innovation was not clear. The results of ourinvestigation in this respect confirm this indeterminacy, as it is the only variable notrelated in a significant way to the dependent variable. Also, in the case of size,conflicting arguments were presented in our theoretical discussion of its relation withthe implementation of JIT. If we consider the impact of size on the different JITdimensions, we may observe that the negative relationship between them establishedin this paper is based primarily on the relationship between size and lot size reduction.Lot size reduction requires greater agility on the part of the operations system and thisability to respond quickly is more likely to be found in small companies, since theyusually present fewer coordination problems.

Managerial implicationsThe results obtained for the explanatory infrastructure variables have importantimplications for the implementation of JIT. In general, terms, our findings reinforce anidea that should be transmitted to the operations managers of companies, that is, theneed to maintain an appropriate organizational infrastructure, with the application of aset of management tools, practices and ideas in different spheres (quality management,new technologies, relationships with suppliers and customers and work organization)in order to support the introduction of JIT practices.

Our findings show that industry does not have a big influence on the degree ofincidence of JIT practices in manufacturing plants. This finding supports the idea thatJIT can be applied in any production context. Nonetheless, some exceptions to thisgeneral result have been found. More concretely, non-metallic mineral products, paperand textile industries present a lower level of adoption of some JIT practices. This isconsistent with the reluctance to the introduction of organizational innovations foundfor these sectors in Spain (Merino, 2003).

As expected, the use of AMT has been found to be related in a very significant wayto the adoption of JIT practices. Although perhaps there is no straightforward technicalexplanation, our results confirm the idea of the existence of a certain alignmentbetween technological and organizational innovation. If we analyze the relationshipwith the four dimensions of JIT, we may observe that, with the exception of thepractices related to plant layout (proximity, order, etc.), the relationship is positive andsignificant. These results are reasonable, since they suggest that the introduction ofAMT helps to reduce set-up times and contributes to the ability to produce small lots.In other words, AMT improves manufacturing flexibility. These results highlight theexistence of substantial complementarities between technological innovation andorganizational innovation, such as JIT practices. Firms seem to understand that bothaspects are not alternatives, but that there are synergistic effects that make advisablethe introduction of changes in technology and equipment together with modificationsin the organization of the production process.

The use of tools for quality improvement and the implementation of JIT are positivelyrelated, therefore confirming the hypothesis formulated in the theoretical section of thepaper. However, it must be emphasized that the results differ depending on the type ofquality tools considered. The positive effect detected appears only for basic tools and notfor advanced tools. This finding suggests that even simple techniques for qualityimprovement can provide the levels of quality required to adopt JIT successfully.

Role oforganizational

context

1059

It can be inferred that, for the implementation of JIT, at the moment companies are notdeeming necessary the use of more sophisticated tools for quality improvement.

It is also necessary to recognize that the impact of qualitymanagement on the differentJIT dimensions is varied. For example, a significant relationship with the implementationof kanban has been identified. This result may be explained by the importance of havinggood quality indicators (minimumdefective product rate) for the kanban planning systemto work well. In order to reach this objective, it seems reasonable to suggest that theapplication of improvement tools will be very useful. In the case of plant layout, therelationship between some of the basic quality improvement practices (for instance, “5S”)and some of the items of such dimensions are evident.

The intensity of the relationships of the plant with suppliers and customers isstrongly associated with the implementation of JIT practices, as was expected in theformulation of the hypotheses. In addition, the relationship with three out of the fourJIT dimensions is statistically significant, with lot size reduction being the exception. Itis difficult to explain the absence of a statistically significant coefficient on lot sizereduction, when a priori this variable was expected to be crucial for close relationshipswith suppliers and customers (fewer problems of material deliveries, betterinformation for production planning, etc.).

Finally, the positive and strongly significant relationship between workorganization and the implementation of JIT practices in the plant shows once againthe importance of employee participation and involvement for the successfulintroduction of new methods and techniques in production organization. This has atwofold implication. On the one hand, workers must be prepared to be flexible and toperform a wide variety of tasks. At the same time, they must have a positive attitude tofacing unexpected problems and be willing to be flexible to adjust to the requirementsof the production system.

LimitationsThe main limitations of this research are those due to the cross-sectional character ofthe data, which prevents definitive statements about the causality of relationshipsamong the variables. This study has the disadvantages arising from research based onsurveys, especially when the answers are of a subjective nature.

As is usualwhendata froma single country are used, thefindingsmaynot be applicablein othergeographical contexts. Several features of Spain shouldbe taken into account for anassessment of this issue. From a cultural perspective, Spain is characterized byhigh-uncertainty avoidance. Moreover, the effort Spanish companies put into innovation isclearly below the average within OECD countries. Finally, there is a strong presence offoreignMNCs in themanufacturing sector, especially in the region the sample comes from.

In general, terms, as these characteristics are shared by the rest of the Southerncountries in the European Union, these findings could be directly generalized tothese cases. Although there are marked differences between Spain and other countries,we consider that most of the conclusions of the paper can be taken as universally valid.Regarding the influence of technological and operational aspects on JITimplementation, such as AMT and quality management, institutional and culturaldifferences are expected not to have a large impact on the nature of that relationship. Inthis context, we should point out that the expansion of MNCs leads to greatercross-country homogenization in innovation processes.

IJOPM28,11

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Regarding the relationship between JIT and human resources, we would argue that,in view of the strong power of unions in Spain and the need for employers to reach anagreement with them before making any change in this area, our conclusions on thisissue are robust. The fact that, even in an unfavourable context, there is a significantassociation between work organization and JIT indicates the relevance of humanresources in the implementation process.

Notes

1. This vision has also been referred to as “big” JIT, whereas the former has been referred to as“little” JIT (Wacker, 2004).

2. JIT has also been deemed to be an innovation in relevant articles in the literature, see, forexample, Snell et al. (2000) and Ketokivi and Schroeder (2004a).

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Further reading

Snell, S.A. and Dean, J.W. (1992), “Integrated manufacturing and human resource management:a human capital perspective”, Academy of Management Journal, Vol. 35 No. 3, pp. 467-504.

About the authorsAlberto Bayo-Moriones is a Lecturer of Human Resource Management at the BusinessAdministration Department of Public University of Navarre, where he earned his PhD. His mainresearch interests are the determinants and effects of organizational innovation and itsrelationship with technical change in the firm. His research has been published in journals such

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as British Journal of Industrial Relations, Industrial & Labor Relations Review or InternationalJournal of Production Economics. Alberto Bayo-Moriones is the corresponding author and can becontacted at: abayom@unavarra.es

Alejandro Bello-Pintado is an Assistant Professor of Business Economics and StrategicManagement in the Faculty of Economics and Business Administration at the Public Universityof Navarre, Spain. His doctoral thesis focused on the strategic management in the oil industry. Hehas published articles in several journals and presented papers in national and internationalcongresses.

Javier Merino-Dıaz-de-Cerio is an Industrial Engineer and Lecturer of Operations and QualityManagement at the Business Administration Department of Public University of Navarre,where he earned his PhD. His main research topics interests are quality management, humanresources management and operations management. His research has been published in journalssuch as International Journal of Production Research, International Journal of ProductionEconomics or Total Quality Management & Business Excellence.

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