Research Journal of Recent Sciences ______ ______________________________ ______ ____ ___ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 103 Operations Practices and Competitive Priorities: Impact of the Operations Strategy on Performance Díaz - Garrido Eloísa, Martín - Peña, María Luz Sánchez - López José María Rey Juan Carlos University, P Artilleros, s/n 28032 Madrid, SPAIN Available online at: www.isca.in , www.isca.me Received 23 rd May April 201 4 , revised 31 st October 201 4 , accepted 12 th June 201 5 Abstract Despite the significant attention that part of the literature has devoted to the operations strategy, most studies analyse th e different variables involved in isolation. This paper proposes a theoretical model to identify the causal relations between thr ee key variables in operations management: operations practices, competitive priorities, and firm performance. The data for this study come from Spanish industrial firms belonging to various sectors of activity, and the hypotheses are tested using structur al equations analysis. The more important findings are that if firms design an operations strategy on the basis of a greater number of structural and infrastructural practices, they will be able to improve their competitive advantages in operations. The re sults also show that competitive priorities in operations can have a direct, positive effect on firm performance. Keywords : Operations strategy, operations practices, competitive priorities, structural equations. Introduction Researchers in strategic management have proposed a different perspective on the achievement of competitive advantage. This alternative view consists of a move from an environmental and market - based (or outside - in) perspective, to one that use s a resource - based and associated dynamic capability (inside - out) approach to increasing competitiveness 1 . Dangayach and Deshmukh 2 identify areas of possible future research, including the need for inside - out focused operations strategy research. Numerous connotations exist to define the term ‘operations strategy’. Nevertheless, it appears that there is consensus on certain issues, for instance, that manufacturing strategy must support competitive strategy and corporate objectives 3,4 , also facilitate manuf acturing objectives in order to achieve a competitive advantage 5 and be focused on a uniform decision - making model within the category of key manufacturing resources 5 - 7 . Any definition of manufacturing strategy must include two key elements, competitive pr iorities and manufacturing decisions and practices. Several studies in operations management have investigated alignment between business and functional level strategies 8, 9 . The problem is that despite the significant attention that part of the literature has devoted to the operations strategy, most studies analyse the different variables involved in isolation. Thus some papers focus on the manufacturing practices 10 - 13 , and ot hers on the competitive priorities 14 - 17 and their relation to firm performance. There is consequently a need for integrated studies that analyse the fit and coherence between the operations practices and the competitive priorities in operations, and the im pact of this fit on the performance. The current work proposes a theoretical model that postulates a number of causal relations between three key variables in operations management – operations practices, competitive priorities, and firm performance – and then empirically tests the hypotheses concerning the proposed relations. Thus the authors overcome the limitation of other studies that only analyse correlations between pairs of variables 18 . The current work should shed light on the operations strategy, and help researchers test its effects in practice, since it jointly analyses the relations between the operations practices, competitive priorities, and firm performance as measures of strategic fit. Two implications derive from this work. First, the work aims to shed light on the operations strategy by describing and analysing the formalisation of the strategy and examining its relation to competitive advantage in operations and the firm’s performance, under the assumption that the two elements making up the content of this strategy (competitive priorities and operations practices) need to be consistent. Second, the research should prove useful in the design and implementation of the operations strategy in organisations, and help guide future research in t his area. The rest of this work is structured as follows. The next section presents the research model consisting of the proposed relations and hypotheses. The third section describes the methodology, which includes the selection of the items used in the empirical analysis, the selection of the sample from which the data is collected, and the data collection and validation through validity and reliability analyses. The empirical analysis uses structural equations, and the results follow. The final section outlines the main conclusions of the research. Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 104 Research model and hypotheses: A key concern of scholarly research in operations management is the contribution of operations strategy to business performance 18,19 . As a result of the decisions that the firm adopts, it can create a structure that enables it to acquire a series of capabilities. The capabilities developed in this functional area have a direct effect on the design and formulation of the most appropriate operations strategy, providing the key to d eveloping the potential of the operations area as a competitive weapon. Thus when a company defines its strategic position, it can focus on the competitive priorities for which it has specific capabilities. The practices that make up the operations strategy can be either structural (capacity and location of plant, technology used in process, level of vertical integration) or infrastructural (quality management, human resource management, production planning a nd control systems, organisation). The competitive advantage of the operations functional area can be achieved through aspects such as cost, quality, deliveries (quick and on time), flexibility (in product and in process), service, and environmental protec tion. Figure - 1 presents the analytical model. According to this model, the set of practices that make up the operations strategy has an effect on firm performance both directly and indirectly through the competitive advantage achieved in the production a nd operations functional area. This work proposes four hypotheses about the relations between the operations practices making up the operations strategy, competitive priorities in operations, and firm performance. The operations strategy that firms define allows them to achieve competitive priorities in aspects such as cost, quality, flexibility, deliveries, service, and environmental protection, because if the firms adopt certain practices in operations structure and infrastructure they can achieve capabi lities upon which to base a competitive advantage. Different authors suggest that operational performance is influenced by the implementation of bundles of manufacturing practices 11,20,21 . On the basis of the above considerations, the first hypothesis is as follows: Hypothesis 1: Firms that design and implement an operations strategy on the basis of a larger number of structural and infrastructural practices achieve more competitive advantages in production and operations. To operationalise the operati ons strategy it is necessary to analyse the competitive priorities, since they orient the decisions to adopt and the practices to carry out within the manufacturing structure and infrastructure. In other words, if managers are to achieve their operations o bjectives and develop competitive advantages in the operations area, they must decide what practices are the most appropriate 17, 22 to align the manufacturing capabilities with the business strategy. A large number of studies have confirmed the relation b etween competitive priorities in operations and structural and infrastructural practices 23, 24, 25 . The determinant factor behind the success of an operations strategy is the way the competitive priorities translate into a set of practices, and the degree of fit between both dimensions offers the key to develop the potential of the production function as a competitive weapon 26, 27 . The second hypothesis follows: Hypothesis 2: The competitive priorities developed have a direct effect on the design of the mo st appropriate operations strategy (structural and infrastructural practices ). Figure - 1 Research model Operations practices Structural practices Infrastructural practices Competitive Priorities (Competitive advantage) Cost/price Quality Deliveries Flexibility Service and Environment Firm Performance Productivity ROI H3 H1 H4 H2 Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 105 A key concern of scholarly research in operations management is the contribution of operations practices to business performance 19 , because operations practices are only valuable if they enhance the performance of an organisation relative to its chosen goals 18 . A strong management commitment towards adopting the practices making up the op erations strategy is associated with superior performance 23 . In recent years a number of studies have analysed the relation between operations practices and performance 2,10,11,28 . The majority finds that implementing more operations practices is associat ed with superior performance. Hypothesis 3: Firms that design and implement an operations strategy on the basis of a larger number of structural and infrastructural practices achieve superior performance. The literature suggests that the results achieved by the operations function contribute to improving firms’ performance and consequently their overall competitive advantage 29 . In other words, achieving a competitive advantage generally suggests that the organisation can develop one or a number of the fol lowing operations priorities compared to its competitors: lower costs, higher quality, greater flexibility, more reliable deliveries, better service, and stronger environmental protection. This competitive advantage can lead to superior firm performance. A large number of empirical studies indicate that developing advantages in aspects such as quality, deliveries, flexibility, and/or cost has a positive effect on firm performance 16, 23, 29, 30 . Hypothesis 4: Firms that design and implement an operations s trategy on the basis of a larger number of competitive priorities in operations will achieve superior performance. Methodology Appendix A reports the items selected, on the basis of the literature review, to measure the variables of the analytical model : operations practices, competitive priorities, and firm performance. Specifically, with regard to the practices making up the operations strategy, the authors consider capacity and location, technology, vertical integration, environmental protection pro grammes, human resource management, quality management, production planning and inventory management, and organisational structure. The questionnaire asked the respondents to say if their production unit carries out investment in each particular practice o r policy or not, and to indicate the importance their firm accords it using a 7 - point Likert scale (1=not important at all, 7=highly important). For competitive priorities this work considers the following competitive priorities: cost, quality, flexibility , deliveries, service, and environment. The respondents were asked to assess their firm’s position in relation to its best competitor for each competitive priority, using a 7 - point Likert scale. To measure the performance the authors use secondary sources of information (Dun and Bradstreet database or Dicodi). Thus the authors calculated the arithmetic mean value over three accounting years of the firm’s ROI and productivity indicators. The authors built their own database using information taken from Dun and Bradstreet’s directory of 50,000 top Spanish firms. Specifically, they extracted a sample of firms to carry out the empirical study, using the following criteria: Industrial firms belonging, according to the Spanish classification of economic activit ies (CNAE), to: DJ (Metallurgy and Manufacture of Metallic Products), DK (Manufacture of Machinery and Mechanical Equipment), DL (Electrical, Electronic and Optical Materials and Equipment), and DM (Manufacture of Transport Materials). Firms with more tha n 50 employees: A total of 1820 firms complied with the above criteria and consequently form part of the current study. The authors chose to use these industrial sectors for various reasons. First, these sectors have been the most commonly analysed in th e specialist literature 2 . Second, firms from these sectors typically have big turnovers and higher - than - average industrial production indices, so they can be considered as making up the industrial backbone of developed countries. As primary source of information the authors used a questionnaire, which they sent by post to each firm from the selected sample, and specifically to the operations manager, or failing this, the chief executive. Before sending the definitive version the authors carried out a p re - test in order to test the validity of the questionnaire designed. This involved conducting personal interviews with both academics and operations management specialists from five companies from the sample. The definitive questionnaire comprised questio ns designed , to evaluate the firm’s competitive priorities and operations practices. The total number of valid questionnaires received was 353, equivalent to a response rate of 19.53%. Before the empirical analysis, the authors tested the unidimensionality, reliability and validity of the scales used to measure the variables shown in Appendix A. If a unique factor underlies the set of variables making up a scale, it is unidimensional. In t able - 1, to test unidimensionality the authors carri ed out an exploratory factor analysis (principal components analysis method, with varimax rotation). For the operations practices, 34 items measuring the 7 dimensions were considered. The cumulative variance explained by the 7 factors is 59.8%. Each of the 34 items loads significantly (greater than 0.4) on at least one factor. Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 106 Table - 1 Results of factor analysis for variable “operations practices” Items 1 2 3 4 5 6 7 Factors Expand workers’ responsibilities .759 .124 .309 .010 - .001 .008 .185 Factor 1 HRM and Organisation Team work .727 .187 .245 - .016 .069 .006 .121 Improve manager - worker relations .695 .062 - .027 .186 .159 .088 .031 Decentralisation of decisions .663 .102 - .069 .152 .123 .090 .077 Increase variety of workers’ tasks .633 .039 .237 .195 .029 .185 .165 Worker training .563 .421 .151 .221 .029 .003 .164 Improve quality of life in work .509 .352 .147 .125 .019 .239 .011 Multi - functional project teams .503 .115 .106 .095 .138 .457 .027 Manager training .401 .316 .066 .276 .073 .120 .217 Statistical control of quality .078 .710 .131 .265 .092 .067 .137 Factor 2 Quality ISO 9000 .138 .654 .083 .034 .010 .217 .227 Quality circles .093 .647 .084 .125 .084 .154 - .014 Total Quality Management (TQM) .285 .612 .001 - .023 .120 .444 .057 Zero - defect programmes .116 .598 .056 .015 .305 .216 .009 Preventive maintenance .278 .556 .100 .132 .253 - .021 - .022 Restructuring of plant .190 .043 .810 .209 .120 .048 .039 Factor 3 Capacity of plant Redistribution of plant .137 .077 .808 .198 .061 .137 .037 Investment in plant, equipment and RandD .069 .111 .585 .002 .170 .143 .233 Expand plant capacity .205 .190 .536 .137 .118 - .051 - .010 Reduce production cycle and delivery time .214 .117 .188 .760 .145 .057 .041 Factor 4 Production planning and control Production and inventory control systems .138 .226 .248 .713 .175 - .028 .164 Just - in - time purchase management .181 .133 .059 .527 - .077 .183 .272 Continuous improvement .310 .419 .196 .514 .136 - .061 - .014 Computer - aided design (CAD) .054 .163 .073 .155 .771 .126 .177 Factor 5 Technology Flexible manufacturing systems .147 .168 .125 .021 .712 - .049 .070 Robots .078 .104 .064 .166 .540 .196 .004 Computer - aided manufacturing (CAM) - .038 .073 .249 - .168 .497 - .078 .456 Reduce machine preparation time .375 .219 .151 .340 .438 .046 - .103 ISO 14001 .064 .327 .040 .042 .012 .803 .087 Factor 6 Environment Environmental management systems .220 .306 .128 .029 .157 .750 .032 Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 107 Items 1 2 3 4 5 6 7 Factors Subcontraction .106 .008 .043 .050 .096 .013 .780 Factor 7 Vertical integration Cooperation with suppliers .262 .201 .070 .249 .051 .018 .645 Integration of IS with suppliers .203 .132 .023 .130 .133 .260 .589 Location and re - location of plant .003 .213 .180 - .033 .080 - .205 .407 The authors initially measured competitive priorities using 18 items representing 6 dimensions (cost, quality, flexibility, deliveries, service, and environment), as t able - 2 shows. Nevertheless, the exploratory factor analysis resulted in 5 factors with a cumulative variance of 61.58%. Flexibility “disappears” as a single dimension. Part of this dimension joins service to form service - flexibility in product, and the re st joins cost to form cost - flexibility in volume. Table - 2 Results of factor analysis for variable “competitive advantage in operations” Items 1 2 3 4 5 Factors Ability to offer different products with large number of characteristics, features, options .720 - .100 - 0.037 0.162 0.106 Factor 1 Service - Flexibility in product Ability to design product and/or process in function of customer needs and demands .657 .268 0.100 0.116 0.073 Ability to offer adequate after - sales service .656 .238 - 0.054 - 0.016 0.248 Ability to introduce quick changes in product creation and design .632 .124 0.264 0.349 - 0.117 Ability to manufacture range of products easily in same installations .576 - .071 0.389 0.274 - 0.055 Ability to provide full information .553 .348 0.232 - 0.067 0.289 Ability to offer defect - free products - 0.01 0.784 0.271 0.238 0.088 Factor 2 Quality Ability to offer product that meets specifications set in design 0.12 0.767 0.192 0.158 0.101 Ability to maximise problem - free time of product functioning 0.35 0.669 0.027 - 0.069 0.199 Ability to offer products when consumer wants them 0.067 0.249 0.780 0.131 0.155 Factor 3 Deliveries Ability to offer products quickly 0.049 0.223 0.772 0.221 0.078 Ability to facilitate orders and returns 0.455 0.031 0.534 0.001 0.249 Ability to reduce product cost - 0.117 0.265 - 0.053 0.728 0.096 Factor 4 Cost - Flexibility in volume Ability to operate at different output levels 0.276 0.022 0.171 0.692 0.140 Speed at which unit can raise capacity after unexpected increase in demand 0.214 0.118 0.219 0.621 0.095 Ability to adjust mix of products quickly and at minimum cost 0.219 - 0.164 0.189 0.407 0.288 Ability to minimise impact of production activity on environment 0.164 0.158 0.095 0.167 0.830 Factor 5 Environment Ability to manufacture products that respect environment 0.089 0.201 0.183 0.197 0.819 Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 108 The authors used two items to measure the firm performance variable. The items have an underlying dimension labelled performance, which explains 96.05% of the total variance , as t able - 3 shows. Table - 3 Results of factor analysis for variable “performance” Items 1 2 Factors Productivity 0.993 0.079 Factor 1 Performance ROI 0.989 0.119 The authors analysed the reliability using Cronbach’s alpha, to determine the internal consistency of the measurement instrument. The values obtained exceed 0.7, which means that the scales used to measure each of the variables proposed in the analytical m odel are acceptable 31 32 . Table - 4 summarises these results. With the unidimensionality and the reliability confirmed, the authors then analysed the content and convergent validity. The content validity indicates that the items considered satisfactorily re present the concepts they are meant to measure. The authors obtained the set of items used (Appendix A) on the basis of a review of the literature. They calculated the convergent validity by measuring the extent to which the different scales used to measur e a variable are correlated 31 . The correlations are generally quite high, and all are significant, which confirms that the measures used for each model variable have a good convergent validity. Table - 4 Mean, standard deviation, correlations and reliability of Practices, Competitive advantage, and Performance Variables Mean SD 1 2 3 4 5 6 7 Reliability Practices Capacity 5.27 0.95 .369 ** - - - - - - .747 Technology 3.23 1.85 .292 ** - - - - - - .734 Vertical integration 4.32 1.28 .383 ** .357 ** - - - - - .722 Quality man. 4.54 1.20 .468 ** .416 ** .342 ** - - - - .701 Planning 5.29 0.99 .248 ** .396 ** .409 ** .534 ** - - - .864 Env. management 5.31 0.92 .450 ** .255 ** .249 ** .515 ** .283 ** - - .811 HRM and Org. 4.87 1.07 .326 ** .429 ** .575 ** .567 ** .425 ** - .864 Comp. advantage Quality 5.36 0.90 - - - - - - .751 Deliveries 4.99 0.93 . 441 ** - - - - - .795 Serv. and Flex. Prod. 7.72 1.08 .421 ** .444 ** - - - - .784 Environment 4.78 1.19 .402 ** .391 ** .411 ** - - - .806 Cost and Flexibility in Volume 4.64 0.85 .337 ** .406 ** .354 ** .363 ** - - .769 Performance Productivity 169 174 - .902 ROI 170 168 - .984 ** *Correlation significant at 0.05 level ** Correlation significant at 0.01 level Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 109 Results and Discussion The authors used structural equations to test the proposed hypotheses. This methodology allowed them to statistically validate the model proposed in figure - 1, through a simultaneous analysis of the system of variables and relations that defines the model 32 . From the data available, the factor analysis resulted in a set of observable variables, specifically the seven variables measuring the operations practices in the operations strategy, the five variables measuring competitive advantages in operations, and f irm performance. These observable variables act as indicators of the three latent variables that represent operations practices, competitive priorities (competitive advantage in operations), and firm performance. The theoretical structure represented in Fi gure - 1 postulates four hypotheses among the variables “Operations practices”, “Competitive advantage in operations” and “Performance”. The structural equation technique requires building two submodels: the structural model and the measurement model. The first describes the causal relations between the latent variables. Figure - 2 illustrates the structural model. The measurement model represents the relations between the latent variables and their indicators and between the different latent variables. After defining the structural and measurement models, the authors then estimated the theoretical model. They carrie d out a first - order confirmatory factor analysis through a structural equations system, using the AMOS 5.0 computer software. They used the robust maximum likelihood estimation method, which throws up fewer problems with non - normal data. Figure - 3 reports the different estimations. Figure - 2 Structural model Figure - 3 Structural and measurement models Technology Vertical Intg Quality Planning HR & Org Environment Capacity Operations Practices Performance Competitive Advantage Performance Service - Flex Quality Deliveries Cost - Flex Environment Technology Vertical Intg Quality Planning HR & Org Environment Capacity Operations Practices Performance Competitive Advantage Performance Service - Flex Quality Deliveries Cost - Flex Environment e1 0.34 e2 0.27 e3 0.28 e4 0.56 e5 0.53 e6 0.59 e7 0.28 e18 0.02 0.10 0.51 0.58 0.17 0.71 e17 0.02 e19 0.09 0.09 e11 0.40 e12 0.39 e13 0.47 e14 0.08 e15 0.41 e16 Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 110 The results show that all the latent variables load significantly on their indicators (significance measured by the t statistic), although the loadings vary in intensity. Table - 5 reports the results of this analysis. Table - 5 Loadings of standardised reg ression coefficients Estimation Service - flexibility --- Competitive advantage 0.635 Quality --- Competitive advantage 0.627 Deliveries --- Competitive advantage 0.683 Cost - flexibility --- Competitive advantage 0.281 Environment --- Competitive advantage 0.637 Environment --- Practices 0.526 HR - Organisation --- Practices 0.765 Prod. planning --- Practices 0.728 Quality --- Practices 0.751 Vert. integration --- Practices 0.529 Technology --- Practices 0.522 Capacity --- Practices 0.583 Performance --- Performance 0.706 Cost - flexibility has the lowest loading, which may suggest that it is not an acceptable indicator for competitive advantage in operations compared to the other capabilities (service - flexibility, quality, deliveries, and environment). All the indicators of operations practices have high loadings, particularly human resources and organisation, quality management, and production planning. This result shows the importance of infrastructural compared to structural practices. The performance dimension loads signi ficantly and highly on the performance variable, which suggests that the right indicators have been used to measure this variable. The authors then evaluated the model by analysing its global fit, using a number of indices. Two of the most commonly used of these are CFI (comparative fit index) and RMSEA (root mean square error of approximation). CFI should be greater than or equal to 0.9, while RMSEA should be less than 0.05 32 . The results suggest that the model has a good global fit to the data, since C FI=0.92 and RMSEA=0.012. Table - 6 shows the results of the estimation of the standardised parameters along with the results of the hypothesis tests. Table - 6 Support for hypotheses Hypothesis Relation Direct effect Support H1 Prac ï‚® CompAd 0.506* YES H2 CompAd ï‚® Prac 0.778** YES H3 Prac ï‚® Perf 0.098 NO H4 CompAd ï‚® Perf 0.170* YES *Significant at 0.05 level, ** Significant at 0.01 level Hypothesis 1 postulates that firms that design and implement their operations strategy on the basis of a larger number of structural and infrastructural practices achieve competitive advantages in operations. The standardised coefficient is 0.506 (significant at the 0.05 level), so this hypothesis can be accepted. Thus implementing structural and infrastructural practices has a direct, positive effect on the achievement of competitive advantages in this area with respect to quality, flexibility, deliveries, service, and environmental protection. Hypothesis 2 postulates that the competitive priorities that firms develop have a direct, positive effect on the design of the most appropriate operations strategy (structural and infrastructural practices). This hypothesis can also be accepted, since the standardised coefficient is 0.778, α<0.01). This result means that the capabiliti es that firms develop in the operations area are critical in the design of the operations strategy in terms of the structural and infrastructural practices that should be selected. The support provided for these two hypotheses shows the level of strategi c fit and internal coherence between the operations practices of the firms examined here and their competitive advantages in operations. This is critical for developing the potential of the production function as a competitive weapon. Moreover, the results show the necessity of the internal fit between the two elements making up the content of the operations strategy (operations practices and competitive priorities). These results are consistent with previous studies 17, 21, 33 . According to Hypothesis 4, firms that manage to develop competitive advantages in operations achieve better performance, and this hypothesis also obtains support (standardised coefficient 0.17, α<0.05). Thus firms that have developed competitive priorities, whether in quality, flexi bility, deliveries, service, or environment, perform better in terms of productivity and ROI. This is consistent with one of the typical assumptions in the operations area about the relation between Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 111 competitive advantages in operations and superior firm pe rformance 30,34 . The data do not, however, support Hypothesis 3, so it cannot be said that greater investments in operations practices to design and implement the operations strategy lead to superior performance. Thus the current research has not found a direct relation between the operations practices of the operations strategy and productivity or ROI. One reason for the failure to support this relation could be the existence of other variables not considered here that may also influence the performance m easures used. In addition, firm performance is a consequence of the contribution of various functional areas (marketing, human resources, R and D, etc.), not just operations. And indeed previous research has obtained similar results 8,22,26,28 . The propose d model explains how operations practices have a positive relation with the achievement of competitive advantage in operations, and how this latter variable is positively associated with superior performance. Consequently, operations practices have an indi rect influence on the achievement of this performance through competitive advantage in operations, but according to the evidence presented here, they do not have a direct effect. Conclusion This work has proposed a theoretical model to analyse the causal relations between three fundamental variables in operations management: operations practices, competitive priorities in operations, and firm performance. As a result of the decisions that the firm adopts, it can create a structure that enables it to acqu ire a series of capabilities. The capabilities developed in this functional area have a direct effect on the design and formulation of the most appropriate operations strategy, providing the key to developing the potential of the operations area as a compe titive weapon. Thus when a company defines its strategic position, it can focus on the competitive priorities for which it has specific capabilities. The results from the empirical analysis carried out here suggest that the strategy firms design in the op erations area has a direct, positive effect on the competitive advantages they achieve in terms of quality, flexibility, deliveries, service, and/or environmental protection, and that the development of these competitive priorities, in turn, has a direct, positive impact on the structural and infrastructural practices that make up the operations strategy. Moreover, both aspects that make up the content of the operations strategy – operations practices and competitive advantages in operations – have a positi ve effect on the firm’s performance. Nevertheless, the results presented here do not provide support for the direct relation between operations practices and firm performance, only for the indirect relation through competitive advantage in operations. Th e absence of a direct effect between operations practices and performance can in part be explained by the fact that this effect transfers to competitive advantage in operations. Moreover, firms obtain superior performance as they develop unique competitive advantages over their competitors, a relation that has gained support in the current research. Thus in general, implementing a large number of structural and infrastructural practices in isolation will not mean a better performance for the firm; these pra ctices must translate into the achievement of competitive advantages in operations over the competitors. In fact, the relations analysed here have been studied very frequently in the operations management literature, although in the past decade authors ha ve generally analysed each relation separately 10,11,15,17,24,34 . The current work is novel in that it has used structural equations analysis to test the relations jointly. This paper offers clear theoretical implications. First, the general model proposed and tested here provides theoretical support for relations between key variables from the production and operations area: operations practices, competitive priorities, and firm performance in terms of productivity and ROI. This advances our understanding of the operations strategy and reinforces the potential of the production and operations function. Second, the work provides theoretical and empirical evidence of the degree of fit and coherence that must exist between the structural and infrastructural pr actices (operations practices) and the competitive priorities – key aspects of the content of the functional operations strategy. The practical implications of the work are also clear. First, the proposed model should prove to be a useful tool to help pro duction and operations managers evaluate the competitive advantages developed in the operations area when they are designing and formulating an effective strategy based on a series of structural and infrastructural practices. Second, the work shows the pot ential importance for the firm of achieving certain operations capabilities as a means of improving its performance. This means that top management should consider operations a key, strategic functional area. Nevertheless, researchers could expand the ana lytical model proposed here in future work to include other variables to do with the environment and the competitive strategy adopted by the firm, in order to confirm the existence of an external fit between the functional operations strategy and the corpo rate strategy. At the same time, it would also be useful to analyse whether aspects such as firm size (measured by number of employees), the industrial sector, or the type of production unit considered (firm, plant, factory, or department) have any influen ce on the proposed model. In the future the questionnaire should, if possible, be sent to more than one manager in each firm to improve the information available. Researchers could also use different performance measures to the ones used here, as well as r eplicate the model in other sectors of activity. Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 112 Appendix - A Operations practices Structural Practices Capacity and Location of installations Reconfiguration of distribution in plant Restructuring and reorganisation of factory Investment in plant, equipment and R and D Expand plant capacity Location and relocation of installations Technology Computer - aided design (CAD) Computer - aided manufacturing (CAM) Robots Numeric - controlled machines Vertical integration Subcontraction of part of manufacturing processes Collaboration relations (stable, lasting and based on trust) with suppliers Integration of Information Systems with suppliers (exchange of information) Environmental protection programmes Environmental management system s ISO 14001 certification Infrastructural practices HRM Increase variety of workers’ tasks Expand workers’ responsibilities Team work Worker training Manager training Quality management and control Total quality management (tqm) Zero - defect programmes Quality circles Statistical control of quality Preventive maintenance Continuous improvement of processes Iso 9000 certification Production planning and control and inventory management Improve production and inventory control systems Reduce machine prepar ation time Reduce production cycle and delivery time Just - in - time purchase management Organisational structure Decentralisation of decisions Improve manager - worker relations Improve quality of life in work Multi - functional teams Competitive Priorities (Competitive Advantages In Operations) Cost Ability to reduce product cost (labour costs, material costs, fixed costs) Quality Ability to offer defect - free products Ability to offer product that meets specifications set in design Ability to maximise problem - free time of product functioning (lasting and reliable) Research Journal of Recent Sciences ______ ______________________________ ______ ____ _______________ ISSN 2277 - 2502 Vol. 4 ( 11 ), 103 - 114 , November (201 5 ) Res.J. Recent Sci. International Science Congress Association 113 Flexibility Flexibility In Volume Speed with which unit can increase capacity after unexpected increase in demand Ability to operate profitably at different output levels (ease of going from large to small batches and vice versa) Flexibility In Product Ability to introduce quick changes in product creation and design Ability to manufacture range of products easily and without modifying existing installations Ability to offe r different products with large number of characteristics, features, options... Ability to adjust quickly and with minimum cost mix of products to be produced (ease with which machinery can go from making one type of product to another) Deliveries Ability to offer products quickly Ability to offer products when consumer wants Ability to facilitate orders and returns Service Ability to offer adequate after - sales service Ability to design product and/or process in function of consumer needs and demands Ability to provide full product information to customer Environment Ability to minimise impact of production activity on diverse components of environment Ability to make products that respect environment Performance Productivity, ROI References 1. Lillis B. and Lane R., Auditing the strategic role of operations, Int. J. of Management Rev., 9(3 ), 191 – 210 (2007) 2. Dangayach G.S. and Deshmukh S.G., Manufacturing Strategy. Literature Review and Some Issues, Int. J. of Operations and Productio n Management , 21(7 ), 884 - 932 (2001) 3. 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