Journal of Manufacturing Technology ManagementThe impact of innovation capability on the performance of manufacturingcompanies: The Greek caseDimitrios Kafetzopoulos Evangelos Psomas

Article information:To cite this document:Dimitrios Kafetzopoulos Evangelos Psomas , (2015),"The impact of innovation capability on theperformance of manufacturing companies", Journal of Manufacturing Technology Management, Vol.26 Iss 1 pp. 104 - 130Permanent link to this document:http://dx.doi.org/10.1108/JMTM-12-2012-0117

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The impact of innovationcapability on the performanceof manufacturing companies

The Greek caseDimitrios Kafetzopoulos and Evangelos Psomas

Department of Business Administration of Food and Agricultural Enterprises,University of Patras, Agrinio, Greece

AbstractPurpose – The purpose of this paper is to provide additional evidence of the impact of innovationon three dimensions of a firm’s performance, namely product quality, operational performance andfinancial performance.Design/methodology/approach – The analysis includes an initial exploratory factor analysis,followed by confirmatory factor analysis and structural equation modelling, in order to investigate therelations between the constructs of the proposed model. A sample of 233 Greek manufacturing firms isused for this purpose.Findings – According to the study findings, “innovation capability” directly contributes to productquality and operational performance. Although it has no direct impact on manufacturing firms’financial performance, it has an indirect impact through the moderator of operational performance.Thus, innovation is an opportunity for a manufacturing firm to improve its performance.Research limitations/implications – The sample of the responding manufacturing companies islimited to small and medium-sized enterprises from one country (Greece). In addition, manufacturingfirms from different sectors have different resources, capabilities and performance.Practical implications – The study offers clear implications for managers who should put additionalemphasis on innovation as it is an important element for achieving improved overall firm performanceand sustainable competitive power.Originality/value – Based on the multi-dimensional structure of innovation, this empirical studydetermines the contribution of “innovation capability” to specific performance dimensions ofmanufacturing companies.Keywords Innovation, Manufacturing industries, Product quality, Financial performance,Operational performancePaper type Research paper

1. IntroductionInnovation is a broad and multi-dimensional concept that refers to all scientific,technological, organizational, financial and commercial activities which lead to, or areintended to lead to, the implementation of technologically new or improved products orservices (OECD, 1997). Innovation has become a key issue at various levels for firms,institutions and governments and its importance has motivated researchers to identifyits various driving forces (Becheikh et al., 2006). The link between innovation andfirm performance is well established in the previous research. Indeed, there is a wealthof evidence in the academic literature indicating a positive relationship betweeninnovation and firm performance in the manufacturing industry (Loof et al., 2002;Cheng et al., 2010). However, some researches indicate a negative link or no linkat all (Capon et al., 1990; Chandler and Hanks, 1994; Subramanian and Nilakanta,1996). There are limited analytical and empirical studies which examine the direct

Journal of ManufacturingTechnology ManagementVol. 26 No. 1, 2015pp. 104-130©EmeraldGroup Publishing Limited1741-038XDOI 10.1108/JMTM-12-2012-0117

Received 6 December 2012Revised 1 June 201331 August 20134 October 2013Accepted 7 October 2013

The current issue and full text archive of this journal is available on Emerald Insight at:www.emeraldinsight.com/1741-038X.htm

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relationship between the innovation dimensions and various aspects of a firm’sperformance ( Jin et al., 2004; Gunday et al., 2011; Hashi and Stojcic, 2010). It is apparentthat much more conceptual and empirical work will be needed to examine the impact ofoverall innovation capability on firm performance dimensions (Loof et al., 2002;Sadikoglu and Zehir, 2010). Therefore, many authors (Evangelista and Vezzani, 2010;Gunday et al., 2011; Li et al., 2012) suggest future research in order to validate previousresearch findings.

The present study contributes to the above mentioned literature gap, since itsobjective is to develop the concept of the “innovation capability” of a firm as the level towhich four innovation dimensions (product, process, marketing and organizationalinnovation) are implemented. The component factors and the key measured variablesfor the construct “innovation capability” are identified through an extensive literaturereview, while the validity and reliability of the above construct is assessed. Theobjective of this study is to provide additional evidence of the impact of the overallinnovation capability of a firm on the three dimensions of firm performance,namely product quality, operational performance and financial performance. Based onthe examination of the above relationships, a conceptual framework is formulated,providing insights into the dilemma described in the existing literature.

The analysis procedure adopted in this study includes an initial exploratory factoranalysis (EFA) to uncover the underlying structure of variables. Then, confirmatoryfactor analysis (CFA) was used to refine the resulting scales in EFA and to determinewhether the number of factors and the related loadings of the measured variables(i.e. indicators) conform to what is expected on the basis of pre-established theory. Thestructural equation modelling (SEM) technique is used to investigate the relationsbetween the “innovation capability” and “product quality”, “operational performance”and “financial performance” of 233 Greek manufacturing companies. The contributionof “innovation capability” to key dimensions of firm performance is clearer and morecomprehensible to managers through the analysis of the present empirical data.

The paper proceeds as follows: in Section 2, we describe the key dimensions ofinnovation introducing the “innovation capability” concept, and the three dimensionsof firm performance. We also develop our hypotheses about the effects of “innovationcapability” on firms’ product quality, operational and financial performance. Section 3describes the research methodology, followed by the data analysis and the respectiveresults. In the next part, findings related to validity, reliability and testing of thestructural model are discussed. Finally, conclusions, managerial implications and futureresearch proposals are presented.

2. Theoretical background and conceptual foundation2.1 Innovation capabilityInnovation capability is the degree of firm innovativeness (Calantone et al., 2002)whilst being a multidimensional concept (Wonglimpiyarat, 2010; Forsman, 2011).Looking at the earlier studies, the question that arises is: what dimensions ofinnovation should be explored to clearly establish an association between overallinnovation and firm performance?

Literature distinguishes different types of innovation and researchers have exploredits classification in different ways ( Jiménez-Jiménez and Sanz-Valle, 2011; Kim et al.,2012). Some studies examined a single type of innovation such as process innovation(Abrunhosa and Moura E Sá, 2008) or product innovation (Prajogo and Sohal, 2004),whereas others explored both process and product innovation (Feng et al., 2008;

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Martinez-Costa and Martinez-Lorente, 2008). Many studies conceptualize innovationrelated to marketing and organizational innovation (Wang and Ahmed, 2004;Evangelista and Vezzani, 2010; Wonglimpiyarat, 2010; Gunday et al., 2011; Chang et al.,2012). Avermaete et al. (2003) claimed that product, process, organizational and marketinnovation were all domains of innovation. Damanpour (1991) distinguishes technicaland administrative innovation. Technical innovation includes new products or servicesand processes that are new to the organization, or changes in the way products aremade or delivered (Avermaete et al., 2003). Administrative innovation refers to theapplication of new ideas to improve organizational structures, systems and processespertaining to the social structure of an organization (Damanpour, 1991; Weerawardena,2003). It includes the organizational and marketing dimensions as they deal with thechanges in the organizational structures (Avermaete et al., 2003; Jiménez-Jiménez andSanz-Valle, 2011). In the OECD (2005), which is the primary international basis ofguidelines for defining and assessing innovation activities, four different innovation typesare introduced. These are product, process, marketing and organizational innovation.

Since the purpose of the present study is to analyze how innovation activity as awhole influences the performance of a firm, it adopts a broad concept of innovationdimensions. In line with the suggested innovation types of Avermaete et al. (2003),Gunday et al. (2011) and the OECD (2005), we adopt and study the four types ofinnovation (product, process, marketing and organizational innovation) that determinean organization’s overall innovativeness. It is hoped that this primary classificationtypology will lead to consistency between studies and an increased ability to generalizefrom a given study of innovation. Therefore, we believe that using the product/process/marketing/organizational typology will provide our analysis with a consistent meansof classifying innovation dimensions.

Product innovation. Product innovation is associated with either the creation of newmarkets or the enhancement of existing products (Chang et al., 2012). It is a difficultprocess driven by advancing technologies, changing customer needs, shorteningproduct life cycles and increasing global competition (Gunday et al., 2011). Productinnovation is a continuous and cross-functional process involving and integrating agrowing number of different competencies inside and outside the organizationalboundaries. However, product innovation is a risky and expensive endeavour, whichresults in low success rates and many projects being terminated midway in thedevelopment cycle (Cormican and O’Sullivan, 2004). The “product innovation”dimension is measured through measured variables that have been used in the studiesof Nassimbeni (2001), Wonglimpiyarat (2010), Tomlinson (2010) and Jiménez-Jiménezand Sanz-Valle (2011).

Process innovation. According to the OECD (2005), a process innovation is theimplementation of a new or significantly improved production or delivery method. Thisincludes significant changes in techniques, equipment and/or software. Processinnovations can be intended to decrease unit costs of production or delivery, to increasequality, or to produce or deliver new or significantly improved products. Theinnovation process refers to the transformation process in an innovation trajectory.Thus, process innovation emphasizes either the re-innovation/reinvention (Rothwelland Gardiner, 1998) or improvement of an existing process through reducing costsand/or increasing the flexibility and performance of the process (OECD, 2005). In moststudies, process innovation is associated with the sequences and nature of theproduction process that improve the productivity and the efficiency of production

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activities (Garcia and Calantone, 2002; De Propris, 2002). It aims to introduce a new elementin production materials, machinery, equipment, processes, task specifications andworkflow mechanisms (Damanpour, 1991). The “process innovation” dimension ismeasured through indicators that have been drawn from the studies of Nassimbeni (2001),Wonglimpiyarat (2010), Tomlinson (2010) and Jiménez-Jiménez and Sanz-Valle (2011).

Marketing innovation. A marketing innovation is the implementation of a newmarketing method involving significant changes in product design or packaging,product placement, product promotion or pricing (OECD, 2005). Marketing innovationis the firm’s ability to publicize and sell the products on the basis of understandingconsumer needs, the state of the competition, costs and benefits and the acceptance ofthe innovation (Yam et al., 2011). Marketing innovations are aimed at better addressingcustomer needs, opening up new markets or newly positioning a firm’s product on themarket with the intention of increasing firm sales (Gunday et al., 2011). The “marketinginnovation” dimension is measured through indicators that have been drawn from thestudies of Yam et al. (2004) and Yam et al. (2011).

Organizational innovation. The OECD (2005) defines organizational innovation asthe implementation of a new organizational method in the firm’s business practices,workplace organization or external relations. Organizational innovations have atendency to increase firm performance by reducing administrative and transactioncosts, improving workplace satisfaction (and thus labour productivity), gaining accessto non-tradable assets (such as non-codified external knowledge) or reducing the cost ofsupplies. Organizational innovation involves changes to administrative processes and/or organizational structures relating to the basic work activities of an organization andits management. Changes in organizational structures and procedures can facilitate thecreation of new products and processes (Chang et al., 2012). The “organizationalinnovation” dimension is measured through indicators that have been drawn from thestudies of Yam et al. (2004, 2011), Gunday et al. (2011) and Forsman (2011).

2.2 Firm performanceFirm performance is considered to be a multidimensional construct (Naser et al., 2004)and is the measurement of a firm’s success and achievements (Yeung et al., 2003). Arestrictive set of financial performance measures may adversely impact on anorganization’s long-term viability, so organizations should develop a broad range ofperformance measures (O’Mara et al., 1998). Garvin (1987), Lee et al. (2001) and Sousaand Voss (2002) deem product quality, coupled with operating and financialperformance of the companies, to be their performance dimensions. Furthermore,Lakhal et al. (2006), following a literature review on strategic management, marketingand operations management, have chosen three performance related dimensions:financial performance, operational performance and product quality. Consistent withprior research, the present study relies on multiple measures of performance to attainrobustness of results. So, three performance related dimensions have been chosen:product quality, operational performance and financial performance.

Product quality. Product quality is a complex, multidimensional factor for which aglobal and uni-dimensional definition does not exist (Sebastianelli and Tamimi, 2002).Product quality refers to a product’s intrinsic and extrinsic quality attributes (Luninget al., 2002). It has a vast number of meanings and encompasses parameters such asfunctional characteristics, physical properties and consumer protection from fraud. It isthe degree to which it satisfies customer requirements (Gill, 2009). In this study, the five

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items of Garvin’s quality dimensions (performance, reliability, durability, perceivedquality and conformance to specifications) are used for measuring product quality.

Operational performance. Operational performance is usually measured as a compositeof several performance dimensions (Ketokivi and Schroeder, 2004) and reflects theperformance of the internal operation of a company in terms of product/process quality,efficiency and productivity (Naser et al., 2004). In this study, the “operational performance”factor is measured through productivity, efficiency and effectiveness of the company’soperations, while the measurement items used are drawn from well-establishedinstruments used in previous studies such as those of Lakhal et al. (2006), Feng et al. (2008),Lin and Jang (2008), Su et al. (2008) and Uyar (2009).

Financial performance. Financial performance is defined as the achievement offinancial and market share objectives (Lin and Jang, 2008). Indicators of businessperformance such as company sales growth, profitability, net profit margin, financialresults and cash flow, are used in the present study. These indicators are drawn fromthe studies of Lee et al. (2001), Conca et al. (2004), Lakhal et al. (2006), Feng et al. (2008),Singh (2008) and Han et al. (2009).

2.3 Development of hypothesesInnovation is critical to a firm obtaining a dominant position and achieving higherprofits (Cheng et al., 2010). It has a considerable impact on corporate performance byproducing an improved market position that conveys competitive advantage andsuperior performance (Walker, 2004). Firms which are more innovative place moreemphasis on management techniques (Baldwin and Johnson, 1996) and reachsustainable levels of higher performance (Hult et al., 2004; Guan and Ma, 2003). A largenumber of past empirical studies have confirmed that there is a positive relationshipbetween innovation and company performance. For example, Avanitis and Hollerstein(2002) conclude that the degree of innovativeness significantly increases theproductivity of knowledge capital. Favre et al. (2000) conclude that innovations havea positive impact on firm profits. Diederen et al. (2002) report that innovative firmsshow significantly higher profits and growth figures than firms that are not innovative.McAdam and Keogh (2004) investigated the relationship between firms’ performanceand their familiarity with innovation and research. They found out that a firm’sinclination to make innovations was of vital importance in the competitive environmentin order for it to obtain a higher competitive advantage. Recently, Cheng et al. (2010)have supported the view that innovation is critical to a firm obtaining a dominantposition and achieving higher profits. Innovation provides organizations with a newmethod of conducting business ahead of the competition and the potential to gain acompetitive edge in the marketplace (Ahuja, 2000). Therefore firms that are successfulat innovation will rate their performance higher than firms that have failed atinnovation (Markham and Griffin, 1998; Bougrain and Haudeville, 2002).

Empirical evidence supports the view that product innovation and processinnovation have a positive effect on a firm’s performance. These two innovationdimensions can be advantageous to a firm in improving its competitive positionrelative to its rivals, as well as its profitability in the market (Cheng et al., 2010). Productinnovations are expected to provide firms with a competitive advantage via thetechnological novelty and improved performance of the product. By contrast, processinnovations provide a competitive advantage via the efficiency/productivity gainsobtained through the introduction of more effective ways of producing (pre-existing)

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products (Evangelista and Vezzani, 2010). Process innovation has a greaterimpact on production cost but less influence on a firm’s sales growth or marketshare than product innovation (Cheng et al., 2010). On the other hand, organizationalinnovation and market innovation deal with the changes in the organizational structureof a company and moves to exploit new territorial markets or new market segmentswithin existing markets (Cheng et al., 2010). The introduction of an organizationalinnovation is important to objectives such as the “reduction of the time needed torespond to customer or supplier needs” and the “improvement of the quality of goods”(Evangelista and Vezzani, 2010). Johne and Davies (2000) found that marketinginnovations increase sales by increasing product consumption and yield additional profitfor firms. According to Kim et al. (2012), organizational innovation andmarket innovation(administrative innovation) increase the efficiency and the effectiveness of managerialsystems and processes by obtaining new resources or adopting new programs. Inaddition, enhancing administrative systems and processes adds value for a firm directlyand for its customers indirectly. Therefore, the following hypotheses are developed:

H1. There is a positive and strong association between innovation capability andoperational performance.

H2. There is a positive and strong association between innovation capability andproduct quality.

H3. There is a positive and strong association between innovation capability andfinancial performance.

Many authors support the view that market share is positively related to high productquality. Garvin (1987) reports that quality improvement makes companies increasetheir market share, product value and price and consequently the financial benefits.Handfield et al. (1998) claim that when product quality is improved, waste is reduced,efficiency is increased and consequently a firm increases its profitability. Furthermore,Du Brin (1995) notes that product quality contributes to sales and market shareincrease. Primrose and Leonard (1987) found also an increase in sales and profits. In thesame way, Jacobson and Aaker (1987) state that product quality has a positive impacton the return on investments, market share and product price. According to Andersonand Gerbing (1988) high product quality positively influences customer satisfactionand consequently the financial performance of a company. Furthermore, Heras et al.(2002) reviewing the research studies, reach the conclusion that improved productquality is related to sales and profit margin increase. Taking into consideration theprevious research referred to above, the following research hypothesis is formulated:

Η4. Improved product quality is positively related to financial performance.

When the production stage of a company is improved, the associate cost can bedecreased, thus, the company can be competitive and enter new markets (Deming,1986). Handfield et al. (1998) state that, the return on assets and company profits areincreased as a result of waste reduction and improvements in efficiency. Furthermore,Heskett et al. (1997) concluded that improved operational performance increases bothsales and profits. The improved financial performance of a company as a result ofimproved operational performance was noted also by Benner and Veloso (2008).

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However, in such a case, the company’s production costs are decreased by developingefficient internal processes. In other words, improving operational performance increasesfinancial performance (Ou et al., 2010). In addition, the findings from the study of Flynnet al. (1995) show that improved operational performance results in fewer defectiveproducts, decreased quality cost, increased productivity, on time product delivery andfinally in increased business performance. Jang and Lin (2008) also reach the conclusionthat operational performance influences market performance positively. Taking intoaccount the above studies, we reach the following hypothesis:

Η5. Improved operational performance is positively related to financial performance.

According to the findings of the empirical research study of Forza and Filippini (1998),product quality is positively influenced by the improvement of process managementpractices. The same conclusion was also reached by the study of Ahire and Dreyfus(2000). Furthermore, Heskett et al. (1997) state that improving operational performanceresults in more attractive products from a customer perspective. Similarly, Handfieldet al. (1998) note that a reduction in product wastage, coupled with improvement inproductivity result in increased product quality and company profits. Furthermore, thestudies of Psomas and Fotopoulos (2009) and Psomas et al. (2011) found that processmanagement practices have a strong and direct effect on quality improvement.Consequently, this study hypothesizes the following:

Η6. Improved operational performance is positively related to product quality.

Figure 1 depicts the conceptual model and a graphical representation of the study’shypotheses.

3. Research methodologyTo test the above hypotheses, a questionnaire consisting of 40 questions was developed bythe authors through a multi – step process. All measured variables of the questionnairewere adopted following a comprehensive literature review. The questionnaire was judgedthrough structured interviews with eight professionals of manufacturing companies andfinally it was pilot tested on 43 manufacturing companies in Greece, proving itsappropriateness and achieving the content validity of the constructs. The results of thepilot survey show the same trends as the results of the final sample. All questions

InnovationCapability

ProductQuality

OperationalPerformance

FinancialPerformance

H1

H2 H4

H5

H3H6

Figure 1.Hypotheses andconceptual model tobe tested

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(37 items) of the seven factors/dimensions were measured on a seven point modified Likertscale (1¼ very low to 7¼ very high). The measurement items used in the survey are listedin the Appendix.

The population chosen for this study was manufacturing companies in all sectorsof economic activity in Greece so as not to bias the final results. The questionnairewas sent by e-mail to 1,000 manufacturing companies that had been recorded on thedatabase of ICAP (the largest business information and consulting firm in Greece).The companies were randomly selected after checking to ensure that large companies didnot have multiple plants included in the sample. The data base also provided names andcontact details of the companies. It was requested that the questionnaire be answered bythe general manager or another manager designated by him/her because of the latter’sfamiliarity with the issues dealt with in the questionnaire. The respondents chosen havethe best knowledge of the innovation process in their organization. The manufacturingcompanies are heterogeneous in terms of sub-sectors and product/process complexity, sothe sample was stratified by size, quality systems registration and sub-sector. Data onthese characteristics were available on the population database and were also collected inthe questionnaire. Finally, a total of 233 valid questionnaires satisfying the criterion forSEM analysis (Wu and Liu, 2010) were collected, yielding a response rate of 23.3 per cent.A profile of the responding firms is provided in Table I.

In our study, the respondents completed the survey instrument individually andindependently within an eight-week period. Consequently the independence of eachpredictor variable was not violated. To measure the equality of variances for a singlevariable or pair of variables, the Levene test is used showing that the p-value for thetest is W0.05 significance level, indicating variation in homogeneity (Feng et al.,2008). Each of the variables was examined individually for unique or extremeobservations, and no case was defined with a threshold value of a standard score up to3 (Hair et al., 2006). By calculating the Mahalanobis d-squared distance, it was foundthat no observations exceed the threshold value of 3 and so no data points are

Profile of the responding firms Number %

Manufacturing companies sectorFood and beverages 84 36Agricultural products 54 23Machinery and equipment 28 12Metal products 19 8Plastic, chemical and associated products 11 5Medicines/cosmetics 14 6Various industrial products 23 10Number of employees0-10 61 2611-50 82 3651-100 35 15101-250 21 9251-500 19 8500 over 15 6Quality systemsISO 9001 119 51ISO 22000 or HACCP 126 54ISO 14000 25 11

Table I.The sampledemographic

characteristics

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deleted from the analysis. As far as the normality of the data is concerned, all themeasured variables in this study exhibit univariate normality and do not suffer fromskew and kurtosis (o±1), indicating, but not guaranteeing, multivariate normality(Hair et al., 2006). In addition, the scatter plot shows constant variance of error terms(Homoscedasticity), while the histogram and normal P-P plots of the standardizedresiduals also indicate normality of the error term.

The analysis adopted in this study includes an initial EFA (principal componentextraction method with varimax orthogonal rotation), to uncover the underlyingstructure of the variables. Then, CFA is used to refine the resulting scales inEFA and to determine if the number of factors and the loadings of the measuredvariables (i.e. indicators) on them conform to what is expected on the basis ofpre-established theory (Narayan et al., 2008). Multicollinearity, unidimensionality, scalereliability and construct validity are undertaken for the study variables as suggestedby Lakhal et al. (2006) and Hair et al. (2006). The model and the hypotheses aretested using SEM via path analysis, as it is a multivariate analytic methodologythat gives insights into the causal ordering of variables in a system of relationships(Fynes and Voss, 2001). The statistical packages SPSS 16 and AMOS 6 were used fordata processing.

4. Data analysis and results4.1 Construct validity of innovation dimensionsEFA and CFA are used to assess construct validity (Hair et al., 2006; Akroush et al., 2011).The purpose of factor analysis in this study is to explore how various items within each ofthe constructs interact with one another, and to develop scales (by combining several closelycorrelated items) to be used in the following analysis on linkage. Factor analytic methods areuseful for observing the underlying patterns or relationships for a large number of variablesand they determine whether the information can be condensed or summarized in a smallerset of factors or components (Gunday et al., 2011). As a result of the EFA, four latent factors(constructs) are established explaining 72.259 per cent of the total variance. These fourfactors are labelled based on the items included in each (product innovation, processinnovation, marketing innovation, organizational innovation). Table II shows the results ofEFA for the innovation dimensions scale components (Kaiser-Meyer-Olkin¼ 0.916,Bartlett’s test of Sphericity¼ 2271.436, p¼ 0.00, eigen-valueW1, MSAW0.80, factorloadingsW0.694). The reliability of the factors of the innovation is confirmed throughCronbach’s α coefficients. The Cronbach’s α values range from 0.831 to 0.885 suggestingsatisfactory level of construct reliability, since when Cronbach’s α values are W0.70, thescale is accepted as reliable (Hair et al., 2006). The uni-dimensionality of the set of measuredstatements for each factor is confirmed. Five items (V005, V006, V008, V013, V016)that demonstrate cross-loading W0.4 on more than one latent factor are dropped becausethey do not provide pure measures of a specific factor.

In order to determine whether the extracted latent factors show acceptable fit to theempirical data, the CFA (maximum likelihood estimation technique) is also applied inaddition to EFA. Thus, a series of tests are also performed to further determine theconstruct validity of the latent factors (Singh, 2008). The results show that the fourfactors revealed using EFA are also confirmed through CFA. The extracted latentfactors show acceptable fit to the empirical data (Table III).

Construct validation also includes tests for face, convergent and discriminantvalidity. The latent factors revealed and their associated items possess a sufficient level

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of face validity, as the survey instrument was mainly adopted from the existingliterature, reviewed by experts in the field and pilot tested (Hair et al., 2006).

Convergent validity refers to the extent to which varying approaches to themeasurement of the latent factors yield the same results. It is demonstrated if a groupof items measure one common factor (Su et al., 2008). The convergent validity of thefour innovation dimensions (latent factors) are confirmed by evaluating the factorloadings (W0.645), the average variance extracted (AVE) (W0.578) and the constructreliability (CR) (W0.897) in all cases (Hair et al., 2006). The AVE estimate is similar tothe composite reliability, but differs in that standardized loadings are squared beforebeing added. It measures the amount of variance for the specified indicators accountedfor by the latent construct. Higher variance extracted values occur when the indicatorsare truly representative of the latent construct. The variance extracted value is acomplimentary measure for the construct reliability value. A value above 0.50 for AVEof any construct is accepted.

Discriminant validity checks whether the items estimate only the assigned latentfactor and no others (Singh, 2008). Discriminant validity is evaluated by comparingthe AVE with the shared variance (i.e. square of the correlation) between each pairof latent factors (Singh et al., 2011). In each case, the AVE is greater than theCorr2, confirming the discriminant validity (Hair et al., 2006) (Table IV). The nomologicalvalidity (significant correlations among the extracted latent factors) and criterion-relatedvalidity of the extracted latent factors are also tested. The results provide strong evidencethat the proposed latent factors of innovation dimensions pass rigorous testing ofthese types of validities.

In the case of the present study, a higher order model is constructed using“innovation capability” as a second-order factor that explains the four first-order

ConstructsFactor loading

ItemsOrganizationalinnovation

Productinnovation

Processinnovation

Marketinginnovation

V017 0.766V018 0.756V019 0.732V021 0.723V020 0.707V002 0.812V001 0.798V003 0.754V004 0.739V010 0.798V011 0.750V007 0.720V009 0.694V012 0.863V015 0.744V014 0.726Eigenvalue 7.850 1.553 1.085 1.074Cumulative variance per cent 49.064 58.769 65.547 72.259Cronbach α 0.885 0.883 0.848 0.831

Table II.Exploratory factor

analysis ofinnovationdimensions

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Fitindices

Innovatio

ndimensions

model

Second

-order

innovatio

ncapability

model

Firm

performance

model

Structural

model

Measurement

model

Acceptable

fitindicesa

Absolutefit

indices

χ2159.574

153.616

141.742

701.026

579.303

0⩽χ2⩽2

dfdf

9596

60353

354

Rootmeansquare

residu

al(RMR)

0.052

0.054

0.042

0.058

0.063

o0.08

Rootm

eansquare

ofapproxim

ation(RMSE

A)

0.054

0.051

0.067

0.065

0.052

o0.08

Increm

entalfitindices

Increm

entalF

itIndex(IF

I)0.971

0.974

0.962

0.926

0.926

W0.90

Tucker-Lewiscoefficient

(TLI)

0.963

0.967

0.950

0.914

0.919

W0.90

Comparativ

eFitIndex(CFI)

0.971

0.974

0.962

0.925

0.925

W0.90

Normed

FitIndex(NFI)

0.932

0.934

0.936

0.911

0.913

W0.90

RelativeFitIndex(RFI)

0.914

0.918

0.917

0.907

0.915

W0.90

Parsim

onious

fitindices

χ2/df

1.680

1.600

2.362

1.986

1.636

o3.0

Parsim

onious

Normed

FitIndex(PNFI)

0.764

0.766

0.805

0.781

0.794

W0.50

AdjustedGoodn

essof

FitIndex(AGFI)

0.892

0.896

0.875

0.792

0.827

W0.50

Goodn

essof

FitIndex(GFI)

0.924

0.926

0.918

0.831

0.859

W0.50

Notes

:a H

airetal.(2006),SadikogluandZehir(2010),S

ingh

etal.(2011)

Table III.The fit indices of thesub-models and theoverall measurementand structural model

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factors (the innovation dimensions). In the second-order factor model, it is hypothesizedthat the “innovation capability” factor explains the association between the fourfirst-order factors of innovation dimensions, thus avoiding the problem of correlatedmeasurement errors (Hair et al., 2006). Wang and Ahmed (2004) also identified asecond-order factor (innovative orientation) through five innovation dimensions(product, market, process, behavioural and strategic innovativeness) to describe anorganization’s overall innovativeness. The overall validity of the second-order factormodel in this study is tested using multiple fit criteria. The results of the second-orderCFA indicate that the fit statistics represent a good fit of the data to the proposedsecond-order measurement model (Table III). In addition, all the second-order factorloadings are positive and statistically significant.

4.2 Construct validity of firm performance dimensionsThe analysis process described above with regard to innovation dimensions is alsofollowed for firm performance dimensions. Thus, the result of the EFA is theestablishment of three latent factors, namely product quality, operational performanceand financial performance (Kaiser-Meyer-Olkin¼ 0.859, Bartlett’s test ofSphericity¼ 2159,462 p¼ 0.000, MSAW0.80, factor loadingW0.761). Three itemswere dropped (V023, V031, V037) because of cross-loadings W0.4 on more than onefactor. The reliability of the extracted factor (Cronbach’s α coefficients W0.893) is alsosupported. Table V represents the results of EFA.

Using the CFA to evaluate the three factors of firm performance produced resultswhich show that all of the standardized regression weights are above 0.6. The index ofthe amount of variance of each latent factor accounted for in its indicators is above therecommended level of 0.50 (Fornell and Larcker, 1981). The convergent validity of the 3latent factors is confirmed (factor loadings W0.761, AVEW0.631 and CRW0.936) in allcases. Discriminant validity is evidenced by the fact that the Corr2 is less than the AVEfor each construct (Table VI).

The fit indices of the firm performance model are indicative of a good fit of the data(Table III).

4.3 Model estimationIn this study the measurement and structural models are performed sequentially(Fynes et al., 2005). In the first step CFA is conducted, while in the second step thehypothesized model is tested (Singh, 2008). The fit indices of the measurement modeland the structural model suggest that the theoretical model has an adequate level ofempirical support (Table III). Thus, these two models fit the data satisfactorily. Afterperforming the above tests, the SEM procedures are applied (maximum likelihood

Latent factors Average variance extracteda Construct reliabilityb (Corr)2c

Organizational innovation 0.578 0.932 0.515Product innovation 0.646 0.927 0.490Process innovation 0.592 0.909 0.490Marketing innovation 0.621 0.897 0.515Notes: aAVE¼Σλi2/n, (number of items i¼ 1,…, n, λi¼ standardized factor loading); bCR¼ (Σλi)2/((Σλi)2+(Σδi)), (number of items i¼ 1… n, λi¼ standardized factor loading, δi¼ error term); cthe highestsquared correlation between the factor of interest and the remaining factors

Table IV.Validity ofinnovationdimensionsperformanceconstructs

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method) to estimate the causal relations between the latent variables and confirm orrefute the hypothesis presented earlier (Η1-Η6). Figure 2 presents the estimatedstandardized parameters for the causal paths and the results of the squared multiplecorrelations for the endogenous factors.

Based on Figure 2, it is apparent that innovation capability has a significant andpositive effect, primarily, on operational performance (Η1: b¼ 0.704, po0.000) andsecondarily on product quality (Η2: b¼ 0.304, po0.002). Thus, the Η1 and Η2 of thepresent study are accepted. Overall, 49.6 per cent of the variation in operationalperformance is explained by innovation capability. By contrast, the Η3 is not accepted(Η3: b¼ 0.158, p¼ 0.119), meaning that innovation capability does not have asignificant effect on financial performance. The company’s financial performance issignificantly and positively influenced by operational performance (Η5: b¼ 0.583,po0.000), however, it is negatively influenced by product quality (Η4: b¼−0.311,p¼ 0.000). Thus, the Η4 is rejected while the Η5 is accepted. Finally, according to thefindings, product quality is significantly and positively influenced by a company’soperational performance (Η6: b¼ 0.314, p¼ 0.001), resulting in the acceptance of theΗ6. Overall 32.5 per cent of the variation in product quality can be explained byinnovation capability and firms’ operational performance. Furthermore, 34.9 per cent of

Latent factors Average variance extracteda Construct reliabilityb (Corr)2c

Product quality 0.690 0.940 0.269Operational performance 0.724 0.888 0.037Financial performance 0.631 0.936 0.269Notes: aAVE¼Σλi2/n, (number of items i¼ 1,…. n, λi¼ standardized factor loading); bCR¼ (Σλi)2/((Σλi)2+(Σδi)), (number of items i¼ 1,…. n, λi¼ standardized factor loading, δi¼ error term); cthehighest squared correlation between the factor of interest and the remaining factors

Table VI.Validity of firms’performanceconstructs

ConstructsFactor loading

Items Financial performance Product quality Operational performance

V033 0.883V034 0.856V032 0.823V035 0.775V036 0.761V024 0.875V025 0.873V022 0.836V026 0.815V028 0.863V030 0.814V029 0.804V027 0.769Eigenvalue 5.531 3.152 1.121Cumulative variance per cent 42.547 66.796 75.415Cronbach α 0.895 0.893 0.906

Table V.Exploratory factoranalysis of firms’performance

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the variation in financial performance can be explained only by operational performance.The structural model parameters are summarized in Table VII. So, these findings supportthe widespread idea that innovation is a key driving force in company success.

5. DiscussionThe majority of the companies participating in the present study are small andmedium-sized manufacturing enterprises (SMEs), given that 77 per cent employ <100employees, and 9 per cent employ more than 100 and <250 employees. In fact, Greekmanufacturing companies in general are SMEs (Panigyrakis et al., 2009). It is worthnoting that in Greece, many companies, irrespective of sector, are family-ownedcompanies (Psomas et al., 2011). So, their small-medium size, based on the number ofemployees, is partly justified.

The present study makes several useful points about the hypothesized modeland its empirical validation. It contributes to the existing research by combiningfour basic innovation dimensions (product, process, marketing and organizational)and introducing the new second order construct “innovation capability”, to developa dynamic model in order to examine its effects on the three dimensions (productquality, operational performance and financial performance) of the manufacturingSMEs’ performance. The “innovation capability” construct takes us a step forwardtowards effectively measuring an organization’s innovative activity. The existingresearches focus on one or two aspects of innovation (product and/or processinnovation). For example, the study of Laforet and Tann (2006) within themanufacturing sector identified that SMEs are more engaged in process innovationthan in product innovation. In addition, the study by Lin and Chen (2007) of TaiwaneseSMEs within the manufacturing and service sectors revealed that technologicaland marketing innovation were the major types of innovation adopted within firms.In this study, the proposed “innovation capability” construct captures the principalelements of four innovation dimensions giving a thorough assessment of a firm’sinnovative capability.

OrganizationalInnovation

0.767

MarketingInnovation

0.657

ProductInnovation

0.582

V001 V002 V003 V004

V015

V014

V012

V020V019V018V017 V021

0.695 0.889 0.892 0.712

0.781 0.747 0.810

0.716

0.807

0.832

0.761 0.783

InnovationCapability

0.763

0.811

0.876

ProductQuality0.325

V022 V024 V025 V026

OperationalPerformance

0.496

V030V029V028V027

FinancialPerformance

0.349

V032

V033

V034

V035

V036

0.759 0.873 0.908 0.765

0.811 0.896 0.866 0.838

0.883

0.919

0.763

0.687

0.673

0.304

0.704

0.583

–0.3110.314

0.158

V009

V010

V011

V007

ProcessInnovation

0.611

0.814

0.645

0.838

0.757

0.782

Figure 2.Theoretical model,showing estimated

standardizedparameters for the

causal paths and theresults of the squaredmultiple correlations

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Relationships

Standardized

regression

weigh

tsSE

p-value

Squaredmultip

lecorrelation

coefficient

Hyp

othesis

test

results

Η1.

Innovatio

ncapability→

operationalp

erform

ance

0.704

0.096

0.000*

0.496

Accepthy

pothesis

Η2.

Innovatio

ncapability→

productqu

ality

0.304

0.106

0.002

0.325

Accepthy

pothesis

Η3.

Innovatio

ncapability→

financial

performance

0.158

0.138

0.119

NS

Rejecthy

pothesis

Η4.

Productqu

ality

→fin

ancial

performance

−0.311

0.102

0.000*

Rejecthy

pothesis

Η5.

Operatio

nalp

erform

ance→fin

ancial

performance

0.583

0.110

0.000*

0.349

Accepthy

pothesis

Η6.

Operatio

nalp

erform

ance→productqu

ality

0.314

0.083

0.001

Accepthy

pothesis

Notes

:NS¼Not

statistically

sign

ificant.*Sign

ificanceat

po0.001

Table VII.Relationshipsbetween constructs

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We must point out that the success or failure of innovation capability depends on afirm’s technological base (Koellinger, 2008). Firms accumulate their technologicalknowledge constantly and continuously over time, so that they are forming their owntechnological base as a set of informational inputs, knowledge and capabilities(Galende, 2006). It is to be expected that more technologically specialized firms are moreinnovative (Garcia-Vega, 2006), since a unique technological base is formed for each firm.Indeed, there are significant differences between firms in terms of their technological base.Wang et al. (2008) point out that firms have different strengths and weaknesses in terms oftechnology-based core capabilities. In addition, firm size provides a number of advantagesand is positively related to dynamic technological innovation performance (Stock et al.,2002). More generally, larger size will allow a firm to accumulate a larger store oftechnological knowledge and capabilities (Damanpour, 1991).

The proposed model that has been presented in this paper has a theoretical basis,while, through its empirical validation, several useful points are revealed. Morespecifically, it builds on the previous work of Loof et al. (2002), Kemp et al. (2003),Sadikoglu and Zehir (2010), Jiménez-Jiménez and Sanz-Valle (2011), Yam et al. (2011)and Gunday et al. (2011) in terms of the operationalization of the relationships that existbetween innovation and various dimensions of a firm’s performance. We extend theexisting research by exploring issues that have been missing in prior studies. Morespecifically, a reliable and valid second-order model has been developed demonstratingthe existence of a broad concept that is determined by the successful implementation ofinnovative activities mainly in SMEs. We introduce the second-order factor “innovationcapability”, that can be assessed indirectly through the assessment of its sub-factors(innovation dimensions), which in turn can also be assessed indirectly through theassessment of their indicators that are directly measured. Based on an extensiveliterature review, this study represents the first research project in the innovationmanagement field that operationalizes innovation capability and empirically tests itsrelationship with the three dimensions of manufacturing firms’ performance.

The next way in which the present study contributes to the literature is that itexamines relationships between firms’ innovation capability and their performancedimensions together in a single model. To our knowledge, this is the first empiricalattempt at defining and confirming the casual path between “innovation capability”,firms’ “financial performance”, “operational performance” and “product quality”. Theinnovative manufacturing firms benefit from improvement in the value of operationalperformance (Η1) and product quality (Η2). The findings are in line with theobservations of Sadikoglu and Zehir (2010), Gunday et al. (2011) and Peng et al. (2011)which show that innovation has a significant and positive impact on firms’ operationalperformance. In addition, Chudnovsky et al. (2006) claim that innovative companiesattain higher productivity levels than non-innovating ones. Their findings alsounderline our suggestion that firms with high innovative capability are rewarded byhigh product quality. Prajogo et al. (2008) also found a significant link between productquality and product innovation, while Koufteros and Marcoulides (2006) report thatfirms that have higher product innovation capability also have higher levels of productquality. In fact, innovation, by exploiting new technologies, is often aimed at improvingseveral aspects of product quality.

Yet another important finding of this study is the positive but weak andnon - statistical significant sign of the relationship between innovation capability andfinancial performance (Η3). It contributes to the current knowledge since doubt existsamong researchers and practitioners about the economic justification for innovation

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and particularly about the direct impact of innovation on the financial benefits for focalfirms. Several researchers have asserted that innovation capability can positivelyimprove firms’ financial performance (Clayton and Turner, 1998; Hult et al., 2004;Jenssen and Randøy, 2006; Keskin, 2006). Oke et al. (2007), similar to the present study,investigated SMEs and found that there is a link between innovation and salesturnover growth in SMEs. The findings of Varis and Littunen (2010) also support theview that the introduction of novel product, process and market innovations ispositively associated with SMEs growth. Nevertheless, the findings of this paper wereconsistent with Panayides (2006) and Yang et al. (2009). They too found that there wasa lack of support for a significant positive relationship between innovation capabilityand financial performance.

However, innovation capability was found to have a significantly positive effect onoperational performance which, in turn, was found to have a significantly positiveeffect on financial performance especially of the manufacturing SMEs. In other words,instead of a direct effect, innovation capability had an indirect impact on financialperformance mediated by operational performance. This lack of a direct relationshipcan be explained by the fact that the implementation of innovation activities make afirm invest in new technology, adopt flexible work practices and address all itsprocesses. So, the introduction of innovation activities entails extra cost and affectsprofitability, especially of the manufacturing SMEs.

Likewise, the present study does not produce any evidence to support theproposition that the improved product quality is positively related to financialperformance (Η4). Our findings are more consistent with the work of Fynes and Voss(2001), whose empirical research has not provided strong support for a qualityperformance – financial performance relationship. However, the evidence provided inthis paper suggests that operational performance does significantly contribute tofinancial performance (Η5). In particular, this research indicates that manufacturingcapability coming from the innovation capability of a company must be taken intoaccount to explain its contribution to financial performance. The present study findingsare consistent with many previous researches (Naveh and Marcus, 2005; Jang and Lin,2008; Ou et al., 2010) and enable us to conclude that operational performance is adeterminant of financial performance.

Finally, the results of the present study suggest that by improving the elements ofoperational performance, a company improves product quality and consequentlymakes the products more attractive to customers (Η6). To our knowledge, this is one ofthe few empirical attempts to define and confirm the casual path between firms’operational performance and product quality.

6. ConclusionIn this study, a theoretical model has been developed that focuses on the Greekmanufacturing sector. All the scales have been tested through rigorous statisticalmethodologies including the pilot test method, EFA, CFA, construct reliability andvalidation of the second-order factor model. Four distinct innovation dimensions havebeen identified on the basis of overall innovation of manufacturing firms. Two of theseinnovation dimensions are characterized by pure process and product orientedinnovation activities; a third is based on the introduction of organizational innovationsand a fourth one is characterized by marketing changes. This approach to the varietyof innovation dimensions is sufficient to depict the different firms’ innovationstrategies within the manufacturing sector.

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Furthermore, important contributions to the existing literature can be derived from theresults of this paper. This study is one of the first attempts to link the innovationcapability of a firm with the three dimensions of firm performance, thus contributing tothe theoretical development of organizational innovation and narrowing the literature gapin several ways. First, enlarging the analysis of innovation beyond the technologicaldomain and introducing organizational and marketing dimensions, provides a muchricher and more complex picture of firms’ innovation strategies and performance. Thestatistical evidence presented in Section 4 has shown that these four different dimensionsof innovation are expressed by a second-order factor, namely firm “innovation capability”.Second, the evidence provided in this paper suggests that the “innovation capability” hasno direct impact on manufacturing firms’ financial performance. This is of course a littledifferent from the traditional innovation philosophy and research. Yet our study doesshow that “innovation capability” directly contributes to product quality and operationalperformance, while indirectly contributing to financial performance through themoderator of operational performance. This result is an important contribution and hasimportant implications (within the constraints of the study sample size). Third, this studyindicates that operational performance must be taken into account to explain its positivecontribution to product quality. Thus, in order for a manufacturing company to increaseits performance and gain competitive advantages, it should focus on increasing the levelof innovation that is implemented. This is potentially a major contribution to theinnovation and operations management literature. Finally, our findings support the factthat innovation strategy is an important major driving force behind firm performance andshould be developed and executed as an integral part of business strategy. In short, thisresult confirms the importance of innovation and provides support for the encouragementof innovation in manufacturing companies.

Managerial implicationsThe findings of the present study carry managerial messages. Managers should putadditional emphasis on innovation capability as it is an important element forachieving improved overall firm performance and sustainable competitive power. Therelationship between innovation capability and firm performance may provide a guideas to how companies should achieve better performance by using innovation. Firmsthat do not focus on the four innovation dimensions may have to look elsewhere to findways to survive. In order to achieve greater advantages from the adoption ofinnovation, managers should focus on practices that directly increase the level to whichthe innovation dimensions are achieved. Having a clear understanding of the exactnature of innovations will help firms to prioritize their market, production andtechnology strategies, to be followed by an appropriate subsequent plan of action. Theconclusions of this study seem to be especially important for smaller and younger firmsand for those firms operating in highly turbulent environments. Thus, this can help amanufacturing SME withstand the current downturn and survive in an unstable andfinancially unhealthy business environment. The fact that the current economicdownturn and financial crisis prevail not only in Greece but in many other Europeancountries too, strengthens this point of view and managerial message. This study hasalso added implications for governments and consultants whose aim is to support thestrategic development efforts of companies (especially SMEs). So, our study offers clearpractical implications for managers who desire to choose strategies and allocateresources in order to improve their company’s operational performance and productquality. Indeed, when innovation becomes a way of life, companies are not competing in

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terms of innovations per se; rather they are competing for company-wide devotion thatwill transform innovation into competitiveness. There is no doubt that the twenty-firstcentury will be credited with being the century of innovation. The message from thisstudy is that creating a successful innovation platform may prove to be the mostcritical catalyst for a company’s success.

Limitations and future research recommendationsAlthough the empirical analysis applied in the present study is based on data from asignificant number of establishments in the manufacturing sector, there are also a numberof limitations associated with this study that suggest future research proposals. First, theempirical validation of the model is based on Greek manufacturing firms, so the resultsmay not be applicable to all company populations. Future research should explore if theresults we observe concerning this sample of companies are applicable to a greaterpopulation of companies from other countries with a different technological base or tospecific subsectors of the manufacturing industry (e.g. the food industry). The use oflongitudinal data and comparisons with this study would provide further insights thatwould assist in generalizing knowledge related to the relationship between innovationcapabilities and firms’ performance. Second, the effects of the internal businessenvironment and endogenous business factors have not been assessed through thepresent study. Thus, it is worth examining the effect of a firm’s characteristics such asleadership, organizational learning, process management, quality management andhuman resources on innovation capability and firm performance. Third, thequestionnaires have no qualitative data. Carrying out interviews with members of thefirms included in the sample would have enriched the paper by providing a betterunderstanding of the causal mechanisms between innovation capability and firmperformance. Finally, future studies could use quantitative variables (e.g. financial indices)for assessment of the financial performance to obtain a more complete view of therelationships studied in this paper.

References

Abrunhosa, A. and Moura E Sá, P. (2008), “Are TQM principles supporting innovation in thePortuguese footwear industry?”, Technovation, Vol. 28 No. 4, pp. 208-221.

Ahire, S.L. and Dreyfus, P. (2000), “The impact of design management and process managementon quality: an empirical examination”, Journal of Operations Management, Vol. 18 No. 5,pp. 549-575.

Ahuja, G. (2000), “Collaboration networks, structural holes, and innovation: a longitudinal study”,Administrative Science Quarterly, Vol. 45 No. 3, pp. 425-455.

Akroush, M., Dahiyat, S., Gharaibeh, H. and Abu-Lail, B. (2011), “Customer relationshipmanagement implementation”, International Journal of Commerce and Management,Vol. 21 No. 2, pp. 158-191.

Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review andrecommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-423.

Avanitis, S. and Hollerstein, H. (2002), “The impact of spillovers and knowledge heterogeneity onfirm performance: evidence from Swiss manufacturing”, in Kleinknecht, A. and Mohnen, D.(Eds), Innovation and Firm Performance, Palgrave, London, pp. 225-252.

Avermaete, T., Viaene, J., Morgan, E.J. and Crawford, N. (2003), “Determinants of innovation insmall food firms”, European Journal of Innovation Management, Vol. 6 No. 1, pp. 8-17.

122

JMTM26,1

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Baldwin, J.R. and Johnson, J. (1996), “Business strategies in more- and less-innovative firms inCanada”, Research Policy, Vol. 25 No. 5, pp. 785-804.

Becheikh, N., Landry, R. and Amara, N. (2006), “Lessons from innovation empirical studies in themanufacturing sector: a systematic review of the literature from 1993-2003”, Technovation,Vol. 26 Nos 5/6, pp. 644-664.

Benner, M.J. and Veloso, F.M. (2008), “ISO 9000 practices and financial performance: a technologycoherence perspective”, Journal of Operations Management, Vol. 26 No. 6, pp. 611-629.

Bougrain, F. and Haudeville, B. (2002), “Innovation, collaboration and SMEs internal researchcapacities”, Research Policy, Vol. 31 No. 5, pp. 735-747.

Calantone, R.J., Cavusgil, S.T. and Zhao, Y. (2002), “Learning orientation, firm innovation capability,and firm performance”, Industrial Marketing Management, Vol. 31 No. 6, pp. 515-524.

Capon, N., Farley, J.U. and Hoenig, S. (1990), “Determinants of financial performance: a meta-analysis”, Management Science, Vol. 36, pp. 1143-1159.

Chandler, G.N. and Hanks, S.H. (1994), “Market attractiveness, resource-based capabilities,venture strategies, and venture performance”, Journal of Business Venturing, Vol. 9 No. 4,pp. 331-349.

Chang, Y., Linton, J. and Chen, M. (2012), “Service regime: an empirical analysis of innovation patternsin service firms”, Technological Forecasting & Social Change, Vol. 79 No. 10, pp. 1569-1582.

Cheng, C., Lai, M. and Wu, W. (2010), “Exploring the impact of innovation strategy on R&Demployees’ job satisfaction: a mathematical model and empirical research”, Technovation,Vol. 30 No. 9, pp. 459-470.

Chudnovsky, D., Lopez, A. and Pupato, G. (2006), “Innovation and productivity in developingcountries: a study of Argentine manufacturing firms’ behavior (1992–2001)”, ResearchPolicy, Vol. 35 No. 3, pp. 266-288.

Clayton, T. and Turner, G. (1998), “Brands, innovation and growth”, in Tidd, J., Bessant, J. andPavitt, K. (Eds), Managing Innovation: Integrating Technological, Market andOrganizational Change, 2nd ed., Wiley, New York, NY.

Conca, F.J., Llopis, J. and Tari, J.J. (2004), “Development of a measure to assess quality constructs”,Journal of Marketing Studies, Vol. 16 No. 1, pp. 12-27.

Cormican, K. and O’Sullivan, D. (2004), “Auditing best practice for effective product innovationmanagement”, Technovation, Vol. 24 No. 10, pp. 819-829.

Damanpour, F. (1991), “Organizational Innovation: a meta-analysis of effects of determinants andmoderators”, Academy of Management Journal, Vol. 34 No. 3, pp. 555-590.

De Propris, L. (2002), “Types of innovation and inter-firm co-operation”, Entrepreneurship &Regional Development, Vol. 14 No. 4, pp. 337-353.

Deming, W.E. (1986), Out of the Crisis, MIT Technology Center for Advanced Engineering Study,Cambridge, MA.

Diederen, P., van Meijl, H. and Wolters, A. (2002), “Innovation and farm performance: the caseof Dutch agriculture”, in Kleinknecht, A. and Mohnen, D. (Eds), Innovation and FirmPerformance, Palgrave, London pp. 73-85.

Du Brin, A.J. (1995), Leadership: Research Findings, Practice, and Skills, Houghton MifflinCompany, Boston, MA.

Evangelista, R. and Vezzani, A. (2010), “The economic impact of technological and organizationalinnovations. a firm-level analysis”, Research Policy, Vol. 39, pp. 1253-1263.

Favre, F., Negassi, S. and Pfister, E. (2000), “The effects of spillovers and government subsidieson R&D, international R&D cooperation and profits”, in Kleinknecht and Mohnen (Eds),Palgrave, London, pp. 201-224.

123

Impact ofinnovationcapability

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Feng, M., Terziovski, M. and Samson, D. (2008), “Relationship of ISO 9000:2000 quality systemcertification with operational and business performance”, Journal of ManufacturingTechnology Management, Vol. 19 No. 1, pp. 22-37.

Flynn, B., Schroeder, G. and Sakakibara, S. (1995), “The impact of quality management practiceson performance and competitive advantage”, Decision Sciences, Vol. 26 No. 5, pp. 659-691.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservablevariables and measurement error”, Journal of Marketing Research, Vol. XVIII Nos 7/8,pp. 39-50.

Forsman, H. (2011), “Innovation capacity and innovation development in small enterprises.A comparison between the manufacturing and service sectors”, Research Policy, Vol. 40No. 1, pp. 739-750.

Forza, C. and Filippini, R. (1998), “TQM impact on quality conformance and customersatisfaction: a causal model”, International Journal of Production Economics, Vol. 55 No. 1,pp. 1-20.

Fynes, B. and Voss, C. (2001), “A path analytic model of quality practices, quality performance, andbusiness performance”, Production and Operation Management, Vol. 10 No. 4, pp. 494-513.

Fynes, B., Voss, C. and Burca, S. (2005), “The impact of supply chain relationship qualityon quality performance”, International Journal of Production Economics, Vol. 96 No. 3,pp. 339-354.

Galende, J. (2006), “Analysis of technological innovation from business economics andmanagement”, Technovation, Vol. 26 No. 5, pp. 300-311.

Garcia, R. and Calantone, R. (2002), “A critical look at technological innovation typology andinnovativeness terminology: a literature review”, The Journal of Product InnovationManagement, Vol. 19 No. 2, pp. 110-132.

Garcia-Vega, Μ. (2006), “Does technological diversification promote innovation? An empiricalanalysis for European firms”, Research Policy, Vol. 35 No. 2, pp. 230-246.

Garvin, D.A. (1987), “Competing on the eight dimensions of quality”, Harvard Business Review,Vol. 65 No. 6, pp. 101-109.

Gill, J. (2009), “Quality follows quality: add quality to the business and quality will multiply theprofits”, The TQM Journal, Vol. 21 No. 5, pp. 530-539.

Guan, J. and Ma, N. (2003), “Innovative capability and export performance of Chinese firms”,Technovation, Vol. 23 No. 3, pp. 737-747.

Gunday, G., Ulusoy, G., Kilic, K. and Alpkan, L. (2011), “Effects of innovation types on firmperformance”, Int. J. Production Economics, Vol. 133 No. 9, pp. 662-676.

Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate DataAnalysis, 5th ed., Pearson Prentice-Hall, Upper Saddle River, NJ.

Han, J., Trienekens, J.H. and Omta, S.W.F. (2009), “Integrated information and logisticsmanagement, quality management and firm performance of pork processing industry inChina”, British Food Journal, Vol. 111 No. 1, pp. 9-25.

Handfield, R., Ghosh, S. and Fawcett, S. (1998), “Quality-driven change and its effects on financialperformance”, Quality Management Journal, Vol. 5 No. 3, pp. 13-30.

Hashi, I. and Stojcic, N. (2010), The Impact of Innovation Activities on Firm Performance Using aMulti – Stage Model: Evidence From the Community Innovation Survey 4, Center for Socialand Economic Research, Warsaw.

Heras, I., Casadesus, M. and Dick, G. (2002), “ISO 9000 certification and the bottom line: acomparative study of the profitability of Basque region companies”, Managerial AuditingJournal, Vol. 17 Nos 1/2, pp. 72-78.

124

JMTM26,1

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser, W.E. Jr and Schlesinger, L.A. (1997),“Putting the service profit chain to work”, Harvard Business Review, Vol. 72 No. 2,pp. 164-174.

Hult, G.T.M., Hurley, R.F. and Knight, G.A. (2004), “Innovativeness: its antecedents and impact onbusiness performance”, Industrial Marketing Management, Vol. 33 No. 3, pp. 429-438.

Jacobson, R. and Aaker, D.A. (1987), “The strategic role of product quality”, Journal of Marketing,Vol. 51 No. 4, pp. 31-44.

Jang, W. and Lin, C. (2008), “An integrated framework for ISO 9000 motivation, depth of ISOimplementation and firm performance”, Journal of Manufacturing TechnologyManagement, Vol. 19 No. 2, pp. 194-216.

Jenssen, J.I. and Randøy, T. (2006), “The performance effect of innovation in shipping companies”,Maritime Policy and Management, Vol. 33 No. 4, pp. 327-343.

Jiménez-Jiménez, D. and Sanz-Valle, R. (2011), “Innovation, organizational learning, andperformance”, Journal of Business Research, Vol. 64 No. 5, pp. 408-417.

Jin, Z., Hewitt-Dundas, N. and Thompson, N.J. (2004), “Innovativeness and performance: evidencefrom manufacturing sectors”, Journal of Strategic Marketing, Vol. 12 No. 4, pp. 255-266.

Johne, A. and Davies, R. (2000), “Innovation in medium-sized insurance companies: howmarketing adds value”, International Journal of Bank Marketing, Vol. 18 No. 1, pp. 6-14.

Kemp, R., Folkeringa, M., de Jong, J. and Wubben, E. (2003), “Innovation and firm performance,scales research reports”, EIM Business and Policy Research, Zoetermeer.

Keskin, H. (2006), “Marketing orientation, learning orientation, and innovation capabilities inSEMs: an extended model”, European Journal of Innovation Management, Vol. 9 No. 4,pp. 396-417.

Ketokivi, M.A. and Schroeder, R.G. (2004), “Manufacturing practices, strategic fit andperformance a routine-based view”, International Journal of Operations & ProductionManagement, Vol. 24 No. 2, pp. 171-91.

Kim, D.Y., Kumar, V. and Kumar, U. (2012), “Relationship between quality management practicesand innovation”, Journal of Operations Management, Vol. 30 No. 4, pp. 295-315.

Kleinknecht, A. and Mohnen, P. (Eds) (2002), Innovation and Firm Performance. EconometricExplorations of Survey Data, Palgrave, Basingstoke.

Koellinger, P. (2008), “The relationship between technology, innovation, and firm performance:empirical evidence from e-business in Europe”, Research Policy, Vol. 37 No. 4, pp. 1317-1328.

Koufteros, X. and Marcoulides, G. (2006), “Product development practices and performance: astructural equation modeling-based multi-group analysis”, Int. J. Production Economics,Vol. 103 No. 8, pp. 286-307.

Laforet, S. and Tann, J. (2006), “Innovative characteristics of small manufacturing firms”, Journalof Small Business and Enterprise Development, Vol. 13 No. 3, pp. 363-80.

Lakhal, L., Pasin, F. And Limam, M. (2006), “Quality management practices and their impacton performance”, International Journal of Quality & Reliability Management, Vol. 23 No. 6,pp. 625-646.

Lee, C.C., Yang, J. and Yu, L.M. (2001), “The knowledge value of customers and employees inproduct quality”, Journal of Management Development, Vol. 20 No. 8, pp. 691-704.

Li, Y., Su, Z. and Liu, Y. (2012), “Can strategic flexibility help firms profit from productinnovation?”, Technovation, Vol. 30 No. 1, pp. 300-309.

Lin, C. and Chen, M.Y.C. (2007), “Does innovation lead to performance? An empirical study ofSMEs in Taiwan”, Management Research News, Vol. 30 No. 2, pp. 115-132.

125

Impact ofinnovationcapability

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Lin, C. and Jang, W. (2008), “Successful ISO 9000 implementation in Taiwan: how can we achieveit, and what does it mean?”, International Journal of Productivity and PerformanceManagement, Vol. 57 No. 8, pp. 600-622.

Loof, H., Heshmati, A., Asplund, R. and Naas, S. (2002), “Innovation and performance inmanufacturing industries: a comparison of Nordic countries”, working paper, SSE/EFIStockholm School of Economics, Stockholm.

Luning, P.A., Marcelis, W.J. and Jongen, W.M.F. (2002), “Food quality management, managementimplementation”, International Journal of Commerce and Management, Vol. 21 No. 2,pp. 158-191.

Markham, S.K. and Griffin, A. (1998), “The breakfast of champions: associations betweenchampions and product development environments, practices, and performance”, Journalof Product Innovation Management, Vol. 5 Nos 5-6, pp. 436-454.

Martinez-Costa, M. and Martinez-Lorente, A.R. (2008), “Does quality management foster or hinderinnovation? An empirical study of Spanish companies”, Total Quality Management &Business Excellence, Vol. 19 No. 3, pp. 209-221.

McAdam, R. and Keogh, K. (2004), “Transitioning towards creativity and innovationmeasurement in SMEs”, Creativity and Innovation Management, Vol. 13 No. 2, pp. 126-141.

Narayan, B., Rajendran, C. and Sai, L.P. (2008), “Scales to measure and benchmark service qualityin tourism industry”, Benchmarking: An International Journal, Vol. 15 No. 4, pp. 469-493.

Naser, K., Karbhari, Y. and Mokhtar, M.Z. (2004), “Impact of ISO 9000 registration oncompany performance: evidence from Malaysia”, Managerial Auditing Journal, Vol. 19No. 4, pp. 509-516.

Nassimbeni, G. (2001), “Technology, innovation capacity, and the export attitude of smallmanufacturing firms: a logit/tobit model”, Research Policy, Vol. 30 No. 2, pp. 245-262.

Naveh, E. and Marcus, A. (2005), “Achieving competitive advantage through implementing areplicable management standard: installing and using ISO 9000”, Journal of OperationsManagement, Vol. 24 No. 1, pp. 1-26.

O’Mara, C.E., Hyland, P.W. and Chapman, R.L. (1998), “Performance measurement and strategicchange”, Managing Service Quality, Vol. 8 No. 3, pp. 178-182.

OECD (1997), Information Technology Outlook, OECD, Paris.

OECD (2005), Oslo Manual: Proposed Guidelines for Collecting and Interpreting TechnologicalInnovation Data, OECD, Paris.

Oke, A., Burke, G. and Myers, A. (2007), “Innovation types and performance in growing UKSMEs”, International Journal of Operations & Production Management, Vol. 27 No. 7,pp. 735-753.

Ou, C.S., Liu, F.C., Hung, Y.C. and Yen, D.C. (2010), “A structural model of supply chainmanagement on firm performance”, International Journal of Operations & ProductionManagement, Vol. 30 No. 5, pp. 526-545.

Panayides, P.M. (2006), “Enhancing innovation capability through relation- ship managementand implications for performance”, European Journal of Innovation Management, Vol. 9No. 4, pp. 466-483.

Panigyrakis, G., Kapareliotis, I. and Ventoura, Z. (2009), “Marketing and corporate profitability:the case of Greek firms”, Managerial Finance, Vol. 35 No. 11, pp. 909-917.

Peng, D., Schroeder, R. and Shah, R. (2011), “Competitive priorities, plant improvementand innovation capabilities, and operational performance: a test of two formsof fit”, International Journal of Operations & Production Management, Vol. 31 No. 5,pp. 484-510.

126

JMTM26,1

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Prajogo, D., McDermott, P. and Goh, M. (2008), “Impact of value chain activities on quality andinnovation”, International Journal of Operations & Production Management, Vol. 28 No. 7,pp. 615-635.

Prajogo, D.I. and Sohal, A.S. (2004), “The multidimensionality of TQM practices in determiningquality and innovation performance—an empirical examination”, Technovation, Vol. 24No. 6, pp. 443-453.

Primrose, P.L. and Leonard, R. (1987), “Performing investment appraisals for advancedproduction competence and additional evidence of its impact on business performance”,International Journal of Operations and Production Management, Vol. 30 No. 6, pp. 548-583.

Psomas, E. and Fotopoulos, C. (2009), “A meta analysis of ISO 9001:2000 research – findingsand future research proposals”, International Journal of Quality and Services Sciences,Vol. 1 No. 2, pp. 128-144.

Psomas, E., Fotopoulos, C. and Kafetzopoulos, P. (2011), “Core process management practices,quality tools and quality improvement in ISO 9001 certified manufacturing companies”,Business Process Management Journal, Vol. 17 No. 3, pp. 437-460.

Rothwell, R. and Gardiner, P. (1998), “Reinnovation and robust designs: producer and userbenefits”, Journal of Marketing Management, Vol. 3 No. 3, pp. 372-387.

Sadikoglu, E. and Zehir, C. (2010), “Investigating the effects of innovation and employeeperformance on the relationship between total quality management practices and firmperformance: an empirical study of Turkish firms”, International Journal of ProductionEconomics, Vol. 127 No. 1, pp. 13-26.

Sebastianelli, R. and Tamimi, N. (2002), “How product quality dimensions relate to definingquality”, International Journal of Quality & Reliability Management, Vol. 19 No. 4, pp. 442-453.

Singh, P. (2008), “Empirical assessment of ISO 9000 related management practices andperformance relationships”, Int. Journal of Production Economics, Vol. 113 No. 1, pp. 40-59.

Singh, P., Powera, D. and Chuong, S. (2011), “A resource dependence theory perspective of ISO9000 in managing organizational environment”, Journal of Operations Management,Vol. 29 Nos 1/2, pp. 49-64.

Sousa, R. and Voss, C. (2002), “Quality management re-visited: a reflective review and agenda forfuture research”, Journal of Operations Management, Vol. 20 No. 1, pp. 91-109.

Stock, G., Greis, N. and Fischer, W. (2002), “Firm size and dynamic technological innovation”,Technovation, Vol. 22 No. 4, pp. 537-549.

Su, Q., Li, Z., Zhang, S.X., Liu, Y.Y. and Dang, J.X. (2008), “The impacts of quality managementpractices on business performance. An empirical investigation from China”, InternationalJournal of Quality & Reliability Management, Vol. 25 No. 8, pp. 809-823.

Subramanian, A. and Nilakanta, S. (1996), “Organisational innovativeness: exploring therelationship between organisational determinants of innovation, types of innovations, andmeasures of organisational performance”, Omega, Vol. 24 No. 6, pp. 631-647.

Tomlinson, P. (2010), “Co-operative ties and innovation: some new evidence for UKmanufacturing”, Research Policy, Vol. 39 No. 5, pp. 762-775.

Uyar, A. (2009), “Quality performance measurement practices in manufacturing companies”, TheTQM Journal, Vol. 21 No. 1, pp. 72-86.

Varis, M. and Littunen, H. (2010), “Types of innovation, sources of information and performancein entrepreneurial SMEs”, European Journal of Innovation Management, Vol. 13 No. 2,pp. 128-154.

Walker, R.M. (2004), “Innovation and organizational performance: evidence and a researchagenda”, Research Working Paper, WP No. 2, Advanced Institute of Management,University of Hong Kong, Hong Kong, June.

127

Impact ofinnovationcapability

Dow

nloa

ded

by U

nive

rsid

ad d

e A

ntio

quia

At 1

6:14

23

Mar

ch 2

015

(PT

)

Wang, C., Iuan-yuan, L. and Chen, C. (2008), “Evaluating firm technological innovation capabilityunder uncertainty”, Technovation, Vol. 28 No. 6, pp. 349-363.

Wang, L.C. and Ahmed, K.P. (2004), “The development and validation of the organisationalinnovativeness construct using confirmatory factor analysis”, European Journal ofInnovation Management, Vol. 7 No. 4, pp. 303-313.

Weerawardena, J. (2003), “The role of marketing capability in innovation based competitivestrategy”, Journal of Strategic Marketing, Vol. 11 No. 1, pp. 15-36.

Wonglimpiyarat, J. (2010), “Innovation index and the innovative capacity of nations”, Futures,Vol. 42 No. 3, pp. 247-253.

Wu, S.I. and Liu, S.Y. (2010), “The performance measurement perspectives and causalrelationship for ISO-certified companies: a case of optoelectronic industry, InternationalJournal of Quality & Reliability Management, Vol. 27 No. 1, pp. 27-47.

Yam, C.M., Guan, J.C., Pun, K.F. and Tang, P.Y. (2004), “An audit of technological innovationcapabilities in Chinese firms: some empirical findings in Beijing, China”, Research Policy,Vol. 33 No. 8, pp. 1123-1250.

Yam, R., Lo, W., Tang, E. and Lau, A. (2011), “Analysis of sources of innovation, technologicalinnovation capabilities, and performance: an empirical study of Hong Kong manufacturingindustries”, Research Policy, Vol. 40 No. 6, pp. 391-402.

Yang, C., Marlow, P. and Lu, C. (2009), “Assessing resources, logistics service capabilities,innovation capabilities and the performance of container shipping services in Taiwan”,International. Journal of Production Economics, Vol. 122 No. 6, pp. 4-20.

Yeung, A.L., Lee, T.S. and Chan, L.Y. (2003), “Senior management perspectives and ISO 9000effectiveness: an empirical research”, International Journal of Production Research, Vol. 41No. 3, pp. 545-569.

Further reading

Simpson, P., Siguaw, J. and Enz, A. (2002), “Innovation orientation outcomes: the good and thebad”, Journal of Business Res, Vol. 59 No. 1, pp. 1133-1141.

Svandven, T. and Smith, K. (2000), “Innovation and economic performance at the enterpriselevel”, Conference Innovation and Enterprise Creation: Statistics and Indicators, Universityof Tasmania, Sophia Antipolis.

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Appendix

Dimensions Items Code

Product innovationThe company introduces new and innovative products intothe market V001The company has the capability to bring in new knowledge ortechnologies to develop new products V002Company efforts to develop new products in terms of hours/persons,team and training involved V003The company has the capability to use new materials, new productfunction and new design V004The company΄s products are modified and improved V005The company enhances the manufacturing technology ofnew products V006

Process innovationThe company has a pioneer disposition to introduce new processes V007The company has the capability to adjust the processes atall levels concerning the production process, inventory, distribution,logistics, etc. V008The company displays clever response to new processes introduced byothers companies V009The company improves existing machinery and equipment. V010The company uses machinery adaptations and develops originalprocessing solutions V011

Marketing innovationThe company has close relationship management with majorcustomers V012The company has good knowledge of different market segments V013The company has a highly efficient sales-force V014The company’s product distribution is efficient V015The company has good knowledge of market conditions V016

Organizational innovationThe company has good coordination and cooperation of R&D, sales,marketing and manufacturing departments V017The company has high level integration and control of the majorfunctions V018The company has a high capacity for developing and gaining access tonew technologies V019The company has a high level of capability at identifying theinnovative strategy of competitors V020The company is highly capable of identifying external opportunitiesand threats V021

Product qualityProduct’s perceived quality compared with that ofcompetitors V022Product’s reliability V023Product’s general performance V024Product’s durability V025Product’s conformance to company’s specifications V026

(continued )

Table AI.Dimensions of

innovation, firmperformance andassociated items

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About the authorsDr Dimitrios Kafetzopoulos, MSc, PhD is a Research Assistant in the Department of BusinessAdministration of Food and Agricultural Enterprises in the University of Patras, Greece. Hereceived a PhD in Quality Management at the University of Ioannina, Greece, in 2011. He hasdealt with issues of Management and his research interests include: Food Safety, QualityManagement, Operational Management, Business Performance, Competitiveness, Innovation andHuman Resource Management. Dr Dimitrios Kafetzopoulos is the corresponding author and canbe contacted at: dimkafe@yahoo.gr

Dr Evangelos Psomas, PhD is a Research Assistant in the Department of BusinessAdministration of Food and Agricultural Enterprises in the University of Patras, Greece. Hereceived a PhD in Total Quality Management at the University of Ioannina, Greece, in 2008. Hehas dealt with issues of Management and Marketing and has worked as a Teaching Assistant inthe University of Ioannina and Technological Educational Institute of Epirus. His researchinterests include: Total Quality Management, Quality Assurance, Food Safety Management,Human Resource Management, Supply Chain Management, Agribusiness and Food Marketing.

Table AI.

Dimensions Items Code

Operational performanceCompany’s productivity V027Company’s efficiency V028Company’s costs of supplies, production and sales V029Company’s process effectiveness V030Company’s delivery of products to customers on time andin the right place and quantity V031

Financial performanceCompany’s profitability V032Company’s financial results V033Company’s net profit margin V034Company’s sales growth during the last three years V035Company’s cash flow V036Company’s market growth during the last three years V037

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