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The TQM Journal

Investigating the role of EFQM enablers in innovation performance

Journal: The TQM Journal

Manuscript ID TQM-09-2018-0124.R1

Manuscript Type: Research Paper

Keywords: Ιnnovation, Manufacturing, Total Quality Management, EFQM

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Investigating the role of EFQM enablers in innovation

performance

Abstract

Purpose – The purpose of this paper is to develop a conceptual framework to investigate how

the EFQM excellence model enablers influence the four types (product, process,

organizational and marketing) of innovation performance of a manufacturing firm.

Design/methodology/approach – The study uses survey data from a sample of 580

manufacturing firms in Greece and employs Structural Equation Modeling (SEM) to test the

developed hypotheses.

Findings – The results reveal that enablers of the EFQM model are either directly or

indirectly associated with the four types of innovation. Furthermore, the findings show that it

is essential for a firm to manage all the facilitating enablers included in the EFQM model,

since they cannot boost innovation when implemented in isolation.

Research limitations/implications –The sample is limited to one country (Greece). In

addition, researchers have to assess the same relationships considering the effect of external

factors such as environmental uncertainty. The potential consequences of enablers on

innovation performance may be mediated by customer or people results.

Practical implications – The empirical findings of the present study help managers to

develop the appropriate quality strategies and allocate the respective resources according to

the desired type of innovation.

Originality/value – Based on the multi-dimensional structure of quality, this empirical study

determines the contribution of EFQM model enablers to specific innovation performance

dimensions of manufacturing companies.

Keywords: Ιnnovation, Manufacturing, Total Quality Management, EFQM

Paper type: Research paper

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1. Introduction

The recognition of quality management (QM) as high-profile activity that is capable of

offering organizations improved performance is widespread around the world (Zeng et al.,

2015). Thus, an increasing number of companies have been implementing QM standards in

order to improve quality and witness the related benefits (Kafetzopoulos et al., 2013).

However, QM alone is not enough for the competitiveness and survival of organizations.

Nowadays, the basis of competitive advantage swiftly shifted from quality to innovation as

the other fundamental component of entrepreneurship, a key factor for the survival and an

important source of competitive advantage of any firm. The body of literature adopting a

comprehensive analysis of the European Foundation for Quality Management Model (EFQM)

quality practices and outcomes is limited (Bou-Llusar et al., 2005). The list is shorter if we

seek analysis based on causal relationships within the EFQM model enablers (leadership,

people, policy and strategy, partnership and resources, processes) since it is mostly based on

studies that test isolated associations (Bou-Llusar et al., 2005). Despite the fact that the aim of

the EFQM model is to support organisations to achieve business excellence through

innovation (Calvo-Mora et al. (2015), only a few researchers have investigated the influence

of EFQM model enablers on the QM-innovation relationship (Raja and Wei, 2014; Santos-

Vijande and Álvarez-González, 2007). Therefore, as Calvo-Mora et al. (2015) propose, future

research should be focused on exploring the EFQM model enablers and investigate the

relationships that are produced between these criteria with the results (Kim et al., 2010).

Heras-Saizarbitoria et al. (2012) and Calvo-Mora et al. (2005) propose the enabler of process

management as a key mediating variable in the EFQM model. However, there is a lack of

studies that specifically test this mediating function (Suarez et al., 2014). In this paper we take

into consideration the EFQM model enablers as innovation drivers and specifically process

management as a key mediator. We also study in depth the development of product, process,

organizational and marketing innovations, as the innovation performance dimensions of a

company.

This offers a significant contribution to the current body of QM by offering a scientific

evaluation of the EFQM model as an operational framework of QM and exploring the causal

relationships among the EFQM’s enablers. Earlier research failed to explain which QM

practices are directly or indirectly associated with innovation (Kim et al., 2012). This study

provides evidence in order to answer two significant research questions: a) Are EFQM model

enablers directly and indirectly related to innovation? And b) is process management a key

mediator of the EFQM model enablers’ effect on innovation? (Suarez et al., 2014).

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Furthermore, the article follows the suggestions of Kafetzopoulos et al. (2015), Kim et al.

(2012), Cole and Matsumiya (2008) and Martínez-Costa and Martínez-Lorente (2008) that

various types of innovation need to be tested to correctly understand the real value of QM on

innovation. Although there is a plethora of research on this issue, the conclusions are not still

clear (Gómez et al., 2015).

In this respect, the purpose of this study is (a) to empirically investigate the existing

relationships among the EFQM model enablers, (b) to assess the mediator role of process

management and to provide an enhanced understanding of how this enabler is associated with

the four types of innovation: product, process, organizational and marketing, (c) to identify

which EFQM model enablers are the most important or influential when managing for

innovation performance and obtaining better results (indirect association).

The rest of the paper is organized as follows. In the next section, a review of previous

literature is presented, followed by the research hypotheses and the related research model.

Section 3 describes the methodology used in this study, including data collection,

measurement scales, measurement analysis, and hypothesis testing, and section 4 presents the

results of the empirical study. Finally, the paper concludes with a discussion of the main

findings, implications from this study, providing suggestions for future research.

2. Theoretical background and hypotheses

2.1 The European Foundation for Quality Management (EFQM) model

Despite the plethora of studies published, there is still a basic difficulty in evaluating

TQM (Sila and Ebrahimpour, 2003). Calvo-Mora et al. (2015) differentiate three types of

frameworks for the implementation of TQM: (a) frameworks based on quality experts or

gurus, (b) excellence models or quality awards, and (c) models extracted from theoretical

and/or empirical research (Suarez et al., 2016). In the European context, the excellence model

most widely used among companies is the EFQM. According to Bauer et al. (2005), for a

measure of content, award models and the EFQM Business Excellence model in particular,

included the idea of self-assessment. The EFQM model, apart from being an unavoidable

reference in quality-related issues, can also be used as an efficient tool to measure the

resources and capabilities of a firm (Ruiz-Carrillo and Fernández-Ortiz, 2005). The EFQM

model comprises nine elements grouped under five “Enablers” criteria (Leadership, People,

Policy and Strategy, Partnerships and Resources and Processes, Products and Services) and

four “Results” criteria (Customer Results, People Results, Society Results, and Key Results)

(Gomez-Gomez et al., 2015; Ruiz-Carrillo and Fernández-Ortiz, 2005). “Results” are caused


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by “enablers”. They are not independent; they must be implemented together and in a

coordinated fashion (Calvo-Mora et al., 2006). The “Enablers” criteria cover what an

organization does and how it does it. In this study, the five EFQM model enablers are

considered as the essential principles and practices for QM to produce the desired effects on

an organization’s innovation results and performance (Calvo-Mora et al., 2015; Sila and

Ebrahimpour, 2003). The EFQM model enablers are measured in the present study through

indicators that have been drawn from the studies of Suarez et al. (2016), Bou-Llusar et al.

(2009), Santos-Vijande and Álvarez-González (2007) and Calvo-Mora et al. (2005) and

Eskildsen et al. (2001).

2.2 The Enablers Criteria and their Interrelationships

Εmpirical work supports the existence of interrelationships between the enabler sides

of the EFQM model, based on the assumption that these criteria are components of the unique

TQM philosophy. More specifically, the criterion related to Leadership asserts that the leader

of an organization should establish the direction for the organization, the main quality goals,

the allocation of resources, and a clear vision of the organization's future (Manders et al.,

2016). Management leadership acts as a driving force in the implementation, development and

improvement of QM within a flexible, innovative and stakeholder oriented organisational

culture (Suarez et al., 2016). The Leadership principles require creating a fertile

organizational environment for sharing ideas, encouraging and recognizing the contribution of

employees and giving them the necessary resources and training (Manders et al., 2016). The

EFQM model ascribes an important role to leadership, with ‘leadership’ considered as the

driver behind ‘strategy’, behind ‘people’ and behind ‘partnerships and resources’ (Zapata-

Cantu et al., 2016). The leadership of the management has a positive influence on policy and

strategy (e.g. Calvo-Mora et al., 2005; Dijkstra, 1997; Eskildsen et al., 2000), considered as

one of the most important factors for the TQM’s success. Leadership should plan to reduce

traditionally structured operational levels and unnecessary positions (Motwani, 2001)

directing the organisations to achieve their results, through the provision of people, resources,

partnerships and the formulation of policies and strategy (Heras-Saizarbitoria et al., 2012;

EFQM, 2003). For example, the recent study of Suarez et al. (2016) showed that the actions

and the commitment of leaders are made effective through the design and implementation of

key processes, suitable resource management, people and the establishment of alliances with

the main suppliers and partners. Calvo-Mora et al. (2006) who analyzed the implicit

relationships among enabler agents of the European excellence model in higher education


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institutions, concluded that ‘leadership and commitment’ has a strong positive influence on

‘policy and strategy’, a significant positive influence on ‘people’ and a weak positive impact

on ‘partnerships and resources’. Furthermore, Eskildsen and Dahlgaard (2000) concluded that

leadership determines the management of people and processes, as well as the formulation of

policy and strategy. This study analyses the role of leadership as the force that drives

organisations to achieve innovation performance, through its influence on “people”,

“partnerships and resources” and “policy and strategy”. Thus, the following hypotheses are

developed:

H1a. Leadership has a positive influence on people.

H1b. Leadership has a positive influence on policy and strategy.

H1c. Leadership has a positive influence on partnerships and resources.

It is important to note that the criterion of “policy and strategy” should not be

detached from “people” and from “partnerships and resources” (Heras-Saizarbitoria et al.,

2012; EFQM, 2003). In this sense, Winn and Cameron (1998) maintain that once it has been

designed, the strategy must be put into practice through the deployment of key processes, a

right human resources management, and the establishment of alliances and other types of

cooperation agreements. Strategy development in a multi-partner, collaborative environment

requires the resolution of the basic dilemma of valuing sustainability and well-being of the

network over the interests of individual actors. The organizational policy and strategy is based

on the current and future needs and expectations of the stakeholders while taking into account

the particularities of the market the organization is working in and the internal organizational

environment (EFQM 2003). Eskildsen and Kanji (1998) demonstrate that policy and strategy

condition people and resources management. Thus, to realise their strategies, organizations

must develop and deploy policies, plans, goals and processes based on the capabilities of their

people and their partnerships and resources. Based on the above, the following hypotheses are

formulated:

H2a. Policy and strategy have a positive influence on people.

H2b. Policy and strategy have a positive influence on partnerships and resources.

Eskildsen and Kanji (1998) show how people and organizational processes are

interrelated while Calvo-Mora et al. (2005) and Eskildsen and Dahlgaard (2000), also

concluded to a positive influence between management of people and process management.

According to the criterion of Partnerships and Resources, the long-lasting objective of the

organization should be to manage external partnerships, suppliers and internal resources in


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order to support policy and strategy and the effective operation of processes. Firms must

cooperate with their suppliers, working closely in specific processes and operations, in order

to increase the value chain of all organizations. Partnerships with suppliers have the greatest

appeal to most companies due to the shared risks associated with the development of new

products (Motwani, 2001). Excellent organisations are linked through alliances with agents

that optimize the value chain. Partnership development with suppliers belongs to the best

principles/ criteria when the experts and companies had to provide their opinion on the

concept of business excellence (Cockalo et al., 2011). Collaboration and cooperation relations

should be based on confidence, honesty and transparency. The criterion of “Processes,

products and services” states that an effective and efficient organisation identifies its core

processes, considering the expectations of customers and other stakeholders (Sila and

Ebrahimpour, 2003). According to the EFQM model, processes are the connecting link

between all the rest critical QM factors (enablers) and the results (Suarez et al., 2016). The

EFQM model is based on the logical assumption that there is an internal structure between the

enabler criteria. It shows that leadership drives policy and strategy, people management and

partnerships and resources, while these three enablers influence an organization’s overall

results through suitable process management (EFQM, 2012). Dijkstra (1997) conducted a

study to identify the enablers’ structure of the EFQM Excellence Model, the results of which

indicate that positive and moderate associations exist between the enabler criteria because of

the existence of a common latent general factor behind them. According to Eskildsen and

Dahlgaard (2000), the three criteria ‘people’, ‘policy and strategy’ and ‘partnerships and

resources’ all have an influence on the enabler of ‘processes’. Heras-Saizarbitoria et al.

(2012)) suggest that the criteria of people, partnerships and resources and policy and strategy

impact the criteria of processes, products and services. Furthermore, many other researchers

(e.g. Calvo-Mora et al., 2005; Su et al., 2003) conclude that the enablers policy and strategy,

people management and partnership and resources have a positive influence on process

management. From the above, the following three research hypotheses are developed:

H3. People have a positive influence on processes, products and services.

H4. Policy and strategy have a positive influence on processes, products and services.

H5. Partnership and resources have a positive influence on processes, products and services.

2.3 Classification of innovation

Innovation is a complex and multifaceted concept (Prajogo, 2016; Helmi Ben Rejeb

and Morel‐Guimaraes, 2011). It refers to all scientific, technological, organizational, financial


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and commercial activities which lead to, or are intended to lead to, the implementation of

technologically new or improved products or services (OECD, 2005). Literature distinguishes

various types of innovation and researchers have explored its classification in different ways.

Some studies examined a single type of innovation such as process innovation or product

innovation (Zeng et al., 2017; Prajogo and Sohal, 2004), whereas others explored both

process and product innovation (Prajogo, 2016; Camisón and Villar-Lópezand, 2014;

Martinez-Costa and Martinez-Lorente, 2008). Many studies conceptualize innovation related

to marketing and organizational innovation (Wonglimpiyarat, 2010; Chang et al., 2012). In

Organization for Economic Co-operation and Development (OECD) Oslo Manual, which is

the primary international basis of guidelines for defining and assessing innovation activities

and collecting - interpreting innovation data, innovation is distinguished between four main

types, namely, product, process, organizational and marketing innovation. The current study

is based on the classification of innovation described in the OECD Oslo Manual (OECD,

2005). It particularly focuses on these four types of innovation, whose relationship with QM

suffers more ambiguity and less investigation in previous literature, compared to other types

of innovation (e.g. radical, incremental, administrative or technological). The same

classification of innovation is also adopted in the studies of Tavassoli and Karlsson (2015),

Kafetzopoulos and Psomas (2015), Avermaete et al. (2003) and Bernardo (2014).

Product innovation: significant changes in the capabilities of goods or services;

Process innovation: the implementation of a new or significantly improved production

or delivery method, including significant in techniques, equipment and/or software;

Organizational innovation: the implementation of a new organizational method in the

firm's business practices, workplace organization or external relations;

Marketing innovation: the implementation of new marketing methods.

The innovation performance is measured in the present study through indicators that

have been drawn from the studies of Yam et al. (2011), Gunday et al. (2011), Forsman,

(2011), Kafetzopoulos et al. (2015) and Prajogo (2016).

2.4 The relationships between process, products, services and innovation types

The enabler related to processes, products and services, is one of the most potentially

rewarding improvement areas for innovation. Gustafson and Hundt (1995) showed that the

QM practices of process focus, was critical to innovation success. Moreover, Perdomo-Ortiz

et al. (2006) identify that process management stand out for the establishment of business

innovation capability. The process management explains the results in relation to product,


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process, organizational and marketing innovation and all these factors in turn ultimately

explain innovation performance. When a firm manages effectively its processes, it pays more

attention to vital issues and avoids activities that do not add value. These same aspects have

also been found to be positively related to the various types of innovation performance (Kim

et al., 2012; Perdomo-Ortiz et al., 2006) such as product, process, organizational and

marketing innovation. The empirical findings by Kim et al. (2012) highlight the critical role

of process management through which a firm can identify potential innovation areas, develop

innovation plans, and produce innovative products. According to Satish and Srinivasan (2010)

management by processes has an impact on product and process innovation. QM has a great

emphasis on processes and it would be closely related to process innovation (Zeng et al.,

2017). In order for a firm to be competitive in the market, it must focus on key process

management activities that fulfil specifications and customers’ requirements. Receiving

immediate and useful feedback from the manufacturing process is instrumental in speeding

new product to the market (Flynn, 1994). This attitude enhances innovation marketing. To

sum up, process management allows firms to develop and document best practices so as to

gain a better know-how for identifying specific problems. Process management is crucial for

innovation to actually occur, contrary to previous arguments stating that organizations

managed by principles of QM are less flexible and have less desire to innovate (Perdomo-

Ortiz et al., 2006). To sum up, processes management is a broad concept that includes

processes that fulfil the expectations of customers and other stakeholders for continuous

improvement and innovation (Sila and Ebrahimpour, 2003). Based on the above, it is logical

to assume that processes, product and service management activities assist firms to improve

their innovation performance and they are directly associated with each type of innovation.

Therefore, the following hypotheses are tested:

H6. Processes, products and services management has a positive influence on product

innovation.

H7. Processes, products and services management has a positive influence on process

innovation.

H8. Processes, products and services management has a positive influence on organizational

Innovation.

H9. Processes, products and services management has a positive influence on marketing

Innovation.


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2.5 The mediating role of processes, products and services management

Previous literature points out the key mediating role that process management play in

the EFQM model (Heras-Saizarbitoria et al., 2012; Calvo-Mora et al., 2005). The EFQM

model is based on the idea that the criterion of processes, products and services is the link

between the remaining critical factors of QM and results (EFQM, 2003). More specifically,

the EFQM model suggests a causal relationship among the different criteria that comprise it,

from left to right, ranging from criteria of a more strategic nature (leadership) to operative

results (processes results) (Gomez-Gomez et al., 2015; Heras-Saizarbitoria et al., 2012;

EFQM, 2003). Suarez et al. (2016) conclude that process management fully mediates the

influence of strategy, alliances and resources management on overall results. The idea is that a

firm’s innovation performance is embedded on its processes and can be strengthened through

their effective management. To sum up, this study suggests that the relationship between

leadership, people, policy and strategy, partnerships and resources and the four dimensions of

innovation is mediated by processes management (although non-hypothesized in order to

keep the model simple).

2.6 The conceptual model

Based on the inter-relations put forward by the EFQM model (EFQM, 2012), the

structural research model, as illustrated in figure 1, was developed in order to analyze the

impact of the EFQM enabler criteria on innovation performance. Each relationship is double

checked using the prior empirical findings based on the EFQM and innovation literature (see

table 1). The empirical validation of the proposed structural model will provide insights into

how excellence in the enablers of the EFQM model explains the achievement of excellent

results in the main types of a company’s innovation performance.

Insert figure 1 about here

3. Methodology

3.1 Sample and data collection

To test the proposed structural model of this study, a survey questionnaire was used as

the data collection instrument. The population of the study was selected from a list consisted

of manufacturing companies from all sectors and size in Greece, so as not to bias the final

results. A total of 2720 questionnaires were e-mailed to the Chief Executive Officer or

middle-level managers and 591 were finally received. To improve the response rate, three

waves of e-mails were sent, resulting in three waves of responses comprising 295, 161 and


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135 questionnaires, respectively. Eleven questionnaires were returned as incomplete and were

not included in the study. Finally, 580 usable responses were obtained. The primary industries

in which the responding firms operate include: 43 metal (7.42 %); 48 machinery (8.28 %); 32

plastic and chemical (5.52 %); 47 wood (8.1 %); 46 leather and textile (7.92 %); 14 drugs and

cosmetics (2.41 %); 215 food and beverages (37.2 %); 75 agriculture products (12.94 %); and

60 other industries, such as electrical equipment and food packaging (10.35 %). With regard

to size, 73% were small firms, 19% medium-sized firms, and 8% large firms. The

predominance of small and medium-sized firms is representative of the Greek industry.

3.2 Response bias

To examine possible bias in self-reported survey data, we compared the responding

companies of the three successive phases in terms of the number of their employees (Kruskal

Wallis test) and the questionnaire items (one-way ANOVA) (Kim et al., 2012), and no

statistically significant differences were detected. Additionally, the One-Way ANOVA test

was used in order to detect possible differences (in the mean value of their measured

variables) among the manufacturing companies’ sub-sectors based on their demographic

profile (small, medium-sized and large firms). Similarly, no statistically significant

differences were found between these groups indicating that there was no bias regarding the

subjects examined, thus, non-response bias is not likely to be an issue in the final sample.

Moreover, the common method variance had to be checked to ensure no major problems with

the data. From the analysis it can be concluded that common method variance is not a major

concern in this study. Consequently, calculating the Mahalanobis d-squared distance, no

observations exceed the threshold value of 3 and so, no data points are deleted, leaving 558

observations for the analysis

3.3 Measures

To design the measurement instrument, previous measurement items addressed in the

literature were used. Following the self-assessment philosophy of the EFQM model, the

measurement instrument was based on its 5 main enabler criteria. These criteria were assessed

through 24 sub-criteria questions. Synthesizing previous studies, four types of innovation

were measured (product, process, organizational and marketing innovation) using 19 items.

Moreover, nine people from Greek manufacturing firms were asked to examine the draft

questionnaire and suggest possible improvements. Before conducting the main survey, pilot-

tests of the questionnaire were carried out in twenty enterprises. Based on the managers'

comments, the measurement instrument of the study and the related items were finalized. A


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seven-point Likert scale was used throughout to measure all the items of the research

instrument. The respondents were requested to indicate their level of agreement with the

items, based on how well they reflected the situation at their work site, ranging from “1 =

strongly disagree” to “7 = strongly agree” (Tsai and Yang, 2013). The initial set of items

included in the instrument was adapted from the original model which has been previously

validated by many researchers (see table 1).

Insert table 1 about here

4. Measurement analysis and results

4.1 Reliability and validity

A range of statistical tests was performed on data obtained from the survey to assess

the reliability and validity of the model as suggested by Hair et al. (2006). The Statistical

analysis software SPSS 22 (Statistical Package for Social Sciences) and AMOS 6.0 (Analysis

of MOment Structures) were used for the statistical processing of the data. First, an

exploratory factor analysis (EFA) was conducted, to check factor loadings of each item

(Sadikoglu and Zehir, 2010). Nine latent factors were extracted - five for the EFQM model

enablers and four for the innovation types - (Kaiser-Meyer-Olkin = 0.943, Bartlett’s test of

Sphericity = 15488.760, p = 0.00, eigen-value>1, MSA>0.80, factor loadings >0.655),

explaining 68.928 percent of the total variance. The examination of factor loadings indicated

that all items loaded significantly on their expected constructs, and didn’t demonstrate cross-

loadings >0.35 on more than one latent factor. Thus, 43 items were used to run CFA and

assess the fit of the measurement model and unidimensionality. Following Kim et al. (2012),

CFA was conducted to examine measurement models of each construct separately. The

goodness of fit statistics showed a good fit of all measurement models to the data. After that,

CFA was again performed to assess the measurement model and the results confirmed the

factors revealed by EFA. The fit indexes for the measurement model (see Table 2) indicated a

good fit of the model to the data based on the recommended criteria: (χ2/df) value <3.0, CFI,

IFI, TLI and NFI > 0.9, SRMR <0.08, and RMSEA <0.08 (Hair et al., 2006). Thus, it was

concluded that all constructs were unidimensional.


Moreover, it’s necessary to assess the reliability of the constructs (Flynn et al., 1994).

The most common method for measuring reliability of self-administered survey

questionnaires involves estimating internal consistency. Cronbach’s alpha coefficient is


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commonly used as a measure of internal consistency. Table 3 shows the mean, standard

deviation, Cronbach’s alpha and correlations of the 9 unidimensional measurement scales

identified in the scale validation process.


The Cronbach's alpha values for the scales ranged from 0.774 to 0.911, exceeding the

recommended 0.70 threshold (Hair et al., 2006). Furthermore, based on the results of CFA,

this study calculated the composite reliability (CR) and the average variance extracted (AVE)

for each construct of the model. The composite reliabilities ranged from 0.760 to 0.950, while

the AVE ranged from 0.502 to 0.750. A value above 0.50 for AVE and 0.6 for CR of any

construct is accepted. The results provided evidence that each construct had an acceptable

level of reliability. Table 1 reports the results of the measurement analysis.

In the last stage, validity was assessed in terms of content, convergent and

discriminant validity. The review of literature as well as the results from the pilot study by a

group of experts in the field provided reassurance about the content validity of the instrument

(Singh 2008). The convergent validity of each construct of the model was assessed by

evaluating the factor loadings and the AVE in all cases as suggested by Tsai and Yang (2013)

and Hair et al. (2006). Factor loadings of all items were >0.5, significant at p values <0.001

and the signs were all positive. Furthermore, each item’s coefficient was greater than twice its

standard error, indicating significant items’ loadings on their respective factor, thus

demonstrating high convergent validity. The AVE values for all the constructs were above the

0.50 threshold (Fornell and Larcker, 1981). The results proved adequate convergent validity

for each construct (see table 1).

Discriminant validity checks whether the items estimate only the assigned latent factor

and no others (Singh, 2008). Discriminant validity, as proposed by Fornell and Larcker (1981)

is evaluated by comparing the AVE with the shared variance (i.e. square of the correlation)

between any pair of latent constructs (Singh et al., 2011). In each case, the AVE was greater

than the squared correlation between each pair of constructs, confirming the discriminant

validity (Hair et al., 2006) (see Table I). The results provided strong evidence that all of the

study constructs were reasonably reliable and valid.

4.2 Hypotheses testing

In this study, the two-step approach was chosen as the most suitable for testing the

hypothesized structural model (Hair et al., 2006). Using this approach, the measurement and


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structural models were evaluated sequentially. The goodness of the measurement model fit

was evaluated and the measured data are presented in Table 2. From this table it is observed

that the basics of goodness of fit, the absolute fit indices, the incremental fit indices and the

parsimony fit indices indicate an acceptable fit of the proposed model (Sadikoglu and Zehir,

2010; Hair et al., 2006). After performing the above test, structural equation modeling (SEM)

was applied (maximum likelihood method) to test the hypothesis of the study, using the

model illustrated in Fig. 1 as the base model. The structural model assesses the weights and

the magnitude of the relationships between the latent variables. Its aim is to confirm the extent

to which the causal relationships of the proposed model are consistent with the available data

(Calvo-Mora et al., 2014). Table 2 shows the results of the structural model, indicating that

the overall fit statistics for the structural model demonstrated an acceptable fit.

Fig. 2 shows the final structural model, depicting the SEM results regarding the

relationship between EFQM enablers and innovation types. Each path in the figure indicates

the associated hypotheses as well as the estimated path coefficients, p-values and square

multiple correlation (R²) for each construct. First, it can be seen that all hypotheses related to

leadership (H1a–H1c) are supported. Additionally, significant paths were found in the

relationships between the EFQM enablers people, policy and strategy, partnership and

resources and processes, products and services, supporting hypotheses H3, H4, and H5.

Finally, the result showed that processes, products and services is a significant and direct

predictor of all four types of innovation. These statistical significances supported hypotheses:

H6, H7, H8 and H9 (see Fig. 2). Table 4 shows the path analysis results of the structural

model.

Insert figure 2 about here


The model explains a substantial amount of variance in people (47%), policy and

strategy (37%), partnership and resources (57%) and processes, products and services (56%).

Moreover, the R² value for the four innovation types indicates a very satisfactory level of

predictability, since the proposed theoretical model explains 68%, 60 %, 62% and 56% of the

constructs’ variance, for product, process, organizational and marketing innovation

respectively. Furthermore, we examined the effects of how the four enablers (leadership,

people, policy and strategy and partnerships and resources) impact directly the four variables

of innovation performance. Looking at the difficulties arising in this kind of analysis, these

direct relationships are not included in a set of hypotheses. Sixteen direct paths (e.g.,


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leadership → product innovation; people → process innovation; policy and strategy →

marketing innovation) were examined to the proposed structural model. The goodness of fit

indices showed that the model has a good fit to the data: χ2/df = 2.21; IFI = 0.944; CFI =

0.944; RMSEA = 0.047; NFI = 0.902; RMR = 0.071; TLI = 0.937 and GFI = 0.883. Eight

paths were additionally found to be statistically significant. These paths were between policy

and strategy and the four types of innovation and between partnerships and resources and the

four types of innovation. In table 5 we can see the total indirect and direct effects of the

EFQM model enablers on product, process, organizational and marketing innovation.


5. Discussion and conclusion

Since the relationship between QM and innovation is rather controversial, this study

seeks to fill the literature gap, enhancing our understanding of interdependence among QM

practices and their relation to innovation. Unlike earlier studies which focused on a list of QM

practices that directly influence innovation (Kim et al., 2012), the proposed model comprises

the five EFQM model enablers as a QM framework, following Amundson’s (1998)

suggestion that researchers should use more complex issues regarding TQM implementation,

and not only TQM practices identified by other studies. The test of the hypothesized structural

model reveals the existence of high causal relationships and strong links among all the

enablers. The relationships are both significant and positive, proving their great

interdependency and supporting the related hypotheses posed in this study. More specifically,

the study confirms the importance of leadership, which is highly stressed in literature.

Research findings show that leadership plays a critical role in the EFQM model. It has a

significant direct impact on people (H1a), on policy and strategy (H1b) and on partnerships

and resources (H1c), and it is indirectly related to processes, products and services. The

results also demonstrate the importance of policy and strategy on people (H2a), partnerships

and resources (H2b) and processes, products and services (H4). This proves that the

significance of an individual enabler is strongly linked to the significance of all others. It

should also be pointed out that appropriate management of people and partnerships and

resources are keys to process management, since both criteria had a significant direct impact

on the processes, product and services criterion (H3, H5). The above findings are in line with

the results of previous studies regarding the EFQM model, such as Calvo-Mora et al. (2006),


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Eskildsen and Kanji (1998) or Dijkstra (1997), which show that implementation, development

and improvement of the criteria of the EFQM model should not be pursued independently.

Furthermore, this paper assesses a firm’s overall innovation performance based on

four key innovation types, named product, process, organizational and marketing innovation.

Overall, the results of this study indicate that the EFQM model enablers are interrelated and

directly influence the four innovation types through the “processes, products and services”

criterion. Appropriate statistical analysis supported the reliability and validity of the proposed

measurement model (Tables 2 and 3), and hence the high predictive power of the EFQM

model enablers as a framework for innovation performance. The study confirms a positive

relationship between the extent to which companies implement TQM and their innovation

performance. Effective implementation of the EFQM excellence model enablers enhances all

four types of innovation performance examined in this paper, especially when they are

collectively implemented. More specifically, processes, products and services is directly and

positively linked to each innovation type (H6, H7, H8, H9) while it mediates the influence of

the other four model enablers.

This study also shows that leadership and policy and strategy have the highest indirect

effect on all types of innovation, followed by partnerships and resources, while people seem

to have the least. However, leadership only indirectly affects the four types of innovation

through the mediating effect of the other four enablers of the EFQM model. On the contrary,

the study shows that partnerships and resources, as well as policy and strategy, have also a

direct significant impact on all four types of innovation. This finding becomes even more

interesting when the contribution of leadership is less in the case of partnerships and resources

(coefficient: 0.265) when compared to the other two enablers of people and policy and

strategy (0.419 and 0.608 respectively). Thus, although partnerships and resources seem to be

underestimated by leadership, the study proves that this enabler has the highest direct impact

on all types of innovation and also the highest impact on processes, products and services,

(0.459) which directly affect innovation. The results of this study build on the results of

previous studies, such as those of Suarez et al. (2016), Calvo-Mora et al. (2005), Prajogo

(2005), or Eskildsen and Dahlgaard (2000) who claim the effective implementation of

excellence models, such as EFQM, turn out to be beneficial for organizations, fostering a

culture of innovation (Calvo-Mora et al., 2014). Dahlgaard-Park and Dahlgaard (2010)

presented a model that had four enabler factors compared to the European Excellence Model’s

five enabler factors, and only one result factor – innovation results – compared to the EFQM

model’s 4 result factors.


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Moreover, few previous studies use mediating variables to test the link between QM

and innovation. Thus, this paper contributes suggesting a more complete model of

relationships between QM (in this case EFQM enablers) and innovation performance. The

proposed model is different from the others in the following three ways. First, with respect to

QM practices, this study utilizes a set of criteria developed and proposed by the EFQM model

which is widely cited in QM studies. Second, with respect to innovation, this study examines

four different dimensions of innovation performance as defined by OECD: product, process,

marketing and organizational innovation. In earlier studies, innovation was mainly

operationalized in terms of only a few types of innovation (Kafetzopoulos et al., 2015; Zeng

et al., 2015; Tsai and Yang, 2013; Prajogo and Sohal, 2004). According to Kim et al. (2012)

this narrow view of innovation may be a barrier that causes a misunderstanding of the

contribution of QM to innovation. Third, our research model contains many potential indirect

effects regarding the impact of the four model enablers (leadership, people, policy and

strategy and partnerships and resources) on the four types of innovation, through the

mediating role of processes, products and services management. Following the QM practices

proposed by the EFQM excellence model fosters a culture of innovation and supports

innovation performance through the deployment of process management. Finally, the study

didn’t reveal any specific linkages between specific enablers and specific types of innovation.

The effect of the various enablers on innovation performance doesn’t seem to vary

significantly according to the particular type of innovation. Instead, QM practices with a high

positive effect on one type of innovation have similarly high effects on all types of innovation

and the opposite. Thus, the results of this study provide additional evidence concerning these

relationships, shedding light into the effect of specific QM dimensions on innovation.

The empirical findings of the present study emphasize the strategic importance of the

EFQM model enablers to a company’s innovation performance providing vital insights for

academics and practitioners interested in this field. Managers may directly improve the

different types of innovation by emphasizing QM tools, techniques and approaches proposed

by the EFQM model enablers. Following the requirements and adopting the principles of the

EFQM model, helps managers to develop a clear and effective policy and strategy, encourage

employees to develop and implement creative ideas, offer the necessary resources and

develop the appropriate partnerships that will enhance process management and support

business innovation. Managers shouldn’t just focus on one or some of the enablers, since the

study’s results showed that there were significant direct and indirect links between enablers

criteria and all types of innovation. Managers should generate a creative synergy among


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individual processes and focus on developing best practices, eliminating waste, avoiding

activities that do not add value and improving efficiency in order to eventually foster

innovation. The study supports the idea that excellent organizations must design, administer

and improve their key processes to improve their innovation performance as a key success

factor in an increasingly competitive and global economy. Finally, it is important for

managers to develop the appropriate quality strategies and allocate the respective resources

for innovation. This study shows that innovation performance is mainly mediated through

process management, while leadership’s role in affecting all other enablers. Managers should

pay close attention to partnerships and resources which proved to have the highest direct

impact on all types of innovation, moreover, they should cultivate close relationships with

their partners in order to increase innovation performance and optimize the whole value chain.

The results of the study help also managers realise the importance of suppliers’ relationships

and management of tangible and intangible resources.

As in other empirical studies, this research acknowledges a series of limitations that is

necessary to bear in mind when interpreting its results. A first limitation is related to the

model relationships. It might be useful for researchers who study organizational phenomena

in complex market environments to assess the same relationships considering the effect of

external factors such as environmental uncertainty. Second, the potential consequences of QM

on innovation performance may be mediated by customer or people results. It will be very

interesting to take into consideration these factors providing researchers with future research

opportunities. A final limitation of the article is related to the limited geographical scope of

the sample of data used. Although the research is focused on examining the association

between QM and innovation across various organizations, it would be very interesting to

extend this scope to a series of European Union countries and compare the results among

these countries. This attempt would verify the findings of the study and improve

understanding of the relationship between QM and innovation.


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EFQM excellence model enablers Innovation performance

Fig. 1. Research model

p < 0.001, p < 0.005

Fig. 2. Structural model

Partnerships and

Resources

R2=0.570

Policy and

Strategy

R2=0.370

People

R2=0.472

Leadership

Product

Innovation

R2=0.675

Process

Innovation

R2=0.598

Organizational

Innovation

R2=0.624

Marketing

Innovation

R2=0.555

Processes,

products

& services

R2=0.561

0.419*

0.608*

H1c

H3

H5

0.219**

0.821*

0.733*

0.790*

0.745*

Η1a

H1b

0.265*

0.173**

H4

0.459*

H6

H7

H8

H9

H2a

H2b

0.346*

0.564*

Le

ad

ers

hip

People

Policy &

Strategy

Partnership

& Resources

Pro

cess

es,

pro

du

cts

& s

erv

ice

s Product

Innovation

Process Innovation

Organizational Innovation

Marketing Innovation

H1a

H1b

H1c

H2a

H2b

H3

H4

H5

H6

H7

H8

H9


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Table 1. Measurement model: loadings, construct reliability and convergent validity.

Constructs and indicators Loadings CR AVE Corr2

Leadership 0.890 0.502 0.366Leadership mission, vision and values 0.657Leadership involvement 0.768Leadership interaction 0.615Leadership culture of excellence 0.693Leadership organizational change 0.797People 0.860 0.531 0.413Management of human resources 0.737People’s knowledge 0.817People’s involvement 0.704Dialogue between the people and the organisation 0.620Recognition to the people of the organisation 0.756Policy & strategy

Policy and strategy is based on needs and expectations 0.840 0.537 0.490Policy and strategy is based on research, learning and external activities 0.578Policy and strategy is developed, reviewed and updated 0.707Policy & strategy is deployed via a schematic key resources 0.710Policy and strategy is based on needs and expectations 0.658Partnerships & resources 0.770 0.638 0.490Management of the external alliances 0.740Management of the economic resources 0.698Management of the buildings, equipment and materials 0.714Management of technology 0.759Management of information and knowledge 0.700Processes, products & services 0.760 0.562 0.483Management of the processes 0.705Improvements via innovation, in order to fully satisfy the customers 0.817Design and development of the products based on the needs of the customers 0.757Production, distribution and attention service of the products and services 0.695Management and improvement of the relationships with customers 0.617Product Innovation 0.940 0.654 0.494The level of newness of the company’s products 0.801The new products and technological knowledge 0.799The frequency of developing new products 0.851New products with technical specifications and functionalities 0.824Advantage over our competitors in terms of the new products we offer 0.765Process innovation 0.950 0.750 0.494Production processes ahead of competitors 0.825We improve the speed and efficiency of our production processes 0.883We use advanced technologies 0.882The rate of changes in the processes and techniques 0.874Organizational innovation 0.950 0.660 0.541Innovative ideas 0.809Computer-based administrative applications 0.846Renewing the supply chain management system 0.865Renewing the organizational structure 0.771Execute innovative activities 0.766Marketing innovation 0.940 0.623 0.541New product pricing techniques 0.795New product promotion techniques 0.845Renewing the design of the current and/or new products 0.836New productd is tribution methods 0.703Renewing general marketing management activities 0.762


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The TQM Journal

Table 2. The fit indices of the overall measurement and structural models

Goodness-of-fit statistics Measurement model

StructuralModel

Absolute fit indicesChi-square(χ2) 1702.233 1737.859Probabilitylevel <0.001 <0.001RootMeanSquareofApproximation (RMSEA) 0.044 0.045Root Mean Square Residual (RMR) 0.054 0.074Incremental fit indicesIncremental Fit Index (IFI) 0.941 0.939Tucker-Lewis coefficient (TLI) 0.935 0.933ComparativeFitIndex (CFI) 0.941 0.939Parsimonious fit indicesChi-square/ degreesoffreedom (χ2/df) 2.086 2.120GoodnessofFitIndex (GFI) 0.873 0.866Normed Fit Index (NFI) 0.903 0.901

Table 3. Descriptive statistics, Cronbach’s alpha, and bivariate correlations

Variables 1 2 3 4 5 6 7 8 9 1. Leadership 1.000 2. People 0.606 1.000 3. Policy &strategy 0.600 0.534 1.000 4. Partnerships and resources 0.569 0.628 0.675 1.000 5. Processes products & services 0.557 0.552 0.543 0.702 1.000

6. Product Innovation 0.349 0.423 0.460 0.537 0.498 1.0007. Process innovation 0.403 0.407 0.436 0.519 0.456 0.731 1.0008. Organizational innovation 0.398 0.457 0.536 0.578 0.478 0.600 0.605 1.0009. Marketing innovation 0.348 0.390 0.456 0.443 0.365 0.655 0.553 0.703 1.000Mean 6.05 5.80 5.67 6.03 6.23 5.39 5.49 5.28 5.10S.D 1.00 1.04 1.14 0.90 0.88 1.03 1.19 1.18 1.18Cronbach’s α 0.850 0.774 0.846 0.854 0.850 0.911 0.854 0.809 0.890


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Table 4.Analysis results of the structural model

RelationshipsStandardized

regressionweights

Standarderror R² Hypothesis

testresults

H1a.Leadership People 0.419 0.077 0.472 Accept

H1b.Leadership Policy & strategy 0.608 0.070 0.370 AcceptH1c.Leadership Partnership & resources 0.265 0058 0.570 AcceptH2a.Policy & strategy People 0.346 0.073 - AcceptH2b.Policy & strategy Partnership & resources 0.564 0.063 - AcceptH3.People Processes, products & services 0.173 0.029 0.561 AcceptH4. Policy & strategy Processes, products & services 0.219 0.042 - AcceptH5.Partnership & resources Processes, products & services 0.459 0.054 - AcceptH6. Processes, products & services Product innovation 0.821 0.234 0.675 AcceptH7.Processes, products & services Process innovation 0.773 0.199 0.598 AcceptH8. Processes, products & services Organizational innovation 0.790 0.228 0.624 AcceptH9.Processes, products & services Marketing innovation 0.745 0.186 0.555 Accept

p<0.001, p <0.005

Table 5Indirect and direct effects of enablers criteria on innovation

Effect to

Effect fromProduct

innovationProcess

innovationOrganizational

innovationMarketing innovation

Total indirect Total indirect Total indirect Total indirect

Leadership 0.428 0.403 0.412 0.389People 0.142 0.134 0.137 0.129Policy &strategy 0.442 0.416 0.425 0.401Partnerships&resources 0.377 0.355 0.363 0.342

Direct Direct Direct DirectLeadership 0.105*** 0.028*** -0.086*** -0.043***People 0.116*** 0.075*** 0.103*** 0.119***Policy &strategy 0.266 0.209 0.330* 0.320*Partnerships&resources 0.380* 0.341* 0.361* 0.213**Processes products & services 0.821* 0.773* 0.790* 0.745* p< 0.001, p < 0.05, *** p > 0.05


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