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European Management Journal 34 (2016) 661e673

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European Management Journal

journal homepage: www.elsevier .com/locate/emj

Excellence management practices, knowledge management and keybusiness results in large organisations and SMEs: A multi-groupanalysis

Arturo Calvo-Mora, Antonio Navarro-García*, Manuel Rey-Moreno,Rafael Peria~nez-CristobalDepartment of Business Administration and Marketing, University of Seville, Av. Ramon y Cajal, N�1, 41018 Seville, Spain

a r t i c l e i n f o

Article history:Received 25 September 2015Received in revised form17 June 2016Accepted 19 June 2016Available online 7 July 2016

Keywords:Knowledge managementTQMEFQM modelBusiness resultsOrganisational sizePLS-SEMMulti-group analysisModerating effects

* Corresponding author.E-mail addresses: schmidt@us.es (A. Calvo-Mora),

García), mrmoreno@us.es (M. Rey-Moreno), rafacris@

http://dx.doi.org/10.1016/j.emj.2016.06.0050263-2373/© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

Following the total quality management (TQM) philosophy and the knowledge management (KM)approach, this contribution aims to study the influence of process methodology and partner manage-ment on KM, as well as the relationships between this variable and key business results. We also analysethe moderating role of organisational size in these previous relationships. The hypotheses proposed inour research model are tested on a sample of 225 Spanish companies with experience in TQM throughevaluations using the EFQM Excellence Model. The partial least squares structural equation modelling(PLS-SEM) approach was used to test the research model. In order to assess the moderating effects oforganisational size, we adopt a multi-group approach using two subsamples with large firms and smalland medium-sized enterprises (SMEs). Our findings indicate that the use of process methodology and theinvolvement of partners are key factors for KM to have a significant impact on the key results of thebusiness, both strategic and operational. Moreover, the organisational size is determinant when ana-lysing the effect of process methodology and partner management on KM. In this sense, processmethodology has a greater effect on KM in the SMEs. On the contrary, the relationship between partnermanagement and KM is more intense in large firms. Finally, it is noted that KM can be effective and canimprove the key business results independently of the size of the organisation.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Organisations must have an appropriate management system tosucceed. The world is changing at an increasingly rapid pace, andthe interdependence between organisations and economies isgrowing and becoming increasingly strong. To maintain competi-tiveness in this environment, an organisation needs to innovate,improve continuously and manage the needs and expectations ofits stakeholders. Total quality management (TQM) proposes a phi-losophy of comprehensive organisational management that fitswith the demands of the current environment, allowing manage-ment to be oriented towards stakeholders and a greater perfor-mance to be achieved (Prajogo & Sohal, 2006).

In addition, in this context of continuous changes and

anavarro@us.es (A. Navarro-us.es (R. Peria~nez-Cristobal).

uncertainty, it is critical that companies develop abilities to createand to acquire knowledge, to learn, to share what is learnt and toput it into practice (Spender & Scherer, 2007). Knowledge man-agement (KM) is associated with business success and with thecapacity of adaptation of the company to the changing and chal-lenging environment, where the threats can be turned into op-portunities. Owing to knowledge, companies can innovate, createorganisational routines, grow, be sustainable and obtain competi-tive advantages (Chen & Huang, 2009).

At this point, we can consider if both management approachesare compatible and complementary. In this regard, the literaturecontributes evidence of relationships and synergies between TQMpractices and the KM process (Asif, de Vries, & Ahmad, 2013;Linderman, Schroeder, Zaheer, Liedtke, & Choo, 2004; Molina,Llor�ens-Montes, & Ruiz-Moreno, 2007).

However, there is no clear evidence showing the models,reference frameworks and key factors with which to obtain aneffective and efficient integration of TQM and KM. This aspect

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673662

would facilitate managerial work to a great extent, as it would helpimprove decision-making and reduce the time taken to reach thedesired results. As Herman van Rompuy, ex-president of the Eu-ropean Council, said, ‘All European organisations, both in the publicand private sectors, are facing new challenges. The increasing pressureto compete on a global stage with limited resources means we all haveto work together to secure our future prosperity, and that of genera-tions to come. The EFQM Excellence Model provides a framework thatencourages the cooperation, collaboration and innovation that we willneed to ensure this goal is achieved’.

Thus, Bou-Llusar, Escrig-Tena, Roca-Puig, and Beltr�an-Martín(2009), Calvo-Mora, Pic�on, Ruíz, and Cauzo (2014) and Kim, Kumar,and Murphy (2010) show how excellence models offer a suitablereference framework for the implementation of TQM. However, asG�omez G�omez, M�artinez Costa, and Martínez Lorente (2015) indi-cate, excellence models and TQM are not the same frameworks, butthey do follow a similar path, and it can be expected that a companywith high scores in excellence models will have high odds of beinga TQM company.

In the literature, theoretical works can be found which analysesynergies between the EFQM model and KM (Benavides &Quintana, 2005; Martín-Castilla and Rodriguez-Ruiz, 2008; Moh-sen Allameh, Khazaei Pool, Jaberi, & Mazloomi Soveini, 2014;Westerveld, 2003). Case studies of companies that manageknowledge or some of its components, such as intellectual capital,through the EFQM model are also frequently found (Chourides,Longbottom, & Murphy, 2003; Kim, Kumar, Kumar, & Hwang,2009; Moradi, Ramazanian, & Momeni, 2011; Se�nov�a & Anto�sov�a,2015; Tabari, Gholipour-Kanani, & Tavakkoli-Moghaddam, 2012).However, there is a lack of research proposing a model that can betested empirically on the suitability of the EFQM model as areference framework for a KM implementation, and how it canpositively influence the key results of the organisation. Studies thatuse a horizontal reading of the EFQM model to analyse questionsrelated to TQM and KM are also limited.

This way of interpreting the EFQM model is a powerful tool foranalysing important concepts of the organisation (Fern�andez-Santos et al., 2010), although it is not as obvious as the traditionalmanner through the criteria and the sub-criteria. Thus, the EFQMmodel is not a set of unconnected criteria. On the contrary, it pre-sents a series of interrelated practices that offer greater continuityand coherence (Martín-Castilla and Rodriguez-Ruiz, 2008). Theinterpretation of the relationships between the criteria and sub-criteria lies in the so-called transverse axes, or horizontal readingof the model, as opposed to its traditional vertical reading. Theexistence of these axes implies adopting a systemic approach tomanagement. This is to say, we will not be able to achieve globalimprovements in the organisation, if we do not simultaneouslyapproach the different aspects of the criteria of the model asinterdependent elements (Calvo-Mora, Navarro-García,& Peria~nez-Cristobal, 2015).

In this study, the transverse analysis of the EFQM model allowsKM to be analysed in organisations that have been subjected to theassessment process, taking into account that KM is not contem-plated in any specific criterion of the model. On the contrary, theaspects related to knowledge are considered in different sub-criteria throughout the EFQM model. More specifically, weattempt to analyse two important aspects for the effectiveness ofKM: process management methodology (PMM) (Asif et al., 2013)and the management of partners (Ju, Lin, Lin, & Kuo, 2006).

In this regard, the value of products and services for the cus-tomers should be optimised, through PMM. Thus, documented in-formation (procedures, technical instructions and records) isgenerated, which is a way of systematising the what, who, how andwhen of what is done in the organisation (a formalisation of the

organisational report). In addition, this documented informationconstitutes a means of contributing evidence of the results that arebeing achieved. This source of information and knowledge must beknown by the management to improve the decision-making pro-cess and by the workers to undertake their work more efficiently(internal knowledge). Conversely, the management of partners iscrucial to obtain a sustainable profit; more specifically, we refer tothe need to establish networks to identify opportunities for po-tential alliances with partners. This type of knowledge is alsonecessary to compete, which is why it is necessary to favour thetransfer of knowledge between organisations.

Finally, the effectiveness of the previous practices and the re-sults that the company achieves can be limited by the size of theorganisation. Desouza and Awazu (2006) and Hutchinson andQuintas (2008) identify a series of peculiarities in small andmedium-sized enterprises (SMEs) that could influence KM: (1)socialisation activity is predominant within the process of knowl-edge creation. (2) In the SMEs, their members have broad and deeplevels of common knowledge. It is knowledge that is assumed andinteriorised by the employees and management. (3) SMEs presentlimitations when creating knowledge internally. Therefore, thealternative is to capture the knowledge from external sources. (4)The loss of knowledge is not a problem in the SMEs, due to theirhigh flexibility and their common knowledge base. (5) In SMEs, theknowledge is managed from a more person-focussed approachthan from a technological aspect.

For this reason, the present research attempts to achieve thefollowing objectives:

1. To verify the reliability and validity of the horizontal reading ofthe EFQM Model to study key aspects of quality managementand KM.

2. To analyse the relationships between three transverse axes ofthe EFQM model (PMM, partner management (PM) and KM)with key business results (KBRs).

3. To study the moderating effect of organisational size betweenthe relationships that exist between the previous variables.

In order to answer the proposed questions, this paper isorganised as follows. Firstly, the literature on QM and KM is ana-lysed. Secondly, the research model and the hypotheses are pre-sented. This is followed by the research method, results anddiscussions. Finally, the conclusions are presented, and the limita-tions and future research of the study are described.

2. Theoretical framework

2.1. TQM and KM

TQM is a comprehensive management philosophy oriented to-wards achieving excellent results in relation to stakeholders(Prajogo&McDermott, 2005). The principles and practices for TQMto produce the desired effects on an organisation's performance areknown as critical factors. Some of the critical factors mostfrequently studied in the literature include leadership and topmanagement commitment, strategic planning, continuousimprovement, customer focus, data-based management, humanresources management, process management and control andsupplier management. Moreover, the literature presents evidenceon the positive effect of this management philosophy on organ-isational performance (Kaynak, 2003; Prajogo & Sohal, 2006).

Bou-Llusar et al. (2009), Calvo-Mora et al. (2014) and Kim et al.(2010) show how excellence models offer a suitable referenceframework that facilitates the implementation and improvement ofTQM. In Europe, the EFQM model is the best-known and most

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673 663

widespread reference when introducing and improving a TQMsystem. Bou-Llusar et al. (2009) and G�omez G�omez et al. (2015)highlight how the use of the EFQM model guarantees that theTQM practices employed form a coherent system.

KM has been of increasing importance in the field of strategicmanagement. According to the knowledge-based approach, it is themost important strategic resource of companies. This is becauseknowledge-based resources are generally difficult to imitate andare socially complex (Teece, 2014). This approach suggests thatcompanies exist to create, share and receive benefit from theirknowledge (Ipe, 2003). Thus, successful companies are those thatcan identify, value, create and develop their knowledge assets(Spender & Scherer, 2007). In the same way that knowledge isconsidered a strategic resource, KM is considered a critical capacityfor organisational success (Chen & Huang, 2009).

This fact has entailed taking a qualitative and quantitative stepin the management of organisations, which see the need toimplement KM systems to increase their competitive capacity. TheKM systems are a set of infrastructures and tools that support theactivities of KM (Alavi& Leidner, 2001); that is to say, they provide acontext that facilitates the creation, storage, transfer and applica-tion of knowledge. Meso and Smith (2000) differentiate betweentechnical infrastructure (information technologies) and that of asocial and cognitive nature (leadership, organisational structure,human resources and the culture). In addition, there is no singlevalid model for managing knowledge, as it depends on the theo-retical vision adopted and the context in which it is applied.

In agreement with the existing literature, TQM and KM sharefactors considered key to the success of implementing both man-agement approaches, for example, motivated workers (Davenport,De Long, & Beers, 1998), employee training, involvement andempowerment, teamwork (Moffett, McAdam, & Parkinson, 2003;Ryan & Prybutok, 2001), leadership and senior management sup-port and commitment, friendly and open organisational culture(Moffett et al., 2003; Ryan & Prybutok, 2001), processes and per-formance measurement (Hung, Huang, Lin, & Tsai, 2005), bench-marking (Hung et al., 2005; Moffett et al., 2003) and informationsystems structure (Hung et al., 2005).

Linderman et al. (2004) go further, by considering that TQM andKM seek the same objective: to create and use tacit and explicitknowledge more efficiently, at the individual and collective level, tocontinuously improve and to obtain better results. Molina et al.(2007) emphasise the importance of technical and social aspectsof quality and KM so that the knowledge transfer process is effi-cient. Asif et al. (2013) show how a series of TQM practices(continuous improvement, statistical control of quality, manage-ment of client satisfaction, process improvement techniques, indi-vidual learning and the methods of new product development)favours the process of knowledge creation.

In short, KM processes do not make sense if they are notdeveloped systematically. To be competitive, organisations need tocontinuously generate and assimilate knowledge and new capa-bilities. Therefore, TQM as a management philosophy based oncontinuous improvement, innovation and learning can serve as acontext and support for the start-up and later development of a KMprocess.

2.2. Vertical and horizontal interpretation of the EFQM model

The aim of the EFQM model is to support organisations toachieve business excellence through continuous improvement,learning and innovation. The model presents a non-prescriptiveworking framework that analyses the relationships between whatan organisation does and the results that it is able to attain,assuming that there are different approaches to attaining

excellence (EFQM., 2013).The EFQM model sets out the management of excellence from a

conceptual structure, based on a series of Fundamental Principles ofExcellence upon which all the later development of the elements ofthe model is based. Nevertheless, when describing the implicationsof excellent management, the model considers two levels of detail:

� Criteria (Enablers and Results): These represent essential con-structs that can describe the actions and consequences thatmust derive from an authentic excellent management in acomprehensible manner and adjusted to the reality of the or-ganisations. These criteria are leadership, policy and strategy,partnerships and resources, people and process, and the fourremaining criteria reflect the results that the organisation at-tains, with respect to their customers, employees, society andother KBRs.

� Sub-criteria: In each criterion, the model sets out a series of sub-criteria that detail the elements that shape the construct onwhich they depend. Their purpose is to provide a more specificand practical guide that translates what each criterion defines atmore operational levels. If we understand that a criterion rep-resents an important construct in management terms, the sub-criteria can be understood as components of the said construct,which allowa greater applicability of the proposals of themodel.

Two immediate conclusions can be drawn from the descriptionof the general structure of the model: Firstly, the criteria, on whichthe development of the model is based, cannot be considered in acompartmentalised manner, and it should certainly not be under-stood that a description of the full complexity involved in themanagement of an organisation could be made solely with thosecriteria. Only those best suited to serve as a conceptual frameworkfor the description of excellence are considered. It could be said thatthese nine criteria represent ‘a vertical’ perspective from which toapproach excellence in management. Secondly, the considerationof other possible constructs important for management calls for ahorizontal reading of theModel, based on themeaning of each of its32 sub-criteria. Each of these horizontal readings (‘transverse axes’)allows the consideration of new factors that allow managementexcellence to be approached from other perspectives (Fern�andez-Santos et al., 2010). In comparative terms, the vertical structure ofthe model would be the equivalent of that used to describe anorganisation on the basis of its organisational units or departments.On the contrary, the analysis of other possible management com-ponents, through axes, would be equivalent to the processesexecuted daily in an organisation and which connect the activitiesor functions of departments.

Thus, it is easy to simplify seeing the model as the sum of ninecriteria without any apparent connection between them. Never-theless, it is impossible to approach the whole complexity ofmanagement without analysing other constructs or axes (Martín-Castilla and Rodriguez-Ruiz, 2008). These axes involve a morerefined reading of the different sub-criteria, seeking in each thoseideas that could be extracted for a better knowledge of the com-ponents of the construct that is attempted to be approached. Fig. 1shows three of these axes: KM, PMM and PM.

If we assumed that the consideration of each and every one ofthe possible elements involved in excellent management would beunapproachable, we must conclude that the analysis of the modelon the basis of transverse axes can help answer the followingquestions: (a) In each case, which sub-criteria of themodel relate tothe new construct to be studied (axis), and what knowledge iscontributed with respect to that question? (b) For a more correctunderstanding of the new construct under study, how is each ofthese sub-criteria interconnected with the others?

1. Leadership

1a

1b. Development of a process

management system and ownership assignment

1c. Involvement of the leaders with

suppliers and partners

1d

1e

2. Policy & Strategy

2a. Establish needs and expectations

2b. Contributions of knowledge to the policy and strategy of the organisation

2c. Balance needs and expectations

2d. Identify and develop the key

process diagram

3. People

3a

3b. Identification, development and maintenance of the knowledge

among the personnel

3c

3d

3e

4 Partnerships & Resources

4a. Manage alliances

4b

4c

4d

4e. Management of the organisation’s

knowledge

5. Process

5a. Description of the system to design and to

manage processes

5b. Description of the system oriented

to the improvement of

processes

5c

5d

5e

9. Key Results

9a. Operational results

9b. Strategic results

Process management methodology

(PMM)

Partner management

(PM)

Knowledge management

(KM)

Enablers of the EFQM Model

Fig. 1. Horizontal Reading of the EFQM Model (Transverse axes). Source: Own production.

Table 1EFQM Model and knowledge management issues.

Fundamental concepts of excellence KM issues

Development and involvement of people Excellent organisations recognise the increasing importance of the intellectual capital of those who comprise them and usetheir knowledge to the benefit of the whole organisation

Continuous process of learning, innovationand improvement

Excellent organisations, through benchmarking activities, must continuously learn, and gather and share the knowledge ofthe people who comprise them, to maximise the learning in the whole organisation

Development of alliances Partners must work together to reach common objectives, each helping the others with their experience, resources andknowledge

EFQM Criteria KM issues

Leadership Refers to practices that help knowledge creation: the promotion of empowerment, creativity and innovation, or establishingincentives so people and groups participate in improvement activities.

Policy and strategy Refers to the need for base policy and strategy upon information on performance indicators, research and learning.People It is necessary to assess: (1) How the knowledge and the competences of the personnel are identified, classified and adapted

to the needs of the organisation.(2) How training and development plans are developed and implemented, which help guarantee that the abilities of thepersonnel are adjusted to the present and future needs of the organisation. (3) How learning opportunities are designed andpromoted at the individual, group and organisational levels.

Partnerships and Resources It analyses how the organisation (1) manages information and knowledge in support of policy, strategy and objectives; (2)identifies the current information and knowledge; (3) provides suitable access to important information and knowledge; (4)uses information technology to support internal communication and information and knowledge management; (5)cultivates, develops and protects the intellectual property to maximise its value for the customer; (6) tries to acquire,increase and use knowledge effectively; (7) generates a climate of innovation and creativity in the organisation by means ofsuitable information and knowledge resources.

Process management Identifies aspects related to the application of knowledgewhen the need is mentioned tomanage the information that comesfrom the customers for the development of new products and services.

Source: Own production.

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A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673 665

2.3. Using transverse axes of the EFQM model for KM

Benavides and Quintana (2005), Calvo-Mora et al. (2015), Mar-tín-Castilla and Rodríguez-Ruiz (2008), and Westerveld (2003)maintain that the EFQM model constitutes an element of stim-ulus and fundamental support to KM, and that there are importantrelationships between the fundamental activities of KM and thefundamental concepts of excellence and criteria and sub-criteria ofthe EFQM Model (Table 1).

Nevertheless, none of the criteria approaches KM through thevertical reading of the model, although a transverse axis can beidentified for KM. In addition, other axes can be found, such ascommunication, social responsibility, creativity and innovation,human resources, PMMor PM (EFQM., 2003). Indeed, these last twoaxes (PMM and PM) are practices characteristic of excellent man-agement, which can favour and strengthen the effect of KM on theresults (Calvo-Mora et al., 2015). PMM is a critical factor of KM,because the knowledge of the organisation becomes formalisedand more explicit through processes, which favours its creation,transfer and diffusion (Molina et al., 2007). In PM, apart from theinformation derived from the internal processes, the managementof the information and knowledge that comes from them isincreasingly important (Ju et al., 2006).

These two axes contemplate different aspects, although com-plementary to the Process and Partnerships and Resources criteria.Thus, the Process criterion makes two essential statements for thedescription of excellent management: (1) independently of thedepartmental structure of an organisation, the identification andmanagement of their processes become key elements for excel-lence; (2) within the whole spectrum of processes that unfold dailyin an organisation, those directly involved with the products andservices provided to the customers must have special attention, asthey are considered to be key processes. However, there is noexhaustive description of the complete development of all the ac-tivities for an excellent process management in the Process criterionitself, but it is extended to others.

For example, when referring to the establishment of owners(leaders) as an essential phase of process management, this activityappears among the sub-criteria of the Leadership criterion. In short,for a complete analysis of process management, an axes-basedanalysis becomes necessary; that is to say, apart from consideringsome sub-criteria of the Process criterion (those of a more generalcharacter), it is also necessary to consider others included indifferent criteria of the model, such as in the case mentioned for‘Leadership’.

Criterion 4 (Partnerships and Resources), however, shows thatwhen using the term ‘Resources’ with excellence criteria, we mustextend our field of vision to understand that not only must thetangible elements linked to the activity of the organisation havespace within the construct, but also those of an immaterial char-acter, fundamentally those associated with information and thebonds, contacts, influences or formal alliances with third parties.Both elements need excellent management in consonance with thestrategy and policies of the organisation. Although most of thequestions related to the excellent management of the material re-sources of an organisation are included in the sub-criteria of cri-terion 4, many questions concern immaterial elements and,especially the theme of alliances, are dispersed through theremaining criteria of the model.

3. Research model and hypotheses

The research model is based on the extent to which the orga-nisation can improve its KBR through KM, for which it will have toapply PMM, in addition to the participation and involvement of its

strategic suppliers and partners (PM) in the process. The KBRs areanalysed as a dependent variable. This variable attempts to deter-mine the benefits to the organisation in relation to the plannedeconomicefinancial, market and process results and the efficiencyof the management of tangible and intangible resources. Moreover,the relationship between these variables may be affected byorganisational size.

3.1. PMM and KM

PMM supposes a way to act that focuses the attention of peopleon the same realities that the client perceives, together with usingcriteria similar to those of the client, to assess the quality of thework done. It involves the organisation being oriented towards theactivities that generate value for the client, which calls for thecreation, sharing and application of the information and theknowledge that comes from the market.

The PMM includes the following activities: assignment of pro-prietors or those responsible for the processes; implementation ofstandardised systems for its management; establishment of ob-jectives and monitoring and measurement systems; and systems ofanalysis and improvement of these processes. These activities needto document the processes, in order to describe what the organi-sation does and, consequently, to make its knowledge and capac-ities explicit (Tang & Tong, 2007). Therefore, PMM facilitates thecreation of knowledge (Asif et al., 2013), as the processes includeconcepts, methods and techniques to support the design, imple-mentation and analysis of the activities that generate value.Accordingly, the information derived from the activities that formthe processes are transformed into knowledge. The PMM also fa-vours the storage and transfer of knowledge (Molina et al., 2007)when cooperating to transform it from tacit to explicit. Therefore,there are two prerequisites for facilitating knowledge transfer: theintention to share it and the capacity to do so. In addition, theimplementation of PMM changes the structure of the company,making it more open and flexible. In this climate, the transfer anddiffusion of knowledge are strengthened (Linderman et al., 2004).Therefore, we propose the following hypothesis:

H1. PMM is positively related to KM.

3.2. PM and KM

In the EFQM Model context, partners are considered to be anyexternal ally of a strategic nature with which the organisationchooses to work, to reach common objectives and to obtain a sus-tained mutual benefit (EFQM., 2013).

Companies that maintain excellent relationships with theirsuppliers and partners can take advantage of synergies and canaccess and exchange new or complementary knowledge, whichallow the generation of value for both parties. This exchange ofknowledge can even be obtained without having to produceexplicit knowledge, as it can be made through the exchange ofpeople or groups with common objectives and cultures that will beable to work together effectively (Davenport & Prusak, 1998).

As noted previously, confidence between the partners is animportant factor that influences the effectiveness of knowledgetransfer. Confidence is associated with the belief that organisationsact coherently and according to expectations (Spekman, Spear, &Kamauff, 2002). Confidence is closely related to the risk and pro-tection of knowledge. A reduction in confidence between organi-sations will be translated into a greater risk of losing criticalknowledge. On the contrary, confidence will encourage the actorsto actively share their knowledge, ensuring that this will not beused against their objectives (Linderman et al., 2004). Therefore, it

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673666

is hoped that an organisationwith greater levels of confidence in itscollaborative relationships with its suppliers and partners managesknowledge in a better manner (Loke, Downe, Sambasivan,& Khalid,2012). Accordingly, we propose the following hypothesis:

H2. PM is positively related to KM.

3.3. KM and KBRs

The KBR in the EFQM model attempts to measure what theorganisation obtains in relation to its strategic results and plannedyield. More specifically, the strategic key results of the econom-icefinancial type (sales volume, share or dividend prices, grossmargins, share profits, profits before interests and taxes or oper-ating margin), as well as those of a non-economic nature, areanalysed (market share, time of launching new products, successindices and process performance), which indicate the success ofimplementing the strategy. The positive relationship between KMand financial results has been confirmed in the study by Huang andShih, (2009). More specifically, Tarí and García Fern�andez (2013)reach the conclusion that the processes of creation, storage,transfer and application of knowledge influence economic resultsthrough greater product diversification, greater client loyalty andincreased automatic control over the work.

The key economicefinancial indicators (treasury, depreciation,maintenance costs and credit qualification) and non-economic in-dicators (performance of processes, partners and suppliers,external resources and alliances, buildings, equipment and mate-rials, technology, information and knowledge) are used by theorganisation to measure its operational efficiency. Hence, studieslike those of Davenport and Prusak (1998) and Tarí and GarcíaFern�andez (2013) indicate how KM helps improve the operationalresults through the development of a global vision of the company,empowerment, improvement in decision-making, reduction of er-rors, teamwork or the training and qualification of the workers.Therefore, the following hypothesis is proposed:

H3. KM is positively related to KBRs.

3.4. The moderating role of organisational size

Durst and Edvardsson (2012) and Gray and Mabey (2005)indicate that the size must be considered an important factorwhen understanding how knowledge is managed, and that the sizeinfluences decision-making processes and the strategic choices thatthe company may adopt. Thus, the simpler organisational structurein the SMEs entails greater centralisation in the decision-making(Jones & George, 2010). They are structures with few hierarchicallevels, in which the strategic and operational decisions are usuallycentralised jointly in the general management (Mintzberg, 1979).With respect to the strategic choices, the small size does not allowaccess to economies of scale and scope, which is why low unit costscannot be achieved. This involves the difficulty of implementingstrategies based on cost leadership (Jones& George, 2010). Anotherimportant factor that limits the competitiveness of the SMEs istheir difficulty in accessing resources, especially those of thefinancial and intangible type. The SMEs have very limited access toavant-garde technological knowledge or to highly qualified labourdue to the high cost. They also have limited access to credit andcapital markets (McAdam & Reid, 2001). This means that they facedifficulty in introducing more expensive, radical and technologi-cally advanced innovations and KM. The KM in SMEs tends to bemore informal and ad hoc, which may lead to a short-term view-point (Laforet, 2013). Furthermore, innovation and KM in SMEs is

usually linked to development orientation, through the continuousimprovement of process management, whereas that of large com-panies is usually linked to research (Desouza & Awazu, 2006).Moreover, the small size and the lower specialisation of theworkforce indicate a greater probability of collaboration andcooperation between the employees (Daft, 2010), which largelyfavours the process management and KM in SMEs. According to thearguments, we propose the following hypothesis:

H4. PMM has a stronger positive effect on KM for SMEs than forlarge companies.

However, when the relationships with clients, suppliers, dis-tributors and other partners and stakeholders are included, theSMEs find greater difficulties in managing such relationships and ingenerating knowledge due to their lower negotiating power andrelational capabilities than large organisations (Jones & George,2010). This implies that in large organisations, given the greatersize and complexity of their networks of partners, the developmentof systematised processes to manage this network influencesknowledge generation to a greater extent than in the SMEs(Arag�on-S�anchez and Rubio-Ba~non, 2005). For this reason, wepropose the following hypothesis:

H5. PM has a stronger positive effect on KM for large companiesthan for SMEs.

Finally, with respect to the relationship between KM and keyresults, McAdam and Reid (2001) concluded that SMEs and largeorganisations have much to gain by developing effective KM sys-tems. These benefits are related to a reduction of costs and animprovement in quality and efficiency. Nevertheless, in SMEs, thereis a predominance of socialisation activity within the process ofknowledge creation. Most of the knowledge is held in the mind ofthe proprietor and some key employees. In addition, managementand employees maintain more direct and constant contact witheach other, which facilitates knowledge transfer and application. InSMEs, the members have broad and deep levels of commonknowledge. Knowledge is assumed and interiorised by the em-ployees and management. It helps mark the behaviour in theworkplace and provides a common reference framework that en-sures communication, transfer and application of the knowledge.Finally, in environments of high competitiveness and mobility ofemployees, the loss of knowledge can be a serious problem. How-ever, in SMEs, this is not considered a real problem, due to theirhigh flexibility, their common knowledge base or to their capacityto retain employees, due to the close social bonds between them(Desouza & Awazu, 2006; Hutchison and Quintas, 2008). From theabove, we can extract the following hypothesis:

H6. KM has a stronger positive effect on KBRs for SMEs than forlarge companies.

4. Methodology

4.1. Sample

The sample consisted of 225 Spanish companies subjected toself-assessment and external assessment on the basis of the EFQMModel. According to data contributed by the Centres of Excellence(an association that unites the efforts of excellence-promotingcentres throughout Spain), the total number of organisations sub-jected to complete assessments during the period 2003e2014 was531. Therefore, there was a 42% response rate. Considering size as acategorical variable, the sample was split into two groups(SMEs ¼ 146; large companies ¼ 79). To this end, the

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673 667

Recommendation of the European Commission 96/280/EC wasfollowed. Therefore, SMEs will be considered to be companies thatemploy <250 people, whose annual business volume does notexceed 50 million euros or whose annual general balance sheetfigures do not exceed 43million euros. Moreover, 83.5% (188) of thecompanies are privately owned, and 16.5% (37) are public organi-sations. Finally, 71.5% (161) of the companies of the sample pertainto the services sector, 19.55% (44) to the industrial sector and 8.95%(20) to the agricultural sector.

4.2. Measures

The variables and their respective measurement indicators wereobtained from the transverse axes of the EFQM model (EFQM.,2003). As previously indicated, the elements that form the axesare the sub-criteria of the EFQM model, although sub-criteria per-taining to various criteria can be combined in each axis. In thiswork, and according to the objectives considered, three transverseaxes were selected (KM, PMM and PM), as well as the KBR (seeFig. 1).

Most studies that analyse the internal structure of the EFQMmodel do not use the original measurement scales or the scoresobtained through the RADAR logic matrices. The literature confirmsthe theoretical reliability of the data obtained through the valida-tion performed by independent external experts (Pannirselvam &Ferguson, 2001). The reliability and validity of the original mea-sures of the EFQM model have also been confirmed by Bou-Llusaret al. (2009) and Calvo-Mora et al. (2014).

The data were collected from the assessment processes ac-cording to the RADAR (ResultseApproacheDeploymenteAssess-ment and Review) logic, which the EFQM model uses to score thelevel of excellence of organisations. The RADAR logic is a dynamicassessment framework and a powerful management tool thatprovides a structured approach to questioning the performance ofan organisation.

The elements of Approach, Deployment, Assessment and Re-view are applied to Enablers; these elements analyse the evidenceof what the organisation is doing. The Results element is used toassess the criteria related to the results. This analyses what theorganisation achieves, as a consequence of the efforts made. Morespecifically, it establishes what an organisation needs to do to

Partner Management

(PM)

Knowledge Management

(KM)

H4

Organisational Size (SME

H5

H1

H2

Process Management Methodology

(PMM)

Fig. 2. Research mode

(EFQM., 2013): (1) Determine the Results that it wants to obtain aspart of the process of producing its policy and strategy; (2) plan anddevelop a series of solidly based and integrated Approaches, whichit takes to obtain the required results, now and in the future; (3)Deploy the approaches systematically to assure complete imple-mentation; and (4) Assess and Review the approaches used, basedon the monitoring and analysis of the results achieved and on thecontinuous learning activities. Finally, it will have to identify,establish priorities for, plan and implement the improvements thatare necessary.

The scoring scale of the RADAR matrices for the Enablers isdivided into five intervals, ranging from values 0 (without evidenceor anecdotes) to 100 (total evidence). For the Results, the scale alsovaries between 0 and 100, but the significance of the extremevalues changes, according to the type of result that is being ana-lysed (trend of the results, fulfilment of objectives, comparisonswith other companies, causes of the results or sphere of applica-tion). Finally, we controlled for sector (1. Agriculture; 2. Industry;and 3. Services) and ownership of the company (public vs. private).

4.3. Data analysis

Two stages were developed in the data analysis using avariance-based, structural equationmodelling (partial least squares(PLS); SmartPLS 3.1.9. software was used {Ringle, Wende, & Becker,2015}). (1) For the whole sample, the research models depicted inFig. 2 were tested allowing the assessment of the measurementmodel and the testing of the linkages proposed between constructs(Rold�an & S�anchez-Franco, 2012). This first stage included a per-mutation algorithm and the measurement invariance of compositemodels (MICOM). (2) The moderating effects of Size were analysedthrough a multi-group comparison approach (MGA), as the Sizetype of variable was categorical (Henseler & Fassott, 2010). Multi-group analysis (MGA) has several advantages: a) It allows re-searchers to determine whether parameters of a measurementmodel and/or the structural model are equivalent (i.e., invariant)across two or more groups (Chin et al., 2012). b) It provides aparticularly strong test of the validity of the measurement modeland replicability of the structural model across settings. c) It is alsoused to make comparisons within a study, whether this is to assesstheoretical differences between subgroups of the same population

Sector

Key Business Results (KBR)

H3

s vs. Large)

H6

Ownership

l and hypotheses.

Table 2Measurement model.

Construct/Indicator (EFQM sub-criteria) Total sample; n ¼ 225 SMEs; n ¼ 146 Large companies; n ¼ 79

Composite Composite Composite

Loadings Reliability AVE Loadings Reliability AVE Loadings Reliability AVE

Process management methodology (PMM) 0.912 0.722 0.916 0.732 0.909 0.7151b. Development of a process management system and ownership assignment 0.871 0.879 0.8692d. Identify and develop the key process diagram 0.867 0.885 0.8415a. Description of the system to design and to manage processes 0.777 0.752 0.8165b. Description of the system oriented to the improvement of processes 0.880 0.899 0.855Partner management (PM) 0.924 0.753 0.925 0.754 0.926 0.7571c. Involvement of the leaders with suppliers and partners 0.866 0.855 0.8902a. Establish needs and expectations 0.916 0.909 0.9272c. Balance needs and expectations 0.871 0.881 0.8634a. Manage alliances 0.814 0.827 0.796Knowledge management (KM) 0.892 0.735 0.891 0.732 0.896 0.7432b. Contributions of knowledge to the policy and strategy of the organisation 0.894 0.902 0.8893b. Identification, development and maintenance of the knowledge in the personnel 0.849 0.872 0.8134e. Management of the organisation's knowledge 0.827 0.788 0.881Key business results (KBR) 0.953 0.911 0.959 0.921 0.944 0.8949a. Operational results 0.952 0.958 0.9429b. Strategic results 0.957 0.961 0.950

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673668

or across populations in the case of multicultural research, or todetermine if samples taken from different sources can be combinedinto a single data set (Fawcett, Wallin, Allred, Fawcett, & Magnan,2011). For MGA, responses were divided into two groups,depending on Size (group 1 ¼ SMEs; group 2 ¼ Large companies).Then, with the use of PLS, the path coefficients were estimated foreach group or subsample (Sarstedt, Henseler, & Ringle, 2011).Finally, the differences between the coefficients' paths were ana-lysed. If they are significant, they can be interpreted as havingmoderating effects. To determine the significance of differencesbetween the estimated parameters for each of the groups, thepermutation test was mainly used. In addition, in a complementarymanner, two approaches were followed. On the one hand, theparametric approach was used considering both equal variancesand different variances (Chin, 2000). On the other hand, a non-parametric confidence approach was used (Sarstedt et al., 2011).

1 Running MICOM in SmartPLS usually automatically establishes configuralinvariance. The statistical output does not apply to this step and is not shown.

5. Results

The measurement model has been designed as a compositefactor model following a reflective approach (Henseler, 2014). Thecomposite factor model relaxes the strong assumption that all ofthe covariation between a block of indicators is explained by acommon factor. This implies that the composite factor model doesnot impose any restrictions on the covariances between indicatorsof the same construct, and composites are formed as linear com-binations of their respective indicators (Henseler et al., 2014). Inaddition, our model is oriented to prediction. Its assessment has tobe based on reliability and validity (Rold�an & S�anchez-Franco,2012). A subsequent PLS path model analysis reveals that allmeasures meet the commonly suggested criteria for measurementmodel assessment as described, for example, by Henseler, Ringle,and Sinkovics (2009) and Hair, Sarstedt, Ringle, and Mena (2012).In this vein, the loadings of both indicators and dimensions exceedthe 0.70 threshold.

Consequently, indicators and dimensions are reliable. Con-structs and dimensions present high internal consistency, as itscomposite reliability indices exceed 0.7. In addition, the convergentvalidity is achieved for all latent variables because the averagevariance extracted (AVE) ratios exceed the 0.5 benchmark (Table 2).

Conversely, Table 3 shows the discriminant validity. According

to the FornelleLarcker criterion, the square root of the AVE of eachlatent construct is greater than its correlationswith any other latentvariable (Fornell & Larcker, 1981). Moreover, we used the hetero-traitemonotrait (HTMT) ratio of correlations (Henseler, Ringle, &Sarstedt, 2015). In this respect, all values are under 0.85. Thus,the discriminant validity is reached, and it can be concluded thatthe main constructs measure different aspects.

In summary, according to the PLS analyses, the measurementmodel is completely satisfactory for our model, both with thewhole sample and with each subsample (SMEs and large com-panies). The following step was to ensure the metric invariance ofthe construct measures; that is, the proposed measurement modeldoes not vary when the size of the company is considered.

For this purpose, a permutation algorithm was used to confirmthat the indicators associated with each construct are invariantbetween SMEs and large companies (Chin et al., 2012). As can beseen in Table 4, the differences between the factorial loads of bothgroups are all non-significant (permutation p-value > 0.05). Theseresults were also corroborated with parametric andWelcheSatterthwaite tests (Table 4).

Furthermore, the permutation algorithm was used to imple-ment the MICOM, described by Henseler, Ringle, and Sarstedt(2016). MICOM is used to determine whether significant inter-group differences are due to inter-group differences in constructs,when assessing composite models. As the indicators in the outermodel determine the meaning of the constructs in the structural(inner) model, the lack of measurement invariance implies thateven though the constructs for, say, the ‘SME’ and ‘Large companies’groups carry the same labels, the labels are deceptive because theconstructs measure different things. MGA tests are suited only incases of measurement invariance, meaning only if the inner modelconstructs measure the same things. MICOM is therefore a logicallynecessary step, prior to conducting MGA. The values of Table 5corroborate the configural,1 compositional and scalar invarianceassuring ‘full measurement invariance’.

Table 6 shows the results of the structural model assessment.Consistent with Hair, Hult, Ringle, and Sarstedt (2013), boot-strapping (5000 resamples; one-tailed Student's t distributionwith

Table

3Discrim

inan

tva

lidity.

Forn

elle

Larcke

rcriterion

Totalsample;n¼

225

SMEs;n¼

146

Largeco

mpan

ies;

n¼

79

PMM

PMKM

KBR

Sector

Ownersh

ipPM

MPM

KM

KBR

Sector

Ownersh

ipPM

MPM

KM

KBR

Sector

Ownersh

ip

PMM

0.85

00.85

60.84

6PM

0.82

00.86

80.82

10.86

80.82

10.87

0KM

0.80

60.80

20.85

70.82

20.80

40.85

60.79

10.81

90.86

2KBR

0.56

10.66

00.67

70.95

40.53

20.70

60.72

20.96

00.62

60.58

30.61

10.94

6Se

ctor

0.05

40.08

90.13

40.16

7n.a.

0.04

50.07

80.12

30.17

6n.a.

0.06

10.07

40.13

50.16

8n.a.

Ownersh

ip0.17

80.14

10.15

20.12

60.32

3n.a.

0.19

70.15

20.14

90.13

70.28

9n.a.

0.18

30.14

70.13

80.12

40.30

9n.a.

HeterotraiteMon

otraitRatio

(HTM

T)To

talSa

mple;n¼

225

SMEs;n¼

146

Largeco

mpan

ies;

n¼

79PM

MPM

KM

KBR

Sector

Ownersh

ipPM

MPM

KM

KBR

Sector

Ownersh

ipPM

MPM

KM

KBR

Sector

Ownersh

ip

PMM

PM0.82

70.82

60.80

3KM

0.81

80.82

10.81

90.84

70.80

90.83

7KBR

0.61

00.72

90.77

80.55

80.77

20.81

80.71

80.65

20.71

3Se

ctor

0.08

90.11

20.16

10.17

80.08

30.12

30.17

20.16

90.07

80.12

20.15

70.16

5Ownersh

ip0.22

30.14

60.13

90.12

80.33

10.21

50.15

60.14

20.13

30.32

10.21

00.15

30.14

40.13

30.28

9

Note:Fo

rnelle

Larcke

rCriterion

:Diago

nal

elem

ents

(bold)arethesq

uareroot

oftheva

rian

cesh

ared

betw

eentheco

nstructsan

dtheirmea

sures(A

VE).Off-diago

nal

elem

ents

aretheco

rrelationsam

ongco

nstructs.

For

discrim

inan

tva

lidity,

diago

nal

elem

ents

shou

ldbe

larger

than

off-diago

nal

elem

ents.n

.a.:not

applic

able.

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673 669

(n�1) degrees of freedom) was used to generate standard errors, t-statistics, and percentile 95% confidence intervals. This analysis wasconducted both for the total sample and for the two subsamples.The three main paths are significant, except for the PMMeKMrelationship in the subsample of large companies. The endogenousconstructs achieve R2 values between 0.392 and 0.536 for KBR,considering those values of moderate character (Chin, 2010). In thecase of the knowledge factor, R2 varies between 0.785 and 0.848. Inthis case, these values are higher than the substantial level indi-cated by Chin (2010).

For the three considered models (total sample, SMEs and largecompanies), the results indicate that neither the sector nor theownership has a direct significant effect on KBR, thus confirmingthe robustness of the proposed model and the solidity of the ob-tained results (Table 6). The predictive relevance of the theoretical/structural model is assessed with the cross-validated redundancyindex (Q2) for endogenous constructs. As all Q2 values are >0, evi-dence pointed to the predictive relevance of our model (Chin,2010). In addition, Table 6 shows the amount of variance thateach antecedent variable explains on each dependent variable, thegreater value being obtained in the case of the suppliers/partnersvariable when explaining the explained variance of the KBR (67%).

Further, Table 6 shows that the indirect effects are significant forthe whole sample, with significant differences being generated,according to the size of the company, in the effect of PMM on KBRthrough KM. Therefore, in small companies, designing a good PMMfrom the point of view of the application of the EFQMmodel can bevital for generating knowledge and obtaining good results. In thePMeKMeKBR relationship, the size did not generate significantdifferences (Table 6).

Once themetric invariance was guaranteed in the measurementmodel and the structural model tested, the multi-group analyseswere performed for hypotheses 4e6, which allowed the testing ofthe moderating role of organisational size on the relationshipsincluded in the research model. To this end, the permutation testwas mainly used (5000 permutation runs; two-tailed 0.05 signifi-cance level), which showed significant differences (p-value<0.05)generated by the size in the PMMeKM (H4) and PMeKM (H5)relationships, there being no significance in the KMeKBR (H6)relationship (Table 7). Moreover, and in a complementary manner,a parametric approach is applied. The moderating effect is exam-ined using a t-test with pooled standard errors. This approach re-quires the data to be distributed normally and/or the variances ofthe two samples to not differ vastly (tParam {EV}). When differentvariances for the two samples are assumed, aWelcheSatterthwaitetest (tParam{NEV}) can be applied (Sarstedt et al., 2011). Both testshave been applied in the comparison, obtaining similar results(Table 7).

The same result is derived from the application of the non-parametric approaches (the bias ecorrect 95% confidence in-tervals). In this case, if the parameter estimate for a path relation-ship of one group (Table 6) does not fall within the correspondingconfidence interval of another group (Table 7) and vice versa, nooverlap exists and it can be assumed that the group-specific pathcoefficients are significantly different with regard to a significancelevel a (Sarstedt et al., 2011). This condition is fulfilled for H4 andH5, but not confirmed for H6. Similar results were obtained whenHenseler's PLS multi-group analysis (Table 7) was applied. Thisnon-parametric significance test showed a significant difference ifthe p-value is < 0.05, as occurs in H4 (p-value ¼ 0.008), or >0.95, asoccurs in H5 (p-value ¼ 0.997), for the difference of group-specificpath coefficients (Henseler et al., 2009). For H6, the differenceswere not significant (p-value ¼ 0.110).

Finally, the overall model was measured through a standardisedroot mean square residual (SRMR) composite factor model

Table 4Metric invariance assessment permutation algorithm and multi-group analysis.

Construct/Indicator Diff (SME-L) Permutation test Parametric test Welche t-value Satterthwaite test (t-value)

95% confidence interval p-value

PMM1b. 0.010 [�0.088; 0.074] 0.301 0.273 ns 0.265 ns

2d. 0.044 [�0.072; 0.065] 0.093 1.415 ns 1.273 ns

5a. 0.064 [�0.129; 0.107] 0.378 0.825 ns 0.826 ns

5b. 0.043 [�0.080; 0.071] 0.076 0.916 ns 0.777 ns

PM1c. 0.035 [�0.074; 0.068] 0.243 0.900 ns

0.965ns

2a. 0.018 [�0.053; 0.049] 0.455 0.742 ns 0.803 ns

2c. 0.018 [�0.075; 0.068] 0.083 0.548 ns 0.507 ns

4a. 0.031 [�0.105; 0.096] 0.117 0.568 ns 0.560 ns

KM2b. 0.013 [�0.048; 0.046] 0.307 0.544 ns 0.496 ns

3b. 0.059 [�0.122; 0.103] 0.116 1.096 ns 0.959 ns

4e. 0.093 [�0.110; 0.091] 0.164 1.296 ns 1.601 ns

KBR9a. 0.016 [�0.028; 0.025] 0.240 0.955 ns 0.856 ns

9b. 0.011 [�0.022; 0.161] 0.161 1.010 ns 0.979 ns

ns ¼ not significant.

Table 5Measurement invariance (MICOM) tests.

Composite Correlation c value (¼1) 95% confidence interval Permutation p-value Compositional invariance?

Compositional invariance

PMM 0.999 [0.997; 1.000] 0.200 YesPM 0.999 [0.999; 1.000] 0.178 YesKM 0.999 [0.998; 1.000] 0.134 YesKBR 1.000 [0.999; 1.000] 0.776 YesComposite Mean Difference SMEeLarge 95% confidence interval Permutation p-value Equal mean values?Scalar invariancePMM 0.017 [�0.033; 0.224] 0.348 YesPM �0.028 [�0.041; 0.243] 0.545 YesKM 0.048 [�0.023; 0.216] 0.251 YesKBR �0.032 [�0.052; 0.176] 0.525 YesComposite Variance Difference

SMEeLarge95% confidence interval Permutation p-value Equal variance?

PMM 0.046 [�0.181; 0.184] 0.842 YesPM �0.055 [�0.188; 0.192] 0.565 YesKM �0.097 [�0.170; 0.177] 0.234 YesKBR 0.019 [�0.123; 0.121] 0.171 Yes

5000 permutation run; two-tailed 0.05 significance level.

Table 6Direct and indirect effects. Bias-corrected 95% confidence intervals and indirect effect of multi-group comparison results.

Total sample; n ¼ 225 SMEs; n ¼ 146 Large companies; n ¼ 79

Effects on endogenous variables Directeffect

t-value(bootstrap)

Explainedvariance

Directeffect

t-value(bootstrap)

Explainedvariance

Directeffect

t-value(bootstrap)

Explainedvariance

Knowledge management (R2 ¼ 0.798/Q2 ¼ 0.580) (R2 ¼ 0.785/Q2 ¼ 0.564) (R2 ¼ 0.848/Q2 ¼ 0.609)H1: Process management

methodology0.252*** 4.919 20.40% 0.344*** 5.256 28.28% 0.113ns 1.507 8.94%

H2: Partners management 0.675*** 13.672 54.13% 0.581*** 8.798 46.71% 0.826*** 13.069 67.65%Key business results (R2 ¼ 0.462/Q2 ¼ 0.423) (R2 ¼ 0.536/Q2 ¼ 0.484) (R2 ¼ 0.392/Q2 ¼ 0.343)H3: Knowledge management 0.677*** 17.473 45.83% 0.722*** 19.940 52.13% 0.611*** 7.269 37.33%Control variablesSector / KBR 0.078ns 1.335 1.39% 0.112ns 1.436 1.89% 0.075ns 1.423 1.24%Ownership / KBR �0.051ns 0.340 0.65% �0.079ns 1.234 1.05% �0.068ns 1.115 0.90%Indirect effect Total sample SME Vs. Large companies SignificancePMM/ KM/ KBR 0.171 0.248 0.069Percentile 95% bootstrap confidence

intervals[0.114; 0.228] [0.163; 0.336] [�0.022;

0.161]Sig

PM/ KM/ KBR 0.456 0.419 0.504Percentile 95% bootstrap confidence

intervals[0.386; 0.534] [0.314; 0.533] [0.344; 0.669] Nsig.

*** p < 0.001, (based on t(4999), one-tailed test); t(0.05, 4999) ¼ 1.645, t(0.01. 4999) ¼ 2.327, t(0.001, 4999) ¼ 3.092; Bootstrapping based on n ¼ 5000 subsamples; ns ¼ notsignificant Sig. denotes a significant difference e indirect effect e at 0.05; Nsig. denotes a non-significant difference e indirect effect e at 0.05.

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673670

Table 7Permutation and multi-group comparison test results and bias-corrected 95% confidence intervals.

Relationship PathSMEs PathLarge Diff.SMEs-Large

Permutation test tParametric

(EV) tParametric

(NEV) Confidence intervals PHenseler

Confidenceintervals

p-value SMEs Largecompanies

Significance

H4 (SME > L) PMM/KM 0.344 0.113 0.231 [0.197; 0.204] 0.015** 2.242** 2.354** [0.317, 0.572] [�0.041, 0.254] Sig. 0.008*H5 (SME < L) PM/KM 0.581 0.826 �0.245 [0.194; 0.182] 0.013** 2.448** 2.706** [0.455, 0.702] [0.707, 0.955] Sig. 0.997*H6 (SME > L) KM/KBR 0.722 0.611 0.112 [�0.153; 0.143] 0.175ns 1.396 ns 1.210 ns [0.655, 0.794] [0.445, 0.769] NSig 0.110ns

Notes: ** Significant at 0.05 permutation test (5000 permutation runs; two-tailed); ns ¼ not significant.Notes: ** Significant at 0.05 (two-tailed t distribution, one- sided test); ns ¼ not significant.Notes: Sig. denotes a significant difference at 0.05; Nsig denotes a non-significant difference at 0.05.Notes: *Significant (one-sided test); ns ¼ not significant.

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673 671

(Henseler et al., 2014). Thus, the values obtained for the completemodel (0.075), the large company model (0.073), and the SMEmodel (0.079) recommend a limit value of 0.08 (Hu & Bentler,1999).

6. Discussion and conclusions

The results support the reliability and validity of the measure-mentmodel (Tables 2 and 3), both for themodel that represents thewhole sample of companies and for the subsamples that representthe SMEs and the large companies. In addition, the property ofmetric invariance is confirmed; that is to say, the measurementmodel does not change when dividing the original sample into twosubsamples (Tables 4 and 5). This result supports the universality ofthe excellence models that can be used as reference for evaluationand improvement by both large organisations and SMEs (EFQM.,2013). Moreover, the results confirm the high predictive power(R2) and high predictive validity (Q2) of the EFQM model as aframework for the implementation and integration of TQM and KMpractices. As can be seen in Table 6, the KM and KBR variablesdisplay substantial R2 values (R2 > 0.67) according to Chin (2010),both for the whole sample and for the two subsamples represent-ing the SMEs and large companies. Moreover, the endogenousvariables present Q2 coefficient values above 0; in particular, theydisplay values of Q2 > 0.34.

With respect to the direct effects represented by the H1, H2 andH3 hypotheses, firstly it is highlighted (Table 6) that PMM plays acentral role in the complete model, and in that representing theSMEs, where a significant direct effect on KM is seen (H1). Inaddition, PMM is usedmore extensively in the SMEs (0.344) than inlarge organisations (0.113). In this sense, in the model that repre-sents large companies, the relationship between PMM and KM,although positive, is not statistically significant. In addition, thisleads to the indirect effect between PMM, KM and KBR of a lack ofstatistical significance. This may be due to PMM being focussedmainly on the key processes, those that are directly involved withthe products and services supplied to the customers. Thus, theSMEs have greater proximity, contact and knowledge with cus-tomers, which explains why their effect on KM is stronger andmoredirect than in large companies (Desouza & Awazu, 2006), More-over, this result may be due to greater collaboration and coopera-tion between employees in the SMEs when putting key processesinto practice (Durst and Runar Edvardsson, 2012), and because theSMEs are more focussed on continuous improvement in their ac-tivities and processes. In this regard, large companies can dedicatemore resources to product innovation, services, processes or tech-nology than the SMEs (McAdam & Reid, 2001). Continuousimprovement is based on the human factor; further, it supposessmall but constant changes that do not require heavy investmentsor monetary expenditure. On the contrary, innovation involvesdrastic changes in the processes and activities of the organisation,

which requires time and a substantial amount of resources, whichis more feasible for large organisations. The importance of thisfactor is also reflected in the high percentages of variance (R2) of theKM variable in both the complete model (20.4%) and that repre-senting the SMEs (28.28%).

Secondly, the management of the main partners of the organi-sations is also a critical variable for KMwithin the framework of theEFQM model. Thus, the direct effect between PM and KM (H2) isstatistically significant both in the model that represents all thecompanies and in the models segmented according to size(Table 6). In addition, management in large companies emphasisesthe importance of PM for KM (0.826). This value is far beyond thatobtained for the same relationship in the SMEs (0.581). Thus, theindirect effect between these variables and KBR is statistically sig-nificant (Table 6). This crucial role in the model is also observedwhen analysing the percentage of variance, which is accounted forby the endogenous KM variable in the complete sample (54.13%), aswell as in those of SMEs (46.71%) and large companies (67.65%).

Thirdly, it is necessary to emphasise how the specific efforts thatcompanies make to manage their knowledge have a direct andsignificant effect on the KBR (H3). This effect is confirmed both forthe whole sample and for the subsamples that represent the SMEsand large companies (Table 6). In addition, no important differ-ences were noted in the values of the indirect effects in this case(PMM/KM/KBR and PM/KM/KBR), although the relationshipbetween KM and KBR is stronger in the SMEs (0.722) than in largecompanies (0.611), explaining up to 52.13% of the variance of theKBR variable.

Finally, on analysing the results of the moderating effect of thesize variable on the direct relationships between the variables of themodel, significant differences (Table 7) are noted in the relationshipsbetween PMM and KM (H4) and between PM and KM (H5). Thesedifferences corroborate the differences indicated in the literature onthe distinctive features presented by themanagement of SMEswithrespect to large companies. More specifically, PMM has a greatereffect on KM in the SMEs, confirming H4. This may be due to thesmaller size and the lower level of specialisation of the workforce,leading to a greater probability of collaboration and cooperationbetween the employees, which largely favours the processes ofcreation, transfer and application of knowledge. In addition, asnoted previously, the SMEs are more focussed on continuousimprovement through processes and not as much towards techno-logical innovation (McAdam & Reid, 2001). On the contrary, therelationship between PM and KM is stronger in large companies,confirming H5. Large companies have greater negotiating powerover their main partners (Gray & Mabey, 2005) and have greaterfinancial means to know themovements and actions of competitorsand other stakeholders. Finally, KM is found to be effective and toimprove the KBR independently of the size of the organisation,when there are no significant differences between both samples.This implies that the H6 hypothesis cannot be confirmed

A. Calvo-Mora et al. / European Management Journal 34 (2016) 661e673672

statistically, although the relationship or direct influence betweenKM and KBR is stronger in the SMEs than in large organisations.

7. Implications, limitations and future research directions

7.1. Theoretical implications

Despite the great interest and several studies published on TQMphilosophy and the EFQM model, empirical research attempting tointegrate the EFQMmodel and KM practices, whether in SMEs or inlarge organisations, is still scarce. The present research attempts tocontribute to a model or reference framework that integrates bothmanagement approaches based on a horizontal reading of theEFQM model, and not on its traditional or vertical reading. Themodel examines the relationship between the transverse axesPMM, PM and KM and their effect on KBR. It is confirmed that themodel is valid and can be used both in large organisations and inSMEs.

In addition, the present study is one of the few to contributeempirical evidence on the factors critical to the success of the KMand TQM initiatives for companies of different sizes. The resultsshow that differences exist, which indicates the importance oforienting the business management based on the size, although KMis effective in both large and small companies. Finally, the modelcan also be used by future researchers, who can test its reliabilityand validity in other contexts using segmentation variables otherthan the size.

7.2. Managerial and practical implications

Organisations can use the vertical reading of the EFQMmodel toidentify areas of improvement in each of the nine establishedcriteria. Nevertheless, this is not the excellence management phi-losophy of the EFQM model. On the contrary, we can identify anobvious first horizontal reading among the agents' criteria and theresults criteria. For example, when analysing the People criterion,we must simultaneously analyse the Results in the people criterion.Nevertheless, several other horizontal readings are not as obviousand establish relationships between the sub-criteria of the Model(transverse axes).

From the point of view of management, the use of the axes canbe considered a powerful management and improvement tool, as itallows us to analyse important concepts for excellent managementusing different sub-criteria of the criteria that compose the EFQMmodel.

Thus, if an organisation wants to analyse its knowledge man-agement, in the context of an excellent management, it must studythree axes: PMM, PM and KM. In addition, the horizontal reading ofthe Model allows an analysis from the perspectives of leadership,policy and strategy, people, alliances and resources and processes(Fig. 1). Specifically, we will be able to identify the areas ofimprovement of KM, with respect to concrete aspects (sub-criteria)of policy and strategy (2b), people (3b) and the alliances and re-sources of the organisation (4e). With respect to PMM, we mustanalyse the areas of improvement in leadership (1b), policy andstrategy (2b) and processes (5a and 5b). In order to identify theareas of improvement in PM, we must focus our attention onleadership (1c), policy and strategy (2a and 2c) and the alliancesand resources (4a). Finally, the proposed model adds the relation-ships and synergies between these three key elements of excellentmanagement and results; that is, we can also analyse the effec-tiveness of the actions undertaken.

In addition, the multi-group analysis allows us to orient the KM,based on whether the organisation is small, medium or large insize. Thus, in both types of organisations (SMEs and large

companies), the development of alliances and cooperation withpartners is important for KM, although this aspect is more deter-minant in large companies. Nevertheless, in the SMEs, the man-agement of internal knowledge (PMM) is more important than inlarge companies. Finally, suitable KM will have a positive and sig-nificant effect on the operational and strategic results of the orga-nisation, independently of the size of the organisation.

7.3. Limitations and future research directions

The interpretation of the results and conclusions of this studyare subject to a series of limitations, principally of a methodologicalcharacter. The first limitation is related to the technique used forthe proposed model: structural equation modelling, which as-sumes the linearity of relationships between the latent variables.The second is related to the notion of causality. Our study consid-ered a soft modelling approach oriented more towards predictionthan causality. Thirdly, due to the eclectic nature of knowledge, itsmanagement is affected by a multitude of factors, and not all ofthem could have been considered in the research. Fourthly, wheninterpreting the results, it is necessary to take into account that thesample corresponds to Spanish companies, to a specific culture ofexcellence and KM; therefore, it cannot be generalised. Finally, theresearch design is cross sectional instead of longitudinal. In thiscase, a longitudinal study can analyse the effects of excellencepractices and KM on the results in more detail.

Some of these limitations allow us to open future streams ofresearch: (1) to introduce new relationships between the excel-lence practices in the conceptual model, as well as between thoseand the results; (2) to analyse the influence of other possibletransverse axes of the EFQM Model, such as communication,creativity and innovation, human resources or corporate gover-nance on KM; (3) to study the possible relationship of excellencepractices and KM in other types of results, for example, related topeople, customers or the company; and (4) to analyse, according tocompany size, the influence of KM on the different types of results(operational and strategic), which form the KBR construct.

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