Enablers of the EFQM model

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1 The current issue and full text archive of this journal is available at Using enablers of the EFQM model to manage institutions of higher education Arturo Calvo-Mora, Antonio Leal and José L. Roldán Department of Business Administration and Marketing, Faculty of Business and Economic Science, University of Seville, Sevilla, Spain Enablers of the EFQM model 99 Abstract Purpose To analyse the implicit relationships among enabler agents of the European excellence model to serve as a framework for the management and improvement of the quality in higher education institutions. Design/methodology/approach The hypotheses set out are based on the structure and meaning of the EFQM model and a review of the TQM literature. The empirical study was developed in 111 Spanish public university centres, and used the partial least squares (PLS) technique to test these hypotheses. Findings The results achieved support the relationships hypothesized and indicate the role of enabler agents as a basis for establishing a management model that leads universities towards excellence. Research limitations/implications The main limitation is related to the notion of causality. Our study has considered a soft modelling approach directed more towards prediction than causality. Practical implications The role of senior management stands out among the key activities. The top management leads the excellence development of key processes in the university through appropriate leadership, strategy formulation, establishment of partnerships, resource allocation, and human resources management. Originality/value The current work is one of the first studies dealing with the testing of implicit relationships set out by the EFQM model among enablers. The comprehension of these relationships provides a guide to implement, develop, assess and improve higher education institutions. Keywords Total quality management, Higher education, European Foundation for Quality Management, Least square approximation, Spain Paper type Research paper Introduction Nowadays, higher education institutions face important challenges such as expansion of systems, necessity of responding to diverse social demands, increase of educational spending, and the need to adapt to the new age of information and knowledge. Thus, the universities get an opportunity for showing their ability to adapt, and their social and intellectual leadership confronting this new environment creatively and innovatively. In any case, the universities must accept the challenge of developing quality in their operations in order to be able to accomplish these actions (Quintanilla, 1999). From the 1990s, being aware of this challenge, one of the emphases of the higher education policies among most European countries has been to stress quality improvement. On account of these policies, different approaches have been adopted for the introduction of quality management in universities, such as self-assessment and external assessment of the institutions, accreditation and certification systems, and different models of TQM (Wiklund et al., 2003). Quality Assurance in Education Vol. 14 No. 2, 2006 pp q Emerald Group Publishing Limited DOI /

2 QAE 14,2 100 TQM presents a strategic option and an integrated management philosophy for organisations, which allows them to reach their objectives effectively and efficiently, and to achieve sustainable competitive advantage (Goldberg and Cole, 2002). Their implementation is based on the European excellence model of the European Foundation for Quality Management (EFQM) which provides a European context. In Spain, the First National Plan for the Assessment of Quality in Universities in 1995, and the Second Plan in 2002, aimed at establishing a series of indicators that gave information about the quality level of processes, products and services in the centres of higher education. Three assessment guides, known as protocols emerged from these: plans for teaching, research and management (the last one clearly inspired by the EFQM model). In this context, the current work aims to test and validate the implicit relationships among the agents that make-up the EFQM excellence model. This relationship framework lays the foundations for actions that could be developed by the universities in order to achieve excellence in management. Moreover, this framework can be useful as an assessment tool, because it offers a profile of strong and weak points in key areas of the administration of these institutions. To achieve these objectives, this study begins with a review of the frequently used TQM implementation frameworks in the higher education field. Subsequently, the enablers or the key implementation factors that shape the EFQM excellence model are described, putting forward and justifying the hypotheses or relationships among them. Subsequently, the empirical work and the methodology are presented, to derive the main conclusions from the study. Frameworks for TQM implementation in higher education There is a body of work that tries to describe and justify how principles and practices of quality management can help improve universities (Biehl, 2000; Kanji et al., 1999; Montano and Glenn, 1999; Spanbauer, 1995; Weller, 2000). The main conclusions of these works centre around two aspects. Firstly, the feasibility of using quality management in the educational context. Secondly, the effectiveness of this management approach for the improvement of educational institutions in areas such as planning, human resources, resource administration, and educational and administrative process management. On the other hand, there are other works which tend to lean on the contributions of quality gurus such as Deming, Juran, or Crosby. This is the case with studies such as Allen (1997), Cullotta and Gonzales (1997), Kosaku (1994), Landesberg (1999) and Martin (1998). These contributions try to show how the doctrines of the great teachers can be transferred from the industrial to the education field, helping to transform the educational institutions. Finally, there are works attempting either to offer models for TQM implementation (Grant et al., 2002; Mergen et al., 2000; Michael et al., 1997; Pires Da Rosa et al., 2003) or to base themselves on existing excellence models, such as the Malcolm Baldrige National Quality Award or the EFQM excellence model. The works of Detert and Jenni (2000), Evans (1997), Farrar (2000), Goldberg and Cole (2002), Johnson (1996), Osseo-Asare and Longbottom (2002), Winn and Cameron (1998) and Zink and Schmidt (1995) can be considered as examples of excellence models applied to the educational field. This study is framed in this last research approach.

3 Relationships between enablers in the EFQM model The EFQM excellence model is a non-prescriptive framework that establishes nine criteria, which any organisation can use to assess the progress towards excellence. These nine criteria are divided between enablers and results. Our model (Figure 1) proposes that there is an inner logic interconnecting the EFQM model s agents. Thus, in order to achieve efficient implementation of TQM, it is not enough to implement actions in isolated areas: there is a need to coordinate efforts that affect the managers, policy and strategy, resources management and alliances, and key process management. In the EFQM model, enablers or agents define what organisations do in order to achieve excellence. Specifically, it is to do with activities related to the leadership of the directors, the management of human and material resources, as well as process management. Moreover, these activities are not independent: they must be implemented together and in a coordinated fashion. Next, the variables that comprise the model are defined, the relationship (two by two) between these variables is established by proposing hypotheses, and these hypotheses are supported by the theoretical and empirical references provided by the TQM literature. Enablers of the EFQM model 101 Leadership Excellent leaders develop and facilitate the achievement of the mission and vision. They develop organisational values and systems required for sustainable success and implement these via their actions and behaviours (EFQM, 2003). The management s commitment and leadership in quality must be visible, permanent and present at all management levels (Dean and Bowen, 1994), since it acts as the guide and promoter of the TQM implementation process. Nevertheless, to be successful, one has to move into action. Here, investment in human, material and financial resources supporting the achievement of the objectives and the development of policies and strategies becomes indispensable (Pires Da Rosa et al., 2003). Moreover, the involvement People Management H1a H2a H3 Leadership & Commitment H1b Policy & Strategy H2c Process Management H2b H1c H4 Partnerships & Resources Figure 1. Research model and hypotheses

4 QAE 14,2 102 of the entire workforce must be fostered and their efforts directed towards improvement recognised. The above aspects are confirmed by empirical studies conducted by some authors such as Eskildsen and Dahlgaard (2000) or Flynn et al. (1994), which demonstrate the significant positive correlation between leadership and the other key TQM implementation factors. From the above, we can extract the following research hypotheses: H1a. The leadership of the management has a positive influence on people management. H1b. The leadership of the management has a positive influence on policy and strategy. H1c. The leadership of the management has a positive influence on partnerships and resources. Policy and strategy Excellent organisations implement their mission and vision by developing a stakeholder focused strategy that takes account of the market and sector in which it operates. Policies, plans, objectives, and processes are developed and deployed to deliver the strategy (EFQM, 2003). Policy and strategy must be put into practice through the deployment of the key processes, suitable policy and staff management, and through the establishment of partnerships (Winn and Cameron, 1998). The theoretical and empirical literature studied focuses on the development and implementation of specific quality policies and strategies (Saraph et al., 1989), and on how these should be integrated into the organisation s policies and strategies (Wilson and Collier, 2000). Other studies make no explicit reference to policy and strategy but analyse it as a fundamental aspect within other dimensions such as the shared vision (Dow et al., 1999). This includes aspects such as the establishment in the organisation of strategic planning processes encompassing the points of view and requirements of internal and external customers. Detert and Jenni (2000) speak of a system thinking which requires all members of the organisation to take into account how their actions affect those of other people in the university institution. In a centre of higher education, this overall vision may be demonstrated using clear goals shared by all: professors, students and managers. These goals must take shape in all the activities of the university via the strategic planning process (Zink and Schmidt, 1995). From the above, we can extract the following research hypotheses: H2a. Policy and strategy have a positive influence on people management. H2b. Policy and strategy have a positive influence on partnerships and resources. H2c. Policy and strategy have a positive influence on process management. People management Excellent organisations manage, develop and release the full potential of their people at an individual, team-based and organisational level. They promote fairness and equality and

5 involve and empower their people. They care for, communicate, reward and recognise, in a way that motivates staff and builds commitment to using their skills and knowledge for the benefit of the organisation (EFQM, 2003). The importance of this yardstick is emphasised by Ahmad and Schroeder (2002) who note that human resource management is the cornerstone on which an important part of the success of TQM rests, since the quality improvement process is one of organisational learning based on people. Essential activities for people management include appropriate selection, reward and professional development (Flynn et al., 1994), the establishment of training plans (Gatewood and Riordan, 1997), the commitment to and involvement with quality (Ahire et al., 1996), or the establishment of an effective communication system (Zink and Schmidt, 1995). In the education field, Detert and Jenni (2000) and Osseo-Asare and Longbottom (2002) emphasise the role of training as a key factor and imply the continuous acquisition of new knowledge and skills by all employees. In short, an appropriate personnel recruitment and selection policy, along with a workforce which is trained, involved and committed to quality and to the improvement of the activities of the organisation must have an effect on performance and improvement of the organisation s key processes. This leads to achievement of better results. From the above, we can extract the following research hypothesis: H3. People management has a positive influence on process management. Enablers of the EFQM model 103 Partnerships and resources Excellent organisations plan and manage external partnerships, suppliers and internal resources in order to support policy and strategy and the effective operation of processes. During planning and whilst managing partnerships and resources they balance the current and future needs of the organisation, the community and the environment (EFQM, 2003). The importance of relationships with suppliers and the management of tangible and intangible resources is an aspect frequently addressed in the literature on quality management (Eskildsen and Dahlgaard, 2000). In the field of higher education, resource and partnership management is not a subject that is particularly discussed in the literature. In spite of this, universities, like any other organisation, must optimise the scarce resources they have, and appropriately manage the suppliers of specific inputs which represent a significant cost in budgetary terms (Osseo-Asare and Longbottom, 2002; Pires Da Rosa et al., 2003). This will lead to better and more efficient management of their processes or key activities. Eskildsen and Dahlgaard (2000), in an empirical analysis of the EFQM model, discovered a significant positive relationship between partnership management and key process management. From the above, we can extract the following research hypothesis: H4. Partnership and resources have a positive influence on process management. Process management Excellent organisations design, manage and improve processes in order to fully satisfy, and generate increasing value for, customers and other stakeholders (EFQM, 2003).

6 QAE 14,2 104 Analysing the structure of the relationships in the EFQM model, process management appears to be the link between the other agents and the results. The key processes of a higher education centre will not be the same as those in other organisations, but once they have been identified, there should be no differences in terms of their management and improvement (Zink and Schmidt, 1995). The key processes are considered to be those that have a significant effect on the critical results for a given organisation (Kanji and Tambi, 1999). In universities, these processes are identified by Zink and Schmidt (1995) and Pires Da Rosa et al. (2003) as the processes of administration and service, teaching and learning, and research. Methodology The data were obtained using a questionnaire following the self-assessment philosophy of the EFQM model. The questionnaires were addressed to the senior staff at the universities. The unit of analysis was the operational centre, i.e. faculties and university schools, since it groups together administrative and managerial tasks, along with other aspects related to teaching and the provision of services to the university community. Pilot study A first version of the questionnaire was sent to ten professors with experience in the quality area and to a dean and deputy-dean who had participated in quality assessments of their centre. Our objective was to establish the content validity of the measurement instrument. One week after the questionnaire had been sent, interviews were conducted to get these individuals comments. These were later incorporated into the final questionnaire (Table II). Sample The population studied was formed by 346 centres of Spanish public universities evaluated under the National Plan for the Assessment of Quality in Universities executed in 1996, 1998, 1999 or A random stratified sample was developed, with the allocation being proportional to the number of centres in each university so as to obtain representative samples of each stratum. The study considered a confidence level of 95 per cent, a sample error of 5 per cent and p ¼ q ¼ 0:5:[1] From this, the optimal sample size ought to be 186 university centres[2]. The number of returned questionnaires was 119, of which 8 had to be eliminated because they had not been completed correctly. As a result, the number of valid questionnaires was 111 representing a response rate of a little over 32 per cent of the population. The sample error obtained with the final sample is 7.76 per cent[3]. The characteristics of the sample are set out in Table I. Measures In order to measure the key implementation factors, we took the EFQM model as a reference, plus an adaptation of this model for the field of higher education developed by the Club Gestión de Calidad (Quality Management Club), an EFQM partner in Spain. In this respect, the information included in the EFQM model is appropriate for developing measurement scales for the nine criteria included in this framework (Eskildsen and Kanji, 1998) (Table II).

7 Frequency Percentage Profile of the surveyed individuals Dean Deputy-dean Director of University School Other senior positions Total Type of study Bio-health or health sciences Experimental Sciences Social and legal sciences Humanities Technical studies Non declared Total Experience in quality 1-3 years (low experience) years (medium experience) years (high experience) Total Enablers of the EFQM model 105 Table I. Sample s characteristic Each scale was reviewed using factor analysis in order to establish its unidimensionality, i.e. that only one trait or latent concept was being measured by the various indicators (items) that comprised each scale tested (Hair et al., 1998: glossary; Henard, 2000). Based on the constitutive definition of the EFQM variables applied to a university context presented earlier, the process management variable was designed as a second-order factor. This implies that this higher order variable is measured by a number of first-order latent variables (LV) or dimensions, that is to say, standards LVs with measured indicators. Therefore, this second-order factor is not directly connected to any indicators (Chin, 1998b). Consequently, we mapped a group of first-order factors or dimensions (i.e. educational, research and administrative processes variables) with respect to this higher level construct (process management variable) (Figure 2). Data analysis and results Partial least squares The research model shown in Figure 1 was tested using partial least squares (PLS), a multivariate analysis technique for testing structural models (Wold, 1985). As a structural equation modelling tool, PLS simultaneously allows the assessing the reliability and validity of the measures of theoretical constructs and the estimation of the relationships among these constructs (Barclay et al., 1995). This method is primarily intended for causal-predictive analysis in which the problems explored are complex and theoretical knowledge is scarce. PLS is an appropriate technique to use in a theory development situation (Wold, 1979), such as this research. We have used PLS-Graph software version 3.00 (Chin, 2003). Process management has been represented as a second-order factor because it is measured by three first-order factors or dimensions: educational, research and administrative processes variables. Following this approach, we have had to make

8 QAE 14,2 106 Table II. Measures Leadership ld1 ld2 ld3 ld4 ld5 ld6 ld7 ld8 Policy and strategy ps1 ps2 ps3 ps4 ps5 ps6 ps7 ps8 ps9 ps10 People management pm1 pm2 pm3 pm4 pm5 pm6 pm7 pm8 Agent/enablers and indicators They develop the centre s mission, vision and values They communicate the mission, vision and values to all levels of the centre They improve their actions, making them fit in with the centre s present and future needs They design an organisational structure (neither a teaching nor a research one) suitable for the centre s policies and strategies They implement a system of key processes or activities supporting the centre s policy and strategy, and its goals They keep intouch with the different stakeholders in order to know their expectations and opinions They encourage student s and staff s involvement in the improvement actions They publicly acknowledge the successes of people and groups in quality improvement actions The centre s policies and strategies are in line with its mission, vision and values The centre s policies and strategies are clearly formulated in writing All the areas in the centre are involved in the process of formulating and communicating the policies and strategies There is a formal process of reviewing and updating policies and strategies The centre s policies and strategies are structured in a Strategic Plan The centre s goals are set out in writing and in a clear and quantifiable manner The goals are communicated at all levels of the organisation The principles of quality are incorporated into all of the centre s policies, strategies and goals There is a procedure allowing for the deployment of the policies and strategies and for their being turned into short term plans The formulation and revision of policies and strategies include the needs and expectations of the stakeholders Identifying the staff s present and future needs regarding knowledge, competencies and skills Developing training plans for the improvement of the staff s knowledge, competencies and skills Promoting actions which support the staff s commitment and involvement in the improvement actions Encouraging the staff s assumption of responsibilities and empowerment to carry out improvement actions Developing suitable channels for sharing and communicating better practices knowledge and experiences Recognising quality improvement related efforts, either at a personal or group level Establishing social benefits and improvement of the staff s services and facilities Encouraging the staff s involvement intopics related to health and safety, the environment, and social and ethical responsibilities (continued)

9 Partnership and resources pr1 pr2 pr3 pr4 pr5 pr6 pr7 pr8 Process management Educational processes ep1 ep2 ep3 Research processes rp1 rp2 rp3 Administrative processes ap1 Agent/enablers and indicators Establishment of partnerships with suppliers to generate value and mutual benefits Development of agreements guaranteeing the exchange of knowledge and experiences with suppliers Making appropriate investments for the development of the centre s policy, strategy and continuous improvement Suitable use of the stock, the materials and the energy Identification and evaluation of the impact of new technologies on the centre Implementation of mechanisms for the collection and use of data supporting the centre s policy and strategy Implementation of mechanisms for the identification of the information needs of the stakeholders Use of information for the continuous improvement of the management system and the services The teaching activity envisages the students needs and expectations The teaching activity envisages the companies needs and expectations The teaching activity envisages the needs and expectations of the community or the society in general The research activity envisages the students needs and expectations The research activity envisages the companies needs and expectations The research activity envisages the needs and expectations of the community or the society as a whole Enablers of the EFQM model 107 The centre makes efforts addressed to identifying and analysing key processes and actions ap2 There is documentary support for processes (field of action, the actions they are made of, validity, etc.) ap3 Responsibilities are allocated for the periodic monitoring and review of the processes ap4 Data are collected about claims and suggestions of the stakeholders, then used to improve the processes ap5 Creativity and innovation are applied to the development of new processes and services ap6 Procedures are developed aimed at guaranteeing the adequate provision of services to the stakeholders ap7 Specific services are developed to support people and students Table II. a choice between a molar and molecular approach to analysis. Whereas the molar attitude represents an emergent construct that is formed from the first-order factors (formative style), in the molecular approach the existence of an overall latent construct is hypothesized and is indicated and reflected by the first-order factors (reflective style) (Chin and Gopal, 1995). The choice depends primarily on whether the first-order factors or dimensions are viewed as causes or as indicators of the second-order factors (Chin, 1998b) (Figure 3). If a change in one of the dimensions or beliefs necessarily results in similar changes in other beliefs, then a molecular model is appropriate.

10 QAE 14,2 Second order factor Process Management 108 First order factor sor dimensions Educational Processes Research Processes Administrative Processes Figure 2. Modelling of process management variable as a second-order factor Indicators ep1 ep2 ep3 rp1 rp2 rp3 ap1 ap2 ap3 ap4 ap5 ap6 ap7 Molar second order factor Molecular second order factor Second order factor Second order factor A B C A B C Figure 3. Molar versus molecular models of second-order factors First order factors or dimensions (ie., A, B and C) cause the second order factor. First order factors can have positive, negative o no correlation First order factors or dimensions (ie., A, B and C) depend on the second order factor. First order factors should be highly positively correlated Otherwise, a molar model is suitable (Chin and Gopal, 1995). In our research, we have decided to model process management as a molar second-order factor. In this way, an increase in the quality of the educational processes does not imply an increase in the quality of the administrative processes. Thus, dimensions are not necessarily correlated and, consequently, traditional reliability and validity assessment have been argued as inappropriate and illogical for this type of high order factor (molar) with reference to its dimensions (Bollen, 1989, p. 222). A PLS model is analysed and interpreted in two stages: (1) the assessment of the reliability and validity of the measurement model; and (2) the assessment of the structural model.

11 This sequence ensures that the construct measures are valid and reliable before attempting to draw conclusions regarding relationships among constructs (Barclay et al., 1995). Measurement model The measurement model in PLS is assessed in terms of individual item reliability, construct reliability, average variance extracted (AVE) analysis, and discriminant validity. Individual item reliability. Individual item reliability is considered adequate when an item has a factor loading that is greater than 0.7 on its respective construct, which implies more shared variance between the construct and its measures than error variance (Carmines and Zeller, 1979, pp ). Since, loadings are correlations, this implies that more than 50 per cent (i.e. loading square) of the variance in the indicator is shared with the construct. Notwithstanding this, in the case of molar second-order factors, the loadings are misleading because the intraset correlations for each block were never taken into account in the estimation process followed by this technique in order to obtain the parameters of this high-order construct. Therefore, it makes no sense to compare loadings among dimensions within a block. The interpretation of this kind of LV should be based on the weights (Chin, 1998a). Like the canonical correlation analysis, the weights allow us to understand the make-up of each LV. That is to say, these provide information about how each dimension or indicator (formative) contributes to the respective construct. However, a concern related to using formative measures deals with the potential multicollinearity among the items or first-order factors (Diamantopoulos and Inklhofer, 2001; Mathieson et al., 2001). In our case, multicollinearity refers to linear inter-correlation among dimensions. When there are nominally different dimensions that actually quantify the same phenomenon to a significant degree and these variables correlate highly with each other, they could be redundant. A principal problem of such data redundancy is that of overfitting in regression analysis models. Therefore, a high collinearity among dimensions would produce unstable estimates, and would make it difficult to separate the distinct effect of the individual dimensions on the construct. With this in mind, a collinearity test was performed using the SPSS programme. Specifically, a variance inflation factor (VIF) test was developed for the three dimensions of the process management variable. The results showed minimal collinearity with the VIF of three dimensions ranging between and (Appendix). According to Kleinbaum et al. (1988, pp ) a VIF over 5 is problematic and over 10 is a problem. These data are far below the common cut-off threshold of 5 to 10. Thus, there is not multicollinearity among the first-order factors that measure the process management variable. Appendix shows the individual item weights and loadings of the variables. Loadings are above 0.7 for all indicators. Construct reliability and average variance extracted analysis. The measures for construct reliability and AVE represent measures of internal consistency and, as discussed earlier, these are not applicable for molar second-order factors. Construct reliability is assessed using a measure of internal consistency: composite reliability (r c ) (Werts et al., 1974). We interpret this value using the guidelines offered by Nunnally (1978, pp ) who suggests 0.7 as a benchmark for a modest reliability applicable in the early stages of research. In our research, all of the constructs are reliable (Appendix). They all have measures of internal consistency that exceed 0.82 (r c ). Enablers of the EFQM model 109

12 QAE 14,2 110 AVE measure was created by Fornell and Larcker (1981). This measure quantifies the amount of variance that a construct captures from its manifest variables or indicators relative to the amount due to measurement error (Chin, 1998a). AVE values should be greater than It means that 50 per cent or more variance of the indicators should be accounted for. Consistent with this suggestion, AVE measures for all LV exceed 0.59 (Appendix). Discriminant validity. To assess discriminant validity, AVE should be greater than the variance shared between the construct and other constructs in the model (i.e. the squared correlation between two constructs). For adequate discriminant validity, the diagonal elements should be significantly greater than the off-diagonal elements in the corresponding rows and columns (Barclay et al., 1995). This condition is satisfied for reflective variables in relation to the rest of the variables (Table III). For the variable modelled as a molar higher order factor, we cannot analyse its situation because of the non-availability of AVE values. As has been previously mentioned, it is inappropriate to apply traditional reliability and validity measures for formative dimensions of a molar second-order factor (Bollen, 1989). Structural model Figure 4 shows the variance explained (R 2 ) in the dependent constructs and the path coefficients (b) for the model. Consistent with Chin (1998a), bootstrapping (500 resamples) is used to generate standard errors and t-statistics. Bootstrap represents a non-parametric approach for estimating the precision of the PLS estimates. This allows us to assess the statistical significance of the path coefficients. All hypotheses were supported since these surpass the minimum level indicated by a Student s t-distribution with two tails and n-1 (n ¼ number of resamples) degrees of freedom (Table IV). H1a-c were proved, although H1c was only marginally confirmed. This shows leadership and commitment variable exerts a significant positive influence on subsequent variables, both directly and indirectly through the policy and strategy. In its turn, this latter plays an outstanding role as the suggested hypotheses H2a-c demonstrate. Moreover, H3 and H4 have also been supported, which supports the influence of the antecedent variables (policy and strategy, people, and partnerships and resources) on the process management variable. Leadership Policy and strategy People management Partnerships and resources Process management Table III. Discriminant validity coefficients Leadership (0.769) Policy and strategy (0.767) People management (0.822) Partnerships and resources (0.779) Process management (n.a.) Notes: Diagonal elements (values in parentheses) are the square root of the variance shared between the constructs and their measures (AVE). Off-diagonal elements are the correlations among constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements; (n.a.) non-applicable

13 People Management R 2 =.4074 Administrative Processes Enablers of the EFQM model (H1a).273* (H2a).407*** (H3).218* Leadership (H1b).753*** Policy & Strategy R 2 =.5680 (H2c).258** Process Management R 2 = Research Processes (H2b).325* (H1c).240 (H4).402**.337 Partnerships & Resources R 2 =.2803 Educational Processes *** p < 0.001, ** p < 0.01, * p < 0.05, p < 0.1 (based on t (499), two-tailed test) Figure 4. Structural model results The research model seems to have an appropriate predictive power for most of the dependent variables given that variance explained, or R 2 s, for endogenous constructs exceed the required amount of 10 (Falk and Miller, 1992, p. 80) (Table V). This way, the mean of the explained variance of the implied variables is Another measure that supports these positive results is the Q 2 test of predictive relevance for the endogenous constructs (Geisser, 1975; Stone, 1974). This test is an indicator of how well observed values are reproduced by the model and its parameter estimates. The cross-validated redundancy measure has been particularly suggested to examine the predictive relevance of the theoretical/structural model (Chin, 1998a). A Q 2 greater than 0 implies that the model has predictive relevance, whereas a Q 2 less than 0 suggests that the model lacks predictive relevance. The results summarised in Table V confirm that the structural model has satisfactory predictive relevance for the endogenous variables, achieving a maximum Q 2 value of Discussion and conclusions Tables IV and V summarise the results obtained in the testing of hypotheses as well as the different effects between variables and the percentage of the total variation of the dependent variable accounted for by each independent variable. Additionally, they will help with the discussion of the results. Firstly, it is confirmed that the leadership and commitment of the senior officers of the centres act as the driving-force of the whole of the quality management and quality improvement process. This can be seen from the effect this variable has on policy and strategy, partnerships and resources, and people and process management. These relationships are confirmed by the work of Eskildsen and Dahlgaard (2000) and

14 QAE 14,2 112 Table IV. Structural model results Hypothesis Suggested effect Path coefficients (b) t-value (bootstrap) Support H1a: Leadership! people management þ * Yes H1b: Leadership! policy and strategy þ *** Yes H1c: Leadership! partnerships and resources þ Yes H2a: Policy and strategy! people management þ *** Yes H2b: Policy and strategy! partnerships and resources þ * Yes H2c: Policy and strategy! process management þ ** Yes H3: People management! process management þ * Yes H4: Partnerships and resources! process management þ ** Yes Notes: *** p, 0.001, ** p, 0.01, * p, 0.05, p, 0.1 (based on t(499), two-tailed test); t (0.001;499) ¼ ; t (0.01;499) ¼ ; t (0.05;499) ¼ ; t (0.1;499) ¼

15 Explained variance Total effect Correlations Indirect effect Direct effect Effects on endogenous variables Policy and strategy (R 2 ¼ ) (Q 2 ¼ ) H1b: Leadership People (R 2 ¼ ) (Q 2 ¼ ) H1a: Leadership H2a: Policy and strategy Partnerships and resources (R 2 ¼ ) (Q 2 ¼ ) H1c: Leadership H2b: Policy and strategy Processes (R 2 ¼ ) (Q 2 ¼ ) H2c: Policy and strategy H3: People management H4: Partnerships and resources Enablers of the EFQM model 113 Table V. Effects on endogenous variables

16 QAE 14,2 114 Wilson and Collier (2000), although in a field different from education. In the latter field, and using the Malcolm Baldrige model as a reference, Winn and Cameron (1998) confirm the relationship between leadership, policy and strategy, and people and process management. Secondly, the policy and strategy must act as a reference for the setting of staff policy and the management of resources and processes. This aspect is highlighted by Ahire et al. (1996) and Eskildsen and Dahlgaard (2000), confirming the relationship between policy and strategy and the management of people and resources. Wilson and Collier (2000) also verify positively the relationship of policy and strategy with the management of people and resources. Winn and Cameron (1998) confirm this relationship positively in the field of education. Thirdly, appropriate management of people is key to the management of processes. In this respect, Eskildsen and Kanji (1998) show that those organisations which make no effort towards motivating or training their employees do not manage to involve them in process improvement. In the empirical literature, there are several studies which corroborate the positive relationship between the management of people and resources (Flynn and Saladin, 2001; Wilson and Collier, 2000; Winn and Cameron, 1998). In the field of education, Detert and Jenni (2000) emphasise the role of motivation and training as key factors affecting the continuous acquisition of new knowledge, skills and abilities by all employees, thus leading them to perform their job better. Moreover, as is confirmed by our study, suitable process development does not depend exclusively on people and the organisation s policy and strategy but also on appropriate management of material and financial resources and the involvement of suppliers in these processes. This aspect is corroborated by the work of Eskildsen and Dahlgaard (2000). Finally, the results of our research also confirm the existence of an internal logic connecting the agent of the EFQM model (Ghobadian and Woo, 1996). The EFQM model proposes a method to evaluate the TQM. This is based on the weighted sum of the criteria it is made up of. That is to say, an individual score is obtained for criteria and an aggregated score is provided for enablers and results. Seen in this way, the evaluation hides useful information on the possible relationships or influences among criteria. In this manner, works such as those of Kristensen et al. (2001) or Eskildsen and Kanji (1998) have analysed the relationships among criteria of the EFQM model. These contributions show how the implementation, development and improvement of the criteria of the EFQM model is not produced independently. The result obtained from the analysis of the structural model and its forecasting ability supports the validity of the European excellence model as a reference framework for the implementation of quality in the field of higher education. Also revealed is the systemic nature of the model, that is to say, the need to consider the agents as a whole. On the other hand, the analysis of the structural model has allowed us to study a part of the causal structure of the EFQM model in depth. Knowledge of this structure may allow universities to lead their management towards excellent process management. In this respect, the principal implications for the management of the universities may be the following. Firstly, for the implementation of any quality improvement initiative, it is necessary to have the leadership and commitment of the senior

17 management of the centres. They must create and disseminate the values of this management philosophy, set goals and objectives that are consistent with these values, and create an appropriate organisation and system to achieve them. This definite management commitment must go hand in hand with a well-defined policy and strategy, implemented and communicated at all levels of the institution. The absence of this prevents measurement of the effectiveness and efficiency of universities or any of their sub-systems, in addition to creating disequilibrium in the allocation of resources within the system. Policy and strategy must be based on the needs and the current and future expectations of the stakeholders, and supported by the mission, vision and values established by the institution. As Osseo-Asare et al. (2005, p. 149) highlight, leadership is a key factor in the success of the TQM implementation in higher education institutions. A committed leadership is not enough to achieve success. Rather this must itself be reflected in the policy and strategy guiding institutions (Winn and Cameron, 1998). In our work, this relationship between leadership and policy and strategy is appreciated analysing the path coefficient between these two variables (0.753) and the explained variance (R 2 ) of the dependent variable (policy and strategy). This reaches a value of 57 per cent (Table V). This is also confirmed in the studies of Winn and Cameron (1998) and Wilson and Collier (2000). Consequently, the planning must be put into practice through appropriate actions aimed at continuous improvement and generating more and more value for the stakeholders. That is to say, via a suitable design, management and improvement of key processes. In relation to this, the EFQM model establishes how what has been planned (policy and strategy) must be put into practice through a key process group (EFQM, 2003). This is revealed in the studies of Winn and Cameron (1998), Wilson and Collier (2000) and Eskildsen and Dahlgaard (2000). The relationship between policy and strategy, and process management can be appreciated from Table V. This important influence can be observed, on the one hand, by the total effect between both variables (0.47) and, on the other hand, by the explained variance (R 2 ) in process management due to policy and strategy (15.4 per cent). With this in mind, each university centre must try to identify its key processes, document them and assign people to be in charge of monitoring and improving them. For these monitoring and improvement activities, it is essential to have a system of indicators to evaluate the effectiveness and the efficiency of the processes, and, in addition, provide transparent external information (students, society, etc.). For the systematic design and management of the key processes, the EFQM model sets out the need for a system that measures the level of implementation. Besides, within the Spanish University System, and as a results of the higher education institutions autonomy, the universities must inform society about the quality of its activities and its management. To meet these two requirements, measurement and information, the Spanish Ministry of Education and Science (2005) has proposed a catalogue of indicators for the Spanish University System. Within this broad catalogue of indicators, point VI refers to the key processes for this type of institution. Human resource management is a basic pillar of successful quality management. Improvement is a process of organisational learning, which is largely based on people. Enablers of the EFQM model 115

18 QAE 14,2 116 Therefore, the participation of the entire workforce in improvement activities must be encouraged, and the efforts made must be rewarded and recognised. We wish, by this conclusion, to highlight the importance set out in the EFQM model, of people in the process of quality improvement and in the appropriate development of the key processes. To achieve this, essential activities are: (1) A correct selection, recognition and career development of the workers (Flynn et al., 1994); (2) the setting up of permanent training schemes (Gatewood and Riordan, 1997); and (3) the commitment and involvement of the personnel in the principles of quality, and their taking part in the principles of quality, and their taking part in the key process improvement activities (Ahire et al., 1996). As seen in Table V, in the results of our research a direct significant effect on the variables of people management and process management exists (0.218). On the other hand, people management explains 13.7 per cent of the process management variable variance. This reinforces the importance of people in process management. Moreover, the universities and their centres, as with any other organisation, must try to optimise the scarce resources they have (monetary, information, infrastructure or technological) and execute adequate control and management of suppliers of specific inputs that represent a significant budgetary cost. In this way, appropriate management of partnerships and resources will have a positive effect on the development of processes. Process management is affected by the efficiency with which resources and alliances are managed (partnership and resources) (EFQM, 2003). As Ward and Chandler (1999) note, this is an especially important theme in universities. Similarly, Johnson (1996) points out how higher education is dominated by financial questions as budget cuts in this area are increasingly more common. That is why a greater attention must be paid to the appropriate management of the scarce resources these institutions have. In our study the relationship between partnership and resources and process management is appreciated via the direct effect (0.402) that exists between both variables. Moreover, partnership and resources is the enabler which explains most variance in the variable process management (26.9 per cent). Limitations The study has certain limitations, which must be considered when it comes to interpreting the results and conclusions arising from it. A first limitation is related to the notion of causality. Although evidence is provided on the causality of the model, this has not really been tested. Our study has considered a soft modelling approach directed more towards prediction than causality. While causation guarantees the ability to control events, predictability allows only a limited degree of control (Falk and Miller, 1992, p. 4). A second limitation is determined by the technique used for the proposed model: the structural equations, which assume the linearity of relationships between the LV (Hair et al., 1998). Thirdly, the study has relied on measurements based on the perceptions of the individuals who participated in it. But it has to allow for the possibility of the perceptions of those surveyed not

19 providing a completely accurate view of reality. Finally, the design of the research employed was cross-sectional instead of longitudinal. In this respect, the quality assessment and improvement is a process which develops over time and whose effects are really appreciated only in the long term. It would, therefore, be appropriate to follow a longitudinal approach for future research. Glossary AVE EFQM LV PLS TQM VIF ¼ Average variance extracted ¼ European Foundation for Quality Management is the body managing the EFQM excellence model. It was introduced at the beginning of 1992 as the framework for assessing organisations for the European Quality Award ¼ Latent variable ¼ Partial least squares ¼ Total quality management is a philosophy and a set of guiding principles that represent the foundation of the continuously improving organisation. ¼ Variance inflation factor Enablers of the EFQM model 117 Notes 1. p is the proportion, or the percentage of units in the sample that possess some characteristic or attribute or fall into some defined class (Cochran, 1977, p. 50). The threshold of p is 0-1. On the other hand, q is the complementary proportion of q, i.e. q ¼ 1 2 p. 2. The optimal sample is obtained applying the equation 5.65 of Cochran (1977, p. 110). 3. Since, the obtained final sample is under the optimal size, the sample error is higher than 0.05, i.e. the value initially set-up. The sample error is also obtained from the equation 5.65 in Cohran (1977, p. 110). References Ahire, S.L., Golhar, D.Y. and Waller, M.A. (1996), Development and validation of TQM implementations construct, Decision Sciences, Vol. 27 No. 1, pp Ahmad, S. and Schroeder, G. (2002), The importance of recruitment and selection process for sustainability of total quality management, International Journal of Quality & Reliability Management, Vol. 19 No. 5, pp Allen, I.E. (1997), The new philosophy for K-12 education: a Deming framework for transforming America s schools, Quality Progress, Vol. 30 No. 2, pp Barclay, D., Higgins, C. and Thompson, R. (1995), The partial least squares approach to causal modelling: personal computer adoption and use as an illustration, Technology Studies Special Issue on Research Methodology, No. 2, pp Biehl, R.E. (2000), Customer-supplier analysis in educational change, Quality Management Journal, Vol. 7 No. 2, pp Bollen, K.A. (1989), Structural Equations with Latent Variables, Wiley, New York, NY. Carmines, E.G. and Zeller, R.A. (1979), Reliability and validity assessment, Quantitative Applications in the Social Sciences, Sage University paper series, Sage, Beverly Hills, CA. Chin, W.W. (1998a), The partial least squares approach to structural equation modelling, in Marcoulides, G.A. (Ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates, Mahwah, NJ, pp