Technical efficiency and innovativeness: some evidence from Brazilian private hospitals

Thiago Chieppe Saquetto

Federal Institute of Espírito Santo, Colatina, Espírito Santo, Brazil

Teresa Cristina Janes Carneiro

Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil

Claudia Affonso Silva Araujo

COPPEAD Institute of Administration of Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

Kleber Fossatti Figueiredo

COPPEAD Institute of Administration of Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil


Hospitals have sought to improve their performance and innovativeness has been highlighted as an ally in this mission. The objective of the research was to verify whether the perception of innovativeness of hospital managers is related to the performance of their organizations. Innovativeness was defined as a measure of the company's ability to innovate and was operationalized through variables related to organizational innovation and the firm's perceived innovation. Performance was defined by the hospital's efficiency in using the resources available to provide services. The results of a survey conducted with managers of 20 private hospitals belonging to the largest health insurance plan operator in Brazil, showed that perceived innovativeness has an inverse relation with technical efficiency: the greater the capacity or propensity of the company to innovate, both perceived by the internal culture of the organization and by its way of acting in the market, the lower the technical efficiency of the hospital.

Keywords: Innovativeness; Technical efficiency; Private Hospitals; Data Envelopment Analysis; DEA.


Hospitals have faced increasing pressure to reduce costs and increase efficiency, and innovation or innovativeness has been highlighted as one of the main drivers of organizational performance, representing an important form of competitive differentiation in the market (Tajeddini et al., 2006; Rhee et al., 2010). However, the study of innovation in hospital projects is still incipient, and research has focused mainly on the study of health innovation systems (Barzotto, 2008, Albuquerque et Cassiolato, 2002) and on the specificities of innovations in hospital services (Vargas, 2006; Isidro-Filho, 2010).

In view of the above, this research aimed to verify whether the perception of innovativeness of managers of private hospitals is related to the technical efficiency of health organizations.

This study regards innovativeness as a measure of the company's ability or propensity to innovate, both perceived by the organization's internal culture and its way of acting in the market. Organizational performance, in turn, is understood as a measure of the efficiency with which the hospital uses the resources it has to provide hospital services compared to other hospitals.


Organizational Performance

The evaluation of organizational performance has received increasing attention from researchers in the last decades (Carneiro da Cunha, 2011), but there is no consensus on how to operate it, since, although the literature on the subject makes numerous performance measures available, none, is considered to be capable of covering all relevant aspects of organizational performance alone (Rogers et al., 1998; Combs et al., 2005). For Slack et al. (1997), the complexity with which companies present themselves in the market makes it impossible to reduce the performance of the business to a single indicator, thus requiring the combination of several indicators to broaden the analysis of business performance.

The first performance studies in the 1950s sought to identify measures that would represent activities within the organizational context (Martindel, 1950; Ridgway, 1956). This idea was reproduced by Drucker (1954) in developing what became known as Management by Objectives. Drucker's studies were complemented by Koontz et O'Donnell (1974), but the late 1970s was marked by criticisms of the measurement models of activities strictly internal to the organization.

The focus of research on performance measurement was then directed to financial indicators (Carneiro da Cunha, 2011). However, the researchers' strong acceptance and use of financial measures in performance appraisal did not prevent them from being criticized as well, because such models left out indicators of consumer satisfaction, employee satisfaction, quality and innovation, considered of great importance for the performance of the business (Ittner et Larcker, 1998). In health organizations, this discussion has attracted special interest from managers (Neely, 2005), especially in private enterprises, where factors such as pressure from health insurance plans force managers to promote innovations that can reduce costs and increase efficiency, without reducing the quality of the service provided (Souza et al., 2009).

Hospital operating performance

Performance measurement can be done using techniques for quantifying the efficiency and effectiveness of business activities (Neely, 2005), and the operational performance, or non-financial performance, comprises all the measures and indicators established for the evaluation of the operations of the business organization (Perera et al., 1997). However, the specificities of hospital organizations make it impossible not only to evaluate them through a single perspective, but also to use traditional performance indicators (Pink et al., 2001).

Marinho et Façanha (2000) affirm that a model of representation of hospital organizations should consider indicators of two categories of variables: input variables; and output type. The input type is subdivided into seven groups of variables: (a) labor inputs, which refer to the variables of the work performed by the hospital labor force (for example, labor force quantities); (b) capital inputs, indicative of the structural resources that impact on the operational capacity of the hospital, such as physical area and number of beds; (c) financial inputs, referring to general expenses for costing and maintenance, such as medicines, food and consumables (excluding those related to capital and labor); (d) general service inputs or support services such as cleaning, laundry and security; (e) specific service inputs, alluding to diagnosis and therapy, such as laboratory tests, radiographs and physiotherapies; (f) patient-related inputs, which describe general characteristics of entry to care, age, sex, clinical status, number of visits, hospitalizations, surgeries, etc.; and (g) environmental inputs or factors, which characterize the general operating environment of the hospital organization, such as the nature of the hospital property, the geographic region of operation, and the characteristics of the population served.

The variables of the output type are subdivided into three other groups: (a) outputs related to the treatment, which describe the care given to the patients or the hospital intervention performed, such as surgeries, outpatient and emergency care, number and term of hospitalization; (b) quality of service outputs, which comprises the actions, structures and conditions related to the general quality of the services provided, such as attitudes towards complaints, liberality in relation to visits, morbidity, mortality and frequency of work accidents; and (c) social outputs, which relate to the social externalities of the services offered by the hospital, such as care in remote areas and care for the needy.

Comparing public and private hospitals, studies conducted by Hollingsworth (2003; 2008) indicate that public hospitals perform better than private ones, whether or not for profit. Likewise, studies conducted in the United States and Germany suggest that private hospitals are less efficient than public hospitals, which is due to the fact that public institutions face resource constraints and, therefore, seek the maximum efficiency of their use (Tiemann et Schreyögg, 2012).

Studies that analyze the efficiency of hospitals in Brazil, using the mathematical model Data Envelopment Analysis (DEA), have frequently used in a combined manner operational and financial indicators and have analyzed mainly hospitals providing services to the Brazilian Sistema Único de Saúde (SUS - Unified Health System), both public and private (Proite et Sousa, 2004; Varela et Martins, 2011, Guerra et al., 2012), and university hospitals (Frainer, 2004; Lins et al., 2007; Ozcan et al., 2010). When analyzing 1,170 Brazilian hospitals, of which 852 are private and 319 public, Proite et Souza (2004) concluded that public institutions tend to be more efficient than private ones, since they would be more focused on improving the quality of services provided, investing more resources than the public. Chart 1 lists the types of non-financial variables most used in hospital surveys, as well as the national and international researchers that used them.

Chart 1. Operational variables used in national and international surveys


Source: The authors.


Although some researchers question (Cho et Pucik, 2005) and others believe that there is still no consensus (Tajeddini et al., 2006), innovation has been highlighted as one of the main factors influencing organizational performance (Hurley et Hult, 1998; Porter, 1990; Rhee et al., 2010). For Simon (2008), since the work of Schumpeter (1934) and Freeman et Perez (1988) there is solid evidence of the relationship between the company's innovativeness and its organizational performance.

In the hospital segment, research has shown that innovations influence the performance of adopters and many hospitals have concentrated efforts to develop innovations and invested more resources in improving their innovative performance (Su et al., 2009; Weng et al., 2011). Part of the innovation research efforts in hospitals has been based on broad approaches to its analysis, such as in studies on health and hospital innovation systems (Albuquerque et Cassiolato, 2002; Barzotto, 2008) and on the specificities of innovations in hospital services (Barbosa, 2009; Isidro-Filho, 2010).

According to the Oslo Manual (OECD, 2005), the fact that a company has produced an innovation is sufficient enough to give it the name of innovative, that is to say, possessing innovativeness. For Hansen et al. (2007), innovativeness is a feature or characteristic of organizations, and among the most widely held concepts is what defines innovative organizations as those that adopt innovations. According to these authors, recent work has added to the concepts of innovativeness, besides the creation and use of innovations, strategic, cultural, social and managerial aspects.

In this research, we chose to use a broad concept of company innovativeness, as suggested by Andressi et Sbragia (2004). According to these researchers, innovativeness is not only a form of innovation, but a state of constant introduction of innovations, either internally or externally. Innovativeness will then be defined, for purposes of this research, as a measure of ability or willingness of the company to innovate, both perceived by the internal culture of the organization and for his way of acting in the market.

The innovativeness of the company, measured according to managers’ perception, has focused on the evaluation of the internal culture of the organization. Organizational culture, in turn, has been treated as a driver for innovations in the company and, from its analysis, is believed to capture the spirit of innovation of the enterprise (Auh et Menguc, 2005). Innovativeness is related to an organization's internal culture, which encourages and enables the emergence of new ideas and new processes, and its evaluation, according to managers' perceptions, has been operationalized through the scale developed by Hurley et Hult (1998). This measure, called organizational innovativeness (OI), was later adapted and revalidated by Tajeddini et al. (2006) and Tajeddini et Mueller (2012) throughout several studies.

Researchers have long emphasized the importance of developing a measure of company innovation from a consumer perspective (Danneel et Kleinschmidt, 2001). In this context, there should be an emphasis on the research carried out by Walsh et Beatty (2007) and Kunz et al. (2010). Walsh et Beatty's (2007) surveys are more related to a corporate reputation assessment, which takes into account opinions about the company or people in particular interest groups. The work of Walsh et Beatty (2007) approximates that proposed by Danneel et Kleinschmidt (2001) for assigning to consumers the centrality in the process of evaluating organizations, but distancing themselves by choosing to evaluate corporate reputation.

Kunz et al. (2010) developed a measure of entrepreneurial innovativeness resulting from consumer perception, which had been termed the Perceived Firm Innovativeness (PFI). This measure evaluates the perception of consumers regarding a series of innovative activities of the company, which broadly attribute a measure of innovativeness to the organization. The basis for consumers to attribute such measure of innovativeness is the information, knowledge and experience they have in relation to the organization being analyzed, and the central elements analyzed are novelty, creativity and their impact on the market.

The complementarity of the PFI and OI constructs for a broad assessment of the innovativeness of companies, as proposed in this study, is based on the fact that the PFI is focused on the perception of the consumers and not on the perception of the managers. However, some important considerations need to be made as to how hospitals provide services to consumers. For Slack et al. (1997), the transformation performed by hospitals can be better understood as providing a pure service, the health service. This is due to the fact that the product generated has characteristics of intangibility, simultaneity between the production and its consumption and a high contact of the consumer with the productive operations. These characteristics insert the consumer into the production environment of the service and enable him to develop a vision in terms of how innovative the hospital organization is based on the information, knowledge and experiences that the hospital itself makes available during the service. In this way, consumer's perception of the hospital's innovative capacity, developed during the service delivery and the way the health establishment operates in the market, is not completely different from the manager's perception of the hospital enterprise.

According to Sousa et al. (2011), many of the requirements for the evaluation of hospital services by consumers are consistent with the efforts expended by hospital administration. This fact makes the PFI construct, originally conceived for the evaluation of the perception of the consumers, a tool apt to evaluate the perception of innovativeness of the hospital enterprise through the perception of its managers, preserving the due adaptations.

In this research, therefore, two constructs of innovativeness will be used to evaluate a broad perception of managers regarding hospital's innovative capacity: OI, developed by Hurley et Hult (1998) and later adapted by Tajeddini et al. (2006); and PFI, developed by Kunz et al. (2010).

The necessary adaptations to the application of the constructs, as proposed in the research, are presented below.


The general objective of this research is to answer the following question: does the innovativeness of a private hospital reflect in its operational performance? To elucidate the issue, it has been broken down into three other more delimited issues: (1) what is the technical efficiency of private hospitals? (2) What is the perception of the innovativeness of managers of private hospitals? and (3) Does the managers' perception of innovativeness relate to the comparative technical efficiency of the hospitals analyzed?

To reach the proposed objectives, the research was structured in two phases: in the exploratory phase, was accessed reports published by the Ministries of Planning and Health, in order to understand the evolution and the current panorama of the hospital sector in Brazil; in the second phase, sample data were collected with the objective of measuring the technical efficiency of hospitals by converting their inputs into health services, verifying the perception of managers' innovativeness, and analyzing whether the perceived innovativeness of managers is related to technical efficiency of the hospital.

The data were collected through a questionnaire sent by e-mail to hospital managers, whose position was occupied by management or sector management. Performance-related variables, such as number of employees, physicians, beds, etc., were filled directly by the managers; affirmations about the hospital were formulated for the variables of innovativeness. These variables were evaluated using a 5-point Likert scale, ranging from totally disagree (1) to fully agree (5). The questionnaires comprise the activities developed by the hospitals in the year 2011. Initially, twenty Brazilian private hospitals, belonging to AMIL, the largest health plan operator in Brazil, were selected to participate in the survey, with a market share of 10.1% in terms of number of beneficiaries, 6.3 million insured lives and a net income of US$ 5.2 billion. However, when checking the data returned by hospital managers, three of them were eliminated from the sample due to inconsistent data. Thus, the final sample consisted of 17 AMIL hospitals: nine located in São Paulo; seven in Rio de Janeiro; and one in Paraná. The names of the hospitals will be kept confidential and, in this research, they are denominated based on their location (SP, RJ, PR).

Operationalization of variables

Performance: as the main objective of private hospitals is to maximize outputs, using existing resources (inputs), the output-oriented model is appropriate for this type of analysis and is in line with previous studies (Chang et al., 2004; Mogha; et al. 2012). Thus, operational performance was calculated through a comparative analysis of the technical efficiency with which hospitals use their resources to provide hospital services.

This research aimed to develop three models of hospital technical efficiency analyzes: Emergencies, Hospitalizations and General Model. The creation of the Emergency and Hospitalization models was motivated by results presented by Weng et al. (2011), considering that the focus of hospital technical efficiency has often fallen to the "product" hospital admissions (Wolff, 2005; Cesconetto et al., 2008). The General Model was defined with the purpose of constructing a hospital efficiency evaluation in a broader way. In addition to analyzing, in a joint way, the technical efficiencies of the hospitals in providing emergency and inpatient services, this analysis included variables related to the medical surgery product. The variables proposed for the three models are presented in Chart 2. In addition, the following moderating variables were considered: the size of the hospital (small - up to 50 beds; medium - between 51 and 150 beds; and large - more than 150 beds), the location (capital or countryside) and the nature of the service provided (general or specialized).

Charte 2. Model Variables


Source: The authors.

The variables were submitted, in each of the models, to the Pearson correlation analysis. As used by Guerra et al. (2012), correlation indices between variables above 0.7 were considered high and led to a deeper analysis of the meaning of the relationship between them: whether causality or redundancy. After considering and identifying the variables that would compose the models, the data were processed through Data Envelopment Analysis (DEA). The method used to analyze the variables is the DEA Constant Returns to Scale (DEA CCR), oriented to the outputs. The DEA mathematical model evaluates the efficiency of decision-making units (DMUs) by maximizing the weighted input-output ratio. In the DEA CCR analysis, the efficiency of each DMU is calculated in relation to the other members of the group (Marinho et Façanha, 2000). The efficiency measure associated with each one is the result of the weighting that allows its maximization, observing the constraints (Carneiro da Cunha, 2011). The main result generated by this mathematical modeling is the technical efficiency indexes of the DMUs. Through them it is possible to generate the ranking of efficiency of the hospitals. The DEA analyzes were carried out with the aid of the R statistical software, through the Benchmarking package, and the other analyzes by the software Excel 2007 and PASW Statistics 18.

Innovativeness: The innovativeness of the company was divided into (a) General innovativeness (GI), measure of the capacity or propensity to innovate, both perceived by the internal culture of the organization and by its way of acting in the market; (b) Organizational Innovativeness (OI), internal company culture that encourages and enables the emergence of new ideas, new products and new processes; and (c) Perceived Firm Innovativeness (PFI), perception of how enduring is the capacity of the company that results in new creative and impactful ideas and solutions for the market. Tables 3 and 4 present the operationalization of variables OI and PFI. The variable GI is the combination of the two. The analysis of Pearson correlations was performed and the correlations above 0.7 were considered undesirable.

Chart 3. Organizational Innovativeness variables


Source: The authors.

Chart 4. Perceived Firm Innovativeness variables


Source: The authors.


Most of the sample hospitals (71%) are located in the capitals of the three states. They are, mainly, medium-sized units (47%), and only two are small. The majority (88%) are general non-specialist hospitals. Of the 17 managers interviewed, 59% hold a Board position and 35% hold a Hospital Administration position; 53% are male. The majority (53%) are between 40 and 50 years old; 47% have been working in the hospital for less than two years and 16% have been working in the hospital for more than 6 years.


Emergency Model: there was a high correlation between the number of physicians and the number of nursing professionals assigned to the emergency room (Pearson's coefficient = 0.703). The existence of a correlation between human resources variables in healthcare enterprises has been a frequent observation (Proite et Souza, 2004; Frainer, 2004; Cesconetto et al., 2008). Such as Cesconetto et al. (2008), variables I5 and I8 were incorporated in a new variable as the sum of the two, called human resources assigned to emergency (I10). Given the number of emergency beds and human resources assigned to the emergency, the hospitals that obtained the best results in the total number of emergency care, and consequently became reference (efficiency of 100%) for the calculations of the other efficiency indexes, were SP3 and PR1. The average hospital efficiency index in the Emergency sector was 31.7% (SD = 34.2). Compared to the hospitals defined as best practices, the hospital that extracted the lowest results was RJ5 (1.82%). The stratification of the hospital technical efficiency indexes of the Emergencies model by size and type of service provided indicated that private hospitals of medium size have the best average technical efficiency index (47%), followed by large hospitals. Despite the small number of hospitals in the sample, the low technical efficiency index of small hospitals, 2.52%, is noteworthy. The general hospital combination of medium size contributed to increase the technical efficiency (72%).

Graph 1. Results of the Operational Efficiency Model called Emergencies


Source: The authors.

Hospitalizations model: the variables total number of hospital nurses and total number of nursing professionals presented a correlation coefficient of 0.794 and are incorporated by means of their sum to a proxy called nursing staff, as well as in the previous model. Given the total number of beds, number of internal physicians of the hospital (routine and on-call) and nursing staff, the hospitals that extracted the best results from the total number of hospitalized patients were: RJ1, RJ3, SP4, SP5 and SP6. In relation to the hospitals considered as the best practices of inpatients, the one that obtained the lowest index in terms of technical efficiency, given the resources at its disposal, was the SP7, with 17.27%. The average rate of hospital efficiency in the hospitalization sector was, approximately, 52.4% (Graph 2). The stratification of hospital technical efficiency indices of the hospitalization model by size, location and type of service indicated that large hospitals have the best average technical efficiency (59%), followed by medium-sized hospitals (58%). Large general hospitals have the highest average efficiency rate (62%) and large general hospitals located in the capitals had an even higher average rate (70%) in the provision of inpatient services.

Graph 2. Results of the Operational Efficiency Model called Hospitalizations


Source: The authors.

General Model: as well as in the hospitalizations model, variables total number of hospital nurses and total number of nursing professionals were incorporated into the nursing staff proxy (I11). The variable total number of beds had a high correlation index with the variable number of operating rooms (0.838); the variable number of operating rooms was also highly correlated with the variables total number of nurses (0.790) and total number of surgeries performed. However, when analyzing the possible relationship between them, it was concluded that there is no redundancy that requires its treatment or elimination. All these variables were maintained in the model. Hospitals that, in view of the available resources (total number of beds, of internal doctors of the hospital, of operating rooms and nursing staff), in 2011 obtained the best results (total inpatients, total emergency care and total surgeries performed) were: SP6, SP5, SP4, SP3, RJ6, RJ4, RJ3, RJ1. Compared to these, the hospital that obtained the lowest technical efficiency index, given its resources, was RJ5, with 32.55% (Graph 3).

Graph 3. Results of the Operational Efficiency Model called Emergencies


Source: The authors.

Table 1, concerning the General Model, presents the generated results, those projected at the technical efficiency frontier and the difference between projected and generated for each output used in the research. In analyzing specifically the inefficient hospitals, they had an average technical efficiency of approximately 72%. Given the projections that would allow them to reach the efficiency frontier, it was observed that they provided approximately 70.19% of projected hospitalizations, 73.74% of emergencies and 73.08% of surgeries. The hospital with the lowest technical efficiency, RJ5, index would require an increase of 8,016 inpatient care, 16,819 emergency care and 3,244 surgeries to project these units to the efficiency frontier. The hospitals that obtained the best general technical efficiency were those of medium size (77%), followed by the large ones (68%). Among the seven hospitals that have achieved maximum efficiency rates, only two are not in a capital. The maximum efficiency hospitals are in majority of cases general hospitals and midsize hospitals.

Table 1. Efficiency ranking of General Model with results achieved and designed and differences


Source: The authors.

R = Realized; P = Projected; D = Difference


The measures of innovativeness (GI, OI and PFI) were obtained by the sum of the indicators that compose them. Thus, can assume the following values: IG, with 12 indicators, ranges from zero to 60; OI, with five indicators, ranges from zero to 25 and PFI, with eight indicators, ranges from zero to 40.

GI: the hospitals that obtained the highest GI perception were RJ5 and PR1, with 57 points each. The smallest measure in terms of innovativeness was SP1 hospital (31 points). The affirmation "our hospital is dynamic" obtained the highest average (agreement) among the managers (4.65); on the other hand, the greatest disagreement was attributed to the statement "our hospital frequently adopts new medical treatments" (mean 2.24).

OI: four hospitals had the highest OI perceptions: RJ5, PR1, SP9 and SP2 (25 points). The lowest perception occurred in the hospital SP1 (15 points). Among all variables analyzed in this study, those related to OI were the ones that obtained the highest averages, indicating agreement by the managers. This result may have been influenced by the centrality of managers in the management of hospital innovation systems.

PFI: dtwo hospitals share the most perceived post of PFI with 37 points: RJ5 and PR1. The lowest perception is of the hospital SP2, with 17 points. As in the GI model, the affirmation "our hospital is dynamic" obtained the highest average response among managers (4,58), indicating agreement; the lowest mean perception of innovativeness was attributed to the statement: "Our hospital often adopts experimental medical treatments" (2,24), indicating disagreement.

The GI measurement results from the combination of the OI and PFI variables. Thus, it was observed that some hospitals were among the highest OI perceptions, but the same was not observed for PFI, culminating in the reduction of GI. Graph 4 shows the OI, PFI and GI ranking of the hospitals surveyed.

Graph 4. Innovativeness Ranking - OI, PFI e GI


Source: The authors.

Innovativeness vs. performance

The third questioning of the research, "was the perception of innovativeness of managers of hospital projects related to the comparative technical efficiency of the enterprises analyzed?", was investigated through a correlation analysis between the measures of innovativeness (GI, OI, and PFI) and performance models (Emergencies, Hospitalizations and General Model). In all, the relationship between innovativeness and operational performance was tested in nine different ways. The analysis of correlations between performance models and measures of innovativeness is presented in Table 2.

Table 2. Innovativeness x operational efficiency


Source: The authors.

*Significant correlation at 5% ** Significant correlation at 1% (N=17)

The innovativeness (GI, OI, and PFI) did not present a statistically significant correlation with the technical efficiency for the restricted operational performance models Emergencies (Operational Efficiency of Emergencies - OEE) and hospitalizations (Operational Efficiency of Hospitalizations - OEH). However, the General Model (General Operational Efficiency - GOE) showed a correlation with statistical significance with GI (-0.637) and PFI (-0.570), both negative. Thus, it can be concluded that: (a) the greater the capacity or propensity of the company to innovate, both perceived by the internal culture of the organization and by its way of operating in the market (GI), the lower the technical efficiency (GOE) of the hospital undertaking; and (b) the greater the innovative capacity of the hospital enterprise, resulting in new, creative and impacting ideas and solutions in the market (PFI), the lower the technical efficiency (GOE). The negative correlation between innovativeness and operational efficiency initially contradicts results of research that state that this relationship is positive (Porter, 1990; Hurley et Hult, 1998; Tajeddini et al., 2006; Rhee et al., 2010). However, the analysis of these results should highlight some of the specificities of innovation in the service sector, as well as the innovative dynamics of hospitals.

Unlike the technicist view of manufacturing innovation, coproduction and immateriality characteristics in service innovations should be emphasized (Isidro-Filho, 2010). The logic of innovation in Brazilian hospitals consists in the evolution of the hospital product, specifically in health, that is, in the adequacy of the hospital to the predominant convention on the hospital services (Vargas, 2006). The purpose of quality in health services is to improve and refine patient care, that is, to innovate the hospital service (Sousa et al., 2011). Although hospitals did not distance themselves from manufacturing in the search for better operational results, the capacity to innovate in health services was shown, in this research, not based on this logic. The perceived innovativeness, especially in the way it operates in the market and in the way the services are provided by the hospital, does not provide gains to the hospitals regarding the number of hospitalizations, emergency care and surgeries, given the resources available to them. However, if managers' perceptions reflect the integrality of hospital innovativeness, it is suggested that the gains of innovativeness are related more specifically to the increase in the quality of hospital services, in consonance with the results of previous research (Hollingsworth, 2003; 2008; Tiemann et Schreyögg, 2012), including those carried out in Brazil (Proite et Sousa, 2004).

When linking the determinants of quality in services with the provision of hospital services, it can be observed that investing in improving the quality of hospital services would not allow a direct improvement in operational performance. On the contrary, by requiring greater availability of resources to improve determinants, such as reliability and responsiveness, operational performance, as measured by the technical efficiency proposed in this research, would be seriously compromised, as attested by Proite et Sousa (2004), Hollingsworth (2003; 2008) and Tiemann et Schreyögg (2012). This research supports, therefore, that the greater the innovation in hospital projects, the greater the investments of the hospital in the determinants of the quality of its services, that is, the higher the quality of hospital services due to the greater availability of medical resources, nurses, nursing technicians, beds and operating rooms. Therefore, as a consequence of these investments, the operational efficiency is lower. In addition, since hospitals normally work with idle capacity due to the possibility of shocks to demand (Marinho et Façanha, 2000), it is suggested that more innovative hospitals provide more resources for a possible attendance of these events. Thus, because of improved quality levels in hospital services, by ensuring reliability and responsiveness to consumers, the result is a decrease in their comparative technical efficiency.


Three objectives guided this study: systematizing and comparing the technical efficiency of private hospitals, assessing the managers' perception of the innovations of these hospitals and verifying the relationship between the hospital's innovativeness and its operational performance. In analyzing the comparative efficiency of private hospitals, it was noted that inefficiencies in the emergency care and/or inpatient care sector do not necessarily imply widespread technical inefficiency. In addition, it was observed that general and large hospitals have higher rates of efficiency. The hospital innovation can differ, in the same enterprise and, as mentioned by the same manager, according to the parameters of innovation defined.

In the private hospitals surveyed, there was a negative relationship between innovativeness (GI and PFI) and operational performance. These findings are aligned with the possibility of these enterprises focusing on improving the quality of their hospital services, even if this leads to the loss of technical efficiency, as already observed in previous studies.

Although this study presents limitations (number of hospitals analyzed and measurement of innovativeness under the perception of the manager only), it is believed that it is relevant when establishing correlations between the variables of innovativeness and operational performance, contributing to the academic discussion about the subject. As future research, it is necessary to emphasize the need for further study in the technical efficiency of private hospitals, the innovative capacity of these enterprises and the performance relationship with the innovativeness, not restricted to the operational performance.


Albuquerque, E. M.; Cassiolato, J. E. (2002), “As especificidades do sistema de inovação do setor de saúde”, Revista de Economia Política, Vol. 22, No. 4, pp. 88.

Amil Participações S.A. (2011), “Demonstrações financeiras acompanhadas do relatório dos auditores independentes”. Dezembro. Disponível em: /amilpar/web/index_pt.html (Acesso em 17 jun. 2012).

Andressi, T.; Sbragia, R. (2004), “Fatores determinantes do grau de inovatividade das empresas: um estudo utilizando técnica de análise discriminante”, Working Paper 1/4, FEA/USP.

Auh, S.; Menguc, B. (2005), “The influence of top management team functional diversity on strategic orientations: the moderating role of environmental turbulence and interfunctional coordination”, International Journal of Research in Marketing, Vol. 22, pp. 333-350.

Banker, R. D.; Conrad, R. F.; Strauss, R. P. (1986), “A comparative application of data envelopment analysis and translog methods: an illustrative study of hospital production”, Management Science, Vol. 32, pp. 30-44.

Barbosa, P. R. (2009), Inovação em serviços de saúde: dimensões analíticas e metodológicas na dinâmica de inovação em hospitais, Tese de Doutorado em Saúde Pública, Escola Nacional de Saúde Pública Sergio Arouca, Rio de Janeiro, RJ.

Barzotto, L. C. (2008), O ambiente de inovação em instituição hospitalar. Dissertação de Mestrado em Administração, Universidade Regional de Blumenau, Blumenau, SC.

Burgess, J. F.; Wilson P. W. (1998), “Variation in inefficiency among US hospitals”, Canadian Journal of Operational Research and Information Processing, Vol. 36, pp. 84-102.

Carneiro da Cunha, J. A. (2011), Avaliação de desempenho e eficiência em organizações de saúde: um estudo em hospitais filantrópicos. Tese de doutorado em Administração, Faculdade de Economia, Administração e Contabilidade, Universidade de São Paulo, São Paulo, SP.

Cesconetto, A.; Lapa, J. S.; Calvo, M. C. M. (2008), “Avaliação da eficiência produtiva de hospitais do SUS de Santa Catarina”, Caderno de Saúde Pública, Vol.24, pp. 2407-2417.

Chang, H.; Cheng, M-A.; Das, S. (2004), “Hospital ownership and operating efficiency: Evidence from Taiwan”, European Journal of Operational Research, Vol. 159, pp. 513–527.

Cho, H.; Pucik, V. (2005), “Relationship between innovativeness, quality, growth, profitability and market value”, Strategic Management Journal, Vol. 26, pp. 555-575.

Combs, J. G.; Crook, T. R.; Shook, C. (2005), “The dimensionality of organizational performance and its implications for strategic management research”, em Ketchen, D. e Bergh, D. (ed.), Research Methodology in Strategy and Management, San Diego, Elsevier. pp. 259-286.

Danneel, E.; Kleinschmidt, E. J. (2001), “Product innovativeness from the firm's perspective: its dimensions and their relation with project selection and performance”, Journal Product Innovation Management, Vol. 18, pp. 357– 78.

Drucker, P. F. (1954), The pratice of management. New York: Harper.

Frainer, D. M. (2004), A eficiência técnica de hospitais universitários brasileiros no primeiro semestre de 2001. Dissertação de Mestrado em Engenharia de Produção, Universidade Federal de Santa Catarina, Florianópolis, SC.

Freeman, C.; Perez, C. (1988), Structural crises of adjustment: business cycles and investment behaviour. London: Pinter.

Grosskopf, S.; Valdmanis, V. (1987), “Measuring hospital performance: A non-parametric approach”, Journal of Health Economics, Vol. 6, pp. 89-107.

Guerra, M.; Souza, A. A.; Moreira, D. R. (2012), “Performance analysis: a study using data envelopment analysis in 26 Brazilian hospitals”, Journal of Health Care Finance, Vol. 28, No. 4, pp. 19-35.

Hansen, E.; Juslin, H.; Knowles, C. (2007), “A relação entre inovatividade, estrutura de capital e criação de valor”, NRC. Vol. 27, pp. 1324-1335.

Hollingsworth, B. (2003), “Non-parametric and parametric applications measuring efficiency in health care”, Health Care Management Science, Vol. 6, pp. 203–218.

Hollingsworth, B. (2008), “The measurement of efficiency and productivity of health care delivery”, Health Economics, Vol. 17, pp. 1107–1128.

Hurley, R. F.; Hult, G. T. M. (1998), “Innovation, market orientation, and organizational learning: an integration and empirical examination”, Journal of Marketing, Vol. 62, pp. 42-54.

Isidro-Filho, A. (2010), Adoção de inovações apoiadas em tecnologias de informação e comunicação, formação de competências e estratégias de aprendizagem em hospitais. Tese de Doutorado em Administração, Universidade de Brasília, Brasília, DF.

Ittner, C. D.; Larcker, D. F. (1998), “Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction”, Journal of Accounting Research, Vol. 36, pp. 1-35.

Koontz, H.; O’donnell, C. (1974), Princípios de administração. São Paulo: Pioneira Thomson Learning.

Kunz, W.; Schmitt, B.; Meyer, A. (2010), “How does perceived firm innovativeness affect the consumer?”, Journal of Business Research, Vol. 64, pp. 816-822.

Lins, M. E.; Lobo, M. S. C; Silva, A. C. M. et al. (2007), “O uso da análise envoltória de dados (DEA) para avaliação de hospitais universitários brasileiros”, Ciência e Saúde Coletiva, Vol. 12, pp. 985-998.

Magnussen, J. (1996), “Efficiency measurement and the operationalization of hospital production”, Health Services Research, Vol. 31, pp. 21-37.

Maniadakis, N.; Thanassoulis, E. (2000), “Assessing productivity changes in UK hospitals reflecting technology and input prices”, Applied Economics, Vol. 32, pp. 1575-1589.

Marinho, A.; Façanha, L.O. (2000), “Hospitais universitários: avaliação comparativa da eficiência técnica”, Economia Aplicada, Vol. 4, No. 2, pp. 316-49.

Martindel, J. (1950), The scientific appraisal of management. a study of the business practices of the well managed companies. New York: Harper.

Mogha, S. K.; Yadav, S. P.; Singh, S.P. (2012), “Performance evaluation of Indian private hospitals using DEA approach with sensitivity analysis”, International Journal of Advances in Management and Economics, Vol. 1, No. 2, pp. 1–12.

Neely, A. (2005), “The evolution of Performance measurement research: developments in the last decade and a research agenda for the next”, International Journal of Operations et Production Management, Vol. 25, No. 12, pp. 1264 -1277.

OCDE. Manual de Oslo. (2005), Diretrizes para coleta e interpretação de dados sobre inovação. 3a ed. OCDE e Eurostat.

Ozcan, Y.A.; Lins, M. E.; Lobo M. S. C. et al. (2010), “Evaluating the performance of Brazilian university hospitals”, Annual Operational Research, Vol. 178, No. 1, pp. 247–261.

Perera, S.; Harrison, G.; Poole, M. (1997), “Customer-focused manufacturing strategy and the use of operations-based non-financial performance measures: a research note”, Accounting, Organizations and Society, Vol. 22, No. 6, pp.557-572.

Pink, G.H., et al. (2001), “Creating a balanced scorecard for a hospital system”, Journal of Health Care Finance, Vol. 27, No. 3, pp. 1-20.

Porter, M. E. (1990), The competitive advantage of nations. New York: The Free Press.

Proite, A.; Sousa, M.C.S. (2004), “Eficiência técnica, economia de escala, estrutura da propriedade e tipo de gestão no sistema hospitalar brasileiro”. artigo apresentado no Encontro Nacional de Economia, 32, João Pessoa.

Rhee, J.; Park, T.; Lee, D. H. (2010), “Drivers of innovativeness and performance for innovative SMEs in South Korea: mediation of learning orientation”, Technovation, Vol. 30, No. 1, pp. 65-75.

Ridgway, V. F. (1956), “Dysfunctional consequences of performance measurements”, Administrative Science Quarterly, Vol. 1, No. 2, pp. 240-247.

Rogers, E. W.; Wrigth, P. M. (1998), “Measuring organizational performance in strategic human resource management: looking beyond the Lamppost”, Center for Advanced Human Resource Studies, paper 135.

Schumpeter, J. (1934), The theory of economic development. Cambridge: HUP.

Silva, F. G. (2009), Avaliação da eficiência técnica dos hospitais da rede São Camilo. Dissertação de Mestrado em Economia, Faculdade de Economia, Universidade Federal do Ceará, Fortaleza, CE.

Simon, D. A. (2008), Desempenho ambiental, inovatividade e desempenho financeiro em empresas da terceira geração petroquímica. Dissertação de Mestrado em Administração, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS.

Slack, N.; Brandon-Jones, A.; Johnston, R. (1997), Administração da produção. São Paulo: Atlas.

Sousa, T. T. S; Bezerra, A. L. D.; Sousa, M.N.A. (2011), “Qualidade do serviço hospitalar patoense: percepção de gestores e clientes de saúde”. artigo apresentado no XXXI ENEGEP, Belo Horizonte, MG, 04 a 07 de outubro de 2011.

Souza, A. A.; Rodrigues, L. T.; Lara, C. O. et al. (2009), “Indicadores de desempenho econômico-financeiro para hospitais: um estudo teórico”, Administração Hospitalar e Inovação em Saúde, Vol. 2, No. 3, pp. 44-45,

Su, S.; Lai, M. C.; Huang, H. C. (2009), “Healthcare industry value creation and productivity measurement in emerging economy”, Service Industries Journal, Vol. 29, No. 7, pp. 963–975.

Tajeddini, K.; Mueller, S. L. (2012), “Corporate entrepreneurship in Switzerland: evidence from a case study of Swiss watch manufacturers”, International Entrepreneurship and Management Journal, Vol. 8, pp. 355-372.

Tajeddini, K.; Trueman, M.; Larsen, G. (2006), “Examining the effect of market orientation on innovativeness”, Journal of Marketing Management, Vol. 22, No. 5–6, pp.529–551.

Tiemann, O.; Schreyögg, J. (2012), “Changes in hospital efficiency after privatization”, Health Care Management Science, Vol. 15, No. 4, pp. 310–326.

Valdmanis, V. (1992), “Sensitivity analysis for DEA models: an empirical example using public vs. NFP hospitals”, Journal of Public Economics, Vol. 48, pp. 185-205.

Varela, P.; Martins, G. (2011), “Efficiency of primary health care spending by municipalities in the metropolitan region of São Paulo: a comparative analysis of DEA Models”, Review of Business, Vol. 32, No. 1, pp.17-34.

Vargas, E. R. (2006), A dinâmica de inovação em serviços: o caso dos serviços hospitalares no Brasil e na França. Tese de Doutorado em Administração, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS.

Walsh, G.; Beatty, S. E. (2007), “Customer-based corporate reputation of a service firm: scale development and validation”, Journal Academy Marketing Science, Vol. 35, pp. 127-143.

Weng, R. H.; Huang, J. A.; Kuo, Y. H. et al. (2011), “Determinants of technological innovation and its effect on hospital performance”, African Journal of Business Management, Vol. 5, pp. 4314-4327.

Wolff, L. D. G. (2005), Um modelo para avaliar o impacto do ambiente operacional na produtividade de hospitais brasileiros. Tese de Doutorado em Engenharia de Produção, Universidade Federal de Santa Catarina. Florianópolis, SC.

Received: Apr. 08, 2015.

Approved: Oct. 11, 2017.

DOI: 10.20985/1980-5160.2017.v12n4.978

How to cite: Saquetto, T.C.; Carneiro, T.C.J.; Araujo, C.A.S et al. (2017), “Technical efficiency and innovativeness: a study carried out in Brazilian private hospitals”, Sistemas & Gestão, Vol. 12, No. 4, pp. 410-421, available from: (access day abbreviated month. year)