Next Article in Journal
Malmquist Indexes of Productivity Change in Tunisian Manufacturing Industries
Next Article in Special Issue
How Corporate Social Responsibility Influences Business Model Innovation: The Mediating Role of Organizational Legitimacy
Previous Article in Journal
Impact of a Balanced Scorecard as a Strategic Management System Tool to Improve Sustainable Development: Measuring the Mediation of Organizational Performance through PLS-Smart
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 12
Issue 4
10.3390/su12041366
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sustainable Orientation of Management Capability and Innovative Performance: The Mediating Effect of Knowledge Management

by
Julio C. Acosta-Prado
1,2,
Oscar H. López-Montoya
3,
Carlos Sanchís-Pedregosa
1,4,* and
Ulpiano J. Vázquez-Martínez
5
1
Faculty of Business Sciences, Universidad del Pacífico, 15072 Lima, Peru
2
Faculty of Accounting, Economic and Business Sciences, Universidad de Manizales, 170001 Manizales, Colombia
3
Faculty of Administration, Universidad del Tolima, 730006299 Ibagué, Colombia
4
Faculty of Economic and Business Sciences, Universidad de Sevilla, 41004 Seville, Spain
5
Faculty of Law and Business Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1366; https://doi.org/10.3390/su12041366
Submission received: 22 November 2019 / Revised: 30 January 2020 / Accepted: 9 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Technology and Sustainability in the Framework of Corporate Social Responsibility (CSR))
Browse Figures
Versions Notes

Abstract

:
The literature suggests that innovation allows organizations to reach a desirable level of sustainability. There is evidence to support the role of knowledge management (KM) as well as management capability (MC) in producing a sustainable approach at organizations. Furthermore, organizations commonly achieve sustainable practices through corporate social responsibility (CSR). In particular, the health sector is increasingly implementing CSR strategies, although with a narrow understanding of the factors to success. Hence, trends lead to asymmetric growth between organizations. This study aims to examine the mediating role of KM in the relationship between MC and innovative performance (IP) in 331 Health Provider Institutions (HPIs). The research reflective model was assessed through Partial Least Squares Structural Equation Modeling (PLS-SEM). According to the results, MC has a positive effect on IP, MC has a positive effect on KM, and KM has a positive effect on IP. Likewise, KM significantly mediates the relationship between MC and IP. Our findings support the importance of KM in addressing MCs in HPIs as it enables innovative practices to address CSR goals to achieve a sustainable impact. Moreover, this study contributes by expanding KM to contexts that are not usually studied, such as health in a South American country.

1. Introduction

During the last decade, it has been shown that environmental, social and economic agendas must be merged into a single agenda of inclusive and sustainable growth [1]. Furthermore, there is a scientific consensus on the importance of sustainability on a micro and macro level [2], which requires organizations to align their strategy to a sustainable approach. Therefore, it is supposed that organizations consider sustainability as a conviction rather than as a requirement, meaning that they desire to apply a sustainable approach in all areas of the business. Sustainability constitutes a complex multidimensional system that integrates economic, social/cultural and ecological/environmental aspects [3]. The complexity of sustainability suggests that organizations need to be prepared to face the new challenges it brings, by equipping themselves with powerful traits such as an innovative approach. In fact, innovation is directly linked to sustainability [4].
Similar findings on sustainability claim relationships between it and both management capability (MC) and knowledge management (KM). Dynamic capabilities strongly influence organizational performance. In fact, specific dynamic capabilities, such as sense and response capability and execution capability, are precursors to strategic corporate social responsibility (CSR) for companies [5]. KM can enable sustainable performance. This can be achieved, for instance, by an interchange of knowledge between organizations to foster social change [6], or by developing Circular Economy business models [7]. Moreover, KM encourages open innovation and consequently supports the development of environmental innovations [8]. However, there is not a clear understanding of how these variables that support sustainability in an organization interact and lead to innovative performance (IP).
On the other hand, several prior researchers [9,10,11] have found a direct and positive impact of corporate social responsibility (CSR) practices on organizations’ adoption of sustainable performance, green practices and green performance. Furthermore, researchers have also identified the role of innovation concerning CSR and organizational performance [12,13,14,15]. Authors like Mousavi [16] suggest that innovation is the solution to reach good levels of sustainable performance in organizations. For example, product innovation based on a sustainable approach features a reduced environmental impact and allows the company to differentiate itself from competitors in the market [17]. In other words, IP also influences CSR while guaranteeing sustainable performance. In fact, the terms are sometimes used interchangeably in the context of organizational activities.
The health sector is a particularly knowledge-intensive service due to the degree of formalization and specialization. This means that professionals are supposed to manage a large body of complex knowledge to improve medical care. For this reason, knowledge management is considered one of the most critical tools in the health sector as it facilities the efficient use of resources, cost reduction, better patient care and prevention education for patients and families [18,19]. Moreover, an effective and sustainable health system implemented through information management will result in better knowledge management [20] and enable better performance in the provision of health services [21]. Indeed, in the quest for a complete management strategy, the inclusion of CSR practices is increasing in the health sector [22,23]. However, it is missing a complete picture that would allow organizations in this sector to successfully fulfill their CSR requirements. In the end, sustainable and innovative health system generation aims to provide quality medical care that covers the largest number of people possible at a low cost.
Based on the above, this study aims to examine the mediating role of knowledge management in the relationship between management capability and innovative performance. This objective leads to the following research question: What is the direct effect of management capability on innovative performance and its indirect effect, mediated by knowledge management, in Colombian Health Provider Institutions (HPIs)?
The study is divided into five sections: this first section introduces the research topic and elaborates on the research problem. Section 2 reviews the relevant literature on the three research variables (management capability, innovative performance and knowledge management). Then, Section 3 describes the methodology. Section 4 presents the results in relation to four hypotheses. Finally, Section 5 presents the conclusions as well as limitations and recommendations.

2. Literature Review

This section seeks to present a theoretical framework based on the literature reviewed. It shows the theory underlying our research constructs. We present this background according to the variables we are aiming to analyze later on.

2.1. Management Capability (MC)

Management capability is a set of skills that enable organizations, through their board of directors, to interpret the environment adequately while adapting to or even influencing it, creating a two-way relationship [24,25]. Furthermore, these organizational capabilities influence the implementation of a long-term vision and the execution of actions to develop innovation capabilities that will foster organizational growth [26,27,28]. Mainly, this capability refers to an organization’s ability to deploy its available resources in order to achieve the desired results. According to resource- and capability-based theory [29,30], it catalyzes organizational resources.
Management capabilities constitute a set of processes to achieve a specific organizational result, thus the indicators reflect aspects of management capabilities [31]. They are essential for organizations to innovate outside determined frames, to draw an innovation path towards sustainability and to invest in an organizational ecosystem [32]. Organizations employ these management capabilities to interact within and outside the organizational context to establish relationships or alliances with other organizations, research centers, higher education institutions and financial institutions, among others. This highlights the important role of management capabilities in dealing with different strategic partners to lead to innovative processes [31]. Therefore, the following hypothesis is proposed:
Hypothesis 1 (H1).
There is a positive relationship between management capability (MC) and innovative performance (IP).
An important factor in innovation is knowledge, therefore an organization needs to know how to access and process knowledge [33]. The interaction between tacit and explicit internal knowledge creates organizational knowledge. This knowledge is specific to a given context [34], meaning that knowledge management implementation must care about the particular context where it is applied. Aspects such as organizational structure or culture influence how people carry out sustainable knowledge-sharing activities. On that basis, the following hypothesis is proposed:
Hypothesis 2 (H2).
There is a positive relationship between management capability (MC) and knowledge management (KM).

2.2. Innovative Performance (IP)

Innovative performance is not defined as an independent construct, but as a set of indicators of innovation capabilities. In general, several innovation measurement approaches are based on a common criterion. This criterion focuses on the development of new products, processes or business models as a real consequence of the success of organizational processes and efforts aligned to innovation [35]. Thus, innovation capabilities represent an important part of the generation of new or improved outcomes that determine the innovative performance [36]. In this way, innovative performance indicators reflect innovation capabilities.
Innovation is crucial to the success of organizations. Innovation transforms science and technology, guides economic growth and development, and provides solutions to eliminate social conflicts and economic crises. In general, it provides benefits to society, the environment and the economy. Likewise, innovation constitutes an essential aspect for managers to achieve a competitive advantage in the health sector because it is a way to promote good practices in sustainability [37].
Innovation can be a tool to address the problems related to sustainability and, in turn, reach new customer and market segments. Organizations that apply a sustainable approach to their innovation processes create value for the development of new products in the market and encourage cooperation among stakeholders [38]. Sustainable innovation is innovation towards more sustainable technologies and processes that are systematic, dynamic, nonlinear and involve a high degree of uncertainty [39]. Therefore, sustainable innovation requires coordination and cooperation.
Decision-making in organizations from different sectors requires special attention to problems of innovation for a sustainable environment. Organizations with sustainable innovation capabilities recognize sustainability as a resource for competitive advantage by featuring their products and services with sustainable attributes [40].
Innovation oriented towards sustainability seeks to contribute to sustainable development regardless of the organization’s performance [41]. Sustainable innovation requires that organizations change their competitiveness strategy to one that is more sustainable [42]. To innovate towards sustainability, organizations need to incorporate ecological and social concerns into the corporative agenda. These changes make it more difficult for organizations to adopt and maintain innovation strategies that will end by influencing different organizational aspects, such as product designs, marketing practices, technological mastery, etc. [43].
For this study, there is no hypothesis regarding innovative performance as it is considered an endogenous variable. However, the relevant literature was reviewed to understand how management capability and knowledge management influence it.

2.3. Knowledge Management (KM)

Today’s world is home to an information society that is entirely interconnected. Organizations recognize the importance of adequate information and knowledge management, as well as their role in the formulation and implementation of the organizational strategy, and their impact on innovation and subsequently on organizational outcomes [44,45,46].
There are several approaches to outline a definition of knowledge management. Based on them, knowledge management can be defined as a set of actions oriented to organize and structure organizational processes, mechanisms and infrastructures aiming to create, store and reuse the knowledge that enables the management of innovation [47,48,49,50,51,52,53,54,55,56,57,58]. This definition considers the vision of organizational knowledge creation and the vision of organizational learning [59,60,61,62,63]. From this, it is understood that the actions described are the product or reflection of an underlying variable, defined as knowledge management.
Knowledge management provides various benefits to organizations, including: (1) preventing the possible loss of information; (2) providing a competitive advantage; (3) the company acquiring greater recognition; (4) continuous learning; (5) increasing the quality of professional services; and (6) helping to meet the needs of costumers [64]. Among the factors that allow or facilitate knowledge management are the organizational culture, the organizational structure, the design of a knowledge management strategy, management support, training and effective leadership. On the other hand, the barriers regarding knowledge management correspond to organizational or individual components [65,66].
Knowledge management is the way for an organization to have a sustainable competitive advantage, besides achieving satisfactory returns. Its implementation requires that employees create, use, manage and share knowledge and information of an organization. This can be explicit (reports, guides) or implicit (transmission of experience among employees) [18]. The literature indicates that proper management of processes within the organization contributes and facilitates sustainable innovation. Knowledge management is supposed to lead to greater innovation capability [67]. The above leads to the following hypotheses:
Hypothesis 3 (H3).
There is a positive relationship between knowledge management (KM) and innovative performance (IP).
Hypothesis 4 (H4).
Knowledge management (KM) mediates the relationship between management capability (MC) and innovative performance (IP) (indirect effect).
Figure 1 shows the study model and the four hypotheses graphically.

3. Materials and Methods

3.1. Design

To answer the research question, we employed an empirical research. The strategy was associative, exploring the functional relationships among the three variables under consideration. The study was explanatory, meaning we set out to test a theoretical model of the relationships among variables (mediation model). Finally, latent variables design (LVD) or structural equation modeling (SEM) was followed, in which two parts of the model are distinguished, an inner model (relationships between latent variables) and outer model (relationships between indicators and defined latent variables). The statistical approach to estimate the parameters of the SEM model was based on variances, also known as partial least squares (PLS-SEM) [68,69].

3.2. Participants

The sample was composed of workers in hospitals in Colombia, also known as Health Provider Institutions (HPIs). These institutions provide health services to people registered in the General System of Social Security in Health (GSSSH). Quality and efficiency are the basic principles in the provision of HPIs’ services. Moreover, they have administrative, technical and financial autonomy. Legally, they all are considered companies as they pursue for-profit goals, although there are some exceptions.
Sampling was non-probabilistic of the intentional type [70]. An a priori statistical power analysis calculated the optimal sample size for the study. Statistical power is the probability of a statistical test (e.g., Welch’s t-test, Spearman’s rho, etc.) finding an effect when it actually exists—that is, rejecting a null hypothesis when it is false [71]. It is also known as the probability of avoiding Type II error. The analysis was executed in G * Power 3.1.9.4 software (Dusseldorf, Germany) [72]. The following input parameters were established for this analysis: a level of significance equal to 0.05 (one tail), expected statistical power of 0.80 (minimum recommended for behavioral and health sciences) [73], expected effect size (f2) equal to 0.02 as it is the smallest effect size of interest (SESOI) [74], and one predictor. The minimum recommended sample size was 311 (Figure 2). On the other hand, the rule of thumb of 10 cases per predictor, 10 times the largest number of structural paths directed at a particular construct in the inner model, suggests a minimum sample size of 20 cases [75].
The sample size was composed of 331 workers. The participants were aged between 20 and 66 years old (M = 36.82, DE = 9.47). Forty-eight percent of the participants belonged to private HPIs and 52% to public HPIs. Regarding the employment relationship with the HPIs, 18% were auxiliaries, 26% were managers or administrative staff, 24% were nurses and 31% were doctors. In terms of education, 2% hold a doctoral degree, 30% hold a specialization, 6% hold a Master’s degree, 40% completed an undergraduate degree, 19% are technicians, and 3% do not have an academic degree. Finally, 69% were women (Table 1).

3.3. Instrument

The measuring instruments were three scales applied in healthcare organizations with adequate evidence of validity and reliability [76,77]. These scales measure the following predictors of health innovation capacity: knowledge management (12 items), management capability (seven items) and innovative performance (three items). The scales have 5-point items ranging from 1 (no activity has been carried out to improve the characteristic of interest) to 5 (positive results have been obtained for the aspect investigated). The instrument development study had a sample size of 107 participants belonging to Colombian HPIs between 2016 and 2017, where a systematic review of the literature was used for the selection of variables on the predictors of innovation capacity in healthcare organizations [78], which were grouped into five categories (organizational culture, management capability, knowledge management, human resource management and variables associated with the organization). A Confirmatory Factor Analysis (CFA) corroborated the theoretical model, providing evidence based on internal structure [77]; also, the alpha coefficients were greater than 0.700 and the corrected item-test correlations were above 0.400—all statistically significant, indicating an adequate level of reliability by internal consistency [76].

3.4. Procedure

Information on the HPIs was obtained from the network of high-complexity hospitals in Colombia between 2016 and 2018. A web application supported data collection. Participation was done voluntarily and on the basis of informed consent. The average response time was 10 min. The information, collected automatically, generated a database for subsequent analysis.
The study complied with the ethical standards of the Declaration of Helsinki of 1975 (ethical principles for medical research involving human subjects). Likewise, the informed consent of the participants was mandatory to consider the responses, and the confidentiality safeguard was respected.

3.5. Data Analysis

Following the research design, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analysis the outlined reflective model. PLS-SEM belongs to the SEM family, with a focus on the analysis of variance [79]. An advantage of this analysis regards the absence of assumptions about the distribution of the data. Due to their algorithms, PLS-SEM can also estimate models with small samples and still produce results with high levels of statistical power [80,81]. SmartPLS 3.2.8 software (Bönningstedt, Germany) [82] was used to perform the analyses. Following the methodology, two models are assessed: outer and inner models. The outer model is assessed through convergent validity (Average Variance Extracted, AVE), divergent validity (Fornell and Larcker criteria, the Heterotrait‒Monotrait Ratio (HTMT) and reliability for internal consistency (outer loadings, Cronbach’s alpha, rho_A and Composite Reliability, CR) were evaluated. The inner model was evaluated through the size and statistical significance of the path coefficients, explained variance (R2), effect size (f2) and predictive relevance (Q2).

4. Results

4.1. Assessment of Reflective Outer Model

The individual reliability of the items is assessed through outer loadings, considering appropriate values to those exceeding 0.708 [79]. Through iterative debugging processes, the KM_7 item of knowledge management was removed from the model, as it had an outer loading equal to 0.687. In the final model (Table 2 and Figure 3), all items have loads above 0.708, ranging from 0.731 (KM_9) to 0.923 (IP_2).
The reliability due to internal consistency of the constructs was evaluated using the Cronbach’s alpha [83], coefficient rho_A [84] and Composite Reliability (CR) [85]. Coefficients greater than 0.800 were considered good [76] as it is the recommended threshold for studies in confirmatory stages. In this study, the three constructs of interest show a high level of internal consistency, exceeding in all cases a value of 0.900 (Table 2).
Convergent validity was assessed by the Average Variance Extracted (AVE), equivalent to the commonality of a construct. AVE values higher than 0.500 are good [86] as they explain more than half of the variability of the items they reflect. The three constructs presented values equal to or greater than 0.600, meaning the convergent validity is supported (Table 2).
Discriminant validity refers to the degree to which a construct differentiates from others. In other words, it enables us to ensure that constructs measure only what they were developed to measure. Discriminant validity was analyzed by two methods. One, the Fornell and Larcker criterion [87], requires that the square root of the AVE of each construct be higher than the correlation with any other construct. This condition is met for innovative performance (Table 3).
The second method was the Heterotrait‒Monotrait Ratio (HTMT) [88], which is the average of the heterotrait‒heteromethod correlations of a multitrait‒multimethod (MTMM) matrix. The statistical discriminant validity test (HTMTinference) employed the confidence intervals for the HTMT calculated by bootstrapping. When the confidence interval does not contain the unit, it means a good discriminant validity. In this study, HTMTinference does not indicate discriminant validity problems for three constructs (Table 4).

4.2. Assessment of Inner Model

4.2.1. Significance and Relevance of Path Coefficients

The results of the bootstrapping procedure with 10,000 samples and using the no sign changes reveal that all inner model relationships are statistically significant. Table 5 shows the significant impact of management capability on innovative performance, direct effect (0.585, p < 0.001) and knowledge management (0.867, p < 0.001). On the other hand, knowledge management has a lower effect on innovative performance (0.290, p < 0.001). Regarding effect sizes (f2), H1 has a moderate effect (f2 > 0.150), H2 has a strong effect (f2 > 0.350) and H3 has a weak effect (f2 > 0.020) [89]. Indirect effect (H4) was also statistically significant (0.251, p < 0.001) with a weak effect size (f2 > 0.020). This results indicates that the model is complementary (partial mediation) [79,90].

4.2.2. In-Sample Model Fit

To assess the fit of the model to the data, the degree of variance explained (R2) of the endogenous construct was calculated. Management capacity presented an R2 greater than 0.300 [91] concerning the two exogenous variables (knowledge management and innovative performance), which is an adequate value (Table 5 and Figure 3). Therefore, the new model tested in this data sample is considered relevant as it holds sufficient predictive capacity.

4.2.3. Out-of-Sample Predictive Power

We used PLSpredict with 10 folds and one repetition to mimic how the PLS model will eventually be used to predict a new observation. Subsequently, we calculated Q2 to evaluate the predictive capability of the research model. Regarding Q2 interpretation, positive values mean that the prediction error of the PLS model is smaller than the prediction error using the mean values. Q2 values above 0.500 are considered adequate [92]. The Q2 results are satisfactory for this criterion, meaning that the endogenous variable management capability is supported (Table 5).

5. Discussion

This study confirms the positive influence of MC on KM and IP (p < 0.001 in both variables), supporting Hypotheses 1 and 2. It was also found that KM positively influences IP (p < 0.001), supporting Hypothesis 3. Furthermore, the study found a mediating effect of KM in the relationship between MC and IP (p < 0.001), supporting Hypothesis 4. Our findings corroborate the technical postulates of the study and lead to new contributions on this topic.
This result indicates that the model is a complementary partial mediation, where KM explains or probably confounds the relationship between MC and IP [93]. This indicates that a portion of the effect of MC on IP is mediated through KM, yet MC still explains a portion of IP that is independent of KM. This partly contradicts Hypothesis 4. While support is provided for the hypothetical mediation relationship, complementary mediation suggests that the model may have omitted another mediator with an indirect path but the same direction as the direct effect [79].
Sustainability is a complex concept and requires different elements for good implementation into an organization’s practices. Prior research found that MC, KM and IP are directly related to sustainability. In fact, prior research devoted to understanding some of these individual variables in the health sector went further in the sense of evaluating the moderating effects of some factor such as (1) competitive environment; (2) learning climate; (3) flexible structure; (4) organizational culture; (5) exploration capability and (6) exploitation capability [94]. However, there was no study that integrated these variables into a single model to assess the contribution of each one to the whole picture. This study provides empirical evidence of these relationships.
Sustainable innovation drives knowledge growth and its dissemination within an organization and between organizations [6], and has an impact on the CSR. Thus, the application of a sustainable innovation approach helps reduce some market inefficiencies [95]. For this reason, and according to this study’s results, implementing a better knowledge management system in the health sector will contribute to developing innovative outcomes by considering the academic perspective [96].
Knowledge management paves the way for innovative performance through knowledge acquisition and the ability to respond to knowledge. In turn, these practices, together with the dissemination of knowledge, are positively and significantly related to the innovative performance of organizations [97]. Additionally, knowledge management practices, namely creation, integration and application, directly impact innovative performance. Thus, knowledge creation has a determinant effect on the speed, quality and quantity of innovation [98]. As knowledge management implementation based on innovation results in positive outcomes for organizations, several implementation models have been proposed [99]. The relationship between knowledge management and innovative performance is aligned with that in other studies and we consider the knowledge management components that enable this relationship.
HPIs represent the center of the relationships among stakeholders, institutions and processes in knowledge generation in the health sector. An HPI represents a complex system that performs as a knowledge organization [100]. Therefore, HPIs require more than a healthcare perspective, as they are also systems that integrate scientific work (knowledge generation) and professional practice (training and service provision). HPI managers are strongly encouraged to embrace this new vision for HPIs, noted for the benefits it grants to society, as they gather together teaching, research, innovation and patient care. In general, these are very powerful organizations with a wide scope that facilitate knowledge generation by developing research, innovation and sustainability. In fact, HPI managers must be aware of the decisive role they play in the production of new technologies and processes within a sustainability framework.
However, the intention of health organizations to implement CSR was found to be asymmetrical in that they have no baseline with which to accurately assess the new CSR-related processes that are being implemented [22,23]. This is crucial given the direct association it has with sustainable performance [4]. According to the results of this study, pursuing innovation while attending to MC and KM is important for maintaining a sustainable approach. However, this should not be too difficult if HPIs commit to good general practices, as this sector by definition requires outstanding knowledge management.
According to our findings, knowledge management processes affect innovation, improving overall organizational performance, which agrees with the conclusions of Durmuş-Özdemir and Abdukhoshimov [101]. Sharing information and innovating go hand in hand and help organizations to keep up with the market [102]. In a knowledge-based society, information is the key element and everyone will be judged by how they manage it. This means that managing and exchanging internal information or working with other institutions have positive effects on innovative performance. In other words, this effect considers the exchange of knowledge as an antecedent of product innovation [103]. Furthermore, this relationship is strongly mediated by the degree of product innovation, and outlines a route towards sustainability.
This study provides some key ideas on how management capability and knowledge management can facilitate better, more innovative performance in the health sector, by presenting a competitive advantage. However, it is recommended that further research be conducted along these lines. Thus, similar studies must be carried out in other service sectors, within private (e.g., retail, transport or communication) or public (urban planning, environmental protection or security) organizations. An interesting goal is to explore how other service organizations align their current business model with a sustainable approach. Moreover, this study only worked with the management capability from a one-dimensional perspective, given the operationalization of the construct. It is suggested that one could address new research questions to broaden the understanding of management capability. For instance, what management capability processes enable the implementation and maintenance (or, conversely, inhibit) innovations in health service organizations, considering specific contexts?
One of the limitations of the study regards the use of self-reported scales, which creates problems related to response styles (e.g., acquiescence or social desirability) [104]. To address this limitation, participation was anonymous, the evaluation was done in a standardized manner, strange response patterns were identified, and atypical or extreme values in the database were removed. Another limitation is the cross-sectional nature of our research design, which excludes the time dimension. However, the data served to validate our hypothetical relationships, and the scales used were chosen to ensure greater precision in the measurement of the constructs and a broader collection of data for the investigation of management capability, knowledge management and innovative performance. Therefore, we recommend that future research use more advanced data collection methods to deepen the knowledge of the causal relationships identified in this study.

6. Conclusions

The objective of the study was to examine the role of knowledge management as a mediator in the relationship between management capability and innovative performance in Colombian Health Provider Institutions (HPIs). The four hypotheses presented through the theoretical review of this study were contrasted and confirmed. The PLS-SEM and PLSpredict methods were used in order to achieve the study’s objective. According to the results, management capability has a positive effect on performance innovation. Also, management capability has a positive effect on knowledge management, and knowledge management has a positive effect on performance innovation. Likewise, we found a significant indirect effect as knowledge management mediates the relationship between management capability and performance innovation.
The main implications of the study comment on both theory and practice. From a theoretical point of view, the proposed model provides evidence of the relationships among the three constructs in the health sector, taking into consideration the importance of each one for a sustainable performance. This relationship has not previously been studied in Latin America, specifically in Colombia. Research in this country focuses mainly on the primary sector and not on the tertiary or provision sector of services. On a practical level, these results encourage politicians, directors and administrative staff in the health sector to stress the attention given to knowledge management processes within organizations, as well as improve their management capability, through program training, the evaluation of internal processes, audits or other mechanisms. This is particularly important for those organizations aiming to implement socially responsible processes (in line with CSR).
Bearing in mind that all members of society are called to contribute to Sustainable Development Goals (SDGs), health organizations play an especially important and natural role here, due to their purpose of prolonging life. However, our findings suggest they are able to achieve more in this regard and should direct their efforts to particular sustainable goals such as ending poverty worldwide; guaranteeing a healthy life and promoting well-being for all at all ages; and finally, promoting innovation through industrialization and infrastructure. In this way, besides the sustainable impact the health sector will have, it will also itself benefit from the improvement of its knowledge capabilities and will consequently be more likely to achieve innovative outcomes.

Author Contributions

J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; methodology, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; software, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; validation, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; formal analysis, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; resources, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; data curation, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; writing—original draft preparation, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; writing—review and editing, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; visualization, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; supervision, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M.; project administration, J.C.A.-P., O.H.L.-M., C.S.-P. and U.J.V.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Measurement items:
1.
Management capability (MC)
  • Achieve a consensus on the importance of innovation to the success of the HPI. (MC_1)
  • Allocate resources dedicated to innovation in the areas responsible for achieving that objective. (MC_2)
  • HPI has leaders who are responsible for innovation management. (MC_3)
  • Innovation processes reduce costs in HPI. (MC_4)
  • Strengthen the willingness to change in the collaborators, in such a way that allows the HPI to adapt to the different variations that surround it. (MC_5)
  • Align daily tasks with HPI plans. (MC_6)
  • Ensure resources (monetary, personal or time) to undertake innovation processes. (MC_7)
2.
Knowledge management (KM)
  • Review the daily activities of the HPI in order to improve the service. (KM_1)
  • Record experiences gained within the organization so they can be shared with members of the HPI. (KM_2)
  • Share knowledge through the different units or areas of the HPI. (KM_3)
  • Work continuously in recognition of user needs. (KM_4)
  • Evaluate new technological advances in order to incorporate them into the daily work of the HPI. (KM_5)
  • Promote the use of Information and Communications Technology (ICT) in order to generate greater benefits for users. (KM_6)
  • Generate learning opportunities for employees (through the completion of tasks, problem-solving, search for alternatives, proof of different options, errors, observation or feedback from other people, a conversation between colleagues). (KM_7)
  • Analyze information related to changes in the environment (trends in patient care, new technologies, new surgical procedures, changes in regulation, etc.) to determine what the HPI will do. (KM_8)
  • Ensure that the information collected serves as input in the daily management of the HPI. (KM_9)
  • Consolidate information systems to organize knowledge. (KM_10)
  • Make your employees more informed about their work. (KM_11)
  • Allow open communication between all members of the organization. (KM_12)
3.
Innovative performance (IP)
  • Introduction of new services to its users. (IP_1)
  • Constantly develop innovative projects. (IP_2)
  • Generate new processes in the HPI (new ways of performing daily work, new surgical procedures, new systems). (IP_3)

References

  1. Schwab, K. The Global Competitiveness Report 2019; World Economic Forum: Cologny, Switzerland, 2019. [Google Scholar]
  2. Zbuchea, A.; Pînzaru, F.; Busu, M.; Stan, S.O.; Bârgăoanu, A. Sustainable knowledge management and its impact on the performances of biotechnology organizations. Sustainability 2019, 11, 359. [Google Scholar] [CrossRef] [Green Version]
  3. Behnam, S.; Cagliano, R.; Grijalvo, M. How should firms reconcile their open innovation capabilities for incorporating external actors in innovations aimed at sustainable development? J. Clean. Prod. 2018, 170, 950–965. [Google Scholar] [CrossRef] [Green Version]
  4. Shahzad, M.; Qu, Y.; Javed, S.A.; Zafar, A.U.; Rehman, S.U. Relation of environment sustainability to CSR and green innovation: A case of Pakistani manufacturing industry. J. Clean. Prod. 2020, 253, 119938. [Google Scholar] [CrossRef]
  5. Ramachandran, V. Strategic corporate social responsibility: A “dynamic capabilities” perspective. Corp. Soc. Responsib. Environ. Manag. 2011, 18, 285–293. [Google Scholar] [CrossRef]
  6. Singh, S.K.; Mittal, S.; Sengupta, A.; Pradhan, R.K. A dual-pathway model of knowledge exchange: Linking human and psychosocial capital with prosocial knowledge effectiveness. J. Knowl. Manag. 2019. [Google Scholar] [CrossRef]
  7. Chiappetta Jabbour, C.J.; Sarkis, J.; Lopes de Sousa Jabbour, A.B.; Scott Renwick, D.W.; Singh, S.K.; Grebinevych, O.; Kruglianskas, I.; Filho, M.G. Who is in charge? A review and a research agenda on the ‘human side’ of the circular economy. J. Clean. Prod. 2019, 222, 793–801. [Google Scholar] [CrossRef] [Green Version]
  8. Singh, S.K.; Gupta, S.; Busso, D.; Kamboj, S. Top management knowledge value, knowledge sharing practices, open innovation and organizational performance. J. Bus. Res. 2019, 4, 40. [Google Scholar] [CrossRef]
  9. Abbas, J. Impact of total quality management on corporate green performance through the mediating role of corporate social responsibility. J. Clean. Prod. 2020, 242, 118458. [Google Scholar] [CrossRef]
  10. Shahzad, M.; Qu, Y.; Ur Rehman, S.; Zafar, A.U.; Ding, X.; Abbas, J. Impact of knowledge absorptive capacity on corporate sustainability with mediating role of CSR: Analysis from the Asian context. J. Environ. Plan. Manag. 2020. [Google Scholar] [CrossRef]
  11. Suganthi, L. Examining the relationship between corporate social responsibility, performance, employees’ pro-environmental behavior at work with green practices as mediator. J. Clean. Prod. 2019, 232, 739–750. [Google Scholar] [CrossRef]
  12. Anser, M.K.; Zhang, Z.; Kanwal, L. Moderating effect of innovation on corporate social responsibility and firm performance in realm of sustainable development. Corp. Soc. Responsib. Environ. Manag. 2018, 25, 799–806. [Google Scholar] [CrossRef]
  13. Bocquet, R.; Le Bas, C.; Mothe, C.; Poussing, N. CSR, innovation, and firm performance in sluggish growth contexts: A firm-level empirical analysis. J. Bus. Ethics 2017, 146, 241–254. [Google Scholar] [CrossRef]
  14. Martinez-Conesa, I.; Soto-Acosta, P.; Palacios-Manzano, M. Corporate social responsibility and its effect on innovation and firm performance: An empirical research in SMEs. J. Clean. Prod. 2017, 142, 2374–2383. [Google Scholar] [CrossRef]
  15. Zhu, Q.; Zou, F.; Zhang, P. The role of innovation for performance improvement through corporate social responsibility practices among small and medium-sized suppliers in China. Corp. Soc. Responsib. Environ. Manag. 2019, 26, 341–350. [Google Scholar] [CrossRef]
  16. Mousavi, S.; Bossink, B.A.G. Firms’ capabilities for sustainable innovation: The case of biofuel for aviation. J. Clean. Prod. 2017, 167, 1263–1275. [Google Scholar] [CrossRef]
  17. Crittenden, V.L.; Crittenden, W.F.; Ferrell, L.K.; Ferrell, O.C.; Pinney, C.C. Market-oriented sustainability: A conceptual framework and propositions. J. Acad. Mark. Sci. 2011, 39, 71–85. [Google Scholar] [CrossRef]
  18. Karamitri, I.; Talias, M.A.; Bellali, T. Knowledge management practices in healthcare settings: A systematic review. Int. J. Health Plann. Manag. 2017, 32, 4–18. [Google Scholar] [CrossRef]
  19. Nicolini, D.; Powell, J.; Conville, P.; Martinez-Solano, L. Managing knowledge in the healthcare sector. A review. Int. J. Manag. Rev. 2008, 10, 245–263. [Google Scholar] [CrossRef]
  20. Yusof, Z.M.; Ismail, M.B.; Ahmad, K.; Yusof, M.M. Knowledge sharing in the public sector in Malaysia: A proposed holistic model. Inf. Dev. 2012, 28, 43–54. [Google Scholar] [CrossRef]
  21. Vasco, E. Quality evaluation in health care services based on customer-provider relationships. Int. J. Health Care Qual. Assur. 2005, 18, 404–412. [Google Scholar]
  22. Brandão, C.; Rego, G.; Duarte, I.; Nunes, R. Social responsibility: A new paradigm of hospital governance? Heal. Care Anal. 2013, 21, 390–402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Rodríguez Cala, A.; Calle Rodríguez, C.; Durán García, N.; Zöller, B.; Pons Rodríguez, A. Responsabilidad social corporativa en los centros de la red hospitalaria de utilización pública de cataluña. Gac. Sanit. 2019. [Google Scholar] [CrossRef] [PubMed]
  24. Bravo-Ibarra, E.R.; Herrera, L. Innovation capability and organizational resources configuration. Intang. Cap. 2009, 5, 301–320. [Google Scholar] [CrossRef]
  25. Brito, L.A.L.; Sauan, P.K.; Brito, L.A.L.; Sauan, P.K. Management practices as capabilities leading to superior performance. BAR Braz. Adm. Rev. 2016, 13, 3. [Google Scholar] [CrossRef]
  26. Schreyögg, G.; Kliesch-Eberl, M. How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization. Strateg. Manag. J. 2007, 28, 913–933. [Google Scholar] [CrossRef] [Green Version]
  27. Amabile, T.M.; Schatzel, E.A.; Moneta, G.B.; Kramer, S.J. Leader behaviors and the work environment for creativity: Perceived leader support. Leadersh. Q. 2004, 15, 5–32. [Google Scholar] [CrossRef]
  28. Amabile, T.M. How to Kill Creativity. Harv. Bus. Rev. 1998, 7, 77–87. [Google Scholar]
  29. Barney, J. Firm resources and sustained competitive advantage. J. Manage. 1991, 17, 99–120. [Google Scholar] [CrossRef]
  30. Barney, J.B.; Mackey, T.B. Testing resource-based theory. In Research Methodology in Strategy and Management; Ketchen, D.J., Bergh, D.D., Eds.; Elsevier: Greenwich, CT, USA, 2005; pp. 1–13. [Google Scholar]
  31. Maurizio, M. Public sector knowledge management: A structured literature review. J. Knowl. Manag. 2015, 19, 530–558. [Google Scholar]
  32. Van Kleef, J.A.G.; Roome, N.J. Developing capabilities and competence for sustainable business management as innovation: A research agenda. J. Clean. Prod. 2007, 15, 38–51. [Google Scholar] [CrossRef]
  33. Song, B.; Li, Y.; Zhao, L. Complementary effect of knowledge management strategy on firm performance: Evidence from Chinese firms. Sustainability 2019, 11, 3616. [Google Scholar] [CrossRef] [Green Version]
  34. Shin, K.; Kim, E.; Jeong, E.S. Structural relationship and influence between open innovation capacities and performances. Sustainability 2018, 10, 2787. [Google Scholar] [CrossRef] [Green Version]
  35. Chong, A.Y.L.; Chan, F.T.S.; Ooi, K.B.; Sim, J.J. Can Malaysian firms improve organizational/innovation performance via SCM? Ind. Manag. Data Syst. 2011, 111, 410–431. [Google Scholar] [CrossRef]
  36. García, O.; Quintero, J.; Arias-Pérez, J. Capacidades de innovación, desempeño innovador y desempeño organizacional en empresas del sector servicios. Cuad. Adm. 2014, 27, 87–108. [Google Scholar] [CrossRef] [Green Version]
  37. Sok, P.; O’Cass, A. Achieving superior innovation-based performance outcomes in SMEs through innovation resource–capability complementarity. Ind. Mark. Manag. 2011, 40, 1285–1293. [Google Scholar] [CrossRef]
  38. Lai, W.H.; Lin, C.C.; Wang, T.C. Exploring the interoperability of innovation capability and corporate sustainability. J. Bus. Res. 2015, 68, 867–871. [Google Scholar] [CrossRef]
  39. Boons, F.; Montalvo, C.; Quist, J.; Wagner, M. Sustainable innovation, business models and economic performance: An overview. J. Clean. Prod. 2013, 45, 1–8. [Google Scholar] [CrossRef]
  40. Varadarajan, R. Innovating for sustainability: A framework for sustainable innovations and a model of sustainable innovations orientation. J. Acad. Mark. Sci. 2017, 45, 14–36. [Google Scholar] [CrossRef]
  41. Adams, R.; Jeanrenaud, S.; Bessant, J.; Denyer, D.; Overy, P. Sustainability-oriented Innovation: A Systematic Review. Int. J. Manag. Rev. 2016, 18, 180–205. [Google Scholar] [CrossRef]
  42. De Medeiros, J.F.; Ribeiro, J.L.D.; Cortimiglia, M.N. Success factors for environmentally sustainable product innovation: A systematic literature review. J. Clean. Prod. 2014, 65, 76–86. [Google Scholar] [CrossRef]
  43. Mousavi, S.; Bossink, B.; Van Vliet, M. Dynamic capabilities and organizational routines for managing innovation towards sustainability. J. Clean. Prod. 2018, 203, 224–239. [Google Scholar] [CrossRef]
  44. Pearce, J.L. Toward an Organizational Behavior of Contract Laborers: Their Psychological Involvement and Effects on Employee Co-Workers. Acad. Manag. J. 1993, 36, 1082–1096. [Google Scholar] [CrossRef] [Green Version]
  45. Calantone, R.J.; Cavusgil, S.T.; Zhao, Y. Learning orientation, firm innovation capability, and firm performance. Ind. Mark. Manag. 2002, 31, 515–524. [Google Scholar] [CrossRef]
  46. Börjesson, S.; Elmquist, M. Developing Innovation Capabilities: A Longitudinal Study of a Project at Volvo Cars. Creat. Innov. Manag. 2011, 20, 171–184. [Google Scholar] [CrossRef]
  47. Madhavan, R.; Grover, R. From embedded knowledge to embodied knowledge: new product development as knowledge management. J. Mark. 1998, 62, 1–12. [Google Scholar] [CrossRef] [Green Version]
  48. Lynn, G.S.; Mazzuca, M.; Morone, J.G.; Paulson, A.S. Learning is the critical success factor in developing truly new products. Res. Manag. 1998, 41, 45–51. [Google Scholar] [CrossRef]
  49. Hsiu-Fen, L. Knowledge sharing and firm innovation capability: An empirical study. Int. J. Manpow. 2007, 28, 315–332. [Google Scholar]
  50. Ju, T.; Li, C.; Lee, T. A contingency model for knowledge management capability and innovation. Ind. Manag. Data Syst. 2006, 106, 855–877. [Google Scholar] [CrossRef]
  51. Lawson, B.; Samson, D. Developing innovation capability in organisations: A dynamic capabilities approach. Int. J. Innov. Manag. 2001, 5, 377–400. [Google Scholar] [CrossRef]
  52. Helen, S. The concept of innovativeness: Should we need to focus? Eur. J. Innov. Manag. 2004, 7, 33–44. [Google Scholar]
  53. Smith, M.; Busi, M.; Ball, P.; Van der Meer, R. Factors influencing an organisation’s ability to manage innovation: A structured literature review and conceptual model. Int. J. Innov. Manag. 2008, 12, 655–676. [Google Scholar] [CrossRef] [Green Version]
  54. Akman, G.; Yilmaz, C. Innovative capability, innovation strategy and market orientation: An emirical analysis in Turkish software industry. Int. J. Innov. Manag. 2008, 12, 69–111. [Google Scholar] [CrossRef]
  55. Cohen, W.; Lenvinthal, D. Absorptive capacity: A new perspective on learning and innovation. Adm. Sci. Q. 1990, 35, 128–152. [Google Scholar] [CrossRef]
  56. Nonaka, I.; Taekeuchi, H. The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation; Press, O.U., Ed.; Oxford University Press: New York, NY, USA, 1995. [Google Scholar]
  57. Nonaka, I. The knowledge-creating company. Harv. Bus. Rev. 1991, 69, 96–104. [Google Scholar]
  58. Nonaka, I. A Dynamic theory of organizational knowledge creation. Organ. Sci. 1994, 5, 14–37. [Google Scholar] [CrossRef]
  59. Brown, J.S.; Duguid, P. Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organ. Sci. 1991, 2, 40–57. [Google Scholar] [CrossRef]
  60. Levinthal, D.A.; March, J.G. The myopia of learning. Strateg. Manag. J. 1993, 95–112. [Google Scholar] [CrossRef]
  61. Kogut, B.; Zander, U. Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 1992, 3, 383–397. [Google Scholar] [CrossRef]
  62. Mowery, D.C.; Oxley, J.E. Inward technology transfer and competitiveness: The role of national innovation systems. Camb. J. Econ. 1995, 19, 67–93. [Google Scholar]
  63. Zahra, S.A.; George, G. Absorptive capacity: A review, reconceptualization, and extension. Acad. Manag. Rev. 2002, 27, 185–203. [Google Scholar] [CrossRef] [Green Version]
  64. Jones, R. Measuring the benefits of knowledge management at the Financial Services Authority: A case study. J. Inf. Sci. 2003, 29, 475–487. [Google Scholar] [CrossRef]
  65. Antonella, M. Barriers and levers towards knowledge management configurations. J. Manuf. Technol. Manag. 2005, 16, 670–681. [Google Scholar]
  66. Sharma, B.P.; Singh, M.D. Modeling the Knowledge Sharing Barriers: An ISM Approach. Int. J. Knowledge-Based Organ. 2015, 5, 16–33. [Google Scholar] [CrossRef]
  67. Marina, D.P. The role of knowledge management in innovation. J. Knowl. Manag. 2007, 11, 20–29. [Google Scholar] [CrossRef] [Green Version]
  68. Ato, M.; López, J.J.; Benavente, A. A classification system for research designs in psychology. An. Psicol. 2013, 29, 1038–1059. [Google Scholar]
  69. Ato, M.; Vallejo, G. Diseños de investigación en Psicología; Pirámide: Madrid, Spain, 2015; ISBN 9788436833249. [Google Scholar]
  70. Kerlinger, F.N.; Lee, H.B. Foundations of Behavioral Research, 4th ed.; Harcourt College Publishers: Fort Worth, TX, USA, 2000; ISBN 9780155078970. [Google Scholar]
  71. Columb, M.O.; Atkinson, M.S. Statistical analysis: Sample size and power estimations. BJA Educ. 2016, 16, 159–161. [Google Scholar] [CrossRef] [Green Version]
  72. Erdfelder, E.; Faul, F.; Buchner, A.; Lang, A.-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar]
  73. Noordzij, M.; Tripepi, G.; Dekker, F.W.; Zoccali, C.; Tanck, M.W.; Jager, K.J. Sample size calculations: Basic principles and common pitfalls. Nephrol. Dial. Transpl. 2010, 25, 1388–1393. [Google Scholar] [CrossRef]
  74. Lakens, D.; Scheel, A.M.; Isager, P.M. Equivalence testing for psychological research: A tutorial. Adv. Methods Pract. Psychol. Sci. 2018, 1, 259–269. [Google Scholar] [CrossRef] [Green Version]
  75. Chin, W.W. The partial least squares approach for structural equation modeling. In Modern Methods for Business Research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 295–336, ISBN 0-8058-2677-7 (Hardcover); ISBN 0-8058-3093-6 (Paperback). [Google Scholar]
  76. López, O.H.M.; Villegas, G.C.A.; Cantú-Mata, J.L. Instrumento para determinar los predictores de las capacidades de innovación en el contexto de las organizaciones de salud. Evaluación de su confiabilidad. Espacios 2018, 39, 28. [Google Scholar]
  77. López, O.H.M.; Villegas, G.C.A.; Catica, J.R.B. Validez del instrumento para determinar los detonantes de las capacidades de innovación en las organizaciones dedicadas al cuidado de la salud. Gac. Med. Caracas 2018, 126, 160–169. [Google Scholar]
  78. Villegas, G.C.; Montes, J.M.; López, O.H. Predictores de la capacidad de innovación en las organizaciones: Revisión Sistemática de Literatura. Espacios 2016, 37, 3. [Google Scholar]
  79. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; Sage: Thousand Oaks, CA, USA, 2017; ISBN 9781483377445. [Google Scholar]
  80. Reinartz, W.; Haenlein, M.; Henseler, J. An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int. J. Res. Mark. 2009, 26, 332–344. [Google Scholar] [CrossRef] [Green Version]
  81. Rigdon, E.E. Rethinking Partial Least Squares Path Modeling: Breaking chains and forging ahead. Long Range Plann. 2014, 47, 161–167. [Google Scholar] [CrossRef]
  82. Ringle, C.M.; Wende, S.; Becker, J.-M. (2015) SmartPLS. SmartPLS GmbH, Boenningstedt. Available online: http://www.smartpls.com. (accessed on 13 February 2020).
  83. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Green Version]
  84. Dijkstra, T.K.; Henseler, J. Consistent partial least squares path modeling. MIS Q. 2015, 39, 297–316. [Google Scholar] [CrossRef]
  85. Raykov, T. Estimation of composite reliability for congeneric measures. Appl. Psychol. Meas. 1997, 21, 173–184. [Google Scholar] [CrossRef]
  86. Nunnally, J.; Bernstein, I. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994; ISBN 9780070478497. [Google Scholar]
  87. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  88. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  89. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Lawrence Erlbaum Associates: Hillsdale, NJ, USA, 1988; ISBN 0-8058-0283-5. [Google Scholar]
  90. Cepeda, G.; Nitzl, C.; Roldán, J.L. Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications; Latan, H., Noonan, R., Eds.; Springer: Cham, Switzerland, 2017; pp. 173–195. [Google Scholar]
  91. Leguina, A. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Southend Oaks, CA, USA, 2015; Volume 38, ISBN 9781483377445. [Google Scholar]
  92. Chin, W.W. How to write up and report PLS analyses. In Hanbook of Partial Least Squares: Concepts, Methods and Applications; Esposito, V., Chin, W.W., Henseler, J., Wang, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 655–690. ISBN 978-3-540-32825-4. [Google Scholar]
  93. Nitzl, C.; Roldan, J.; Cepeda, G. Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016, 116, 1849–1864. [Google Scholar] [CrossRef]
  94. López, O.H.; Acosta-Prado, J.C. Relación entre la gestión del conocimiento y la capacidad de innovacion en instituciones de salud (Colombia). Espacios 2019, 40, 11. [Google Scholar]
  95. Lin, M.; Wei, J. The impact of innovation intermediary on knowledge transfer. Phys. A Stat. Mech. Its Appl. 2018, 502, 21–28. [Google Scholar] [CrossRef]
  96. Greenhalgh, T.; Robert, G.; MacFarlane, F.; Bate, P.; Kyriakidou, O. Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Q. 2004, 82, 581–629. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Saqid, M.; Hassan, M.; Shaukat, S. Impact of knowledge management practices on firm performance: Testing the mediation role of innovation in the manufacturing sector of Pakistan. Pakistan J. Commer. Soc. Sci. 2014, 8, 99–111. [Google Scholar]
  98. Mardani, A.; Nikoosokhan, S.; Moradi, M.; Doustar, M. The relationship between knowledge management and innovation performance. J. High Technol. Manag. Res. 2018, 29, 12–26. [Google Scholar] [CrossRef]
  99. Wu, I.; Hu, Y. Open innovation based knowledge management implementation: A mediating role of knowledge management design. J. Knowl. Manag. 2018, 22, 1736–1756. [Google Scholar] [CrossRef]
  100. Jaramillo, H.; Latorre, C.; Albán, C.A.; Lopera, C. El hospital Como Organización de Conocimiento y Espacio de Investigación y Formación; Editorial Universidad del Rosario: Bogotá, Colombia, 2008; ISBN 978-958-8298-99-3. [Google Scholar]
  101. Durmuş-Özdemir, E.; Abdukhoshimov, K. Exploring the mediating role of innovation in the effect of the knowledge management process on performance. Technol. Anal. Strateg. Manag. 2018, 30, 596–608. [Google Scholar] [CrossRef]
  102. Celtekligil, K.; Adiguzel, Z. Evaluation of data sharing in production firms and innovation orientation in the effect of management capability on operational performance. Procedia Comput. Sci. 2019, 158, 781–789. [Google Scholar] [CrossRef]
  103. Markovic, S.; Bagherzadeh, M. How does breadth of external stakeholder co-creation influence innovation performance? Analyzing the mediating roles of knowledge sharing and product innovation. J. Bus. Res. 2018, 88, 173–186. [Google Scholar] [CrossRef]
  104. Caputo, A. Social desirability bias in self-reported wellbeing measures: Evidence from an online survey. Univ. Psychol. 2017, 16, 245–255. [Google Scholar] [CrossRef] [Green Version]
Sustainability 12 01366 g001 550
Figure 1. Research model and hypotheses. MC = Management capability; KM = Knowledge management; IP = Innovative performance.
Figure 1. Research model and hypotheses. MC = Management capability; KM = Knowledge management; IP = Innovative performance.
Sustainability 12 01366 g001
Sustainability 12 01366 g002 550
Figure 2. Minimum recommended sample size based on a priori power analyses.
Figure 2. Minimum recommended sample size based on a priori power analyses.
Sustainability 12 01366 g002
Sustainability 12 01366 g003 550
Figure 3. Assessment results of path coefficients and variance explained (R2).
Figure 3. Assessment results of path coefficients and variance explained (R2).
Sustainability 12 01366 g003
Table
Table 1. Sociodemographic characteristics of participants.
Table 1. Sociodemographic characteristics of participants.
Characteristicn%
Gender
Female21868.65
Male10031.45
Employment
Auxiliaries5918.27
Manager or administrative staff8526.32
Nurses7824.15
Doctors10131.27
Type
Private15948.04
Public17251.96
Academic degree
Doctoral41.24
Master’s195.90
Specialization9830.43
Undergraduate13040.37
Technician6319.57
No academic degree82.48
Table
Table 2. Assessment of convergent validity and internal consistency reliability.
Table 2. Assessment of convergent validity and internal consistency reliability.
Construct/IndicatorsOuter LoadingsWeightsAlpharho_ACRAVE
Management capability (MC) 0.9270.9270.9410.695
MC_10.8360.170
MC_20.8570.171
MC_30.8260.169
MC_40.7990.166
MC_50.8530.181
MC_60.8500.178
MC_70.8130.165
Knowledge management (KM) 0.9330.9350.9430.600
KM_10.8110.117
KM_20.8290.121
KM_30.7960.115
KM_40.7460.121
KM_50.7950.139
KM_60.7700.127
KM_80.7660.114
KM_90.7310.106
KM_100.7750.115
KM_110.7510.107
KM_120.7410.110
Innovative performance (IP) 0.9020.9040.9390.836
IP_10.9140.366
IP_20.9230.382
IP_30.9070.345
Note: CR = Composite Reliability; AVE = Average Variance Extracted. Detailed definitions of each indicators could be found in Appendix A.
Table
Table 3. Assessment of discriminant validity using Fornell and Larcker criterion.
Table 3. Assessment of discriminant validity using Fornell and Larcker criterion.
ConstructManagement CapabilityKnowledge ManagementInnovative Performance
Management capability0.834
Knowledge management0.8670.774
Innovative performance0.8360.7970.914
Note: On diagonal, square root of Average Variance Extracted (AVE); Intercorrelations between constructs are presented below the diagonal.
Table
Table 4. Assessment of discriminant validity using the Heterotrait‒Monotrait Ratio (HTMT).
Table 4. Assessment of discriminant validity using the Heterotrait‒Monotrait Ratio (HTMT).
ConstructManagement CapabilityKnowledge ManagementInnovative Performance
Management capability 0.913 [0.888; 0.935]0.930 [0.910; 0.946]
Knowledge management 0.862 [0.830; 0.889]
Innovative performance
Note: Heterotrait‒Monotrait Ratio (HTMT) are presented above the diagonal; numbers in brackets represent the 90% bias-corrected and accelerated confidence intervals derived from bootstrapping with 10,000 samples.
Table
Table 5. Inner model results and predictive performance summary.
Table 5. Inner model results and predictive performance summary.
HypothesisPath Coefficientt-Statisticp-Value95% BCCIf2R2Q2
H1 (MC → IP)0.5859.1450.000[0.455; 0.711]0.3030.7200.684
H2 (MC → KM)0.86759.1790.000[0.833; 0.890]3.0410.7530.735
H3 (KM → IP)0.2904.3430.000[0.150; 0.416]0.074
H4 (MC → KM → IP)0.2514.3290.000[0.131; 0.358]
Note: 95% BCCI = 95% bias-corrected confidence intervals.

Share and Cite

MDPI and ACS Style

Acosta-Prado, J.C.; López-Montoya, O.H.; Sanchís-Pedregosa, C.; Vázquez-Martínez, U.J. Sustainable Orientation of Management Capability and Innovative Performance: The Mediating Effect of Knowledge Management. Sustainability 2020, 12, 1366. https://doi.org/10.3390/su12041366

AMA Style

Acosta-Prado JC, López-Montoya OH, Sanchís-Pedregosa C, Vázquez-Martínez UJ. Sustainable Orientation of Management Capability and Innovative Performance: The Mediating Effect of Knowledge Management. Sustainability. 2020; 12(4):1366. https://doi.org/10.3390/su12041366

Chicago/Turabian Style

Acosta-Prado, Julio C., Oscar H. López-Montoya, Carlos Sanchís-Pedregosa, and Ulpiano J. Vázquez-Martínez. 2020. "Sustainable Orientation of Management Capability and Innovative Performance: The Mediating Effect of Knowledge Management" Sustainability 12, no. 4: 1366. https://doi.org/10.3390/su12041366

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop