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Article

Knowledge Management in Relation to Innovation and Its Effect on the Sustainability of Mexican Tourism Companies

by
Sergio Ochoa-Jiménez
*,
Beatriz Alicia Leyva-Osuna
,
Carlos Armando Jacobo-Hernández
and
Alma Rocío García-García
Department of Administrative Sciences, Instituto Tecnológico de Sonora, Cajeme 85000, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(24), 13790; https://doi.org/10.3390/su132413790
Submission received: 24 November 2021 / Revised: 9 December 2021 / Accepted: 9 December 2021 / Published: 14 December 2021
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Browse Figures
Versions Notes

Abstract

:
An essential aspect of today’s companies is to convert their knowledge into innovative applications that have a sustainable effect; therefore, this study focuses on determining the relationship between the three mentioned constructs. To verify this, a questionnaire was applied to 492 companies in the tourism sector in southern Sonora, Mexico, whose information was captured in SPSS and analyzed with structural equations in the SmartPLS software. The main findings affirm that knowledge management has a direct, positive, and significant relationship with sustainability and innovation, as well as a positive and direct relationship between innovation and sustainability. Companies can focus on knowledge management that will generate innovation and in turn will be distinguished by achieving sustainability, with a possible generation of competitive advantage, as well as sustainable innovation.

1. Introduction

Today, more than in the past, companies are exposed to constant changes in their internal and external factors, which require organizations to adapt in a timely and appropriate manner due to the dynamic context in which they operate. In addition, organizations, besides contemplating their economic purposes, must take into account the concerns and regulations of natural resources with an ecological and social approach, which requires a change in the business paradigm with a properly structured orientation aligned with sustainable development (SD) [1,2]. In this regard, Martins et al. [3] comment that the definition of the SD concept widely accepted in the literature corresponds to the Brundtland report, which states that it consists of development that meets the needs of the present, preserving resources so that future generations can meet their needs [4]. In this sense, organizations should exercise an appropriate management of the areas, as well as their tangible and intangible resources in a balanced way, from their socio-environmental and economic-environmental fields according to the triple bottom line model [5].
In the literature and in business practice, knowledge management (KM) has been conceptualized as a core strategic resource for all types of organizations [3,6,7]. Furthermore, they have emphasized that it has become the vital factor and asset that ensures the value proposition of companies in their quest to gain balance in the three domains of SD [8,9,10], and also as a facilitator of innovation [11,12,13,14], which has been considered a key element in SD [1,15,16]. KM can be defined as the process of creating, using, sharing, storing, and managing knowledge and information within an organization to achieve its objectives [17]. In this way, from the very complexity of the concept of sustainability, KM can contribute to manage it [9,18,19] by simplifying the management, adaptation, and implementation of sustainable practices.
There is great potential in strategic approaches to knowledge sharing that support the implementation of sustainability in organizations [20]. In measuring the impact of KM on the performance of sustainable practices and eco-innovations, it is found that KM contributes to SD, as do information and communication technology (ICT); however, ICT tools are considered to be fundamental to KM practices, as they propose that these are the means to drive the knowledge itself [8].
On the other hand, [21] Abbas and Sağsan conducted a study to know how KM processes impact on SDE (sustainable development entrepreneurial) and green innovation activities, considering SD practices related to the three domains (economic, social, and environmental) and four KM practices, (1) creation, (2) acquisition, (3) sharing, and (4) application of knowledge. They obtained in their results that KM has a significant impact on SDE and green innovation performance, as well as green innovation has a significant impact on SDE. While the application of KM knowledge creation showed negligible impact on social sustainability and ICT, similarly, the application of knowledge acquisition generated a negligible impact on social sustainability. The rest of the KM activities indicated a significant impact on the remaining two domains of SDE and green innovation.
This raises the next question: What is the relationship between KM, innovation, and sustainability? Subsequently, the objective of this paper is to identify the relationship between KM with innovation and sustainability, the latter from its meaning oriented to the model of sustainable development [5]. The knowledge derived from this study can make contributions from the theoretical perspective to the body of literature regarding these constructs and their link to conduct sustainable development initiatives in organizations; in addition, it can provide from the practical perspective to the adoption of a business behavior based on and integrated to sustainability with systematized and strategic management practices.
In this sense, this article is divided into five sections. A first introductory part contains the contextual, historical, and precedent elements that support the objective of this research. This is followed by an in-depth review of the literature on the three constructs already mentioned and the development of the four hypotheses that guide this document. The third section is composed of the method, with a description of the participants, the instrument used, and the statistical tests of the same to assert its reliability and validity. The fourth section describes the findings of the study and finally includes the discussion and conclusion.

2. Literature Review and Hypothesis Development

2.1. Knowledge Management and Sustainability

From the theoretical framework, the role of knowledge in obtaining sustainability is recognized [22,23], coupled with empirical support [24,25,26,27,28], as well as a proposal for planning sustainable activities in tourism [29]. Roxas and Chadee approached KM from the concept of orientation knowledge management (OKM) and its correct implementation, which facilitates the transformation of relational capital that in turn simplifies the adoption of sustainable practices [30]. Knowledge management and its dimensions are essential for the sustainability of organizations, mainly in companies in transition countries [31]. The same results were evidenced in companies in Iraq, highlighting that, despite the strong relationship between knowledge management and organizational sustainability, it is a priority for companies to focus on solid processes to store, share knowledge, and have a greater sustainable impact [32].
Knowledge management is “inextricably” linked to corporate sustainability [33], and the positive relationship of KM with sustainability is also evidenced as that approach that creates, obtains, exchanges, directs, and aligns the knowledge of organizations, in terms of processes and practices [34,35,36,37], as well as mediating and even comparing it with ICT [38,39], which in turn potentiate innovation or innovative capabilities in terms of SD, thus the following hypothesis arises:
Hypothesis 1 (H1).
There is a positive and significant relationship between knowledge management and sustainability.

2.2. Knowledge Management and Innovation

With an interesting theoretical contribution, it is argued that KM has the possibility of influencing innovation and competitiveness [40]. A company with knowledge management capacity will be more innovative [41], which can support that knowledge management capacity and has a positive effect on innovation performance [42,43,44]. There are several studies that empirically demonstrate that KM contributes to innovation [45,46,47], since there is a positive and significant relationship between both aspects [48,49,50]. In particular, it is shown that there is an influence of KM on organizational innovation [51,52]; also, KM can go beyond an organization, with inter-organizational applications and derived from this an effect on innovation [53]. As a complement to the above, an agricultural KM framework for innovation (AKM4I) is presented through a case study [54].
KM drives innovation, as well as the performance of companies in industry groups, i.e., KM is a mediator [14]. In another relationship-type view, KM strategies directly impact various types of performance through innovation, which is driven by them [13]. Despite the evident relationship, it is valuable to identify the mediating effects that allow both constructs to remain positive. To achieve sustainable innovation derived from knowledge management, organizational learning acts as a mediator [55]. KM has a mediating effect by positively relating with management capabilities to have a positive effect on sustainable innovation [56], and innovative culture potentiates the evidenced relationship between knowledge management and business innovation [57]. Finally, the following hypothesis emerges:
Hypothesis 2 (H2).
There is a positive and significant relationship between knowledge management and innovation.

2.3. Innovation and Sustainability

Innovation focused on sustainable development is a topic increasingly included in academic publications [58], and this is confirmed by documentary research of publications from 2003 to 2019, in which it was found that sustainability is of relevance in the evolution of the open innovation paradigm [59]. It highlights the links between innovation and sustainability [60,61,62], showing that digital transformation tools support the value creation process in the long term [63]. However, despite finding a significant relationship between innovation and environmental sustainability, the impact it may have varies from company type [20].
Top management support, governance initiatives, and financial resources are the most important factors influencing the implementation of sustainability-oriented innovation practices [64]. Similarly, collaboration among supply chain organizations facilitates innovative practices that generate economic, social, and environmental benefits [65]. However, it is not only sufficient to innovate, but to do so with a focus on sustainable development [66], as green innovation practices [67] and innovative ideas [68] promote it. An attempt was made to assess an inverse relationship where sustainability commitment has a direct effect on the innovative business model, which was not possible to prove [69].
Understanding the green innovation product as a result of the relationship between innovation and sustainability may be a priority for both theory and practice [70], so that there is a close relationship between both constructs, to the extent that they have been conceptually united as sustainability-oriented innovation and sustainable innovation. Thus, being that involves making changes in the values and philosophy of the organization, in its products and practices and processes to meet the specific objective of building environmental and social value without neglecting the economic aspect [15,71], and consequently, generating theoretical reviews of sustainable entrepreneurship [72] and sustainable business models that support sustainable innovation [73,74,75,76]. In relation to the above, the third hypothesis of this research arises:
Hypothesis 3 (H3).
There is a positive and significant relationship between innovation and sustainability.
The above supports the three hypotheses described above, which in turn allow the proposal of the following theoretical model in Figure 1, which gives orientation and guidance to the present research.

3. Materials and Methods

3.1. Participants and Questionnaire

The research focused on tourism businesses in Ciudad Obregón, Navojoa, Huatabampo, and Álamos, cities located in the southern part of the state of Sonora in Mexico, particularly those dedicated to providing food and lodging services, from which a convenience sample of 492 businesses was selected from the National Statistical Directory of Economic Units (DENUE) of the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía) [77].
In a face-to-face visit to each organization, the director, manager, or person responsible for the organization was asked to answer a questionnaire made up of 15 indicators that measure the construct of knowledge management [78], 22 of innovation [79], and 17 of sustainability [80]. The response options were on a Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. The information generated was captured in the Statistical Package for the Social Sciences (SPSS) [81], from which a database was generated and processed in the SmartPLS software [82].

3.2. Construct Measurement

The types of statistical tests used in this study to assess the reliability and validity of the constructs, as well as to test hypotheses, are shown in Table 1.
The reflective measurement model was used, where the latent construct precedes the indicators, thus exposing their causality [85]. The indicators are causal of the same construct and correlate with each other; when a construct is highly reliable, an indicator can be omitted, and the construct does not change its meaning. The consistency of these indicators is evaluated through factor loadings. According to Table 1, the evaluation of the model was performed through internal consistency, convergent validity, and discriminant validity [83]. The values of load factor, Cronbach’s alpha, and the composite reliability must be higher than 0.70 [86]; consequently, the questionnaire indicators that did not meet the minimum requirements were excluded. Table 2 shows the indicators that formed the model, as well as the evidence of the reliability of the constructs for this research since all the results exceed the minimum necessary value; likewise, the reliability of the indicator and the average variance extracted (AVE) is met since the total variance of the indicators in each latent construct is ≥0.5 [87].
Discriminant validity ratifies the difference between one construct and another. Three aspects are evaluated: (1) Fornell–Larcker criterion, (2) cross-charges between indicators and latent constructs, and (3) the HTMT matrix, checking that the variance of the average extracted from each construct is greater than the square of the correlations between construct and other latent constructs of the model [84,88]; that is, it is the variance that a construct shares with other constructs of the model. Table 3 shows that the Fornell–Larcker criterion of discriminant validity is met, since the value of the variance between two constructs is smaller than the variance of each individual construct [88].
The cross-loadings of all indicators of each construct are compared and must exceed with their loadings that of other constructs of the model [89]. In Table 4, the cross-loadings of the indicators of the corresponding construct exceed their value compared to the loadings of the other constructs, thus meeting this discriminant criterion.
Finally, the heterotrait–monotrait ratio (HTMT) criterion considers the validity in a complete way, since it determines the correlations between indicators that measure the same construct (monotrait–heteromethod) and indicators that measure other constructs (heterotrait–heteromethod), for which the HTMT ratio must be less than 1 [90]. This criterion is also met according to the results in Table 5.
According to the internal consistency, convergent validity average, and discriminant validity tests presented, the measurement model of this study is reliable and valid, and therefore the structural model can be evaluated, which is presented in the following section.

4. Results

Once the reliability and validity of the reflective model was assessed in the PLS-SEM, the next step consisted of evaluating the structural model with the following indicators: levels of collinearity through the variance inflation factor (VIF), assessment of R2 and Q2, effect size (f²), and magnitude and statistical significance of the path coefficients [83,84].

4.1. Collinearity

The constructs for this study are of the reflexive type; however, it was decided to verify the existence of collinearity between them, since this is a regression study. The values with respect to the variance inflation factor (VIF) can be seen in Table 6 and in which it is deduced that there is no problem of collinearity since it results in a VIF < 5.0 with a tolerance level of >0.20 [83].

4.2. Coefficient of Determination (R²) and R² Adjusted

The prediction of the structural model is evaluated through the coefficient of determination (R²), which explains the variance of the endogenous or dependent constructs, given by the exogenous or independent constructs, whose value must be ≥ 0.1 [91]. Based on the results in Table 7, the adjusted R² of the endogenous construct innovation has a value of 0.288 (28%) and sustainability 0.516 (51%).

4.3. The Test of Q² (Stone–Geisser)

The Q² test (Stone–Geisser) is another criterion for the evaluation of the relevance of the constructs in the prediction of the structural model, whose values must be greater than 0 and positive [86]; in addition, the values ranging from 0.02 as (small), 0.15 (medium), and 0.35 (large) are categorized [83], and according to Table 7, the value of innovation is 0.168 and sustainability is 0.272. Substantiated on the above, it can be confirmed that there is a predictive validity of the model with medium value.

4.4. Standardized Root Mean Square Residual (SRMR)

As part of the evaluations of the measurement model and the structural model, the standardized root mean square residual (SRMR) criterion is so far the only one that can make a global evaluation of both models. The adjustment to the global model is acceptable, since according to Table 7, it has a value of 0.069 < 0.08, without being <0.05 qualified as optimal [86].

4.5. Effect Size (f²)

The test to determine the effect size (f²) within the model is calculated considering that the value of (f²) is 0.02 (small), 0.15 (medium), and 0.35 (large) [92]. According to Table 8, the effect of knowledge management with innovation and sustainability is of a large or strong effect, even in the omission of an exogenous construct; however, innovation with sustainability presents a small effect in the omission of some exogenous constructs not present in the model.

4.6. Structural Model and Hypothesis Test

The structural model can be seen in Figure 2, which was designed based on a theoretical model in which the constructs and their indicators were constructed and selected according to the theory. This allows the identification of the factor loadings of the indicators of the constructs, the path coefficients or betas, as well as the results of the R², and according to the theory, the hypothetical relationships for the study are established.
To assess the hypotheses, the bootstrapping technique is used in PLS-SEM, with which the population can be represented with the observed sample [89]. In this case, the bootstrapping calculation was completed in PLS-SEM software through 5000 interactions, which replaces the original sample of 492 tourism businesses, obtaining the results shown in Table 9.
The betas or path coefficients represent the relationship between the constructs of the model through the contribution made by the exogenous construct to the explained variance of the endogenous construct [86]. It is taken as a basis that it is a considerable correlation if the value is in the range of 0.16 to 0.19, if it is >0.20 the interrelations between the constructs are considered important, if the beta value is >0.30 to 0.50 it is considered strong, and >0.50 very strong [93]. According to Table 9, knowledge management maintains a very strong correlation with sustainability and innovation, while innovation with sustainability maintains a considerable correlation, all in a positive sense; thus, it is complying with the first parameter for testing the hypothesis.
The T Student and P value statistics allow the measurement of the significance of the betas in the model. The T Student with a risk of 5% (95% confidence level) is accepted when it is ≥2.0; then it is statistically significant [94]. In the case of the results in Table 9, the T Student is significant for all the correlations presented in β in the constructs of the model.
In the same way, the bootstrapping calculation in PLS-SEM software yields the significance values for the p-value, which measures the probability that the obtained result presents a difference which is due to chance [95] or the association is not due to chance [96]. The considered values in this study for p value are p ≤ 0.05, significant; p ≤ 0.01, considerably significant; and p ≤ 0.001, highly significant [97]. According to the results in Table 9, the hypotheses of the positive and significant relationship between knowledge management and sustainability, knowledge management and innovation, and innovation and sustainability are highly significant, thus accepting the first three hypotheses of the study.

5. Discussion and Conclusions

Knowledge management has been analyzed in different studies in relation to sustainability [35], innovation [98], and jointly [57], which has allowed the identification of how KM has an impact on the constructs of innovation and sustainability. This opens the way to deepen understanding in this topic and its behavior with different constructs, as well as to identify its behavior in relation to sustainable innovation. The present study empirically evidences the positive relationship between knowledge management and sustainability [21,22,23,24,25,26,27], and so the knowledge and new ideas of the members of the organization, which subsequently become actions, have a direct bearing on sustainability, especially in the social aspect and mainly in improving the conditions of its employees.
It is confirmed that knowledge management plays a significant role in innovation [40,41,42,43,45,46,48,49,50]. In this way, the knowledge generated, assumed, or shared in the company improves products and/or processes, generating new ways of working. Furthermore, they are innovative and that will allow a growth of the company. The theoretical argument is practically reiterated that sustainability is of relevance and is related to innovation [59,63,64,66,67,68], i.e., that these innovations in the company are aimed at making its activities and development sustainable.
The main theoretical contribution of the study is to ratify the relationship between knowledge management and innovation; especially focused on the generation of ideas by the members of the company and their consequent application to generate innovation, as well as sustainability as an effect of the above. Likewise, the aspect of sustainability is reinforced as an outstanding area of study. There is a great opportunity to study in depth what has been called sustainable innovation [1,15,47,55,74], green innovation [21,44,67], and any other denomination to refer to the fusion of two of the constructs studied, since the theoretical review generates a reflection on the subject and the possibility of generating future studies focused on this topic.
In the administrative practice and management of companies, this research allows for the highlighting of the importance of knowledge management in companies, so that it can support the ideas generated in it by its members to become practical applications that generate knowledge and this in turn leads to innovative practices, as well as new products, services, or internal work processes. This is characterized with a focus on balancing economic benefits with care for the environment and the social aspects in which the company is involved [5,99], especially in relation to its personnel, to the people who are part of it.
The relevance of the study and its contribution have been highlighted; however, it is essential to mention that the research does not consider other factors or constructs related to sustainability, innovation, and knowledge management and that it was conducted in a particular region and productive sector; in addition, the sustainability construct in its measurement is more focused on the economic and social aspects. As a result, it is suggested for future research to incorporate other constructs that, according to the theoretical review, are relevant to the study, such as technology and organizational learning. Moreover, it is convenient to broaden the framework of the study to other sectors of commercial, service, or industrial activity, as well as to focus it on other geographical contexts. Finally, in the integration of measurement instruments, greater emphasis should be placed on the economic, social, and environmental aspects in a balanced manner, since these are the three relevant aspects that make up sustainability.

Author Contributions

S.O.-J.: Formal analysis, funding acquisition, investigation, methodology, project administration, validation, writing—original draft and editing. B.A.L.-O.: Data curation, software, formal analysis, investigation, methodology, validation, writing—original draft and editing. C.A.J.-H.: Formal analysis, methodology, validation, writing—review and editing. A.R.G.-G.: Methodology, validation, writing—original draft and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Program for Promotion and Support of Research (PROFAPI: 2021) of the Instituto Tecnologico de Sonora (ITSON).

Institutional Review Board Statement

This article followed all ethical standards for carrying out research.

Informed Consent Statement

Not applicable for studies not involving humans or animals.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 13 13790 g001 550
Figure 1. Theoretical model.
Figure 1. Theoretical model.
Sustainability 13 13790 g001
Sustainability 13 13790 g002 550
Figure 2. Structural model (inner model).
Figure 2. Structural model (inner model).
Sustainability 13 13790 g002
Table
Table 1. PLS-SEM evaluation: statistical tests.
Table 1. PLS-SEM evaluation: statistical tests.
Evaluation of the Reflective Measurement Model
1.
Internal consistency (Cronbach’s alpha and composite reliability)
2.
Convergent validity (Reliability of the indicator and the average variance extracted—AVE)
3.
Discriminant validity (Fornell–Larcker criterion, cross-charges between indicators and latent constructs, and the HTMT matrix)
Evaluation Structural Model
1.
Coefficient of determination (R²)
2.
Predictive relevance (Q²)
3.
Magnitude and statistical significance of the path coefficients
4.
Effect size (f²)
Source: Hair et al. [83] and Martínez and Fierro [84].
Table
Table 2. Internal consistency and convergent validity.
Table 2. Internal consistency and convergent validity.
Convergent ValidityInternal Consistency
Construct
(Indicators)		Load
Factor
(λ)		Reliability of the IndicatorAverage Variance Extracted (AVE)Composite Reliability (FC)Cronbach’s Alpha (α)
Knowledge management 0.5700.8690.811
Employees come up with new ideas and knowledge0.7380.754
The company learns from the environment by being attentive to change0.7160.716
Activities are carried out in the company to convert knowledge into action plans0.7770.761
Everyone is informed of the company’s performance0.7430.753
New ideas from employees are presented to others in the company0.7810.789
Sustainability 0.9010.9220.628
Employees are well remunerated0.7600.789
There is concern for the health of its employees0.6940.704
Employees are provided with benefits in addition to those provided by law0.7760.749
Employees are provided with training and development0.7760.762
Innovation 0.7430.8380.565
Employee initiatives to create new ideas are recognized0.7440.754
Employee creativity is encouraged0.7860.791
New ways of achieving success are constantly being created0.8080.805
Improving products, work processes, and ideas that have been implemented over time0.8050.783
Correct mistakes and turn them into opportunities to improve processes, services, or products0.8320.828
New ways of working are generated in order to compete0.8040.805
Innovate to achieve company growth0.7760.779
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 3. Fornell–Larcker criterion.
Table 3. Fornell–Larcker criterion.
Knowledge ManagementInnovationSustainability
Knowledge management0.755
Innovation0.5380.792
Sustainability0.7040.5040.751
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 4. Cross-charges.
Table 4. Cross-charges.
Knowledge
Management		InnovationSustainability
Indicator 10.7540.4450.540
Indicator 20.7160.3440.506
Indicator 30.7610.4200.473
Indicator 40.7530.4020.557
Indicator 50.7890.4150.575
Indicator 60.6120.4360.789
Indicator 70.4600.4330.704
Indicator 80.4780.3140.749
Indicator 90.5480.3250.762
Indicator 100.4520.7540.395
Indicator 110.4300.7910.414
Indicator 120.4300.8050.409
Indicator 130.3730.7830.343
Indicator 140.4150.8280.390
Indicator 150.4330.8050.409
Indicator 160.4400.7790.421
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 5. Heterotrait–monotrait ratio (HTMT).
Table 5. Heterotrait–monotrait ratio (HTMT).
Knowledge ManagementInnovationSustainability
Knowledge management
Innovation0.626
Sustainability0.8960.610
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 6. Collinearity statistics (VIF).
Table 6. Collinearity statistics (VIF).
ConstructKnowledge ManagementInnovationSustainability
Knowledge management 1.0001.407
Innovation 1.407
Sustainability
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 7. Prediction model.
Table 7. Prediction model.
Construct R² R²
Adjusted 		Q² SRMR
Knowledge management 0.069
Innovation0.2890.2880.168
Sustainability0.5180.5160.272
Table
Table 8. Effect size (f²).
Table 8. Effect size (f²).
ConstructKnowledge ManagementInnovationSustainability
Knowledge management 0.4070.548
Innovation 0.046
Sustainability
Source: Own elaboration with data obtained from SmartPLS software.
Table
Table 9. Hypothesis test.
Table 9. Hypothesis test.
Hypothesis Path
Coefficients
β 		T Score p ValueAccepted/Rejected
H1. KM → SUS0.60914.9180.000Accepted
H2. KM → INN0.53812.3390.000Accepted
H3. INN → SUS0.1763.6950.000Accepted
Source: Own elaboration with data obtained from SmartPLS software.
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Ochoa-Jiménez, S.; Leyva-Osuna, B.A.; Jacobo-Hernández, C.A.; García-García, A.R. Knowledge Management in Relation to Innovation and Its Effect on the Sustainability of Mexican Tourism Companies. Sustainability 2021, 13, 13790. https://doi.org/10.3390/su132413790

AMA Style

Ochoa-Jiménez S, Leyva-Osuna BA, Jacobo-Hernández CA, García-García AR. Knowledge Management in Relation to Innovation and Its Effect on the Sustainability of Mexican Tourism Companies. Sustainability. 2021; 13(24):13790. https://doi.org/10.3390/su132413790

Chicago/Turabian Style

Ochoa-Jiménez, Sergio, Beatriz Alicia Leyva-Osuna, Carlos Armando Jacobo-Hernández, and Alma Rocío García-García. 2021. "Knowledge Management in Relation to Innovation and Its Effect on the Sustainability of Mexican Tourism Companies" Sustainability 13, no. 24: 13790. https://doi.org/10.3390/su132413790

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