Knowledge and Enterprises in Developing Countries: Evidences from Chile

Knowledge management is a fundamental tool in order to obtain competitive advantages in organizations. In this paper, we present an interesting study about how enterprises in a developing country like Chile manage their knowledge by using variables linked with three interesting concepts related to knowledge: innovation, learning, and knowledge sharing. From this information, six clusters of enterprises and two special cases with different behaviors in knowledge management and different results are identified. From this information, some conclusions are extracted: (1) clusters 4 and 5 are the best in knowledge management (best results) and, thus, other enterprises should replicate their behaviors; (2) the Government of Chile should promote more knowledge management in order to improve the country’s performance; (3) chemical industry is highlighted like one of the most important one related to knowledge; and finally, (4) enterprises with a high investment in knowledge are also enterprises with high results. These results are very interesting in order to understand the knowledge activity in a developing country like Chile.


Introduction
At present time, knowledge is considered a key resource for countries and organizations. Specifically, knowledge management is considered as an essential tool in order to obtain competitive advantages in organizations (Nonaka and Takeuchi 1995;Davenport and Prusak 1998;Drucker 1998;Davel and Snyman 2007;Ajmal et al. 2010); therefore, the process is important to all governments, but with special emphasis in developing countries because it is the real key to create value.
However, the intangible character of knowledge linked to the heterogeneity of firms hinders contributions related to knowledge management. In this way, despite the numerous studies which have been undertaken in recent years, there is currently no commonly accepted model by all stakeholders that helps to manage knowledge in enterprises (Choi and Jong 2010). Besides this information, a gap in the literature is identified: Research in knowledge management area is essential in order to support decision making process in organizations by using knowledge, and this topic is still under discussion because there is not an agreement about it.
In this context, an interesting analysis is presented showing how enterprises in a developing country with a growing benefit manage their knowledge in order to identify if this process should be replicated in other countries following its example. This is very important because there is not much research on this topic, although it is interesting for governments, managers, and scholars. Besides this information, Chile is selected and a cluster analysis is realized with the aim of comparing different behaviors and identifying their similarities and differences. These results have implications for some stakeholders: First, it is interesting for Chilean enterprises, because it explains what they do and what they should do. Second, the results could help the Chilean government because the analysis shows how the behavior of its enterprises is. Third, the analysis supports the decision making process in other developing countries, both in governments and enterprises.
The paper is structured as follows: First, a bibliographic review about knowledge management is presented, analyzing its importance for enterprises and its relationship with innovation, learning, and knowledge sharing. Then, the methodology is detailed specifying data collection and sample. In the following section, results are analyzed explaining how Chilean enterprises manage their knowledge. Finally, the conclusion includes the main findings of the research, future research possibilities, and limitations.

Theoretical Framework
Knowledge management has been used in enterprises from their very first origins, when artisans, soldiers, or scholars shared their experience and the secret of their success to trainees (Hansen et al. 1999;King et al. 2007). It was not until the 1990s, however, that it was recognized as a relevant tool in organizations (Obeso et al. 2012). Since that time, its popularity as an excellent management tool has increased rapidly as can be demonstrated by the growing number of research in the area (Edvardsson 2009).
There are several recognized benefits of Knowledge Management, for example: improving operational efficiency, increasing adaptation capacity in a changing and fast environment, strengthening the organizational culture based on people, and improving relationship capacity between workers, suppliers, customers and other stakeholders (Daedalus Report 2002). Knowledge is therefore recognized as a key organizational resource (Zyngier and Venkitachalam 2011) with a strategic value because it cannot be imitated easily by competitors (Zack 1999). Successful Knowledge Management can be a key element in an organization's survival (Liao et al. 2008).
Similarly, there is not a commonly accepted definition of knowledge management (Hlupic et al. 2002) because it includes different activities such as data collection, analysis, storage, diffusion, or use (Lancioni and Chandran 2009). There are many well-known definitions such as Hibbard's in 1997, which defines knowledge management as a process to captain knowledge presented in people, papers, or databases and distribute them in order to obtain an improved performance. Another definition is Kebede's in 2010 which defines knowledge management as managing the process and tools linked with knowledge with the aim of taking advantage of its potential and supporting the decision making process by facilitating innovation and creativity within the organization and obtaining a competitive advantage. Obeso et al. (2012) came up with a review based on knowledge management definition, and they identify the following actions related to knowledge management process: create, identify, acquire, develop, distribute, use, share, and accumulate. In turn, these actions could be included in the following three categories: innovation (creating and developing knowledge), learning (identifying and acquiring knowledge), and knowledge sharing (distribution, use, sharing, and accumulation knowledge); these categories are explained in the following subsections, and they will be used in order to select the variables.

Innovation and Knowledge
Innovation is an idea which is perceived as something new (Rogers 2003), and it is considered an essential output in organizations in order to improve their performance and standing in society, where innovation influences economic and social change (Sorensen and Stuart 2000;Amar and Juneja 2008). The relationship between innovation and knowledge management is seen in the definition of the concept of innovation. For example, the one proposed by Herkema (2003) defines innovation as a knowledge process with the aim of creating new knowledge and developing solutions. Plessis also offers a definition linked with knowledge by defining the concept as, Bthe creation of new knowledge and ideas to facilitate new business outcomes, aimed at improving internal business processes and structures and to create market driven products and services^ (Plessis 2007: p. 21).
However, innovation not only is new knowledge (Romer 1990) but also includes other situations such as processes, techniques, or organizational designs (Dougherty and Hardy 1996). Kline and Rosenberg (1987) identified the following innovations: a new product, a new process of production, the substitution of a cheaper material, the reorganization to increased efficiency or lower costs, or an improvement in instruments or methods of innovating.
In this sense, knowledge management supports innovation processes, generates ideas, and exploits the thinking of an organization. Plessis (2007), moreover, posits that knowledge management creates an environment for innovation to take place. Following Amar and Juneja, the relationship between innovation and knowledge can be seen as analogous to the growth of a plant: BThe seed is the tacit knowledge, the fertilizer is the explicit knowledge and the soil is human creativity; all three are essential to get innovation^ (Amar and Juneja 2008: p. 299). Miles exposes in his paper that Bthis phenomenon could be thought of as the revitalization of innovation studies in the face of the knowledge-based economy^ (Miles 2000: p. 388).
Following Pavitt (1984), companies adopt different behaviors according to the innovation process, identifying supplier-dominated firms, production-intensive firms, and science-based firms. Supplier-dominated firms are those enterprises belonging traditional sectors, generally small and with a low capacity of innovation. Production-intensive firms are linked to enterprises where machines and economies of scale are the most relevant sources for their competitiveness. And, finally, science-based firms are enterprises where R&D and technology presents the most relevant characteristics from them.

Organizational Learning
Some studies identify a relationship between knowledge management and organizational learning. For example, in a study by Jerez-Gomez et al. (2005), knowledge management has a positive influence in organizational learning, and this link was confirmed by Liao and Wu (2010). In this sense, knowledge management is a strategic key in order to obtain organizational learning.
Organizations that invest in organizational learning obtain better results related to knowledge creation than others that do not (Boisot 1998). Organizational learning and knowledge management therefore need one another in order to survive (Loermans 2002). The basic aim of organizational learning is to develop new knowledge and increase knowledge existing in an organization (Pemberton and Stonehouse 2000). As Moustaghfir puts forward, Borganizational learning mechanisms enable this interconnectivity between knowledge assets and constantly renew and enhance their value ( Moustaghfir 2009: p. 352).
The relationship between knowledge management and organizational learning is also seen in the simple definition of learning: a process to acquire knowledge (Wikstrom and Norman 1994). Hubber (1991) defines organizational learning as an organization's capability to self-adapt to the environment, to be flexible, and to generate actions known as a fast response. The concept of organizational learning could also be defined as the process of obtaining knowledge and developing abilities in employees in order to improve performance (Addleson 1999).

Knowledge Sharing
Knowledge sharing between employees is essential in order to be a competitive enterprise (Chow and Chan 2008). In this sense, literature shows that knowledge sharing is linked with reductions in production costs, reductions in production time, improved team performance, improved innovation activity, and increased firm performance (Hansen 2002;Cummings 2004;Arthur and Hungley 2005;Collins and Smith 2006;Mesmer-Magnus and DeChuch 2009). Moreover, in difficult situations, knowledge sharing can increase the credibility of a firm's commitment by making performance drivers (McEvily et al. 2000).
Knowledge sharing is defined as Bthe provision of task information and know-how to help others and to collaborate with others to solve problems, develop new ideas or implement policies or procedures (…) and it can occur via written correspondence or face-to-face communications through networking with other experts, of documenting, organizing and capturing knowledge for others^ (Wang and Noe 2010, p. 117). Knowledge sharing thus includes behaviors linked with acquiring knowledge (Chow and Chan 2008). Knowledge sharing is related with knowledge creation, considering knowledge sharing as an antecedent (Nonaka 1991). In addition, it is linked with the process of transforming individual knowledge into organizational knowledge (Foss et al. 2010). The process can be internal (linked with employees) or external (linked with clients or suppliers) (Renzl 2008).

The Case of Chile
Chile is one of Latin America's fastest-growing economies. In the past 20 years, the country has registered an annual average per capital growth of around 3.8 %. Since 1973, this country has presented a high level of institutional stability applying policies of structural reforms and economic openness (Brida et al. 2011). However, productivity and investment have been decreasing over the last decade. Chile has two challenges: improving its productivity and achieving equal opportunities (World Bank website 2012). Analyzing time, it is considered a developing country (PNUD 2011) with a GDP per capita of about $14,413 USD (Banco Central de Chile 2011), and it is characterized by an unequal distribution of wealth (Naciones Unidas and Gobierno de Chile 2011).
Following a study published by the Economic Commission to Latin America and the Caribbean (CEPAL) and the Iberoamerican General Secretariat (SEGIB), sustainability and better income distribution in Latin America are possible by orientating policies to innovation (Cimoli 2010). However, Latin American countries have not heavily invested in this area and their innovation processes are linked with adaptations rather than scientific discoveries. Moreover, these countries have more patents in traditional sectors than in innovative sectors such as biotechnology and information technologies (CEPAL 2008). Only Chile and Brazil have overcome this low-level investment (Cimoli 2010). From this information, Chile has been selected. Chile is an interesting case study and its businesses have influenced these results. Therefore, this case study could be an example to other developing countries in the same region where innovation is increasingly becoming more important.
Knowledge is thus presented as a fundamental tool for Chile, and this paper analyzes how Chilean enterprises manage their knowledge identifying and comparing different clusters of enterprises. This analysis will demonstrate what cluster of enterprises obtains better results by investing more in knowledge.

Main Question
The previous sections brings to light the significance of manage knowledge suitably in enterprises. Nevertheless, there are some problems hindering this management related basically to the intangible character of knowledge (Demarest 1997) and the heterogeneity of the organizations (Obeso et al. 2012); thus, there is not a rule applicable to each case. These problems highlight the need of continued research in this field in order to achieve an agreement and provide information to support decision making process in enterprises.
In this scenario, an original study about how Chilean enterprises manage their knowledge is presented, answering to a need in the literature derived from the difficulty of manage knowledge in the heterogeneous enterprises and developing countries, especially the case of Chile. In order to justify the originality, a search in the Web of Science database in June 2015 with the following requirements has been realized: -Years: 1900-2014 -Topic: Knowledge Management -Words in title: BChile-Research area: Business Economics -Document: papers In the search, six documents have been obtained, and after a review of abstract, the conclusion is that these are not papers related to the present research in this database; thus, the analysis is original. Therefore, with this analysis, information about an original question answering a gap in the literature is presented, with the aim of supporting decision making process linked to knowledge for governments, enterprises, and scholars.
To sum up, with the aim of providing new evidence about the significance of knowledge management and tools in order to suitably manage this intangible in the enterprises, the final question is, How do Chilean enterprises manage their knowledge?

Research Methodology
A descriptive research design was selected for the research in line with the aim of the paper. Data has been collected in Chile by Latin America and Caribbean (LAC) Enterprise Survey, an initiative of the World Bank, between May 2010 and April 2011. This data is more updated at the present time. Following the information about the data, the sample for Chile was selected using stratified random sampling in order to make sure that the final total sample includes establishments from all different sectors and obtains unbiased estimates for different subdivisions of the whole population. A total of 1034 questionnaires are being analyzed, including general information, infrastructure and services, sales and supplies, innovation and degree of competition, land and permits, crime, finance, business development services, business-government relations, labor business environment, and performance.
Besides the categories proposed in the previous section (theoretical framework), 33 variables have been used to the analysis (see Table 1). The first category, named as description, includes variables related to the country. They are important when it comes to answer the question related to describe how the different groups are: Are there any similarities between enterprises belonging to the same industry? And between enterprises localized in the same region? Are there any similarities between enterprises with a same size?
The second category is named innovation, and it includes variables related to create something new in the enterprise. Then, a new business is to create something new; thus, the business idea is the first innovation in the enterprise. Besides this, variables linked to the founder and his/her idea are selected. In addition, questions linked to the development of products and services inside the firm are in this category. In this way, variables related to those tools used to promote innovation activity in the enterprise are in the analysis (Internet used to develop new products and services, use programs to support innovation, programs to support innovation financing, prevision programs to support innovation, and use programs to increase goods and services).
Following the theoretical framework, the category named as learning has been recognized, where variables related to the experience are identified, including the experience that employees have before their incorporation to the firm (years of experience, employees with secondary school, employees with a degree, and most difficult skill to find) and also those experience that the enterprise provides (use programs to offer technical assistance, programs to offer technical assistance financing, prevision programs to offer technical assistance, use programs to increase sales in Uses programs to obtain export certification domestic market, use programs to increase quality of goods/services, use programs to reduce energy consumption, category require more training, uses formal training for employees, uses external training for employees, uses internal training for employees and programs to train financing). Finally, the last category, like the identified in the previous section, is linked to knowledge sharing by the enterprises. In this way, variables linked to programs to make business alliances with suppliers or clients, financing entities and exporting activity are identified.
Then, a cluster analysis where Chilean enterprises are classified depending how they manage their knowledge is realized. Cluster analysis is a statistics technique based on Bclassified objects (that is to say respondents, products or other entities) where each object is very similar to others in the same cluster^ (Hair et al. 1999, p. 492). Clusters have internal homogeneity and very high external heterogeneity if they are compared to others clusters (Hair et al. 1995). Then, the different behaviors between clusters are compared using a descriptive technique and analyze the more interesting variables linked with knowledge management.

Managing Knowledge in Chilean Enterprises
First, a hierarchical cluster analysis has been applied using the average linkages between groups method using only knowledge variables. With the results and using the dendrogram obtained, an imaginary line has been drawn on the first quartile and eight clusters of enterprises have been identified (see Table 2).

Results: Clustering Chilean Enterprises
The first cluster is composed of 756 enterprises with a medium average of sales per employment (52,926). It is composed mainly of enterprises from the food, fabricated metal, retail, and chemical industries (more than 50 %; see Table 2). Studying the size of the enterprises, this cluster is formed by medium and large enterprises (small enterprises are only 28 %; see Table 3). It has a medium average of enterprises investing in knowledge activity: It does not highlight whether it is a high-level or low-level investment (see Table 4). In relation with public support, there is a small percentage of enterprises receiving public financing, not including public support for employee training where 32.5 % of enterprises receive funding (see Table 5). And finally, all enterprises in cluster 1 in which the respondents are also the founders of the firms were selected and their motivations were analyzed (see Table 6). Linked with this point, 33.6 % of founders in cluster 1 found the enterprises developing a new idea and only 30 % replicating a business idea by others.
The second cluster is composed of only 30 firms with low average sales per employee (35,214). It is composed mainly of food, retail (with a special weighting), and fabricated metal industries (50 % in total; see Table 2). In relation with the size of firms, the composition is similar to cluster 1: It is composed mainly of medium and large enterprises, and only 28 % are small (see Table 3). Analyzing knowledge variables, cluster 2 highlights investment in programs linked with innovation, learning, and knowledge sharing with a high percentage of enterprises using these programs in comparison with other clusters, with the exception of programs for employee training (see Table 4). Also, in comparison with other clusters, cluster 2 obtains more public support for their initiatives than others (see Table 5). And finally, when analyzing the origin of the business idea, results show that more than 55 % of firms were created replicating an idea developed by other people, meaning that the ideas of enterprises in cluster 2 are not very innovative.
Cluster 3 is composed of 123 enterprises, and it is the cluster with the lowest scores (measured with average sales per employee variable; see Table 2). This result can be explained by the composition of the cluster: More than 55 % of firms are small companies and only 12 % are large (see Table 3). In relation with the industries, cluster 3 is formed by enterprises from the food (34 %), fabricated metal (11.4 %), and plastic (9.8 %) industries (see Table 2). In relation with knowledge variables, cluster 3 stands   Table 4) and also by the fact that it has received less public support than other clusters (see Table 5). Finally, the origin of the business idea is analyzed, and in this case, there is no clear winner with respect to the origin of the idea (see Table 6). The fourth cluster is composed of only 28 enterprises, and it has medium to high average sales in comparison with the others clusters (see Table 2). Cluster 4 is composed mainly of medium enterprises (more than 42 %), 28.6 % being small and 28.6 being large firms (see Table 3). Knowledge activity is important in this cluster, where most enterprises invest in programs linked with innovation (see Table 4), and they also obtain a significant level of public support for these programs in comparison with other clusters (see Table 5). Finally, cluster 4 is the most innovative one because no enterprises have replicated ideas developed by other people.
The fifth cluster is composed of 65 enterprises, and they have the most average sales per employment (with the exception of cluster 7 which is a special case; see Table 2). It is composed mainly of the chemical (23.1 %) and food (15.4 %) industries (see Table  2), and it has a high percentage of large enterprises (almost 50 %). Like cluster 4, cluster 5 pays attention to knowledge activity and firms investing in these programs (see Table 4) and they obtain more public support for their knowledge activity in  comparison with the other clusters (see Table 5). Finally, around 50 % of enterprises were created based on a new idea and only 16 % were created replicating an idea developed by other firms (see Table 6). Cluster 6 is composed of 28 enterprises where more than 74 % of them are small and medium (see Table 3). It is mainly composed of the food and fabricated metal industries (more than 46 %) and their results are poor (see Table 2). In relation with knowledge activity, cluster 6 has a medium investment in programs which promote innovation activity, learning, and knowledge sharing (see Table 4), and there are a high number of enterprises in comparison with other clusters obtaining public support for innovation and employee training.
The seventh and eighth clusters represent a special case in the chemical industry because they are both composed of only one enterprise (see Table 2). In the case of cluster 7, special case 1, a large enterprise is identified (see Table 3) with the best result measured across sales per employment with a figure about 250,000 (see Table 2). Special case 1 invests heavily in some programs linked with knowledge to offer technical assistance, increase goods and services offered, increase sales in the domestic market, increase quality of goods and services, reduce costs, obtain certificates, and train employees (see Table 4), and it obtains public support only for the final program: training employees (see Table 5). Special case 1 seems to be an old enterprise because the respondent is not the founder and thus there is not information about the business idea (see Table 6).
Finally, special case 2 is also composed of a large enterprise in the chemical industry (see Table 3). In this case, the results are poorer than in special case 1 (see Table 2). However, there are similarities between their knowledge management because special case 2 invests in knowledge activity: It invests in programs to make business alliances, increasing offered goods, operating in new markets, increasing sales, increasing quality, reducing costs, obtaining a certificate, and training employees (see Table 4), and it obtains public support to make business alliances and train employees (see Table 5). In this case, the respondent is the founder, and the business idea was created by modifying an idea encountered in previous occupations by the founder (see Table 6).

Discussion
Eight different behaviors have been identified in order to manage knowledge in Chilean enterprises: six clusters and two special cases. The majority of firms are in cluster 1, so this cluster represents the most common behavior managing knowledge. In general, cluster 1 is represented from all the industries and sizes of firms, and its position is average in relation with the knowledge and innovation (see Tables 4, 5, and 6).
The best results are obtained by cluster 5 and the worst results are obtained by cluster 3. These clusters are very different in relation to descriptive variables: While cluster 3 is composed mainly of small and medium enterprises and the principal industry is food, cluster 5 is composed of large enterprises and the principal industry is chemicals (see Tables 2 and 3). Consequently, their knowledge behavior is very different: On the one hand, with respect to innovation, the investment in programs to support this activity is more than double in the case of cluster 5 compared with cluster 3. Cluster 5 also receives more public support than cluster 3 (see Tables 4 and 5). In addition, Table 6 demonstrates that cluster 5 is more innovative because 50 % of the enterprises were created developing a new idea, while in cluster 3, this percentage is only around 27 %. Regarding learning, the difference is also very clear: The percentage of enterprises investing in this activity is always greater in the case of cluster 5 in comparison with that in cluster 3, and public support is also high (see Tables 4 and 5). Finally, linked with knowledge sharing, the percentage of enterprises in cluster 5 is more than double in comparison with that in cluster 3 (see Tables 4 and 5). With this data, the idea is about that there are some enterprises with high investment in knowledge and good results measured across sales by employee, and there are some small enterprises with a poor investment in knowledge with results lower than others. This idea is linked to previous contributions like Kebede (2010) who relates knowledge to competitive advantages in enterprises.
Cluster 4 represents similar behavior to cluster 5 linked with knowledge activity. The principal difference is their composition, because cluster 5 is composed mainly of large enterprises while cluster 4 is composed mainly of medium-sized firms (see Table 2). The percentage of enterprises investing in knowledge in cluster 4 is almost always a little lower than in cluster 5 (see Tables 4 and 5). The results in the case of cluster 4 are the second best behind those of cluster 5 (see Table 1), and enterprises formed in cluster 4 are also innovative because more than 58 % of enterprises were created developing a new idea. Thus, cluster 4 is a special cluster that follows closely behind cluster 5.
From this information, enterprises interested in improving their situation using knowledge management should replicate the behavior of clusters 4 and 5, that is to say, they should invest more resources in knowledge. This is in agreement with previous contributions explained in the theoretical framework section, where Zyngier and Venkitachalam (2011) consider knowledge as a key organizational resource, and Liao et al. (2008) identifies this resource as essential in order to achieve survival.
Cluster 2 has similar behavior to cluster 1 with an intermediate position; however, it has some particularities. First, their results are lower than in cluster 1 although their composition in terms of size of enterprise is very similar. Thus, the following question (see Tables 2 and 3) is considered: If the composition is similar, why do enterprises in cluster 2 obtain worse results than in cluster 1? In the case of cluster 2, the principal difference is the origin of business idea: More than 44 % of enterprises were created replicating an idea developed by other firms; thus, they are not innovative firms (see Table 6).
In the case of cluster 6, the results are the second worst. This difference can be explained with knowledge investment, because although it has a medium level of investment in knowledge activity, in the case of knowledge sharing, it is less than in the other clusters (see Table 4), and these enterprises do not receive public support for this part of knowledge management (see Table 5).
In addition Cimoli (2010) recognized a positive relationship between innovative politics and the sustainability in Latin America identifying Chile and Brazil as the first investors. Results show that clusters' receiving more public founds related to innovation are more innovative and have better results. Therefore, knowledge has a positive influence in the growing of countries. In this way, the government of Chile should promote knowledge in the clusters, focusing their efforts in those where knowledge is not the key.
Therefore, cluster 5 is presented as the best cluster with the best knowledge activity, followed by cluster 4. Cluster 1, which represents the majority of firms, is in a medium position. Clusters 2 and 6 have the worst results of all the other clusters, and they present some differences in knowledge management: Cluster 2 is composed of noninnovative firms and cluster 6 does not invest in knowledge sharing in comparison with the others. Finally, cluster 3 has the worst results and the lowest investment in knowledge management.
As a final point, two special cases have been identified and both of them are in the chemical industry. Special case 1 is a large and old firm with excellent results. This enterprise focuses its efforts in learning, ignoring knowledge sharing, and innovation. This strange situation can be explained by the fact that it is an enterprise with a probable long history (the respondent is not the founder) and with sufficient internal resources to obtain good results in terms of knowledge. This special case, where enterprise has a big experience, is according to the statement formulated by Nonaka and Takeuchi (1995: 8), Bthe most interesting learning come from the direct experience.^This firm could be considered as a learning organization, defined as an organization which follows the principles of organizational learning, that is to say, these firms pay attention to the knowledge process and try to develop capabilities in their employees in order to increase their effectiveness (Kim 1998).
Special case 2 is also a large firm but, in this case, the respondent is the founder. Thus, it is in its first generation-a new enterprise. This point could explain the lower results in comparison with other enterprises, having only 26,666 million by employee. This enterprise invests homogeneously in all parts of knowledge.
Briefly, the study has demonstrated, once more, the significance of knowledge management, which is not another tool, but it is the key for enterprises and governments. This idea is agreed on previous contributions like Nonaka and Takeuchi's viewpoint (1995), who analyzed Japanese companies. Chile bets on innovation and knowledge, and this investment is being rewarded. And, those Chilean enterprises including knowledge management as a normal process in their firm have good results.

Conclusion
Starting from Chilean enterprise data, a cluster analysis has been proposed in order to know how these enterprises manage their knowledge. Results show that clusters 4 and 5 are the best in knowledge management (best results) so other enterprises should replicate their behaviors, and they also obtain the better general results. In addition, enterprises from non-innovative clusters are obtaining lower results. This is in line with the literature in knowledge management area, and it highlights the relevance of knowledge management process influencing the enterprise's performance.
Thus, the main academic contribution is that, in a field as heterogeneous as the enterprise, the study contributes to the theory showing evidence about highlighting knowledge management. In this way, discussion includes the relationship between results obtained and previous works in the knowledge management area.
Moreover, this study has some managerial implications; on one hand, a very interesting result related to government's investment is identified: Those enterprises which are receiving public funds for innovation and creating knowledge obtain better results (both general and specific of knowledge management) than others who are receiving less public funds. This highlights the influence of the governments in the enterprises, and the situation of enterprises has a direct relationship with countries' health.
In addition, other very interesting result is related to government's investment. Those enterprises which are receiving public funds for innovation and creating knowledge obtain better results (both general and specific of knowledge management) than others who are receiving less public funds. This highlights the influence of the governments in the enterprises, and the situation of enterprises has a direct relationship with countries' health.
These results are very interesting because, once more, knowledge management highlight has been demonstrated. It may be obvious, but these lines of evidences are necessary in order to persuade enterprises about including knowledge management process as something essential. The study shows that there are a lot of enterprises where knowledge is not relevant yet. And, the firms need to know results like we present.
Besides this, the main contribution for scholars and the academic area is clear: This study remarks knowledge management highlights for enterprises and governments using an example that had not been used previously: Chile. Because of the heterogeneity of the enterprises, different studies explaining the same (knowledge management highlight) are important. In addition, the study exposes the role of the governments in this process in enterprises.
In the same way, this paper is interesting for Chilean government, because it shows the situation of enterprises in this country. Thanks to this paper, the Chilean government knows that their public funds related to innovation are used by enterprises with good results of knowledge management and also in general. Consequently, the government also knows that there are some enterprises that still need more motivation on the topic. In short, the Chilean government knows that its efforts are being rewarded, but they still need to motivate knowledge management process in enterprises.
Finally, this paper is interesting for other countries, especially for those of the same region (Latin America) because they have characteristics in common with Chile; therefore, for them, the study could be a reflection in that it could be them if they invest more in knowledge management. They know that Chilean progress is a fact, and investment in knowledge management could be one of its secrets. The same reason is applicable to the enterprises; they could be an example in innovative Chilean firms.
Linked to special case 1, some future lines of research are identified. In this way, it could be very interesting to analyze this enterprise's internal organization in order to identify where the investment in learning activity is best utilized. In addition, this study could be replicated in other developing countries in the future and compared with this study in order to see the differences between them because it could help governments to orientate their enterprises' knowledge activities.
The study has some limitations. First, related to knowledge sharing, there are no variables in the questionnaire used linked to share knowledge with clients and suppliers. Second, the analysis is static because the information is only about the recollected information up only once between 2010 and 2011. Finally, it has the limitations derived from the data collection by the World Bank.