Diseno, medicion y analisis de un modelo para la gestion del conocimiento en el contexto de una Universidad Mexicana.
DESENHO, MEDICAO E ANALISE DE UM MODELO PARA A GESTAO DO CONHECIMENTO NO CONTEXTO DE UMA UNIVERSIDADE MEXICANA
CONCEPTION, MESURAGE ET ANALYSE D'UN MODELE POUR LA GESTION DES CONNAISSANCES DANS LE CONTEXTE D'UNE UNIVERSITE MEXICAINE
In recent times, knowledge has become the main asset of production as opposed to the tangible assets that previously droved manufacturing-based markets (Kemp et al., 2002). As such, knowledge must be recognized as a resource that needs to be managed. According to Carrillo (2001), in the nature of the new global environment, sooner or later, we will distinguish two great blocks of species in the world of organizations: those that manage their knowledge and those that are extinct.
The increasing importance of knowledge lies in how its application is adding value to services. That is the case of Higher Education (HE) institutions in which education is a knowledge-based activity. The relevance of knowledge in the new economy has led organizations to re-examine and renovate their strategies, processes and technologies based on a Knowledge Management perspective (Luo & Lee, 2013; Zhang & Zhao, 2006). Facing this situation, institutions (e.g., Universities) require of strategies and models that allow them to manage their knowledge (Fullwood, Rowley & Delbridge, 2013). A starting point, from which Universities can learn to do so, would be to look at previous research (e.g., Skyrme & Amindon, 1998; Davenport, De Long & Beers, 1998; Holsapple & Joshi, 2000; Liebowitz, 1999; Chong & Choi, 2005; Wong, 2005; Pentland et al., 2011), in which a series of KM critical success factors have been identified. A second source of learning for Universities to implement KM initiatives would be to analyze the existing KM models (e.g., Nonaka & Takeuchi, 1995; Snowden, 1998; Bukowitz & Williams, 1999; Wiig, 1997; Eppler & Sukowski, 2000; Heisig, 2002) and determine how these models can contribute to their own KM initiatives.
However, despite the identification of critical success factors for KM and the availability of different KM models, there is still a persistent discussion about the role of information technologies in KM initiatives, on the one hand, and on the role played by social and cultural aspects, on the other. Davenport et al. (1998), comment that the field of KM has been traditionally dominated by information technologies. Nevertheless, today the roll of people in KM processes is being recognized, increasing the interest from a perspective focused on people (Earl, 2001). Organizations begin to realize that technology is not the solution for all KM problems, reason why the focus is going towards people (Grundstein, 2013; Poole, 2000). The theory of Nonaka and Takeuchi (1995) agrees with this idea. They focus their attention on the conversion of individual tacit knowledge to collective explicit knowledge, through individual and organizational processes of learning and perceived KM as a set of structural administrative initiatives that support learning of employees in the organization. This theory is based on cultural, organizational, human and social aspects of knowledge (Ackerman et al., 2003).
In contrast to the perspectives that consider information systems are the solution to KM problems, others have suggested that people, their interrelations and attitudes are fundamental aspects to succeed. It seems then that both perspectives should be fore-grounded when analyzing KM initiatives. Consistent with this idea, the study by Scholl et al. (2004), argues that the future of KM focuses on a better integration of KM activities to business processes, as well as a greater concentration in the interface man-organization and a better adjustment between technological aspects and human factors.
This study agrees with the view expressed by Scholl et al. (2004), and proposes a holistic KM model that integrates organizational, cultural, structural and technological elements. These elements are required in organizations in order to support and facilitate the processes of creation, storage, transference and application of knowledge.
The remaining part of this paper is organized as follows: Section two discusses the theoretical background of the study; it starts by establishing a KM definition to further discuss a series of KM enablers and KM processes that are located at the core of the concept. Drawing on this body of literature a KM model is introduced in section three. Section four introduces the research methodology and includes the methods used for data collection, the sample characteristics and the operationalization of variables. Section five presents the data analysis and suggests a series of hypotheses that emerge from the study. A final section discusses the conclusions, implications and limitations of the study.
Different authors (e.g., Grundstein, 2013; Prusak, 2002; Lehaney et al., 2004; Hinds & Pffefer, 2003; Huysman & Wit, 2002; De Long & Fahey, 2000; Karlsen & Gottshalk, 2004; Satayadas et al., 2001; Horwitch & Armacost, 2002; Mertins et al., 2003; Friedman & Prusak, 2008; Horwitch & Stohr, 2008; Chan et al., 2013; Trusson, 2014) have defined KM from complementary perspectives. Most of these definitions agree in the following points:
* KM requires a set of organizational practices related to strategy, technology, environment and people (KM enablers).
* KM tries to improve the knowledge processes of creation, storage, sharing and use (KM processes).
* KM tries to improve organization productivity and the quality of decision making (KM benefits).
In a previous work by one of the authors of this paper, KM was defined as "the set of methods, tools, structures, and necessary initiatives that organizations need to create, store, transfer and apply knowledge in the organizationvalue-adding processes, in order to gain a competitive advantage over their competitors" (Rivera, 2007, p. 17). Taking this definition as the point of departure, what follows reviews existing research on KM enablers and processes, and how the relations among them have been hypothesized in previous studies. This review is further used to develop a KM Model to be applied within the context of a HE institution in Mexico.
For the purpose of this paper KM processes are defined as the necessary activities that need to be performed so that knowledge can be created, stored, shared and applied by the members of the organization in order to reach a better organizational performance.
Different classifications have been suggested for the necessary KM processes to managing organizational knowledge. Generally, these classifications may vary in the number of activities within a rank of three to eight. Davenport and Prusak (1998), propose as KM processes the generation, codification, coordination and transference of knowledge. The model proposed by Probst et al. (1998), considers eight KM processes: identification, acquisition, development, transference, use, retention, evaluation and knowledge goals. A study conducted in 1,000 German companies and 200 European companies, concluded that four knowledge activities are the most relevant for organizations: apply, distribute, generate and store (Mertins et al., 2003). These processes coincide with the classification by Alavi and Leidner (2001), of creating, storage/retrieval, transferring and application, which has been widely accepted in the field of KM (Handzic & Zhou, 2005). These processes as proposed by Mertins et al. (2003), and Alavi and Leidner (2001), are taken as the starting point and discussed below.
Knowledge creation. Refers to the process through which new knowledge is developed within existing knowledge. This process has been widely studied by Nonaka and Takeuchi (1995). In their work, they analyzed how knowledge is created and shared, and the conditions that support knowledge creation. In their knowledge creation model, they present four ways of knowledge conversion: socialization, exteriorization, combination and internalization; by means of these, knowledge expands from the individual to the organizational level. This was further complemented by Nonaka and Konno (1998), who studied the fundamental conditions that must exist in organizations so that knowledge can be created. They introduced the concept of "ba", and defined it as the shared context of those who take action and interaction in the process of knowledge creation (Nonaka & Teece, 2001). In this context, an environment in which tolerance and freedom allow to continuously learn is strongly recommended (Handzic & Zhou, 2005).
Knowledge storage. This process implies that knowledge has to be organized and deposited in different forms, such as documentation of best practices, written documents, structured information, codified knowledge, documented procedures and tacit knowledge (Alavi & Leidner, 2001). Storing knowledge by constructing knowledge repositories allows organizations to develop an organizational memory. Many of the KM practices initiate constructing knowledge repositories with the objective of capturing and storing knowledge for its later access and use (Grover & Davenport, 2001). Markus (2001) mentions three factors that must be considered when developing effective knowledge repositories: i) to provide time and suitable resources to document knowledge, ii) to count on appropriate incentives for motivating employee participation and contribution (formal and informal incentives as well as an open culture that allows knowledge sharing), and iii) to have intermediaries to organize, synthesize and translate the information in effective knowledge that can be used.
Knowledge transfer Implies to distribute knowledge where it is needed to be applied (Pentland, 1995). The objective of knowledge transfer is to distribute the correct knowledge to the correct people at the correct time. Several technological applications have been developed to facilitate knowledge transfer; some of the most outstanding are the email, discussion forums, videoconferencing, intranet systems and the internet. Channels to knowledge transfer can be formal and informal, personal or impersonal (Alavi & Leidner, 2001) and their application will depend on the type of knowledge to be transferred. In order to enable knowledge transfer, organizations must generate a suitable organizational environment as well as provide an infrastructure that facilitates knowledge sharing. This implies that: i) knowledge has to be accessible through the use of expert directories that can be used to identify specialists in certain areas, ii) technological infrastructure has to be promoted to facilitate knowledge distribution using intranets, email and virtual working, and iii) KM initiatives need management support through the creation of an environment of trust and a friendly knowledge culture.
Knowledge application. Refers to the use of knowledge in order to reach a competent performance (Pentland, 1995). The final objective of KM is to use knowledge to benefit the organization. Only the productive use of knowledge will translate intangible assets in tangible results (Handzic & Zhou, 2005). Without an effective use of knowledge, all the efforts in the development, storage and transference of knowledge are in vain. Although, the possession of knowledge does not guarantee automatically its successful application in daily work; there is a wide variety of factors that inhibit the effective use of knowledge, reason why it is necessary to take into consideration some aspects to assure that knowledge is used effectively, such as knowing users necessities, promoting a supportive work environment and designing physical facilities to promote an effective communication, among others.
KM enablers are defined as the set of organizational, cultural, structural and technological elements existing in the organization that support and facilitate the KM processes of creation, storage, transference and application of knowledge. Previous literature has identified a number of KM enablers that might be relevant to support KM processes when implementing KM initiatives. Among other studies, the work by Wong (2005) becomes relevant for the purposes of the current study. In his work, Wong made a comparison of KM enablers proposed by different authors that are required to succeed in KM initiatives (Table 1).
A similar exercise was conducted by Rivera (2007), who identified different KM models and the KM enablers suggested in these models (Table 2).
From the contributions by Wong (2005) and Rivera (2007), a set of KM enablers persistently appear as critical to support the processes discussed above. These are: leadership, culture, human resources, information technologies, structure and control.
Leadership plays a critical role to succeed in KM (Garavan, Carbery & Murphy, 2007; Holsapple & Joshi, 2000) because it is a decisive factor during the implementation of cultural, organizational and technical changes in organizations (Handzic & Zhou, 2005). Eppler and Sukowski (2000) place leadership as the main element in the pyramid of necessary platforms, norms, processes and tools to have an effective KM. In the same way with the continuous improvement programs, management support and commitment is a key element to succeed in KM initiatives (Davenport et al., 1998). A study conducted in 431 American and European companies confirms that 67% of the executives interviewed admit that the greatest obstacle to manage knowledge--culture--can be attacked with a greater sense of leadership (Holsapple & Joshi, 2000). Different authors comment about the function of leadership within KM initiatives. Eppler and Sukowski (2000), and Beckham (1999), make emphasis on the role of leaders in providing motivational aids and the necessary time and space in order to share knowledge, given that KM initiatives sometimes fail due to the lack of resources such as time, human and financial resources (Wong, 2005).
Organizational culture has been recognized as one of the most important enablers or inhibitors of KM (Handzic & Zhou, 2005; Holsapple & Joshi, 2000; Lin, 2006) and as the greatest challenge in the practices of KM (Zhou & Fink, 2003). It is in the culture where non-spoken norms are about how knowledge is distributed in the organization and between individuals. A study made by the Journal of Knowledge Management (Lin, 2006) revealed that culture is one of the greatest obstacles faced by the people responsible for managing knowledge. Of the 431 executives interviewed in this study, 80% stated that culture prevents the development and introduction of KM strategies and programs in their organizations. Only through the creation of a culture of trust and collaboration, can knowledge sharing and organizational effectiveness be improved (Sveiby & Simons, 2002). Moreover, trust also requires tolerance to mistakes and failures, which must be seen as part of the learning process of KM implementations (Kannan, Aulbur & Haas, 2011).
In addition to leadership and culture supporting KM activities, human resource initiatives become critical to support those of KM. On the one hand, organizations need to provide training and development to their employees so that they can understand the purpose of a KM initiative and have the competencies required to participate in the KM activities (Liebowitz, 1999); on this regard, special attention should be given to the development of competencies required to use information technologies. On the other hand, there is a need to use incentives in order to motivate employees so that they share their knowledge with other members of the organization (Wong, 2005). When giving incentives for participating in KM processes, organizations must provide the correct incentives according to the raised objectives. According to Chase (1997), unproductive incentive programs are one of the greatest obstacles for implementing KM initiatives. As in the case of technological solutions, incentive programs must be customized, since different interests in the organization and its employees are present.
The critical role of Information Technologies (IT) has been acknowledged in many studies (Tambe & Hitt, 2012; Woodman & Zade, 2012; Heisig, 2009; Mertins et al., 2003; Wong, 2005; Lin, 2006; Trusson et al., 2014). Handzic and Zhou (2005), comment that there are two types of enablers: first, the organizational environment, integrated by organizational culture, leadership, organizational structure and measurement; and second, the technological infrastructure, which includes a great variety of information and communication technologies.
Whereas the organizational environment contributes to create a knowledge friendly climate, the technological infrastructure facilitates the processes for KM. This technological infrastructure can help managing the stored explicit knowledge in internal or external databases, as well as maintaining employees in contact for sharing the knowledge they own, which is not documented (Mertins et al., 2003). In general, ITs have been reported to support several processes to store, transfer and apply organizational knowledge (Lin, 2006). Among the ITs most commonly used by organizations we can mention the intranet, email, forums of discussion, tools for managing documents, video-conferences, and other tools to support communities and electronic learning (Spek & Carter, 2003).
Recent KM research has revealed that organizational structure plays a more important role than organizational culture and information technologies in the processes of knowledge sharing (Zhou & Fink, 2003). Different studies have tackled the subject of organizational structure as a fundamental element to succeed in KM initiatives. Some of the elements that emphasize these studies are those related to Communities of Practice (CoPs), social networks, formal and informal hierarchies inside organizations and the creation of spaces to promote interactions between employees. Handzic and Zhou (2005), argue that companies can use a wide variety of organizational forms such as teamwork, social networks and CoPs, to create an atmosphere that supports collaboration and knowledge sharing (Lave & Wenger, 1991). Facilitating the creation of informal groups of collaboration between employees is one of the more effective means to promote knowledge sharing (Snowden, 2000).
Similarly, it has been found that the elimination of hierarchies of status in order to promote knowledge sharing can contribute to the success of KM initiatives (Karlsen & Gottschalk, 2004). This elimination of hierarchies also entails changing the organization facilities in order to ease collaborative work. The creation of special spaces to facilitate employee interaction (coffee rooms, dining rooms for employees, etc.), has been identified as highly useful to promote the flow of ideas and knowledge between employees in an informal but very effective way (De Long & Fahey, 2000).
Measurement and control continue being one of the greatest challenges in KM initiatives and one of the least developed aspects in KM. This might be due to the difficulty of measuring something that cannot be seen, as it is knowledge (Bose, 2004). In spite of the difficulty of companies for measuring the benefits of KM, Skyrme (2003), comments that not only a measurement system has been developed, but in addition to this, he argues that today there is a great variety of new methods that combine different indicators to measure the intangible assets of an organization. Supporting this point of view, the APQC (2001) affirms that measurement is possible, although it is not a simple task.
Generally, when organizations have tried to measure the impact of KM initiatives on the general performance of the organization, they have used three different approaches: financial measurements, non-financial measurements and a combination of the two (Chang, Hsu & Yen, 2012). Mertins et al. (2003), remark that one of each three companies uses soft and hard indicators to evaluate the results of KM initiatives. What is clear is that before defining a method to measure the impact of KM initiatives, organizations must clearly define the objectives of this measurement (Skyrme & Amidon, 1998).
Drawing on the literature reviewed in the previous section, this part of the paper introduces the Knowledge Management Model developed in the current study, as shown in Figure 1. The model is composed by six enablers: leadership, culture, structure, human resources, information technologies and measurement. Altogether, these six enabling conditions are expected to facilitate the processes of knowledge creation, storage, transference and application. It is important to clarify that these four processes and six enablers are not discrete, independent and isolated, but they are rather dynamic and interdependent. Thus for KM initiatives to succeed it is not required of an excellent performance in a single activity, but rather to support the four activities as an integrated process.
This study asserts that the proposed model holds the potential for explaining how KM enablers relate to KM processes. Three main objectives of the proposed model are set up:
1. to evaluate the degree of development of KM processes: creation, storage, transference and application;
2. to evaluate the degree of implementation of KM enablers: leadership, culture, structure, human resources, information technologies and measurement;
3. to establish the relations among the elements of the model within the context of the case study.
Sample and Data Collection
Data was collected through the application of a 65 question paper-based survey to the whole board of directors of a Mexican University. For each item, the survey requested the participant to evaluate in a five-point Likert-type scale (1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree and 5 = strongly agree) the way they perceive each one of the exposed statements. In total, 36 employees were surveyed. Despite the fact these employees represents only 15% of the total population of the University, research subjects were selected for several reasons: they were familiar with the multiple aspects related to a KM implementation; according to a group of research informants, they showed an authentic interests in KM; and their perceptions provided insights about an influential group critical for the success of the KM initiative.
A meeting session was carried out in order to apply the survey to all research participants. A cover letter explained the goal of the study and ensured participants that their opinions were kept anonymous and confidential. Additionally, a glossary of terms was included at the end of the survey to ensure that research participants had a consistent and clear view of the concepts included. Prior to the application of the survey, a pilot test was carried out to improve the final version of the instrument. This pilot test was also applied to 15 Master degree and four Ph.D. students who provided feedback to improve the instrument design. In this pilot test, the clarity of each statement as well as the suitable operation of each item was carefully revised.
Table 3 shows research participants' demographic information, including gender, age, positions held at the University and number of years working for the institution.
Operationalization of Variables
All constructs were measured using different items. The operationalization of these constructs was primarily adapted from previous studies but modified to be applied in the context of HE institutions. The items used to measure each construct are summarized as follows (Table 4):
TABLE 4. List of items of each construct Item Enablers No. I. Leadership 1.1 Resources 1.1.1 Financial resources 19 1.1.2 Human resources 29 1.1.3 Time 35 1.2 Strategy 1.2.1 Use of information technologies 12 1.2.2 Clear and spread KM strategy 24 1.2.3 Awareness of tools, activities and benefits of KM 42 1.3 Leader by example 1.3.1 Leader by example 6 1.4 Creating an appropriate work environment 1.4.1 Appropriate work environment 32 II. Culture 2.1 Trust 2.1.1 Freedom and trust to new possibilities 10 2.1.2 Knowledge credit 21 2.1.3 Trust to resolve doubts 38 2.2 Environment 2.2.1 Knowledge is power 2 2.2.2 Job security 15 2.2.3 Attitude toward mistakes 33 2.2.4 Employees collaboration 44 III. Human Resources 3.1 Incentive systems 3.1.1 Establishment of incentive systems 31 3.1.2 Non-financial incentives 43 3.1.3 Financial incentives 4 3.2 Recruitment and retention 3.2.1 Knowledge positive attitude 36 3.2.2 Valuable employees retention 23 3.3 Role design 3.3.1 Personal development opportunities 16 3.3.2 KM integration to daily activities 25 3.3.3 Role required skills 11 IV. Information Technologies 4.1 Effectiveness 4.1.1 Existence of Information technologies 27 4.1.2 Applications and databases 14 4.1.3 Benefits of information technologies 5 4.2 Efficiency 4.2.1 Use, maintenance and support 45 4.3 Required knowledge and skills 4.3.1 Required knowledge and skills 22 4.4 Integration 4.4.1 Integration to role activities 39 V. Structure 5.1 Organizational forms 5.1.1 Informal activities 13 5.1.2 Formal activities 41 5.1.3 Hierarchical levels 20 5.1.4 Departments interaction 30 5.2 Knowledge networks 5.2.1 Teamwork and empowerment 1 5.2.2 Communities of practice 46 5.3 Physical facilities 5.3.1 Buildings, offices and work spaces 8 VI. Measurement 6.1 Economic impact 6.1.1 Financial incentives 48 6.2 Indicators 6.2.1 Financial indicators 49 6.2.2 Non-financial indicators 50 6.3 Feedback 6.3.1 Feedback for improvement 51 6.4 Intellectual capital 6.4.1 Metrics to measure intellectual capital 52 Item Processes No. P.1 Create P.1.1 Creation of new ideas and knowledge 26 P.1.2 Learning between employees 7 P.1.3 Sharing knowledge with clients and suppliers 18 P.1.4 Freedom and trust to new possibilities 10 P.1.5 Attitude toward mistakes 33 P.1.6 Personal development opportunities 16 P.2 Store P.2.1 Documenting key knowledge and lessons learned 9 P.2.2 Efficient processes to classify and store knowledge 40 P.2.3 Documented procedures 3 P.2.4 Applications and databases 14 P.2.5 Maintenance to physical facilities and electronic means 34 P.2.6 Time 35 P.3 Transfer P.3.1 Learning between employees 7 P.3.2 Sharing knowledge with internal clients and suppliers 18 P.3.3 Personal development opportunities 16 P.3.4 Buildings, offices and work spaces 8 P.3.5 Job security 15 P.3.6 Hierarchical levels 20 P.3.7 Knowledge is power 2 P.4 Apply P.4.1 Applying the appropriate knowledge 47 P.4.2 Applying the acquired knowledge 17 P.4.3 Context of the problem 28 P.4.4 Support to apply new ideas 37 P.4.5 Required skills and knowledge 22 Source: Own elaboration.
Data Analysis, Hypothesis Definition and Discussion
In order to carry out the statistical analysis of the data, different software was used: SPSS version 15.0, Eviews version 5, Masters version 5 and Minitab version 15. Data analysis proceeded in two complementary stages. An initial stage assessed the overall fit of the model (Table 5) and its convergent validity (Table 6).
The overall model fit was assessed in terms of the Root Mean Square Error of Approximation (RMSEA). In practice it has been found that a value near 0.05 or less indicates a good fit to the model; a value of 0.08 or less indicates an acceptable fit to the model (Steiger & Lind, 1980). As shown in Table 5, indices of Culture, Human Resources, Structure, Creation, Store and Apply exhibited a good fit with the data collected, whereas indices of Leadership, Information Technology, Measurement and Transfer showed an acceptable fit to the model. This in turn showed that the defined variables modeled the data well.
Convergent validity was assessed through reliability of question items. Reliability of a scale is used to examine internal consistency by calculating Cronbach's alpha value (Nunnally, 1979). Two Likert-type scales were analyzed, the first scale evaluated the level of implementation of KM enablers integrated by 41 items and the second evaluated the degree of development of KM processes integrated by 24 items. Table 6 shows the factor loadings of the measurement items. For all items, these factors exceeded the recommended level of 0.5; indeed, the value of all items ranged from 0.71 (ITs, store and apply) to 0.89 (leadership). Based on these results it can be concluded that the instrument has a high reliability (0.96), indicating that the obtained results are consistent and coherent.
In the second stage, once the overall fit of data to the model and the convergent validity were assessed, two Spearman correlations analysis for non-parametric data were run in order to evaluate the existing relations between KM processes and KM enablers. The levels of correlations found are shown in Table 7 with a confidence level of 99%.
In addition to the correlations found between KM processes and KM enablers, research participants were asked to assign a number from 1 to 6 to the KM enablers that they perceived to be the most important in supporting KM processes. Results are presented in Table 8, which shows how culture is perceived to be the most influential enablers to support KM processes. In contrast, IT is one of the least relevant enablers to support KM processes.
According to the results introduced in Tables 7 (secondary hypothesis) and 8 (core hypothesis), the following hypotheses are suggested to be explored in further studies:
* Hypothesis 1. Culture is perceived as the most influential enabler to knowledge management processes.
* Hypothesis 2. Information technologies are perceived as the least influential enabler to knowledge management processes.
* Hypothesis 3. Leadership has the greatest positive impact in the process of knowledge application.
* Hypothesis 4. Culture has the greatest positive impact in the process of knowledge creation.
* Hypothesis 5. Structure has the greatest positive impact in the process of knowledge storage.
* Hypothesis 6. Structure has the greatest positive impact in the process of knowledge transfer.
As shown by previous research, culture has been regarded as the most critical feature in supporting knowledge management processes (Heisig, 2009; Richter & Pawlowski, 2008; Bick & Pawlowski, 2009). As measured in this study, aspects such as trust (Muneer, Iqbal & Long, 2014), collaboration among co-workers (Liebowitz, 2012) and tolerance toward mistakes (Vera & Crossan, 2005), have been found to be influential in supporting knowledge sharing.
In relation to the hypothesis about IT as the least influential enabler to knowledge management processes, other studies are also in line to this finding showing that the enabler Information Technology has been overrated several times (Krzakiewicz & Cyfert, 2012). Similarly, authors such as Andreeva and Kianto (2012), and Davison, Ou and Martinsons (2013), have also found that this enabler (ITs) has no impact on KM initiatives unless cultural and social aspects are also considered.
Similarly, previous research (Rasula et al., 2012; Fullwood et al., 2013; Mas-Machuca & Martinez-Costa, 2012; Singh & Kant, 2008) has concluded that organizational structure is also critical in processes of knowledge storage and transfer as suggested by the finding of our study presented in Hypotheses 5 and 6. Saenz et al. (2012), for example, found that an environment where interactive dialogue is promoted -dimension of organizational structure- facilitates the generation and transfer of new ideas. In contrast, Miller et al. (2007), found that creating knowledge-oriented practices can be designed to create a supporting environment for knowledge flows. Finally, our study found a positive relation among the enabler leadership and the process of knowledge application. These findings are highly consistent with previous studies on KM research (Garavelli et al., 2004; Von Krogh et al., 2012).
Figure 2 shows the structural model in which the correlations related to hypotheses three, four, five and six are represented; this to avoid that the model creates confusion. It is necessary to take into account that this is an exploratory study, what means that the six hypotheses suggested are tentative assertions about the relations between KM enablers and KM processes. Further research needs to be conducted in order to test the hypothesis.
A second concern for discussion points to the empirical findings of the study. According to the perception of the Board of Directors surveyed, culture was perceived as the most influential KM enabler to KM processes, while information technologies were perceived as the least influential. When KM enablers were correlated in an independent way, structure, culture and leadership were found to have the greatest positive impact in KM processes. In that way, the current study argues for the critical role that the social, human and cultural aspects play in KM initiatives (Heisig, 2002; Lehaney et al., 2004; Huysman & Wit, 2002; Nonaka & Takeuchi, 1995).
Organizations and institutions can thus create an environment where continuous participation and learning opportunities are promoted; open communication is the rule rather than the exception; barriers between departments and hierarchies are to be eliminated; and trust among all employees is developed to explore new possibilities. It has also become clearer that information technologies play an important role in KM initiatives but not sufficient to succeed. Focusing only on information technologies when KM initiatives are implemented might overcome the relevance of other critical aspects to be considered.
Implications and Conclusions
This study has developed and applied a KM model to the context of a HE institution in Mexico. The proposed model, which is an initial contribution of the current study, shows that KM processes can be facilitated through a set of KM enablers. As shown in our research, this model emerged from the review of a series of studies and KM models.
Additionally, we argue that the proposed model is holistic, since it considers the cultural, social, human and technological aspects of an organization as well as an integral knowledge cycle for creating value (Ruggles, 1997; Alavi & Leidner, 2001; Mertins et al., 2003; Handzic & Zhou, 2005; Gairin & Rodriguez-Gomez, 2011). The validity and reliability of the model also suggest that it can be used as an instrument to evaluate KM initiatives in HE institutions. Furthermore, when evaluating KM initiatives through the proposed model, measurable results can be obtained to identify areas that require of improvement.
The study has practical implications for Mexican Universities in particular and for HE Institutions in general, which can be categorized into four dimensions. First, Universities can benefit from the use of a KM such as the developed in this paper, to identify, develop and evaluate their knowledge assets, in order to improve the knowledge sharing process that impacts core activities such as teaching, researching and consulting. Second, the findings suggest that for KM initiatives to succeed, Universities need to deploy not only financial resources but also non-financial schemes; whereas resources such as investment in facilities, economic rewards and information technologies seem to be relevant, other aspects such as development of trust, embracement of a friendly work environment and leadership by example are also needed. Third, although the use of Information Technologies is important to support KM initiatives, Universities must not undermine the social, human and cultural character of knowledge processes; aspects such as power, informal relations, status and rules of thumb must be brought to the fore. Fourth, the model proposed in this paper is to be seen by Universities as a dynamic model in which enablers and processes are interconnected and in continuous change, thus requiring flexibility in its application and continuous monitoring.
This study is not without limitations that require caution and further research. Since the study was conducted within the context of a Mexican University, what has been found may not hold true in other contexts. For that reason, it is recommended to test the model in different organizations, considering that culture differences can influence employee perception about how they perceive KM processes and KM enablers. A second limitation relates to the data collection method used. In order to improve reliability and validity of findings, the use of different measuring instruments such as participant observation, semi-structured interviews and analysis of physical artifacts is recommended. A final limitation points to the cross-sectional approach taken in the study. It might be desirable to conduct a longitudinal research to identify how KM processes and KM enablers develop over time.
(1) Part of this paper is based on the Master degree thesis project by Gibran Rivera Gonzalez, and is a result of the project SIP-IPN 20152105.
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Gibran Rivera Ph.D. en Estudios de la Informacion Instituto Politecnico Nacional Distrito Federal, Mexico Correo electronico: firstname.lastname@example.org Enlace ORCID: http://orcid.org/0000-0003-2805-5524
Igor Rivera Ph.D. en Ingenieria Industrial Instituto Politecnico Nacional Distrito Federal, Mexico Correo electronico: email@example.com Enlace ORCID: http://orcid.org/0000-0003-0768-3740
CORRESPONDENCIA: Av. Te 950, Delegacion Iztacalco, Colonia Granjas Mexico, C.P. 08400 Ciudad de Mexico, D. F. Mexico. UPIICSA-SEPI, Cubiculo 20.
ENLACE DOI: http://dx.doi.org/10.15446/innovar.v26n59.54320.
CLASIFICACION JEL: D83, M10, M15.
RECIBIDO: Abril 2013, APROBADO: Diciembre 2014.
TABLE 1. Comparison of KM enablers Skyrme & Amidon Holsapple & Davenport KM enablers (1998) Joshi (2000) et al. (1998) Management Knowledge Leadership Senior management leadership and leadership support support Culture A knowledge Knowledge-friendly creating and culture sharing culture Technology A well- Technical developed infrastructure technology Standard and infrastructure flexible knowledge structure Strategy Strong link to Clear purpose and a business language imperative A compiling vision and architecture Measurement Measurement Link to economic performance or industry value Roles and Organization responsibilities infrastructure Processes Systematic Control and Multiples channel organizational coordination for knowledge knowledge transfer processes Continuous learning Rewards and Chance in recognition motivation practices Other Resources KM enablers Liebowitz (1999) Hasanali (2002) Management Support and Leadership leadership and leadership support Culture Knowledge- Culture supporting culture Technology Knowledge ontologies IT and repositories KM infrastructure systems and tools Strategy A KM strategy Measurement Measurement Roles and A CKO or equivalent Structure roles responsibilities and a KM and infrastructure responsibilities Processes Rewards and Incentives to recognition encourage knowledge sharing Other KM enablers Wong (2005) Chong & Choi (2006) Management Management Management leadership and leadership and commitment and support support support Culture Culture Knowledge-friendly culture Technology IT Infrastructure to information systems Strategy Strategy and purpose Measurement Measurement Performance measurement Roles and Organizational Teamwork and responsibilities infrastructure empowerment Processes Processes and Knowledge structure activities Rewards and Motivational aids recognition Other Resources Training Training and education Human Benchmarking resource management Employee involvement Source: Adapted from Wong (2005). TABLE 2. KM enablers identified in previous KM models Knowledge Management Reference Knowledge Knowledge Fraunhofer Management Creation Model Model (Heisig, Assessment Tool (Nonaka & KM enablers 2002) (APQC, 1999) Takeuchi, 1995) Leadership [check] [check] [check] Culture [check] [check] [check] Information [check] [check] technologies Control [check] [check] Roles and [check] [check] organization Human [check] [check] Resources Knowledge Knowledge Intellectual Management Management Capital Model Diagnostic Maturity Model of IBM (Vorbeck (Bukowitz & KM enablers (KMMM, 2001) et al, 2003) Williams, 1999) Leadership [check] [check] Culture [check] [check] Information [check] [check] [check] technologies Control [check] Roles and [check] [check] organization Human [check] [check] [check] Resources Source: Adapted from Rivera (2007). TABLE 3. Demographic characteristics of research participants Demographic characteristics Frequency % Gender Female 10 27.8 Male 26 72.2 Age Under 35 1 2.7 35-45 16 44.4 45-55 14 38.8 Over 55 5 13.8 Position held at the University Director 1 2.78 Assistant director 3 8.33 Head of department 32 88.8 Years in the institution From 5 to 10 7 19.4 From 10 to 15 9 2 From 15 to 20 4 11.1 More than 20 16 44.4 Academic degree Ph.D. 3 8.3 Master degree 21 58.3 Bachelor degree 12 33.3 TOTAL 36 100 Source: Own elaboration. TABLE 5. Results of the overall model fit Root Mean Square Error of Approximation Not (RMSEA) Acceptable Acceptable Good Enablers Leadership 0.059 [check] Culture 0.036 [check] Human resources 0.048 [check] Information 0.071 [check] technologies Structure 0.044 [check] Measurement 0.080 [check] Processes Create 0 [check] Store 0.04 [check] Transfer 0.051 [check] Apply 0.027 [check] Source: Own elaboration. TABLE 6. Results of reliability analysis Reliability Items No. Enablers 0.95 41 Leadership 0.89 8 Culture 0.72 7 Human resources 0.73 8 Information technologies 0.71 6 Structure 0.85 7 Measurement 0.86 5 Processes 0.91 24 Create 0.73 6 Store 0.71 6 Transfer 0.78 7 Apply 0.71 5 Total reliability 0.96 65 Source: Own elaboration. TABLE 7. Results of correlation analysis between enablers and processes Create Store Transfer Apply Leadership 0.675 0.615 0.707 0.73 Culture 0.74 0.565 0.677 0.681 Human resources 0.622 0.437 0.711 0.625 Information technologies 0.423 0.656 0.364 0.471 Structure 0.634 0.674 0.777 0.699 Measurement 0.513 0.621 0.476 0.573 Source: Own elaboration. TABLE 8. Relevance of KM enablers as perceived by research participants Enabler Mean Leadership 4.11 Culture 4.77 Human resources 3.47 Information technologies 2.97 Structure 3.5 Measurement 2.16 Source: Own elaboration.
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|Title Annotation:||estrategia y organizacion; texto en ingles|
|Author:||Rivera, Gibran; Rivera, Igor|
|Date:||Jan 1, 2016|
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