Knowledge management relates to the way organizations acquire, manage and share the knowledge efficiently and effectively into the organization (North & Kumta, 2018). It is important for the organizations to make knowledge management objectives that can facilitate the security of the information being shared. The first objective of knowledge management is management of organizational memory in which all the information and the pertinent knowledge is properly being accumulated and stored effectively (Girard, 2015). Moreover, Huawei has been storing the technological information and the knowledge available in terms of the innovation, ideas, technology and intelligent data in such a manner that they are stored effectively. The second objective is to make sure that the employees within the organization have proper access and understanding of knowledge available into the organization. Similarly, Huawei has a knowledge management system in which relevant data and the significant information related to the employees are being managed in an effective way that the employees are facilitated by the information being retrieved.
Further, knowledge management has the objective of maintaining accurate information in order to give the required information as and when required. It relates to the purpose of availing complete and timely information through the quality knowledge being transmitted (Wu, et al., 2019). Huawei has been utilizing the accurate knowledge and skills into their products and securing a prominent position into the Internet of Things (IoT) domain. The objective also includes the driving competitiveness which can be facilitated through the proper use of knowledge. It is being argued that the organizations are expected to use the required knowledge to make tactical stakes and competitive strategies for overcoming the competitors. Similarly, Huawei has knowledgeable resources are different from the competitors which has been giving the company competitive advantage over the competitors (Fu, et al., 2018). The company has been using cyber security and user privacy protection through meeting the distinct relevant standards.
Knowledge management strategy is made by the organization in the form of the plan that outlines the activities needed to be taken to manage and control the information and data for increasing the efficiencies into the organization (Mohapatra, et al., 2016). Further, Huawei has a strategy that aligns with the knowledge available at the organizational departments and also aligns with the knowledge management process, focusing on the employees and technology for meeting the objectives of the knowledge management of the company. The company has an organized system or plan that is being used to manage and store the information pertaining to the innovations and the technological improvements (Wang, et al., 2016). The following are the models that can be used for the knowledge management strategy of the company:
Nonaka’s SECI Model describes the way an organization uses and manages explicit and tacit knowledge available into the organization (Bandera, et al., 2017). Explicit knowledge is a structured knowledge that can be documented, stored, managed and articulated for the purpose of effective use of the knowledge being transferred and learnt. Besides, tacit knowledge is gained through the experiences and can be subjected to personal knowledge that can be sensitive and dynamic. This type of knowledge is difficult to share, manage and transferred containing a lot of human interpretation (Mohajan, 2017). Moreover, Huawei’s explicit knowledge is the knowledge of new equipment, technology and the digital assets held by the company that has the ability to be managed and stored in a coherent manner. Further, tacit knowledge of Huawei is learning of the employees that has been gained through training which the company finds it difficult to manage successfully. This model has four modes in which the knowledge is being conversed which are discussed as above:
Source: (Thornton, 2011)
This process is also called tacit to tacit knowledge sharing. Socialization is considered as the process of knowledge creation in which knowledge is being created through the observation and communication between the communities (Allal-Chérif & Makhlouf, 2016). It seeks to develop a virtual space that provides an opportunity for the communities to interact and share knowledge. Similarly, Huawei has created a communication platform for the increased social communication of the employees and the managerial people. The company has developed an environment in which the communities can have a conversation and are able to transfer and receive the tacit knowledge.
In externalization, tacit knowledge is being shared by use of the concepts of explicit knowledge. It can be done through the use of written documents which becomes the basis for sharing the tacit knowledge and also supports the interactions from which the new knowledge is created (Mohajan, 2017). Similarly, Huawei has been using socialization for the creation of knowledge. The company has been making efforts through the issue of magazine which is Huawei People which is proved to be communication vehicle for the increased sharing of tacit information between the employees and managerial staff.
It means the combination of explicit knowledge in which the knowledge is explicitly gained and utilized explicitly. In this, the knowledge is attained from inside or the outside sources and is combined and articulated in such a manner that it seeks to develop a new knowledge that needs to be acquired (Bandera, et al., 2017). Moreover, Huawei has been using the combination process for the knowledge sharing by the import of the technological system that has facilitated the company to manage the knowledge that is being gained through the use of the new technology and also has a predefined system for the managing of the technology.
When the explicit knowledge takes the form of tacit knowledge then it becomes the part of the knowledge of the individual. Employees within the organization use the explicit knowledge which ultimately help to create the tacit knowledge through the internal process efficiently to improve productivity (Zhang, et al., 2016). Similarly, Huawei has been providing opportunity to the employees to utilize innovation and technology which is the explicit knowledge and use that knowledge to gain the productivity by making it part of the basic knowledge which is specifically required for the daily operations.
Knowledge management maturity model (KMMM) is an engineering approach for attaining the maturity in the knowledge management. It seeks to use a systematic, quantifiable and disciplined approach for the operation and maintenance of knowledge (Kuriakose, et al., 2011). Thus, maturity model of knowledge management seeks to analyze the strengths and weaknesses of the knowledge and also the identification of the development levels into the knowledge management process. In addition, by the application of KMMM in case of Huawei, it can be understood that the knowledge management at this company makes an efficient use of the external knowledge and uses a well-defined engineering approach for the measurement and evaluation of th
Fig: Opinion of people in different job position regarding Knowledge Management
Source: (Bagheri, et al., 2013)
The model divides the maturity level in five levels which are being determined by the key parameters. The key parameters are the specific areas in which the maturity levels are identified in an organization. These key parameters are discussed as above:
This parameter precisely relates to the organizational culture and the strategies pertaining to the internal guidelines that support the knowledge management process. Under it, it has awareness, participation, motivation and rewards and mentoring and success planning (Pee & Kankanhalli, 2009). Further, when these parameters are seen in case of Huawei, it can be said that the company has been using level of maturity to understand the practical level of the knowledge and also makes sure each of the employee is making participation to the activities of the knowledge management. Besides, the company utilizes the evaluation measurement to analyze that each of the employee is benefitted by the rewards and is motivated to participate into the knowledge management process. Also, Huawei has been making the efforts through the mentoring of the activities that can be used in the process of knowledge management and also measures the level of maturity through the various areas.
Under the knowledge management maturity model, the next parameter is the process which defines the policy and strategy of knowledge management that ultimately helps an organization to measure the effectiveness of the process. The stage where the knowledge management plan is being made, the organizations seek to develop a criterion which requires a process to define the level of maturity (Kuriakose, et al., 2011). Moreover, Huawei has been making effective process for the policy and strategy development that will outline the core methods and the main purpose of the knowledge management at the company. Besides, the knowledge management process used by the company for the acquiring, sharing, using and maintaining the quality of the knowledge are the key parameters that will ultimately help Huawei to make the identify the maturity level.
The organizations determine the technological infrastructure as the parameters being used for the determining the level of maturity in the knowledge management. The technological infrastructure includes the network, Artificial Intelligence techniques, Explicit and implicit knowledge management and the technology integration (Cawthorne, 2015). Similarly, Huawei has been using the effective network connectivity as the parameters and also techniques such as content management and collaboration technique for the increased ease of the company to acquire and maintain the knowledge.
Knowledge management is crucial for every organization to improve the efficiency that requires the participation of the employees in order to attain and share the knowledge on a daily basis. The employees can be motivated to share knowledge by the rewards and recognition being used by the organizations. Besides, undertaking of the effective collaboration can be a useful method for the knowledge sharing which can be undertaken by the creation of the atmosphere for the increased communication (Hu & Zhao, 2016). However, the employees of Huawei have been provided with the information and communication channel that can be used to share knowledge and process the meetings. Apart from this, training programs are being held by the managers of the company that persuades the employees to participate into the knowledge sharing process. This is also be undertaken by the effective mentoring and success planning of the company for the increased motivation of the employees.
There is huge role of big data analytics into the knowledge management. Big data analytics gives an opportunity for the organizations to manage the data and store efficiently. It has been part of Internet of Things (IoT) wherein the organizations face the process of data capturing and then goes through the digital interfaces as per the needs of the organizations. Further, the role of data analytics is to automate the process and innovate the findings translating into the actions by the human interventions and the feedback loops (Cawthorne, 2015). However, Huawei has recognized the importance of Big Data and has accumulated the big data integration by the implementation of a platform that provides intelligent data for the knowledge management capabilities (Huawei, 2017). This implementation has ultimately helped the company to manage the acquired knowledge through the facilitation of the quality audits, data lifecycle management and increased data management that has helped the company to solve the issues and problems through the better data governance solutions benefiting in the reduction in the time to be wasted into the long data preparation.
References
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Bagheri, R., Eslami, P., Mirkfakhraee, S. & Yarnjali, M., 2013. The Evaluation of Knowledge Management Maturity Level in a Research Organization. Australian Journal of Basic and Applied Sciences, 7(2), pp. 11-20.
Bandera, C. et al., 2017. Knowledge management and the entrepreneur: Insights from Ikujiro Nonaka’s Dynamic Knowledge Creation model (SECI). International Journal of Innovation Studies, 1(3), pp. 163-174.
Cawthorne, J., 2015. Knowledge Management and Big Data: Strange Bedfellows?. [Online] Available at: https://www.cmswire.com/social-business/knowledge-management-and-big-data-strange-bedfellows/[Accessed 26 October 2019].
Fu, X., Sun, Z. & Ghauri, P., 2018. Reverse knowledge acquisition in emerging market MNEs: The experiences of Huawei and ZTE. Journal of Business Research, Volume 93, pp. 202-215.
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Girard, J., 2015. Defining knowledge management: Toward an applied compendium. Online Journal of Applied Knowledge Management, 3(1), pp. 1-20.
Huawei, 2017. Huawei’s Big Data Analytics Platform Named ‘Most Innovative Data Governance Solution’. [Online] Available at: https://www.huawei.com/en/press-events/news/2017/10/Most-Innovative-Data-Governance-Solution[Accessed 26 October 2019].
Hu, B. & Zhao, Y., 2016. Creative self-efficacy mediates the relationship between knowledge sharing and employee innovation. Social Behavior and Personality: an international journal, 44(5), pp. 815-826.
Kuriakose, K., Raj, B., Satya Murtey, S. & Swaminanthan, P., 2011. Knowledge Management Maturity Model: An Engineering Approach. Journal of Knowledge Management Practice, 12(2).
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Mohapatra, S., Agrawal, A. & Satpathy, A., 2016. Designing knowledge management strategy. In: Designing Knowledge Management-Enabled Business Strategies. s.l.:Springer, pp. 55-88.
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Wang, Z., Wang, N., Cao, J. & Ye, X., 2016. The impact of intellectual capital–knowledge management strategy fit on firm performance. Management Decision, 54(8), pp. 1861-1885.
Wu, K. et al., 2019. Enhancing corporate knowledge management and sustainable development: An inter-dependent hierarchical structure under linguistic preferences. Resources, Conservation and Recycling, Volume 146, pp. 560-579.
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