Managing Knowledge In Organizations: Technologies And KM Audit Essay.

Organisations are engaging with knowledge management projects and strategies to harvest the value of knowledge in order to stay competitive and be innovative. Knowledge management

(KM) is the process of systematically managing individual, group and organizational knowledge.

Research and projects in the field of knowledge management concentrates mainly on finding effective ways of managing this knowledge through social and management perspectives, as it resides in human memories; managing is seen as a human-oriented process rather than one that is technology-based. However, the increasing power and importance of information communication technology (ICT) means that it may now be possible to harness the capacity of such technologies to find solutions that will be of value in managing knowledge.

Identify any THREE (3) technologies used in managing knowledge and conduct an in-depth research on the product by highlighting the following:

• the product’s features,
• how it works,
• which part of the SECI model it supports,
• the advantages and disadvantages,
• how to improve the product.

You   are required    to   perform    a   simple   KM audit    at your   organisation     (or   a  unit    within   your organisation).        The  purpose   of  conducting   this  KM  audit  is   to  understand    how   people    in your organisation    perceive  the  role  of  KM  and  at  the  same  time  enable  you  to  identify   where   your organisation's  strengths  and opportunities   lie  in managing  knowledge.

The KM audit survey   questionnaire    is attached in Annex A.    It is  in divided  into  five  areas:    Process,    Leadership, Culture,  Technology  and  Measurement;   and,  is  based on a scoring system   with  a scale ranging from one to five: (1 = none, 2 = poor, 3 =fair,  4 = good, and 5 =excellent).

Get feedback on your survey from at least five colleagues/  employees.    Their   responses    can then   be averaged and related to the closest number  on the scale for each of the survey   questions.

Discuss the inadequacy  or lack of KM practices in the organisation  with   respect   to the five  KM  audit areas.

Based   on  the  audit   survey  analysis,   discuss   the   recommendations    and   matter concerning     the implementation   of KM in your  organisation.

a. Recommend   a   solution    for   each of the   KM  audit   areas  that   can    help  to   improve    the organisation's   KM efforts  or initiatives.    Discuss your solutions with appropriate examples.

b.Discuss the  barriers   of implementing    the  recommended   KM solutions,   and suggest some   ideas on how they can be overcome.

Part A

Knowledge management refers to the process that looks after and tries to solve the critical organizational problems like survival and adaptation with the constantly changing environment both inside and outside the organization. Knowledge management fundamentally embodies the crucial processes of the organization that in according to solve requires effective compilation of information and data and the receipting capability of information technology mixed with human capacity. Knowledge management essentially is a deliberately created framework of interactions among agents of the organization by which the different components of the organization are managed that participate in the basic knowledge processes for producing a strong and enriching knowledge base of the organization (Wiig, 2012).

As asked in the question, three technologies that are used in managing knowledge are case-based reasoning systems, group decision support systems and artificial neural networks.

Case-based Reasoning System is a method that is constructed in order to record the problems or situations that were previously solved which are in one word known as cases. Case based reasoning essentially is a procedure that indicates one’s intent to learn the tricks of the trade. This system not only inspires a user to learn cases but also rejuvenates the memory organization. Case-based Reasoning Systems essentially refer to a case library attached with a software system that for analyzing and evaluating the cases which are similar in nature along with the information associated with it. The case library that is being utilized in a case based reasoning system consists of a huge number of varied ideas across a vast range of industries and other business functions. Each and every case that is stocked into the case library consists of the competitive environment that is that lies underneath along with the management priorities, environmental conditions, values and experience that is essential in learning the basics of every business. Now a case library when installed with a software system becomes the technology that is used for managing case-based reasoning system (Kolodner, 2014). A software system helps in indexing each of the cases in such a way that a single search initiated by a user will result in a number of cases which are similar in nature. The software is such that the search results that are presented by the system to the user will also display reasons as to why those certain cases were selected. In case the system fails to find the exact case file searched by the user then it is coded in such a way that the system yields cases which are most similar in nature. Adaptation rules are also provided in order to encode a certain adaptation procedure. Sometimes the adaptation of a case may lead to generation of a new case that shall also be included in the case library. The case based reasoning system is primarily created on the basis of cognition-psychology. The concept of case based reasoning system presents the opinion that people essentially are of the nature that they use previously acquired experience in order to react in a particular way. Case based reasoning system is broadly about storing different problems and recording the solution to each of these problems, thus saving them as cases on the basis of experience (Riesbeck & Schank, 2013).

1. Case-based Reasoning System

The term group decision support system refers to a system that is designed in order to assist or aid the managers in making decisions in regards to complex or complicated problems. Decision making by a manager involves much communication with the superiors, subordinates and other stakeholders in and out the organization which occupies much of the time of a manager. However the utilization of a personal decision support system tool will definitely save the time of the manager and help him in taking effective decisions.  However there are certain arguments regarding the proper implementation of this knowledge implementation tool, that is when a decision supporting system is integrated with the context of office communication then only its optimum utilization can be achieved.

A number of pioneer group decision support systems have been built in the past five years. For instance decision room group decision support system that utilize a public screen and personal terminals have proven to be very effective in the process of decision making. These decision rooms have gained popularity in the recent past and have been used by many companies to hold board meetings (Bonczek, Holsapple & Whinston, 2014).

The Group Decision Support System design or architecture depends upon the type or nature of group decision to be taken, the tools to be utilized and also on the availability of communication channels. Group communication can be divided into four different domains namely spatial distance (in which decision makers are in the same location); Temporal distance (in which decision makers are either in the same location or are at different locations and provide their inputs at different points of time); Centralization of control (in which the ultimate decision is taken by the group leader); and Degree of cooperation (in which there is a possible conflict regarding decision making). Therefore the Group Decision Support System has to develop systems that match the criteria as mentioned in each of the above conditions. In addition to this some modern techniques used by the Group Decision Support System are the utilization of artificial intelligence techniques and multiple criteria decision methods.

There are three major categories of Decision Support System (DSS), namely they are Active Decision Support System, Passive Decision Support System, Cooperative Decision Support System and Communications-driven Decision Support System. In case of a Passive DSS it is not able to result in decision making suggestions. It just works as an assistant. The Active DSS obviously has the ability to produce valuable decisions in the decision making process. Cooperative DSS does provide the feature of building a framework where a decision has to be taken regarding several packages of the organization. A communication driven support system refers to the support system in which active communication, collaboration and shared view decision making is encouraged (Galegher, Kraut & Egido, 2014).

2. Group Decision Support System

Artificial neural networks are essentially computer software that has been created keeping in mind the neural networks in biology. A model of artificial neural network is similar to a system that promotes learning and is created by simple units that is imposed into a highly interconnected network. Neural networks are generally categorized as a soft computing technique. Soft computing fundamentally refers to a number of techniques that cover a selected number of fields pertaining to various categories in artificial intelligence (Yadav, Yadav & Jain, 2014).

Artificial neural networks have been used by the companies in various fields ranging from medical diagnosis, assessment of stock market to the recognition or identification of handwriting. For instance, the artificial neural networks have displayed their capability to outperform traditional models that are required for forecasting purposes in regards to the common problems pertaining to business classification and problems regarding predictions. Hence the benefits of utilizing the artificial neural networks in various applications may be unlimited.

In spite of the above advantages there are certain problems regarding the use of artificial neural networks. These problems are that the structural factors that form the base of the particular technique are not stable. Therefore there is much pressure to identify and recognize the trends within the time period of their validity and also the time when they are no longer valid. Another major problem is that the artificial neural networks though have proved it to be an efficient tool of forecasting in comparison to other popular forecasting tools like regression analysis but the entire process of decision making incorporated by it is a difficult task to understand and verify. Companies refuse to take up any tool for forecasting which does not have a clear model that can be easily understood and verified. In case of many applications, it is recommended to obtain knowledge from trained neural networks for the purpose of users so that they get a better understanding of the problem. If the forecast produced by a neural network could be simply understood and further explained, then this technique could have been adopted by more and more firms. This would definitely lead to well deserving acclamation for this technology and would lead to increase in the number of applications of the artificial neural network technology.

The only solution to the above mentioned problem is creating a descriptive neural network. This task briefly can be achieved with the help of three steps. The first step is that a forecasting model on the basis of neural network has to be created with the help of neural network construction system. Secondly the required rules have to be obtained from the trained neural networks and thirdly whatever the hidden rules of forecasting have been obtained in the previous step have to be incorporated in the framework that is produced by the neural network construction system in order to create a descriptive neural network. Descriptive neural network is an artificial neural network that constitutes of enough descriptions of the particular knowledge of the area for which it is applied. This is done in order to ensure that not only forecasting is produced but also the justifications for such forecasts can be explained (Daniel, 2013).

3. Artificial Neural Networks

Now to link the application of knowledge management with a particular product, the approach with which the particular task is carried out is known as content centered approach. In order to develop a product centric approach towards knowledge management at first the separation between the knowledge imparted and the person creating or making it has to be stopped. The best practice is to create a repository in which a company can store its best practices and documents supporting it for further reference in the future.

The product that is going to be discussed in this study has been developed with the help of content centered approach and helps a particular organization by making the process of decision making utterly simple and quick. The concerned product has the name as Knowledge Framework as it aims to break down the huge repository of knowledge collected by a particular organization into small units of relevant data. The product aids in all the processes of the organization including audit. An auditor can easily carry out the audit of the firms by going through the Knowledge Framework as he will get all the relevant information in a clear and concise way. It stands by the recognition of the strong points of the organization that is the areas of success of the organization and the related business processes, the major or noticeable points that are embedded these business processes in which decision making is required, the individuals that utilize content and the content itself.

The portfolio of content upholds the specific nature and types of content that is required by the organization to pack and deliver to entities who can treat them as knowledge. This documents which are referred to as packs may include specifications of products, quotations, case studies and other relevant information. The product recognizes and displays the fact that the company has enough experience and expertise and must store it with utmost care for future reference. The Knowledge Framework also has the ability to identify potential areas of investment in order to improve the knowledge base or intellectual capital of the organization (Sultan, 2013).

The Knowledge Framework product as such utilizes all the three techniques of knowledge management. It promotes the case based reasoning system by creating a repository of all the incidents or events that had been damaging for the organization and the process in details in which the organization could recover from the situation. It not only records the details of the case and the solution but also the environment both inside and outside the organization at the time of the calamity, the individual reactions by the higher authorities and the projected loss due to the happening and the actual loss that was incurred. An added feature of the product is that the product can search the database management systems and data warehouses in order to link a particular case or situation with similar occurrences and suggest the user relevant solutions along with the solution that is already provided in the case (Brodie & Mylopoulos (Eds.), 2012).

Part B: KM Audit

The product Knowledge Framework also integrated group decision support system in its features. This means that the product is also featured for group decision making. The product promotes communication between different components and contenders of business and comes up with relevant suggestions and decisions. It enables an user to create a personal database so that the user can access the information collected by the tool that is required by the user to take a potential decision. This creation of a personal database also helps the user to store important and relevant information that is required for effective decision making.

Lastly the product utilizes a neural network for creating and linking this vast data. It creates a fine framework of information that is ultimately used for the purpose of creating a forecasting or predicting tool. Therefore the product also comes with the feature of forecasting by utilizing all the stored data and information.

The SECI model is fundamentally a model that lists down the steps in which knowledge that is of explicit nature and tactic can be converted into organizational knowledge.

The product supports both the tactic to tactic (Socialization) dimension and the explicit to explicit (Combination) dimension of the SECI model. This is because the tactic to tactic dimension essentially refers to socialization that is interpersonal interaction between different stakeholders of business. Knowledge Framework supports such communication and obtains important information by this process which is essential for case based reasoning system. Secondly the product also supports the combination or explicit to explicit dimension or part of the SECI model. This is because it does use databases and communication networks that are a fundamental part of neural networks. Knowledge framework as a tool obtains information from both inside and outside the organization for making accurate forecasts, which is again enough evidence that it represents the combination part of the SECI model (Jones & Sallis, 2013).

The advantage of the product Knowledge Framework lies in the fact that it utilizes all the three models of knowledge management. Thus it is a perfect integration of knowledge management both individually and on the basis of group decision making. It gives a particular user the liberty to browse through a library of cases and even suggests similar cases from large scale databases. It creates a intellectual network where an user can share and store his data relevant for the process of decision making.  Due to the fact that the product also utilizes a neural network it can also be used for the purpose of forecasting.

Survey Questionnaire and Scoring System

The only disadvantage of the product lies in the fact that the user interface used by the product is not developed as such. With a handful of features to be used, the user interface of the product is somewhat mechanical in nature and does not give the user the ease of using it. The layout or the outlook of the product is very timid and does not highlight the features that it holds.

The only major area where the product can be improved is the user interface of the product. The user interface should be re-designed and made in a way so as to attract the users. The timid outlook of the product should be changed and more importance should be given to the vast number of features that it holds. The design of the interface should be such that all these features are highlighted at the very starting page of the application. This will not only inform the user of the vast resource that the product contains but also the techniques that it uses in order to enable such features. Another small issue is that the general speed of the product should be improved, that is it should be a bit fast so that the user can get his desired information in no time.

Knowledge management audit was performed in the organization. A survey was conducted from five employees in the organization. Their responses were averaged and then it was related to the closest number on the scale for each survey question. The questionnaire has been provided in the appendix. The responses as averaged were recorded as follows.

P1 – 2

P2 – 1

P3 – 3

Total  - 6

Comments for KM Process – The organization is still not prepared for the implementation of knowledge management as the score accounts to be really low.

L1 – 3

L2 – 2

L3 – 2

Total – 7

Comments for KM Leadership – There is not enough leadership in the organization to motivate the staff of the organization and convince them about the implementation of knowledge management in the organization.

C1 – 4

C2 – 2

C3 – 1

Total – 7

Comments for KM Culture – The culture that prevails in the organization is not appropriate for the implementation of knowledge management. This is because the employees of the organization have no accurate information as to what is going to happen and they hold all sorts of misconceptions regarding knowledge management. Moreover no initiatives have been taken on the part of the management to clear the doubts of the employees. No prior induction was executed as to what is knowledge management and how it will help the employees.

Recommendations for Improvement

T1 – 4

T2 – 4

T3 – 2

Total – 10

Comments for KM Technology – The technology that is planned to be implemented in the organization for creating a knowledge management database is reviewed and fits in perfectly with the organization. Only rigorous training has to be provided to the staff in order to make them utilize the repository of knowledge.

M1 – 2

M2 – 2

M3 – 3

Total – 6

Comments for KM Measurement – The measurement of the knowledge management to be implemented in the organization has not fetched satisfactory results. No proper step has been taken in order to link the knowledge with the financial results. The company has been using the SECI model for measuring the impact of the process but has developed a research base for the achieving the desired results.

Total Score for all the five sections – 36/75

Comments for Future Action – The organization should be more concerned with the employees as to how they are taking this change in technology and should be more focused about making the technology more employee-friendly. Tools to measure the implementation of knowledge management and ways to link it with financial results should be developed. But most importantly, the management should make sure that the employees are interested and energetic about the technology and are eager to use it. Monetary incentives on the basis of performance in regards to the usage of the new technology could result in initiative and interest among the employees.  

The key problem as identified from the knowledge management audit is that the employees those who have been delegated with the task of implementation of knowledge management in the organization have not been able to execute the task properly. This is because they have been in the fear of being over burdened with work. The thought of compiling data from every database possible and creating a knowledge source for further reference seemed to be huge for the employees. Another major issue was that the implementation of knowledge management was not included by the management in the list of company goals which reduced the importance or urgency of the issue and was taken for granted by the employees. Moreover there was not enough focus upon the non technical aspects of business. Introduction of hard core technology had already de-motivated the employees as they were of the opinion that the management now placed their trust more in machines than in human capacity and on top of that no training or induction as such was conducted in the organization that could clear the misconceptions of the employees. The team assigned with the specific task of knowledge implementation was also not competent enough, thus could not find the exact areas where the employees needed further knowledge. Even the content managers needed further training in order to carry out their tasks properly.

Barriers to Implementation

The recommendation for the above mentioned issues are that at first the organization should have conducted a formal induction regarding the introduction and utilization of knowledge management  and that it was being done in order to aid the employees. Moreover skilled training and bonuses should also have been declared in order to motivate the employees and in increasing their interest regarding the implementation of knowledge management. Secondly the implementation of knowledge management in the organization should have been included within the list of company goals so that the issue receives due importance and urgency. The team assigned with the purpose of implementation of knowledge management in the organization was not skilled enough. Moreover the members were scared of the huge burden of information that they had to collect and compile in order to convert it into a base of knowledge. Therefore it is evident that they lacked enough training and knowledge about the entire process which hampered the entire framework of implementation of knowledge management in the organization. Similarly the content managers should also be skilled enough in order to execute the task in an accurate and error free way (Liebowitz & Frank (Eds.) ,2016).

As mentioned in the question the five leadership areas in the knowledge management audit are Process, Leadership, Culture, Technology and Measurement.

The process pertaining to the implementation of knowledge management in the organization was very poor. This is because it was unplanned in nature and lacked enough thought in regards to the process of implementation. This is evident from the fact that the first step in the implementation process that is orienting the employees and other stakeholders of business with the knits and grits of knowledge management and how it would help the staff in executing the daily operations of business and create a strong foundation of knowledge for the organization was not explained at all. The sudden implementation of knowledge management without prior preparation hampered the entire process.

The leadership in the firm was not up to the mark. This is because of the fact that though the management being a leader thought in accordance with the interest of the company and took the decision of implementing knowledge management, it did not delegate the responsibility well to the lower hierarchy of authority. As a result of which the employees were demoralized with the decision of implementation of knowledge management and thought that the management was going to transform the entire work force mechanical. Had there been enough leadership in the company then the employees could have been explained by their leaders in a detailed way and remove their misconceptions regarding the implementation of knowledge management in the organization.

There is no major issue regarding the culture that pertains in the organization. All the employees of the organization believe in achieving the goals or objectives as listed down by the company and believe in sustainable growth of the organization rather than short term gains. The only issue that persists is that the workforce is focused upon achieving the goals as listed down by the company that they pay no heed to other goals. Therefore the management should make it a point to include the implementation of knowledge management in the list of company goals so that the task is achieved with due care and sincerity.

The technology used by the management in order to implement knowledge management in the company is a type of software that blends both the features of group decision support system and artificial neural networks. The only disadvantage is that the application does not include case based reasoning system. Whatever may the case be enough training is required on the part of the employees who will be using the technology and should be imparted with such level of training.

The measurement of the knowledge management initiative by the organization has been done with the help of SECI model. The results have not at all been satisfactory due to the above mentioned issues and can only be recovered when these issues are mitigated with the stated recommendations (Rollett, 2012).

The major barrier that lies in the path of successful implementation of knowledge management is the management of the organization. The management of the organization over emphasizes on the part of knowledge management and less on the part of people who will be utilizing it. The heart of an organization lies in its employees who run it. Therefore enough attention should be given to the employees, their reactions to the introduction of a new technology in the organization and whether they are happy with the tool of improvement in the business. To be more simple the management should stop over emphasizing on the aspect of implementation of knowledge management and create an atmosphere that the employees feel comfortable with the new change and as a matter of fact they welcome it. The management should make it a point to convince the employees that the implementation of knowledge management will definitely make the work easier for the employees. The proceedings as to how their work load will decrease, how they will get access to crucial information as well as solutions to organizational problems in a single click should also be explained to the employees. The management in order to promote the concept of utilization of knowledge management in the company should also design incentive or bonus systems that will encourage the employees to be interested in the process of implementation. To be more precise the concept that knowledge management will ultimately aid the employees should be sold by the management in one or the other way (Holtshouse, 2013).

References

Bonczek, R. H., Holsapple, C. W., & Whinston, A. B. (2014). Foundations of decision support systems. Academic Press.

Brodie, M. L., & Mylopoulos, J. (Eds.). (2012). On knowledge base management systems: integrating artificial intelligence and database technologies. Springer Science & Business Media.

Daniel, G. G. (2013). Artificial neural network. In Encyclopedia of Sciences and Religions (pp. 143-143). Springer Netherlands.

Galegher, J., Kraut, R. E., & Egido, C. (2014). Intellectual teamwork: Social and technological foundations of cooperative work. Psychology Press.

Holtshouse, D. K. (2013). Information technology for knowledge management. Springer Science & Business Media.

Jones, G., & Sallis, E. (2013). Knowledge management in education: Enhancing learning & education. Routledge.

Kolodner, J. (2014). Case-based reasoning. Morgan Kaufmann.

Liebowitz, J., & Frank, M. (Eds.). (2016). Knowledge management and e-learning. CRC press.

Riesbeck, C. K., & Schank, R. C. (2013). Inside case-based reasoning. Psychology Press.

Rollett, H. (2012). Knowledge management: Processes and technologies. Springer Science & Business Media.

Sultan, N. (2013). Knowledge management in the age of cloud computing and Web 2.0: Experiencing the power of disruptive innovations. International journal of information management, 33(1), 160-165.

Wiig, K. (2012). People-focused knowledge management. Routledge.

Yadav, J. S., Yadav, M., & Jain, A. (2014). Artificial neural network. International Journal of Scientific Research and Education, 1(6), 108-117.