Evaluation and Recommendation of Knowledge Management Technologies
Introduction
Today, knowledge management (KM) is perceived by many businesses as a source for achieving competitive advantage. For this reason, many firms have been forced to modify the business environment by utilising knowledge and its capabilities (Thompson and Walsham 2004). In most organisations, KM requires technological tools to maintain operational strategies, processes, create, and disseminate the best knowledge to the key employers and other stakeholders. Malhotra (2001) reveals that when enterprises apply knowledge management technologies for decision-making processes, they deliver the right information to the correct people, and at the time the when the information is needed. Moreover, knowledge management technologies aid leaders to accumulate human intellect and experience required for the benefit of implementing critical management decisions (Dalkir, 2013). Therefore, it is essential to evaluate the existing KM technologies in order to recommend the most appropriate technologies that can be applied by organisations to facilitate knowledge management and decision-making (Becerra-Fernandez, and Sabherwal 2015). Since technology advances have eased some of the installation and integration of knowledge management, this paper focuses on recommending and evaluating the right KM technologies for the World Bank, as one of the well-known organizations aiming at establishing effective ways of developing knowledge and experience while sharing it with its customers. The analysis will be laid on multi-criteria decision analysis (MCDA), with the model being used to make the decision on the most suitable KM technology that World Bank can use to process its decision.
Review of the KM Technologies
Knowledge management technologies in many firms constitute the key component of KM systems. KM technologies are intrinsically not different from the concept of information technologies; but KM technologies are more fundamental, as they focus on the management of the knowledge and intelligence processing. Tsui (2005) points out that for competitive advantage and innovation in the businesses, KM technologies support management systems and ensure that managers are positioned strategically to manage KM arrangement, especially ICT infrastructure. On the other hand, Malhotra (2001) argues that technology is just an enabler of knowledge management, as it enables organisations to facilitate the real knowledge, store it, and ensures that experience and knowledge are intertwined (Liao 2003). This means that when corporations want to distribute human intelligence, KM technologies are the suitable platforms that need to be applied. In essence, KM technologies allow managers in organisations to foretell the accurate information to dispense and the people who need that information. Malhotra (2001) proclaims that some of the modern technologies such as database and groupware applications store a low volume of information and other data relating to the people, but the technologies cannot be extensive enough to store human knowledge. For this case, it is evident that organisations need KM technologies, as the system does not only focus on storing human data, but also their intelligence and information that is conceptually sensitive for the businesses.
Dalkir (2013) classifies KM technologies according to factors such as communication, collaboration, learning, employees’ intelligence, and personal tools. Technologies that sustain KM in modern organisations include networking technologies, E-learning technologies, artificial intelligence (AI), and Web 2.0 technologies (Dalkir 2013). In the use of each of this type of knowledge management technologies, it is important for an organisation to note that some if the technologies cannot be offered as standalone software, but most of the technologies serve as a module that can be combined with other technological products. For the case of the World Bank, the firm uses KM technologies such as combining video interviews and hyperlinks for the purpose of documenting and reporting information systematically (Becerra-Fernandez and Sabherwal 2014). As such, the organisation depends on reports and documents to record the knowledge of the recruit and employees who are close to retirement. Although this type of KM technology is beneficial to the company, it is noted that the organisation needs to implement a more useful and fundamental KM technology that will improve the aspect of decision making in every department within the enterprise. The current KM technology at World Bank allows the institution to enhance its communication system and solve many problems that may influence the management decisions without extensive work or responsibilities (Becerra-Fernandez and Sabherwal 2014). However, based on the needs of the company and its goals, it will be essential to consider adjusting its KM technology or even implement a more detailed technology that will manage and store human knowledge in all dimension.
Artificial Intelligence (AI)
The concept of artificial intelligence (AI) technologies includes systems used for knowledge acquisition, case-based reasoning technologies, electronic discussion group, device based simulations, systems set to support decision making, video conferencing systems, and business resources planning technologies (Becerra-Fernandez and Sabherwal 2014). Based on a review presented by Thakur (2012), artificial intelligence framework is substantial to the businesses, as the technology assists organisations to manage, develop, and communicate about their intangible assets, that is, the human knowledge and information. Similarly, Sabri (2011) posits that artificial intelligence (A1) has an essential role in the management of KM because the system gives the organisations an opportunity to have knowledge based technologies that will attain human attributes and replicate the human abilities and experience in ethical decision making. From this argument, it can be said that A1 is a KM technology that can influence the performance of an organisation, in particular when it comes to the aspect of distributing information.
Web 2.0 Technologies
The Web 2.0 technology is the latest emergent form of system that businesses use to support the KM concept and implement decision relating to the aspect of management of human experience and information (Becerra-Fernandez and Sabherwal, 2014; Sultan, 2013). The key examples of KM Web 2.0 technologies include wikis and blogs, and they both serve the same purpose of promoting the control of human knowledge in the enterprises. Web 2.0 comprise applications that organisations can use to facilitate communicating information distribution and collaborating the information being disseminated on the particular website (Alavi and Denford 2011; Levy 2009). The system is essential to KM as it has dynamic content, networked structures, and allows anyone managing human information to do some online editing before storing the information (Razmerita, Kirchner, and Sudzina 2009). Thus, when chosen for management services in the business, Web 2.0 can assist the managers to collect valuable knowledge to enhance that all individuals in the organisation participate in the administration activities.
E-learning Technologies
Radwan (2015) expresses that E-learning has become popular in the organisations due to the rapid growth and development of the communication technology platforms and Internet. Apparently, in the current businesses knowledge and learning cannot be separated. Hence, when organisations use the E-learning technology, they encourage the sharing of the stored knowledge and provide a mechanism to ensure that communication is efficient and happens appropriately (Yilmaz 2012; and Sammour et al., 2008). Therefore, if an organisation use E-learning platform for knowledge management, this will be vital because the system will provide structured knowledge content that will support employees in the workplace.
Networking Technologies
Rollett (2012) writes that several networking technologies support various processes of knowledge management. The role of networking technologies is that the system provides an appropriate infrastructure that is vital to the implementation of KM systems and administering the knowledge that is already acquired by a firm. Ahoorani and Banihashemi (2011) clearly state that networking technologies have significant impacts on the KM planning processes, maintaining knowledge, and assessing knowledge, which might be a challenging task for the organisation using other KM technologies. From this case, KM applications in enterprises can be implemented on the basis of different technologies, but for an organisation that focuses on serving its customers in different markets and maintain development, networking technologies will bring competitiveness, and offer support for business operations (Malhotra 2005).
Figure 1: Different types of KM technologies
Identification of the Decision Criteria
A number of complexities can influence a decision process, especially if the decision relates to a specific problem and organisational context (Zopounidis and Doumpos 2016). In reference to a study by Thokala et al. (2016), MCDA is defined as a theory that covers any decision-making process with complexities or with multiple objectives. In the case of selecting the most prominent KM technology for World Bank, the Multi-Criteria Decision Analysis (MCDA) will be considered with various method of MCDA reviewed before choosing the most appropriate decisions regarding KM case at the World Bank. The MCDA technique analysed in this section are the AHP, MAUT theory, and discrete choice experiments (DCEs).
The Analysis of the MCDA Methods
Figure 2: The Multi-Criteria Decision Analysis (MCDA) approach
Analytical Hierarchy Process (AHP)
This is a critical approach of MCDA aimed at evaluating different criteria for decision-making (Saaty 2008). According to the approach, for people to make standard decisions, it is relevant to identify several alternatives, a hierarchy of analysis criteria, and pairwise comparison where alternatives for the solution of a particular problem are required (Cinelli, Coles, and Kirwan 2014). The AHP technique is important to decision makers, as the model allows them to define the problem, structure the hierarchy of the decision, and construct set of elements that are compared against each other. Despite the advantages of the AHP technique, the model has a weakness in that it requires decision makers to compile hierarchies in different applications and set AHP hierarchy with scores of elements (Ishizaka and Labib, 2009). Consequently, this forces the decision maker arrange these items in clusters so that the components do not differ in any situation.
Multi-Attribute Utility Theory (MAUT)
MAUT is described as the theory of performance aggregation in which the decision maker is required to identify utility functions and weight of different attributes assembled for application in a particular criterion (Cinelli, Coles, and Kirwan 2014). MAUT model has an obvious strength, which is the feature of this method. MAUT allows the decision maker to use the utility functions to measure and evaluate the options that will enhance meeting the criteria or the situation influencing the organisational performance (Dolan, 2010). In addition, the comprehensive literature shows that in the cases where MAUT decision system support is used to implement decision associated with technology, relative success has been experienced (Velasquez and Hester 2013). However, this method is data intensive, and in most cases, data for decision-making may not be available. Lastly, the method has heavy implications in the organisational financial sector, hence making it expensive to sustain.
Discrete Choice Experiments (DCEs)
In this approach, the decision makers choose two hypothetical alternatives that are different in terms of various characteristics such as performance, the outcomes that they will deliver, and the benefits they can offer to the organisation (Cinelli, Coles, and Kirwan 2014). When DCEs is used for decision-making, many people feel that the technique have several experimental benefits to the decision maker and the organisation where decisions are passed. De Bekker-Grob et al. (2010) emphasise that DCEs are better than other MCDA techniques. In this approach, the alternatives in this mode are more realistic, and the choices tasks are not theoretical, which makes the decision outcomes more valid. The method has a weakness, which may influence the result of the decision implemented (Louviere et al., 2010). With no doubt, the hypothetical nature of DCE may make the technique become a disadvantage to the decision maker, as it makes it work better in areas of more investigation concerning the alternative solutions
Table 1: Comparison of the strengths and weaknesses of MCDA methods
| The MCDA methods | Strengths | Weaknesses |
| Analytical Hierarchy Process (AHP)
| · It is simple and flexible to use · It is systematic, hence can be used to institute decisions for complex situations · It can work together with other decision making models such as BSC | · It always involves more than one person · A hierarchy to analyse option is always needed in this model |
| Multi-Attribute Utility Theory (MAUT)
| · Have a utility functions to measure situations · Successful in making decisions relating to technological situations | · The approach is data intensive · It is costly to execute and maintain |
| Discrete Choice Experiments (DCEs) | · Allows the decision maker to choose two hypothetical alternatives that are more realistic · Gives the decision maker valid outcomes | · The model is hypothetical in nature, hence not suitable for immediate decisions · Requires a lot of research on the problem |
The Use of Analytical Hierarchy Process (AHP) for Solving KM Technologies
Figure 3: Justification of the AHP approach for decision-making
For this case, the analytical hierarchy process (AHP) is chosen to make decisions on the KM technology issue due to its extensive advantages. The advantages of using AHP to provide KM decisions include its flexibility, simplicity to use, and strengths of measurement (Velasquez and Hester 2013). The technique is flexible due to the various formats that are available. This shows that the feature of flexibility make the method justifiable, as the aspect of flexibility makes it a supportive method that can be used to pass decision for different users and different situations (Velasquez and Hester 2013). Furthermore, AHP is utilised in collaboration with a Balanced Scorecard (BSC), a context for performance assessment, which decision makers use to rank alternatives and choose the best model to solve the problem. The use of BSC together with AHP helps a decision-maker assess the performance of each alternative, weigh distinct options, measure, and compare them aimed at selecting the prominent solution for effectiveness (Velasquez and Hester 2013).
Conclusion
As this report has demonstrated, knowledge management has become an important area for organisational improvement through integrating technology in the concept, which can easily be adapted by the World Bank. In the competitive business environment, knowledge is a valuable resource that can enhance business success and improvement. Throughout this research, different KM technologies have been analysed aimed at demonstrating some of the key technologies that organisations can use to manage knowledge and transmit it for the advantage of the business. The study also presents the concept of MCDA, as a theory that is applied by decision makers to pass or implement decisions that will strengthen the management and dissemination of KM. Therefore, with different KM technologies for a financial institution that operates in the domestic and international market being available, selecting the most suitable technology will require the organisations to plan wisely, create technologies that will reinforce KM, maintain the KM, and assess the KM for improvement purposes.
Recommendation
Following the evaluation of different form of KM technologies, it is recommended that the World Bank should use networking technologies to run and control knowledge management. The technology can be employed to ensure that information is shared appropriately and tacit and explicit knowledge is stored safely for future use. Besides, networking technologies will help the World Bank to address the real needs in the management of KM. Unlike other forms of KM technologies, network technologies enhance knowledge integration, which is important to the organisation that relies on information from both internal and external corporate. Ideally, the technology will ensure that the company has explicit knowledge available in such a well-organized and formalised form.
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