Knowledge Management and Decision Making
Introduction
In today’s turbulent and ever-changing business market, knowledge management is of undeniable significance in decision making. Contrary to the past where companies hired consulting experts to offer assistance in settling for the most profitable decision, the firms have replaced this trend by hiring knowledge managers to support decision making. Knowledge management (KM) can best be described as the approaches and practices that are utilized by the contemporary organizations in capturing, storing as well as distributing knowledge for the growth of the organization uniformly. As stated by Dalkir and Beaulieu (2017) KM has fundamental effects on organizational decision making. Knowledge management specialists, therefore, play a crucial role in guiding decision makers in regard to the situation where they are supposed to make complex choices in the business sector that is not only characterized by operating risks but also doubts and conflicts. The whole decision-making move is grounded in the forecasting outcome which is derived knowledge management operations. Thus, KM is an essential aspect of strategic decision making. According to Moon et al (2015), knowledge management is not the primary problem that is faced by organizations today but the primary challenge lies in the absence of the needed expertise to managing the existing knowledge in guaranteeing real decisions. This report presents, a description of the selection criteria regarding the expertise of candidates with respect to KM and uses a multi-criteria decision analysis (MCDA) method to provide a selection model.
Knowledge management is challenging since specialists are required to acquire knowledge that is related to data, information and skills gathering from other people in order to transform the acquired insights into knowledge that would, in turn, play part in improving the decision making process in general (Huynh et al, 2015 p.16). Information technology offers undeniable assistance in regard to the decision making, by enabling by creating a wide platform where knowledge can be acquired. However, technology on its own is inadequate as people are categorized to be the primary determinant of the success of the process. This as a speedy and accurate process offers a more improved ground for the selection of the best choices since decision makers are able to make informed judgments. In KM specialist selection stage decision making should mainly be grounded in skills and experience. Knowledge management role necessitates specific skills needs time in order to be obtained. Proficiency in gathering and distributing information and its application in decision making is the base of an effective KM specialist (Karna, Supriana & Maulidevi, 2016). Skills and abilities are highly required for an incorporation of adequate knowledge that is needed for the position. This can also incorporate more conceptual skills which are challenging to assess based on their status such as strategic planning, relational and communication skills. However, several aspects such as price, flexibility, and speed must be accounted for in selecting the most suitable KM expert for the company.
Job selection represents one of the most important decisions that holds direct impact on the organization, employees, and stakeholders. There are some criteria that should be considered in job selection and the complexity of these decisions leads to a Multi-criteria decision making (MCDM) issue. Based on García-Peñalvo and Conde, (2014) KM is all about communication in the acquisition and distribution of knowledge in general which is done orally or in written formats for all the needed reports. In addition, adequate managerial competence in the inclusion of the capacity to come up with actionable strategies and create priorities within the existing resources that best encourages work consistency and efficiency are required. This means that the expert should be possessing authoritative organizational abilities that incorporates flexibility of individualized operations. It cannot be denied that organizations are fully dependent on reliability particularly when it comes to making decision based on the fact that this act determines the failure or success of the firm (Dalkir and Beaulieu, 2017). Knowledge is acquired from the learned skills and exposure to actual operation. Knowledge can best be described as the accommodation of understanding that is achieved via education and learning exposure in regard to the specific role. This means that the best candidate for the position is not only the individual with adequate experience but one that has the highest ability to apply the acquired skills in enhancing wellness. The KM job is highly specialized and therefore, it necessitates detailed and comprehensive knowledge within the narrowed scope of gathering and distributing information (Karna, Supriana & Maulidevi, 2016).
According to Riaz and Khalili, (2014) a KM expert acts as the consultant within the organization to whom the decision makers seek information from in order to make informed choices. These selection criteria that necessitates the presence of skills and knowledge is guided by the notion that, the candidates that hold less familiarity in regard to their role are incapable of doing and fulfilling the requirements of their positions. There is, however, an existing uncertainty and conflict in regard to choosing a knowledgeable and skilled expert over an experienced one. In that, all these aspects are necessary for developing a more productive expert given that decision making and management processes are very crucial in operations (Tayali and Timor, 2017). Work-related skills and competence are required for an individual to fully commit and succeed in this position. In this context, a Multi-criteria decision is required in addressing the selection problem and enhancing productivity through solid and reliable decision making (Tayali and Timor, 2017). Return on investment is the classic decision criteria that best applies to the case in consideration of a number of aspects such as cost, flexibility and risk level. In that, the company needs to consider the needs of its stakeholders as the prime priority. The objective of its operations is to increase the general earnings of the stakeholders based on their investment. In this context, if the KM role is one that will lead to increased expenses with reduced gains then it means that the risk regarding costs is high. The implementation of the approach is one that is flexible and ease on the ground that the roles can be changed based on the needs of the organization. High investment gains align directly with low costs thus, the selection should best consider the price of the expert and made direct comparison of the expected gains while weighing the anticipated risks such as cost (Riaz and Khalili, 2014).
Siemens (2014) asserts that MCDA is a challenging decision-making instrument in today’s business world. Due to the complexity of global business setting, some MCDA strategies and models have been recommended for organizations that seek to settle on the most probable decisions that strategically position their operations. MCDA methods are used extensively as the most accurate and feasible decision methodologies across different industries ranging from manufacturing, trade, energy and so on (Brigui-Chtioui & Saad, 2011). These techniques lead to the improved decision excellence by generating the development of competent, clear and coherent organizations. Some of the most used methods include multiple attribute value theory (MAUT), Evaluation Matrix (Evamix) and Analytic Hierarchy Process (AHP) (Cinelli, Coles and Kirwan, 2014).
To begin with, MAUT method, as indicated by the term the approach is dependent on distinct utility operations which directly permits the assessment of the involved risks outcomes (Brigui-Chtioui & Saad, 2011). The risk is expected to arise from two different modes that are the expected outcome or value. MAUT is therefore beneficial in weighing risks that are related to a given decision. It involves undeviating inquiring of the decision makers based on their choices by allowing them to assess the values over the risks. Shareholders engagement in decision making is not only a necessity but also essential. MAUT offers a transparent path in reference to the risks and benefits indicators based on its capacity to handle different problems at the same time (Tayali and Timor, 2017). The challenge is, however, derived on the ground that the method generates much information thus creating complexity in the evaluation. The methods allow the assessment of risks and doubts directly on the ground of the developed utility theory. In other words, MAUT is involved in the evaluation of risky options and makes comparison with the decision maker’s preferences to the satisfaction of the company. In making different choices information is needed and for this method the utility benefits create the foundation for these decisions (Tayali and Timor, 2017). Based on the operation of the method, it is rather evident that MAUT is not suitable for assessing external issues on the ground that much time would be needed to generate the needed choices and justification for every choice (Brigui-Chtioui & Saad, 2011). The macro environment is particularly complex which makes the approach unsuitable.
On the other hand, the AHP method is concerned with the creation of appropriate hierarchies, determining key priorities and evaluating logical consistency within the decision making setting (Ginevičius, Kalkaska’s & Kazokaitis, 2011). In regard to hierarchy’s construction, this involves gathering information during knowledge elaboration and ensuring that the human mind acknowledges the relations. Since humans are incapable of processing information simultaneously the approach helps in simplifying data which is part of the logical concepts. Therefore, hierarchies are valuable in assisting individuals in settling for the most constructive decisions. These hierarchies do not necessarily refer to levels but the breakdown of information (Marttunen, Lienert and Belton, 2017 p.15). AHP pairs different elements for comparison and creating priorities based on their importance. This arrangement is based on the consistency, values and the associated risks. The AHP can also consider the reliance amid different evaluation approaches by the mode of constructing the most fitting hierarchies. If the choices are limited they are placed on locations based on their needs while connecting them to the highest hierarchy. On the other hand, if the preferences regarding the selection criteria are unlimited the hierarchy is then placed within the decision-making issue (Madžar, 2015). All the identified elements represent an opportunity for comparative evaluations of the existing pairs which help in highlighting the significance of an element over the other. This is then finalized through alternatives evaluations.
AHP supports stakeholder’s engagement in decision making. Different social participants are also encouraged to play part by offering their opinions. This leads to the comparison of different preferences judgment which in turn expresses the observation point for all the stakeholders and helps in gathering their preferences and ideas (Madžar, 2015). It becomes possible also to promote consistent communication amongst the players. This is because they are all required to work collaboratively in the quest of settling for a more beneficial assessment criteria and aims while highlighting the significance. This approach is flexible and easy to modify given that it can be applied in extensive decision-making issues at all levels of operations. In different settings, the use of comparative pairs is achievable between different criteria’s on the ground that ranking and categorization is grounded on importance (Khondoker et al, 2014). In this context, different strategies combination with AHP is achievable. AHP permits the utilization of direct and secondary information which has been useful in making sound and fast choices without having to compare.
Evamix mainly involves the assessment and evaluation of the existing alternatives (Ginevičius, Kalkaska’s & Kazokaitis, 2011). The highest ranked alternative acquires the position on the basis of dominance and performance. In that, different criteria’s are evaluated within the matrix based on their performance which is achieved when the associated gains are higher than the expected risks. The decision-making process is thus, focused on assessing the applicability and use of all the systems. This implies that the preferences of the decision makers are evaluated uniformly which leads to the transformation of knowledge into more extensive basis. Ordinary and cardinal weights are assigned to the alternatives after the evaluations (Madžar, 2015). If the alternative is feasible and effective with regard to fulfilling the needs of the organizations it is categorized under the cardinal rank. The evaluation matrix fully facilitates stakeholder’s participation by permitting contributions from different social players in weighing the options which are combined by applied in demonstrating distinct viewpoints ranging from economic to social concerns as raised by the stakeholders. It, therefore become easier to promote communication between the involved players on the ground that they are all required to work collaboratively in the development of goals and action plans the method is mainly used to deal with different decision issues at a range of geographical positioning (Brigui-Chtioui & Saad, 2011). The approach is mainly applied in regional as well as urban development which acts based on alternatives scale standardization to establish the most suitable strategy for a given role.
With respect to the selection of a KM specialist for the organization, the most suitable MCDA method for decision making is AHP (Wątróbski and Jankowski, 2015). This is because for the selection complexity AHP is the most effective decision-making instrument. The intention of AHP is to assist individuals or decision makers within an organization in settling for the right choices even in demanding situations. A feasible decision can be established through the use of prescribed evaluations based on outwardly concrete decision options. This method works on the placement of value to the suitable criteria with the objective of settling for the best choices and development of solutions. In other words, AHP is a statistical and psychological method which challenges the decision makers to evaluate their choices based on the choices importance and value. The methods permit the participation of stakeholders based on its capability to balance views and evaluations outcomes from different grounds. AHP offers understandings in regards to the rationale behind several decisions (Ginevičius, Kalkaska’s & Kazokaitis, 2011). With more people involved in the selection process, this means that making decision in regard to the best choice is difficult given that people are entitled to distinct views.
AHP can, therefore, be applied in creating transparency throughout the process which helps in settling for the best options. In turn, these aspects offer more support to the end decision. With abstract choices in regard to KM specialist selection, AHP is more applicable and feasible given that it creates grounds for comparison. In addition, unlike all the other MCDA methods which only seek to offer rational choices, AHP is objective in nature (Stefanović et al, 2016 p.152). The criteria are one that is grounded on specific, achievable, relevant and feasible solutions. In that the method mainly challenges the decision makers to weigh the existing options not only on the basis of value but also while considering their achievable nature. In this context, every member is fully informed of the position’s requirements and its feasibility when making decision. By weighing the risks and values the actors will thus settle for sound choices (Brigui-Chtioui & Saad, 2011).
In conclusion, it is evident that expert’s selections represent one of the most fundamental aspects of carrying out a business in the contemporary world. In this context, there is a need to incorporate MCDA in solving decision making complexity and uncertainties. In that where a number of individuals are involved there is without a doubt a possibility of generating more issues which in turn affects efficiency. Knowledge management is one of the most beneficial business aspect given that it influences the decision made. Decision making using AHP for the KM expert selection is the most valuable since it provides insights as to why certain criteria are rational over another while encouraging stakeholder’s participation.
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