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Mgt602 Entrepreneurship Management Answers Assessment Answers

Describe the transition from information driven decision making to knowledge creation in the development of Knowledge –Based Decision Support Systems (KBDSS ) using three technologies

  • Technologies for knowledge modelling and representation; for reasoning and inference; and making use of web-based systems
  • Taken together, advances in service systems have contributed greatly  to the knowledge economy with reasoning now provided through thelearning facilities of Artificial Intelligence (AI).
  • AI, together with the group decision support services provided by GDSS, can allow knowledge to be captured through communication exchanges between individuals and between units, organisations and the external environment.

Knowledge sharing can be seen as as the sharing of understanding of systems, processes and workplace relations through individuals’ stories and their contextualizing of those stories through personal experiences.

  • Importantly communication patterns operating within, and between networks, such as online discussion forums can be identified and explored using social network analysis.
  • Communication patterns which, once identified, can provide insights into individuals, groups online participation rates.
  • Social network analysis can be used to look deeper into the underlying network structure of individuals in discussion forums.
  • Analysis can determine the relative participation of individuals and through rates of connectivity, relationships operating between them.

Answer:

Introduction

The paper demonstrates the evaluation of the usefulness of the decision-making tools that reflects upon the decision-making style. It will provide with the comparison as well as contrast that critically evaluates the decision-making process and style. It will evaluate and examine the decision-making system and techniques that will engage group decisions and enhance sustainable results. It will critically examine emerging technologies and tools for the effective decision-making process. The virtual team works together to facilitate innovative or creative ideas to bring success to the organization. Thus, the paper will focus on organizational change management skills and decision-making that will improve group functioning.

Discussion of Scenario A

There were 800 people in the mid-sized Australian organization where 80 people were widely involved in the Research, Development, and Design (RD&D) work. The primary objective of the organization is to recognize the creative as well as innovative capabilities of their RD&D staff members. To distinguish their business from their competitors, the firm makes wise use of the capabilities of their staff (Duan & Xiong, 2015). To research the creative or innovative capabilities of RD&D staff members the company has developed a project that is carried out by the employees. The organization will get the significant information of the staff members that could be accessed by using email-based communication among the staff members of RD&D. The company has mapped the rates and directions along with email connectivity of the employees. The organization has developed a map of the unstructured information of RD&D that is provided in the emails of the employees. The map provides a node that recognizes detail information of the employees along with their workstation in which they are logged in (Fan, Lau & Zhao, 2015). The number of lines into or out of the nodes indicates the intensity of traffic and the lines that connect different nodes and directions indicates the rates of connectivity. To prepare tacit unstructured information and to discuss the information provided in an explicit email, the project seeks an interpretation of the decision made. The RD&D staff members will be provided with different meeting rooms with a facilitator, visual aids and a group support system.

The project will make wise use of communication technology system that will support the meetings of the staff members, and in turn, it will help the staff members to get data implicit or explicit information. To develop this process the resources that are being provided in each meeting room will provide instructions to the group process (Hashem et al., 2015). It will also ensure that each staff member present in the meeting room must have cooperation. Also, it will help the groups to develop explicit information.

The following table presents the nodes and the clusters of six staff members of RD&D department who has been chosen to participate in the meeting.

SR/No.

Clusters

Nodes identified per cluster

1

40

4

2

32

7

3

38

3

4

04

4

5

19

4

6

75

4

The table below presents the key members of clusters those who are chosen to attend the meetings. It shows the staff members of RD&D based on the meeting rooms and the cluster.

SR/No.

Rooms

Clusters

Key Members Selected to Attend Meetings

1

A

1

Corporate Research and Development Staff

2

B

2

Research and Development staff

3

C

3

Technician

4

D

4

Senior Staff, Research and Development

5

E

5

Senior Research and Development staff

6

F

6

Production team leader

To combine some clusters of different components, the clusters help in connecting a massive number of objects. Every cluster those who are similar to each other are chosen by grouping a set of numbers in the same group (Hartmann, Zaki, Feldmann & Neely, 2016). It is entirely based on segmenting customer portfolio that is again based on various demographics, transaction behavior, and behavioral attributes. Hierarchical techniques are determined to be the standard technique that is being used for the clustering of the nodes. In this method among the set of clusters, the endpoint is different while the numbers present in every cluster appears to be the same. By recognizing the capabilities of the staff members in the RD&D department the clusters and nodes were chosen that differs from the business of their competitors (Lee, Ardakani, Yang & Bagheri, 2015). The emails of the staff members provide with the unstructured information based on their capabilities. To identify the capabilities, several meeting rooms have been arranged for the meeting. Communication technology system will be used by the project to organize the meeting. This will eventually help in selecting the particular key members those who will attend the meeting.

In the particular analysis dangling nodes has not been used because dangling nodes is used to remove or delete a node from the cluster (Bello-Orgaz, Jung & Camacho, 2016). Whereas, in the analysis, no particular clusters has been eliminated because eliminating a cluster would make it difficult to select the key members. Instead of dangling nodes, unconnected nodes have been used to choose the key members. The map shows two unconnected nodes that indicates the members those who are not selected for the meeting. As the members are not chosen to attend the meeting hence there capabilities will not be identified (Hazen, Skipper, Boone & Hill, 2018). In this particular scenario making use of unconnected nodes makes it easier to select the staff members of RD&D department who will participate in the meeting.

The primary purpose of selecting the clusters is thoroughly based on connecting the objects of a similar nature. While doing the business analysis the two key members those who are chosen to attend the meeting are Corporate Research and Development Staff and Research and Development staff who are nearer to each other. To some extent, the job of both the departmental staff is the same (Marjani et al., 2017). Thus they are selected for the meeting.

Discussion of Scenario B

The Leximancer Concept map provides with the significant relationship among the concepts such as lines and placement and importance of the concepts. Management, open, customer, communication, focused and results are the six themes that are presented within the map. Each theme is presented inside a balloon. All the balloons except focused are interlinked with each other that demonstrates the act that all have some relationship with each other (Sivarajah, Kamal, Irani & Weerakkody, 2017). It is observed from the map that all the themes are connected with the concept of communication. It clearly states that to execute the business successfully there must be proper communication between the management and field staff. Thus, every organization communication plays a significant role in executing the business effectively to proceed with the work smoothly.

In the particular scenario, the Leximancer concept analysis is entirely based on wide service-based Australian company that possesses 200 service staff that reports to 40 managers all over ten service centers. The service level support provided by the employees is recognized by the company. To implement this process, the organization have developed a project that detects the vital support provided by the management and the roles and responsibilities of the staff members (Huang, Wang, Zhang & Zhang, 2017). Thus, a telephonic and face-to-face interview of the field staff and the management have been organized, and the results of the interviews have been recorded. It has been analyzed from the map that the relationship amongst the staff members and managers must be strong to deliver efficient work to achieve the target. The Leximancer Concept analysis presents the fact that the concept focused in not connected with any of the other concepts. This means that the management of the organization is not focused on their work to achieve the organizational goals. This will greatly affect the organization to get better results as expected (Güçdemir & Selim, 2015).

The Leximancer Concept analysis presents the fact that the field staff members of the organization communicate with each other with the help of sending emails, interacting through the training process, creating an open atmosphere and using a simple language. All these factors greatly help in making appropriate decisions related to the business that will help in getting better results. The communication is entirely related to the execution and the procedure of the business to achieve the goal. It is noticed that communication amongst the managers and customers are not of a high level where the communication is usually based on the needs of the customers. To present their demands and provide relevant feedback based on the requirements the customers are not connected directly with the managers (Bansal, Sharma & Goel, 2017). Also, it is observed that to provide the customers with appropriate resources support availability plays a significant role. Support availability helps the customers to facilitate with the technologies as per their needs. It provides profitability to the organization if it fulfills the needs of the customers. Hence, the map presents the fact that there is a high level of support provided to the customers.

On the other hand, it can be seen that there is proper communication amongst the field staff members and the organizational management which is determined to be open. The management team and the field staff members discuss the key issues openly to make an appropriate decision related to the project. It is acknowledged that the decision-making process will be greatly affected if proper communication between the managers and the field staff members does not take place. Hence, a high level of communication results into proper execution of the business activities. The map presents the factor that communication strategy is greatly linked with the customers that will be beneficial for the company to develop its business effectively. Therefore, communication is regarded as a channel for transferring knowledge and exchanging information between different staff members (Choi, Chan, & Yue, 2017). The role of the managers is determined to be vital as it comprises of some significant factors such as leading, planning, organizing and controlling. Based on the analysis the managers are constantly focusing on satisfying the requirements of the customers. Thus, to satisfy the needs of the customers the field staff members and the managers must have a mutual understanding related to the incentive payment system. It is because the managers are not at all ready to offer incentive payment system due to a shortage of funding capacity to the customers.   

Support availability has proved to be beneficial for the organization as per the results. It is essential for an organization to have a better support system to execute the business accurately. According to the results, better support has been provided to the customers as well as the staff members that will enhance profitability. It is also acknowledged that there must be a change in the customer service management to execute greater openness. This particular change helps in making an appropriate decision to get outstanding results. Therefore, to make better decisions based on the needs of the customers’ customer-service communication is significant.

Recommendations

The management team of the organization is highly recommended to execute its work effectively to attain the organizational goals. It is important to select the clusters by analyzing the fact that which nodes are nearer and connected. The organization could exceed with its process of choosing the key members who will participate in the meeting by utilizing the method of soft clustering. The use of the soft clustering method is to bring all the data together into a single node for all the customers. It is helpful in detecting the appropriate members those who are chosen to participate in the meeting. However, it is regarded as a relevant method to execute the clustering process.

 It is also recommended to the management team that it must consist of a proper connection with every concept that is present in the Leximancer concept map. All the themes should be connected directly with each other have a better way of doing work. It is also significant that there should be a strong connection between the themes such as customers, field staff, communication, management, and results. If all these concepts are not linked with each other systematically than it will affect the results. The managers and field staff are recommended to concentrate on providing high-quality work to the organization that will help in achieving the target. The management team will be unable to satisfy the demand of the customers if they are not focused on their work that will put a negative impact on the result of the company (Wei, 2016). In an organization, there must be high-level communication amongst the customers and the managers to understand the demand of the customers to satisfy their needs. Thus, it is significant for the firm to follow communication strategy systematically. Proper support availability will be provided to the staff members and customers which will be profitability to organizational members. Within the organization, this will result in better management of the projects. It is also recommended to the organization to implement customer-service communication that fulfills the requirements of the customers. Thus, it is essential for the management to be interlinked with the concepts or themes that are presented in the Leximancer Concept map.

Conclusion

Presently most of the virtual teams in the businesses work mutually to facilitate outstanding results for the firm. The mutual understanding of the virtual team puts a positive impact on the organization to make the relevant decision. It is observed that making a relevant decision based on the organizational activities will help the organization to get better results. However, the paper provided with the discussion of two scenarios A and B that is based on evaluating decision-making tool. It reflected upon the decision-making styles and contrast that considers the appropriate levels of rationality. Therefore, the paper examined the decision-making system and techniques.

References

Bansal, A., Sharma, M., & Goel, S. (2017). Improved k-mean clustering algorithm for prediction analysis using classification technique in data mining. International Journal of Computer Applications, 157(6), 0975-8887.

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.

Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

Dedi?, N., & Stanier, C. (2016, November). Towards differentiating business intelligence, big data, data analytics and knowledge discovery. In International Conference on Enterprise Resource Planning Systems (pp. 114-122). Springer, Cham.

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.

Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.

Güçdemir, H., & Selim, H. (2015). Integrating multi-criteria decision making and clustering for business customer segmentation. Industrial Management & Data Systems, 115(6), 1022-1040.

Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data–a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36(10), 1382-1406.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.

Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1-2), 201-211.

Huang, W., Wang, H., Zhang, Y., & Zhang, S. (2017). A novel cluster computing technique based on signal clustering and analytic hierarchy model using hadoop. Cluster Computing, 1-8.

Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP, 38, 3-7.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access, 5, 5247-5261.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.

Wei, G. (2016). Picture fuzzy cross-entropy for multiple attribute decision making problems. Journal of Business Economics and Management, 17(4), 491-502.


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