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Examine and evaluate decision making systems
Course:- Other Subject
Reference No.:- EM132187199




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Learning Outcomes

a) Select and evaluate the usefulness of a range of decision making tools and reflect on your decision-making styles and contrast with other styles to determine the respective levels of rationality and intuition utilised

b) Compare, contrast and critically evaluate sources of data as influences for decision-making in a range of business contexts

c) Examine and evaluate decision making systems and techniques to engage group decisions and analyse how these can enhance sustainable outcomes

d) Critically examine emerging tools and technologies for decision making

Context:

Working environments are constantly changing, with contemporary organisations readily adopting new and innovative ways to bring groups of people together. One of the ways organisations have overcome globalisation demands is through the formation of virtual teams. Virtual teams/groups are where individuals, rather than working face-to-face, operate in networked environments supported in their communication and physical presence through technology and virtual group membership.

Here, skilled empowered individuals are required to operate outside their traditional organisational workplaces, instead working across the city or across the world, joined together by systems of technology, but working in physical isolation from one another.

This growth in virtual groups has come at a time of rapid and multidimensional change where established, traditional approaches to communication have been force fitted over newly emerging workgroup structures. Managers will clearly need to take on and make good use of available and emerging technologies to enhance their own their group's functioning and their organisation's decision making and change management skills.

Understanding the use of technology, the psychosocial costs and communication challenges in this technology-based environment will become essential ingredients for effective virtual managers and is the focus of Assessment 3. Organisations, keen to harness information-based resources held in the form of data in staff members' heads and evident in unstructured form in their emails, texts and conversations will need to use appropriate technology and group process in collaboration between staff to capture the resources. Information -based resources in tacit or unstructured data form can be mined and translated into explicit form to identify key organisational assets. Resources once identified can be leveraged to provide the enterprise with competitive advantage.

Instructions

To successfully complete Assessment 3 students are required to;

- Read and discuss both Scenario A AND Scenario B
- Provide an individual Research Analysis covering all required fields

Scenario A

Is set in a mid-sized Australian organisation of 800 people, 80 of whom are involved in research, development and design (R, D &D) work. The organisation is keen to identify the innovative/creative capabilities held by their RD&D staff and use these capabilities to differentiate the business from their competitors. The organisation has set up a project to research R, D & D innovative/creative capabilities held tacitly by staff and in their unstructured communication.

Tacit information can be best accessed and converted to knowledge by accessing email -based communication between RD&D staff. RD&D staff members' emails and their rates and direction of connectivity have accordingly been mapped over a one-week period. The results of this research audit have been made available on the attached Map of R, D & D unstructured Information contained in emails.

Scenario A Data

The map identifies each staff member and the work station they have logged onto, by node (circle with a number inside). Rates of connectivity are shown by lines connecting the different nodes and the direction and intensity of traffic by the numbers of lines into/out of nodes.

The project will call for interpretation of the map and decisions made on who to bring together to discuss the content and collaborate in making tacit unstructured information drawn from emails explicit and therefore suitable for use by the organisation. Several ‘meeting rooms' will be made available to selected RD&D staff, each with a facilitator, visual aids and a groups decision support system to link and summarise up meeting room outputs.

This project will make use of a group-based communication technology system to support meetings between staff members and help make staff members' tacit data/information explicit. To facilitate this process resources available in each meeting room will guide group process and ensure that each meeting room's participants collaborate with one another, and with groups in other meeting rooms in generating explicit knowledge.

Task

- After considering the map constructed from 70+ company staff members' emails over a one- week period you are required to help make sense of the tacit data held in those emails
- You should start by identifying naturally occurring clusters of R, D & D staff. Number those clusters one to six and show the nodes (numbered staff and terminals) you have decided to include in each cluster
- Move to the next step of selecting two key members (use rates of inter connectivity- connecting lines) from each of the clusters to attend the meeting areas and work with facilitators and GDSS technology in generating explicit knowledge
- Identify each cluster from one to six and show each cluster's constituents. In the table below.

Discussion Questions:

- How did you decide on type and size of each of the six clusters?

- What was the basis for your selection of nodes in the clusters?

- Did you include ‘dangling nodes' in your analysis? Why or Why Not?

- Did you include unconnected nodes in your clusters? Why or Why not?

- What was your justification for selecting two R.D & D staff per each cluster to attend meeting areas over others in different clusters?

Scenario B

This case is centred around an Australia wide service-based organisation. Head office is in Melbourne, Australia and the organisation consists of 200 service staff, across 10 service centres, reporting to 40 managers. The organisation is keen to determine levels of service support provided by their staff and has instituted a project to capture what it means to be a service person and identify perceived support provided by management.

A series of telephone and face-to-face interviews have been conducted across field staff and management and the results recorded. The words recorded in individuals' stories captured through interviews have been processed by Leximancer, a qualitative analysis tool capable of making sense of vast amounts of data.

Assessment 3 Scenario B Data

The Leximancer process of analysis can define meaning through deep analysis of large amounts of textual data.

Task

- Take note of the relative size of the balloons (concepts) and the lines connecting, or not collecting, the concepts.
- Consider the Leximancer content analysis map showing the concepts surrounding field staff, managers and customers and the communication, or lack of it between these concepts.
- Consider the map carefully, identifying key concepts and their relative relationships, then describe the more important and less important relationships shown.

Discussion Questions:

- What form does the field service staff members' communication take?
- Who are they most likely communicating with, and about what?
- Is there a high level of communication between managers and customers?
- How important has support availability been?
- How open is communication between field staff and management?
- Describe the nature of results communicated by field staff
- What part do managers play in communicating results?
Use the results of your analysis to inform your decisions on the following.
- Are managers sufficiently customer facing to justify an introduction of an incentive payment system for them. Why or Why Not?
- Should the organisation bolster management's role or rather establish empowered self- managed field service teams to better reach customers?

- Has support proved to be effective enough to justify support increases
- Should customer-service communication be changed towards one of greater openness. Why or Why Not?

Suggested format

A possible structure for your Research Analysis is shown below.

- Cover page
- Executive summary
- Introduction
- Body
o Discussion of Scenario A
o Discussion of Scenario B
- Recommendations
- Conclusion
- Appendices

You may make any necessary assumptions; however, any significant assumptions should be detailed in your Research Analysis.

Attachment:- Scenario_data.rar

Answered:-

Verified Expert

In the today's environment business procedure are changing and therefore organization has to improve their technology so that they create or innovate new techniques that provide benefits to their firm. therefore to increase the impact of globalization context many companies have made virtual teams so that they easily compete with the changing business environment. many scholars suggested that the virtual teams can strategically communicate with their consumer base and thereby help to fulfill their demands. In accordance with this context two scenarios are provided that simply based on the working environment of the organization.



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  1. user image
    len2187199

    Demonstrates use of high quality, credible and relevant resources to support and develop ideas. Demonstrates use of good quality, credible and relevant resources to support and develop arguments and statements. Shows evidence of wide scope within the organisation for sourcing evidence Demonstrates use of high- quality, credible and relevant resources to support and develop arguments and position statements. Shows evidence of wide scope within and outside the university for sourcing evidence

  2. user image
    len2187199

    Referencing in Support of Argument Percentage for this criterion 10% Demonstrates inconsistent use of good quality, credible and relevant resources to support and develop ideas. Demonstrates use of credible and relevant resources to support and develop argument, but these are not always explicit or well developed.

  3. user image
    len2187199

    Percentage for this criterion 20% argument lacks supporting evidence. Audience cannot follow the line of reasoning. not always clear and logical. Line of reasoning is often difficult to follow. flow of ideas and arguments. Line of reasoning is easy to follow. clear and well supported by evidence. Demonstrates cultural sensitivity. demonstrating a clear flow of ideas and arguments. Engages and sustains audience’s interest in the topic, demonstrates high levels of ethical, diversity, and business sensitivity Effective use of diverse presentation aids, including tables, data sets, graphs, spreadsheets

  4. user image
    len2187199

    Effective Difficult to understand Information, arguments Information, arguments Information, arguments Expertly presented; the Communication for audience, no and evidence are and evidence are well and evidence are very presentation is logical, logical/clear structure, presented in a way that is presented, mostly clear well presented; the persuasive, and well poor flow of ideas, presentation is logical, supported by evidence,

  5. user image
    len2187199

    data handling, making, justifying recommendations. Demonstrated ability to process data sets and holistically, define relationships between data concepts and formulate conclusions make recommendations and decisions Analysis and evaluation reflect sound judgement, intellectual independence, rigor and adaptability. Exhibits an ability to Read between the lines through insightful intuiting of material presented in cases. Can readily justify results of research into data and defend intuitive interpretation data analytics.

  6. user image
    len2187199

    data nor of relationships between data. relevant concepts to data analytics, in decision making, and in justifying decisions, recommendations. Well demonstrated capacity to explain and apply relevant concepts on data handling, making, justifying recommendations. Demonstrated ability to process data sets and holistically, define relationships between data concepts and formulate conclusions make recommendations and decisions

  7. user image
    len2187199

    Supports interpretations of data through careful analysis and provides conclusions based on that analysis, Does not confuse logic and emotion in data handling, decision making nor making recommendations Decisions and recommendations are based on analysis. Demonstrates a capacity to explain and apply Discriminates between assertion of opinion and information substantiated by robust evidence from analysis and understanding of results of data analytics. Does not confuse logic and emotion in data handling, decision making nor making recommendations Decisions and recommendations are based on analysis. Discriminates between assertion of opinion and information substantiated by robust evidence from analysis and understanding of results of data analytics. Does not confuse logic and emotion in data handling, decision making. Recommendations and Decisions are based on sound data analysis. Well demonstrated capacity to explain and apply relevant concepts on

  8. user image
    len2187199

    Understanding of Decision Analytics and use in making decisions, forming Recommendations Percentage for this criterion 20% Limited understanding of key concepts required to meet the requirements of the case studies. Confuses logic and emotion in data handling, making decisions and forming recommendations. Reaches conclusions without consideration of relationships between data entities, concepts, themes. Some understanding of key concepts required to meet the requirements of the case study. Confuses logic and emotion in data handling, decision making and in making recommendations. Reaches conclusions without consideration of date, information presented. Analysis and evaluation of data sets do not reflect clear understanding of

  9. user image
    len2187199

    Percentage for this criterion 20% making, including some evaluations. and evaluation of appropriate use of models to meet specific task environments frameworks and evaluation of appropriate use of models to meet specific task environments Able to use understanding of data analytics to inform selection, uses of decision models. Covers appropriate matching of decision models with outcomes from research and data analytics

  10. user image
    len2187199

    Application of Conceptual Frameworks to data Driven Decision Making Little or no reference made to conceptual frameworks, understanding of data analytics or theoretical models of decision making Basic coverage of at least one relevant conceptual framework, understanding of data analytics and can provide one model of decision making Clear coverage and comparison of at least two models and some understanding of data analytics and relation to frameworks for decision Well-developed understanding of the field of data analytics and relation to decision making as demonstrated by coverage of multiple models and frameworks A sophisticated understanding of data analytics and relation to the decision-making field. Demonstrated understanding of multiple models and conceptual

  11. user image
    len2187199

    Learning Rubric: MGT602 Assessment 3 Research Analysis Assessment Attributes Fail (Unacceptable) 0-49% Pass (Functional) 50-64% Credit (Proficient) 65-74% Distinction (Advanced) 75 -84% High Distinction (Exceptional) 85-100% Application of Conceptual Frameworks in interpreting unstructured, patterned data Percentage for this criterion 30% Unable to analyse and make sense of patterns of data and provide a useful interpretation of that data Makes some sense of the data patterns but does not provide a useful interpretation Demonstrates a sound interpretation of data patterns and provides a useful interpretation Demonstrates a good understanding of patterns of data. Shows an ability to interpret and apply the results of sense making analysis to specific task environments A sophisticated understanding of the pattern recognition and sense making process. Demonstrated sound understanding of the outcomes of analysis. Able to apply this understanding across different situations and task environments Able to discriminate between and operate on different types of unstructured data. Provides sound interpretations of unstructured data and an ability to make explicit



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