Research Analysis Report
This is my assessment please have a look and I want to do as best as possible it’s a small assessment please follow the assessment criteria.
This assessment is design for you to critically reflect on key themes of the subject .
the challenge should be meaningfull challenge it should be real challenge it cant be something imaginary.
it should be relevant to myself like the challenge should be why did i came to Australia instead of going to US.
which course should i choose after the completion of my studies i move to temporary resident and then permanent resident and then citizenship like
after that did you make a intuitive decision or rational decision or bounded rationality decision.
need to include VUCA environment in decision making part.
they are 4 criteria need to address follow the brief.
MGT602_Assess3_Research Analysis 1 of 4
A SSESSMENT 3 BRIEF
Subject Code and Title MGT602 Business Decision Analytics
Assessment Research Analysis Report
Individual/Group Individual
Length Up to 2,000 words
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
Submission
12 week class: By 11:55pm AEST/AEDT Friday of Module
6.1 (Week 11)
Intensive 6 week class. By 11.55pm AEST Friday of
module 6.1 (week 6)
Weighting 40%
Total Marks 40 marks
Context: Workplaces today are changing rapidly in parallel with rapid advances in technology and
means of communication with teams separated in terms of space and time, that is different
geographic locations and time zones, not only within countries, but also across continents. This is
something that we in Torrens adapt to on a daily basis as part of a global organisation. We need to
take into account not only time and space, but other elements of diversity to collaborate effectively
for organisational outcomes.
Task:
This assignment is designed for you to critically reflect on key themes for this subject: individual and
group decision-making processes; sources of data and analysis, including usage of data analytics for
organisational decision-making, and the influence of bias in effective decision-making.
MGT602_Assess 3_Research Analysis 2 of 4
In doing so, you should consider a meaningful work challenge – a project that requires many
decisions to be made- that is relevant for you- in your current, or recent workplace. [NOTE: Torrens
University is a workplace and you have a special role in it.].
CHECKPOINT: Submit a draft of your outline and discuss your report proposal with your Learning
Facilitator by WEEK 9.
Your task is to analyse the project from the perspective of decision-making points/stages. In the
analysis, you are to consider:
1. The sources of data, and the use of data analytics to identify trends/ patterns that form
the evidence for decision-making;
2. Show visualisation of the decision-making process, and analytics to support the decision-
making;
3. Select at least three decision-making tools and technologies from within the subject
modules and show their application for your project. Consider if the decision would be
the same/ different by using multiple methods.
4. Present the findings of your results in a business style report that includes clear headings
to guide the reader and visualisation of the data sources/trends/ patterns, and is
underpinned with evidence from relevant contemporary literature, including major
resources from within the subject modules.
5. Reference according to the APA 6th. Ed. reference style guide, available at Student Hub
@ Torrens.
Submission Instructions:
1. Submit your report to Turn-It-In.
2. Submit your report and TII report via the Assessment link in MGT602 Business Decision
Analytics on the Student Portal.
3. The Learning Facilitator will provide feedback via Grade Centre in the Student Portal.
Feedback can be viewed in My Grades.
MGT602_Assess 3_Research Analysis Page 3
Assessment Criteria
Fail
(Unacceptable)
0-49%
Pass
(Functional)
50-64%
Credit
(Proficient)
65-74%
Distinction
(Advanced)
75 -84%
High Distinction
(Exceptional)
85-100%
1. Critically reflect
on the key themes for
this subject: individual,
group decision-making
and business analytics,
and reduction of bias for
more effective business
decision-making.
(25% -10 marks)
Little or no authentic
personal reflection on
individual, group and
business decision-making and
the use of analytics for
organisations.
No, or limited reflection on
bias
Some personal reflection on
individual, group and business
decision-making and the use of
analytics in organisational
decision-making is evident,
Some reflection on bias is
evident.
A clearly authentic personal
reflection on individual, group
and business decision-making
and the use of analytics is
evident, with clear links to the
influence of bias on decision-
making.
Comprehensive reflection on
individual, group and business
decision-making and the use
of analytics, with clear links to
the influence of bias on
decision-making.
Comprehensive and sophisticated
reflection on individual, group and
business decision-making and the
use of analytics is evident, including
implication of bias and blind spots
for decision-making.
2. Examine and
analyse at least three
decision-making tools
and technologies to
demonstrate their
application in the
workplace.
(25%- 10 marks)
Little or no reference made
to conceptual frameworks
and explicit decision-making
tools and technologies.
Basic coverage of at least three
decision-making tools and
technologies, with reference to
conceptual frameworks, data
analytics and their workplace
application.
Clear analysis and comparison
of at least three decision-
making tools and technologies,
with reference to conceptual
frameworks, data analytics and
their workplace application.
Well-developed analysis and
comparison of at least three
decision-making tools and
technologies, with reference
to conceptual frameworks,
data analytics and their
workplace application.
Sophisticated analysis of at least
three decision-making tools and
technologies, with reference to
conceptual frameworks, data
analytics and their workplace
application.
3.Prepare a business
style report with clear
headings and visual
interpretation and
presentation of data
trends/ patterns
(25%- 10 marks)
Report lacks logical
coherence and clear
structure
Limited data analysis and no
visual representation of data.
Report has basic logical
flow and structure.
Basic concepts and analysis are
explained
Attempts basic visual
representation of data.
Report has a clear
logical flow and
structure.
Clear expression of
concepts and analysis.
Adequate visual
representation of data
trends/ patterns
Report has a strong
logical flow and
structure.
Accurate Executive
Summary.
Conclusion accurately
captures key learning.
Clear visual representation of
data trends/ patterns
Excellent logic and structure.
Concise, accurate Executive
Summary
Conclusion accurately captures
deep learning and insights.
Creative and engaging visual
representation of data
trends/ patterns.
4.Support your
argument with relevant
contemporary literature
including major
resources from within
the subject modules.
(25%- 10 marks)
No, or few references from
within subject learning
resources.
Many errors in text citations
and reference list according
to APA referencing
guidelines.
Makes some attempt to use
relevant references from within
subject learning resources.
Several major and minor errors
in text citations and reference list
according to APA referencing
guidelines.
References support an
adequate argument and
application of major concepts
from within subject learning
resources.
Several minor errors in text
citations and reference list
according to APA referencing
guidelines.
References support a mostly
comprehensive argument and
application of major concepts
from within subject learning
resources.
Strong level of accuracy of in-
text citations and reference
list according to APA 6th ed.
style guidelines; minor errors.
Sophisticated references support
comprehensive argument and
application of major concepts from
within subject learning resources.
High level of accuracy of in-text
citations and reference list
according to APA 6th ed. style
guidelines.
MGT602_Assess 3_Research Analysis Page 4
- Learning Rubric: MGT602 Assessment 3 Research Analysis Report 40% (40/100 marks)
Cover page – Torrens Group Assessment Cover Page
EXECUTIVE SUMMARY
An executive summary should summarize the key points of the report. It should restate the purpose of the report, highlight the major points of the report, and describe any results, conclusions, or recommendations from the report. It should include enough information so the reader can understand what is discussed in the full report, without having to read it.
|
Executive Summary (Sample only) Methods of analysis include trend, horizontal and vertical analyses as well as ratios such as Debt, Current and Quick ratios. Other calculations include rates of return on Shareholders Equity and Total Assets and earnings per share to name a few. All calculations can be found in the appendices. Results of data analysed show that all ratios are below industry averages. In particular, comparative performance is poor in the areas of profit margins, liquidity, credit control, and inventory management. Recommendations discussed include: |
subject matter methods of analysis Findings Conclusions Recommendations (note that conclusions and recommendations can be bulleted) |
https://articles.bplans.com/writing-an-executive-summary/
Excerpt from Woodward-Kron, R. (1997) Writing in Commerce: a guide to assist Commerce students with assignment writing, (Revised edition), Centre for the Advancement of Teaching and Learning, The University of Newcastle.
Table of Contents
1. Introduction 4
2. Scope/Brief 4
3. Analysis of ancillary decisions 4
4. Analysis of data 4
5. Decisions made using different decision making models 4
5.1. Decision Making Model 1 4
5.2. Decision Making Model 2 5
5.3. Decision Making Model 3 5
6. Key themes 5
7. Conclusion 6
8. References 6
9. Appendices 6
Appendix 1: 6
Appendix 2: 6
Appendix 3: 6
Appendix 4: 6
1. Introduction
Mention briefly about the company
2. Scope/Brief
Clearly mention what the challenge is (Use only one challenge)
Example –. My challenge was that “I had to make a decision to select a country between Australia, Canada or the UK, to get the citizenship before I am 30 years old”
3. Analysis of ancillary decisions
Other decisions to make, once the making decision is taken, i.e. Australia
Example – Which university to study, which course to study, which state to study, ect. (explain the factors considered).
4. Analysis of data
analysis of data (qualitative/quantitative), show the decision route/path, progress chart for the destination
5. Decisions made using different decision making models
5.1. Decision Making Model 1
(Use the actual tools that you used back then and discuss the pros and cons of the model in the context of your challenge, visualize where necessary, show how you made the decision)
Identify the biases and blind spots back then, and how they affected your decision
5.2. Decision Making Model 2
(discuss how the decision would have been different, under another tool (DM tool 2) the pros and cons of the model in the context of your challenge, visualize where necessary, show how you made the decision)
5.3. Decision Making Model 3
(discuss how the decision would have been different, under another tool (DM tool 2) the pros and cons of the model in the context of your challenge, visualize where necessary, show how you made the decision)
6. Key themes
Reflecting on your challenge, show what key themes (learned in this subject and enlisted below) would have helped you to make a better decision back then. Use of these key themes highly dependent upon your challenge, and you do not need to show the use of all but the relevant key themes.
Module 1
Use of data, information, knowledge and wisdom in decision making, Use of decision making models – Rational, Intuitive and Balanced DM models, their advantages and disadvantages, Personality types and their effect in DM
Module 2
Rational, Bounded Rational, Dual Processing (System 1,2 and 3) Recognition Primed DM models their advantages and disadvantages
Module 3
Individual and Group DM, their advantages and disadvantages, Biases and Blind spots, Group think, working in virtual teams, stages of development of teams, importance of diversity and their challenges
Module 4
Biases, Blind spots, Ethics and Morals in DM, Heuristics and its use in DM,
Module 5
Competitive DM and Collaborative DM, Strategic DM in VUCA environment, importance of leadership style in DM, Knowledge management system
Module 6
Use of AI, Business Intelligence systems, Management Information system, big data, data mining, and other systems for DM, their advantages and disadvantages, Use of machine learning, Decision Support Systems and Group Decision Support Systems
7. Conclusion
Tell this subject made you a better decision maker in the future. Identify what you learned about yourself and working in teams.
8. References
Intext referencing (direct/paraphrasing) needs page numbers (except for web-pages)
Reference list should be in alphabetical order in hanging indent form
No hyperlinks allowed
9. Appendices
Appendix 1:
Appendix 2:
Business Decision Analytics- Research Analysis Report
Torrens University
Executive Summary
This research report provides complete insight on Business Decision Analytics in the perspective of Torrens University. The author of this report assuming a major role in Torrens University has prepared the report for the major decision of maintaining service quality and increases the student enrollments. Firstly, the report commenced with the selection of group decision making as various departments were involved. Secondly, the sources of data pertaining to the issue was planned in an organized manner. This included archives of records from university, customer feedback and employee feedback in regard to the customer service quality. Subsequent to this, the different decision making systems such as descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics were evaluated for effective decision making. Finally, the influence of bias was critically examined and different cognitive biases were evaluated.
Table of contents
1.
Introduction
4
2.
Process of individual and group decision making
4
3
.
Sources of data and analysis
5
4.
Data analytics for organizational decision making
8
5.
Influence of bias in effective decision-making
10
6.
Conclusion
11
References
12
1. Introduction
Torrens University Australia is a part of Laureate International Universities, the renowned global network with 70 accredited universities and higher education institutions and has students of about 1 million worldwide (Torrens University Australia, 2020). Torrens University has the major aim of bringing new, contemporary, career focused and international perspective to higher education (Torrens University Australia, 2019). The University design courses in a way it provides the graduates to develop internationally oriented skills which facilitate them to be a valuable employee to any organization. Torrens offers graduate and post graduate courses in different disciplines and campuses extend over various destinations such as Sydney, Melbourne, Adelaide, Brisbane, and NSW. This is a report prepared by the Customer service Manager at Torrens University.
In the current contemporary world, the educational universities and other related institutions are facing lot of issues and the institutions are adapting approaches to deal with the issues. Bruni, Carubbo, & Sarno, (2018) has stated that system thinking is the most common approach that most of the business leaders adapts to solve the major issues in a rapid manner. Particularly the service quality which is perceived to be the major issue in Universities is declining slowly and due to this many potential students are diverted to rival universities . The issue of customer dispersion due to the long waiting time to connect to the executives is the common issue in educational universities. This slowly aggravates and shared in social media which became a risk factor for university reputation. Resolving the issue of customer service is becoming very challenging and effective decisions has to be made using a range of decision making tools to improve the customer service and increase the enrollment of students
2. Process of individual and group decision making
There is a perception that individual decision making is the simplest process. But while considering with group decision making, it becomes one of the complex issue for the customer service manager to determine which the better and effective one is . In certain small issues like sharing university promotional activities to a set of customers individual decision making is perceived to be more productive. While for admission procedures, discounts and other customer related issues, group decision making with different departments such as customer service, admission, and payment facility proved to be the wiser choice.
(Yan, Liu, & Skitmore, 2018) has suggested that in regard to the issue of strengthening the customer service in the Torrens University, group decision making will be effective as the issue involves different departments and employees . As a first step of resolving the issue, following ancillary decisions for the issue was developed
· Identify strategies to improve customer satisfaction
· Plan suitable training to the customer service staff
· Implementing necessary software and adapt social media techniques to digitalize the admission procedures
· Develop costing for bringing the above
3. Sources of data and analysis
has expressed that for identifying suitable decisions for an issue, it is very inevitable to collect necessary data pertaining to the issue and make an analysis and this will give better clarity for the issue and facilitate effective decision making. In this study, the customer service quality has to be monitored for improvement. Hence before planning a decision, relevant data has to be collected and following key points have to be noted:
· The data should be right and valid to the identified issue
· The data should facilitate accurate conclusions
· The data should inform the decision making process
Right data and appropriate analysis and tools will lead to a simple and clear decision making process . To improve the data analysis and subsequent decision making process, following steps have to be performed.
Data driven Decision Making process
Figure 1: Source: Adapted from
Defining the question
: As stated by Identifying the sources of data commences with the right question. The question should be designed in a way it qualifies potential solutions to the issue . In this study, the question is “
What are the tactics to improve the students’ enrollment
?”
Set Measurement practices: Firstly, what kind of data to be measured has to be decided. In answering the above question, following data have to be collected
· Number of employees engaged in direct admission
· Number of enquiries and number of admission in the previous years
· Details of training and working hours of employees of the concerned department
· Efficiency and effectiveness of the computer department including the software details
· Feedback from existing customers about the service of the university
Secondly, suitable measurement technique for the collected data has to be identified with the following key questions
· What is the time frame?
· What will be the unit measure?
· What factors are to be included such as enrollments, admissions, fees etc
Collection of data
Suitable sources for the data have to be determined. In regard to the data pertaining to previous years, the archives in the records office of the university can be gathered. A questionnaire or direct discussion with the employees provides information pertaining to employee involvement in the customer service. For the customer feedback, a questionnaire with questions relevant to their experience with the customer service of the University can be developed and distributed through messages, emails and social media.
Analyzing the data
The data gathered from the archives will help to make comparison with the existing data. Responses collected from employees and customers are recorded and fed in Pivot table of MS Excel . In this, the data is sorted and filtered on the basis of certain variables and helps to calculate mean, median and standard deviation. On manipulating this, exact data is identified and based upon the requirement for further manipulation, data collection is extended.
Interpreting the results
After analyzing the data, the results are interpreted and following key questions are raised to measure the outcomes:
How is the collected data relevant to the research question?
Does the data support any objections?
Is the conclusion drawn properly or are there any limitations?
The results of the data analysis process helps for effective decision making
4. Data analytics for organizational decision making
In the perspective of customer service in Torrens University, data analytics involves collecting and gathering different information pertaining to customer service in order to obtain actionable insights in order to assess the strategies and design efficient customer service. The term data analytics is divided into 4 different types and aligned to decision making process.
Descriptive analytics: As said by Lepenioti, Bousdekis, Apostolou, & Mentzas, (2020) this facilitates the analysis in a comprehensive perspective of key metrics within the University. The real time data collected through questionnaire and historical data from archives of records are analyzed to develop significant insights for the challenge in regard to customer service. This is the basic type of analytics to determine the actual reasons behind any major issues. Through this descriptive analytics, the university can gain understanding on the past performance, initiate strategies on the basis of observations for the future outcomes and impact of the strategies on the current performance.
Diagnostic analysis:
The next phase of understanding the ins and outs of data analytics subsequent to descriptive analytics is diagnostic analytics. After evaluating the descriptive data, the analysts get into the problem deeply. By using the technique of drilldowns and queries, the major cause of the issue is eliminated . In this, the data collected from archives are ensured against any other data to uncover answer to “Why the customer service has declined”. Using Diagnostic analysis, Torrens University develops the capability of developing new idea to identify dependencies and distinguish prototypes. With this, the university is able to obtain better perception in regard to the problems and issues in customer service. Conversely, the information gathered through diagnostic analysis has to be maintained else data collection in future for any other purpose will become time consuming. To implement diagnostic analytics, Torrens University should have to be equipped with efficient and integrated Business Information dashboard with data assimilation, participating filters and drilldown capabilities.
Predictive Analytics: A good decision making lies on right predictions. This comprises of analysis of past patterns and trends and predicts the future outcomes accurately. Since this analysis uses the exact trends of the University, it helps in ascertaining the realistic goals for the decision making process. With this, it becomes easy for determining propensity and expectations of different customer groups and hence considered as a valuable tool. Moses, (2016) has expressed that this analysis implements various algorithms and statistical methods for predicting the probability of future outcomes. Contrary to this, Fortunny & Martens, (2013) has stated that this analysis does not provide 100% accuracy as the assumptions drawn are based on predictions. Many large organizations implements this approach to understand the customer behavior.
Prescriptive analytics: This relates to Big Data and Artificial Intelligence and major objective of this analytics is to recommend suitable action for addressing future problems. This helps Torrens to gain adequate understanding of the basic reasons for the problem and develop suitable action plan. The analytics uses combination of mathematical models, collected data and business rules . The data here refers to the historical data from archives, employee data and customer feedback. Business rules are the preferences, practices, procedures and other restrictions of the University. Operational research, statistics, machine learning and language processing are some of the mathematical models. This seems to be quite complicated but have huge impact on the operations and resolving the issues and develop growth.
Data Analytics
Figure 2: Source: Adapted from
5. Influence of bias in effective decision-making
Julmi, (2019) expresses that decision making is a natural activity based on the acquisition of knowledge and the result of this might be either reasonable or illogical. There are different reasons that influence the decision making process such as personality and experience of the individuals. The bias of individuals can either be a hindrance or enabler to the decision making process. The bias of the individuals is measured from the psychological perspective in terms of set of needs and preferences. Abraham Maslow Theory is the most significant theory on the basis of motivation. According to the Maslow motivational theory, the basic needs of individual has to be satisfied before desires and higher level needs.
A cognitive bias is a thinking error that happens when an individual or individuals are processing data and information around them and affect the decision making process . Cognitive bias is of four types and they are discussed below:
Actor-observer bias: This is the most common bias that is developed with the tendency to characterize own actions to external causes while linking other people’s behavior to internal causes.
Anchoring bias: This relates to the tendency of trusting the very first information received regardless of the genuineness and quality of the information. This bias can be used to set the preferences of others by putting the first information on the top of the table.
Availability Heuristic: This is a type of bias where the information comes to the mind is given higher value than others. Higher credibility is given to this information and be likely to misjudge the probability of same type of things in the near future.
Halo effect: The overall impression of an individual influences the way the character is perceived. Mostly the physical appearance influences the way the other qualities are rated.
Apart from the above there are also certain factors that contributes into the biases such as Emotions, Motivations, Mind ability, and social pressure. Multiple biases influences the thinking and subsequently affects the decision making process.
6. Conclusion
This report clearly elucidates the customer service quality in universities and educational institutions are slowly declining which will bring severe downfall in the industry. In order to maintain the reputation and market share, it becomes essential for Torrens University to plan suitable decision making process and initiate the decision of increasing the number of student enrollment. Data driven decision making is the technique that will be adapted to improve the customer engagement. Finally, the data analytics was implemented to inspect, cleanse, transform and model the data with the major aim of implementing efficient decision making process.
References
Bruni, R., Carubbo, L., & Sarno, D. (2018). An Overview of the Contribution of Systems Thinking Within Management and Marketing. (D. 10.1007/978-3-319-61967-5_13, Ed.) New Economic Windows , 241-259.
Cech, T., Spaulding, T., & Cazier, j. (2018). Data competence maturity: developing data-driven decision making. (https://doi.org/10.1108/JRIT-03-2018-0007, Ed.) Journal of Research in Innovative teaching and learning , 139-158.
Delen, D. (2018). Research challenges and opportunities in business analytics. (https://doi.org/10.1080/2573234X.2018.1507324, Ed.) Journal of Business Analytics , 2-12.
Fortunny, D., & Martens, D. (2013). Predictive modeling with big data: is bigger really better? (D. 10.1089/big.2013.0037, Ed.) Big Data , 215-227.
Hogarth, R., & Soyer, E. (2015). Providing information for decision making: Contrasting description and simulation. (https://doi.org/10.1016/j.jarmac.2014.01.005, Ed.) Journal of Applied Research in Mamory and Cognition , 221-228.
Julmi, C. (2019). When rational decision-making becomes irrational:a critical assessment and re-conceptualization of intuition effectiveness. (https://doi.org/10.1007/s40685-019-0096-4, Ed.) Business Research , 291-314.
Lepenioti, k., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. (https://doi.org/10.1016/j.ijinfomgt.2019.04.003, Ed.) International Journal of Information management , 57-70.
Miller, A. (2014). Introduction to Using Excel® Pivot Tables and Pivot Charts to Increase Efficiency in Library Data Analysis and Illustration. (https://doi.org/10.1080/01930826.2014.903365, Ed.) Journal of Library Administration , 94-106.
Moses, B. (2016). Algorithmic prediction in policing: assumptions, evaluation, and accountability. (https://doi.org/10.1080/10439463.2016.1253695, Ed.) Policing and Soiety , 806-822.
Osmani, J. (2016). Are Groups the Best Way to Make Decisions? A Literature Review. (Doi:10.5901/ajis.2016.v5n1p301, Ed.) Academic Journal of Interdisciplinary Studies , 301-309.
Philips-Wren, G., Power, D., & Mora, M. (2019). Cognitive bias, decision styles, and risk attitudes in decision making and DSS. (https://doi.org/10.1080/12460125.2019.1646509, Ed.) Journal of decision systems , 63-66.
Sivarah, U., Kamal, M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. (https://doi.org/10.1016/j.jbusres.2016.08.001, Ed.) Journal of Business Research , 263-286.
Sultan, P., & Wong, Y. (2012). Service quality in a higher education context: An integrated model. (D. 10.1108/13555851211278196, Ed.) Asia Pacific Journal of Marketing and Logistics , 755-784.
Torrens University Australia. (2019, October). International Students. Retrieved August 10, 2020, from torrens.edu.au: https://www.torrens.edu.au/studying-with-us/international-students
Torrens University Australia. (2020, July). LAUREATE INTERNATIONAL UNIVERSITIES. Retrieved August 10, 2020, from Torrens.edu.au: https://www.torrens.edu.au/about/torrens-university-australia
Wang, Y., Kung, L., & Cegielski, C. (2018). An Integrated Big Data Analytics-Enabled Transformation Model: Application to Health Care. (D. 10.1016/j.im.2017.04.001, Ed.) Information & management , 64-79.
Yan, P., Liu, J., & Skitmore, M. (2018). Individual, Group, and Organizational Factors Affecting Group Bidding Decisions for Construction Projects. (https://doi.org/10.1155/2018/3690302, Ed.) Advances in Civil Engineering , 1-11.
Defining the question
What are the tactics to improve the students’ enrollment
Set measurement practices
Decide what to measure
Decide how to measure
Collection of data
Archives of records
Employee Feedback
Customer Feedback
Analyzing the data
Pivot Table in MS Excel
Interpreting the results
Measuring the outcomes with the Past performance
Develop Suitable decision
3
RESEARCH ANALYSIS REPORT
1
RESEARCH ANALYSIS REPORT 14
Business Decision Analytics (MGT602)
Name of the Student: Dipkumar D. Patel
Student ID: 00308693T
Name of the University: Torrens University
Assessment 3: Research Analysis Report
Word count: 2039
Reference: APA
Table of Contents
Introduction 3
Scope and brief 3
Analysis of decision to address the issue
4
Analysis of data 5
Applying of different decision-making model to reach the final decision
8
The rational model
8
The dual process model
9
The bounded rationality
10
Key themes 10
Module 1
10
Module 2
11
Model 3
11
Model 4
11
Module 5
11
Module 6
11
Conclusion 12
References
13
Introduction
For the conduction of the successful and completion of the project at the desired timeline, decision-making is one of the crucial tools. It is a general notion that each step of the business required to make decision, failure in seeking decision can resulted into failure in the business. In a business, each project consists of many phases, from the development of the strategy to final delivery of the project. Each step required critical decisional making. In business organisations, employees with the decision-making skills are preferred over other employees. This assignment focusses on applying the decision-making tool to analyse the ongoing issue faced by the Torrens University after conducting data collection and data analysis related to entire issue. Apart from this, it also includes discussion on the three decision making tools and critical reflection in the themes discussed in the module.
Scope and brief
The scope of this report is related to the challenges faced by the organisation in the past few years. All the organisations work with the aim of growth and expansion which is their core objective while conduction of the business. It must be noted that revenue generation of any organisation is completely dependent on the selling of their products. In context of the Torrens University, revenue is generated by the number of admissions taken by the students in the University. In the past few years, Torren University witnessed the reduction in number of the customers which are student in this context. As mentioned by Strandvik, Heinonen, and Vollmer (2019), all the business is customer centric, customers are given prime importance as the generation of revenue solely dependent on them buying the product. Currently, Torrens University is suffering from financial constrain as number of customers are decreasing each year. This issue has been emerged due to the poor customer relationship between university and customers. Customers failed to get good services during the time of admission which resulted into customers dissatisfaction. Apart from this, limited staff in all the department limits the quality of services provided to the customers. As stated by Dorantes, and Peterson (2020), failure of the organization in addressing the needs of the customers due to limited staff members can have poor impact on the business, as customers tend to disperse when their satisfaction level decreased.
Analysis of decision to address the issue
After analysing the current situation which is responsible for causing potential financial strain on the university is poor customer service given by the organisation. It has been seen that customer service and responsibilities taken by the staff members are related to each other. It has been seen that staff members of the University failed to provide the desire services to the customers due to two reasons, one is number of staff is limited and most of the staff members are untrained to handle the customer issues during the time of admission efficiently which resulted in long waiting hours of customers and unsolved queries. As an outcome, the quality of the service has been critically reduced. There is need to take two decisions immediately so that these issues can be solved for the long term. One is hiring of the required number of trained and skilled staff and another is given training to the existing staff. Apart from this, given special training to the staff to prepare them during the time of admission when they must deal with large number of customers and solved their queries. As noted by Kanten, and Darma (2017), training of the employees of the organization helps in increasing their productivity and quality service they provide to the customers. The good services given to the customers can help in building effective relationship with them, as an outcome, customer loyalty would be increased, and revenue can be generated.
Analysis of data
Before analysis of the data, it is necessary to collect the data related to the issue. The research variables based on which data needs to be conducted is the factors responsible for reduction of number of customers in the Torrens University. Although, secondary research has been conducted to identify the potential factors which can affect the customer satisfaction. Apart from this, quantitative data has been collected by conducting the questionnaire-based survey. The questions developed in the survey are based on the secondary research conducted on the issue. The participants of the survey are staff members and customers seeking admission in the organisation. The data collected in the form of numerical mode which can be predicted using the statistical method.
1. Are the services provided to the customers meet the quality criteria?
2. what are the factors responsible for poor services provided to the customers?
3. what solutions can be taken to improve the situation currently existing?
4. Can improving of IT infrastructure help in solving the admission time challenge?
5. Is training of staff and hiring of new staff improve the services delivered to the customers?
Thus, on conduction of analysis of the data, it has been proved that improvement of the IT technology in the organisation and increase in the number of the trained staff can be helpful in increasing the quality of services delivered to the customers. It must be noted that each employee of the organisation must be provided with training to improve their IT skills and communication skill for better conduction of the business (Haque, 2018).
Applying of different decision-making model to reach the final decision
It has been seen that there are different decision-making models which play the crucial role in understanding the issues and help in applying the correct decision based on the situation occur. There large number of decision-making model available which play the role in providing the knowledge related to the issue and determine the potential solution based on the analysis conducted. People involved in business must acquire the knowledge gained from the decision model. To analyse the current situation of the Torren University, the three models have been studied and analysis to make the decision is done based on the knowledge provided by them. The three models selected for the study are rational model, dual process model, and the bounded rationality.
The rational model
This model is considered as the classical model of the decision making. It is a multi-step process which helps in making choices between the alternatives. It is a sequential process in which specific path has been followed. In rational method path includes, formulation of the goal, identifying the criteria for making the decision, then identification of alternatives for the issue, conduction of the analysis, and making of final decision (Gough, & Boaz, 2017). This model assumes that people make the decision which provides maximum benefits at the minimum investment. It also assumes that individual have complete information based on which they take the decision. It measures the existing criteria for which data can be collected and analysed. It also assumes that an individual has cognitive ability, resources, and time to analyse the alternatives of the issue. This model considered only those factors which can be quantified. It failed to consider the ethical values and personal emotions of the individual. This model is often considered as unrealistic and over-simplified assumption (Djulbegovic, Elqayam & Dale, 2018). Using the rational model, organisation can conduct the thorough analysis of the existing issue and identify the core reason which resulted in increase in the issue in the business process. The benefit of this model is that it can be obtained scientifically and encouraged to gain the information which is scientifically correct. This model can help in identifying the core reason of low satisfaction of the customers towards the organisation.
The dual process model
In the dual process model, the communication between different situation and circumstances is done utilising the architectural manner. As mentioned by Diederich, and Trueblood (2018), this model provides the quick processing of the decision making and strategic thinking practices. It gives preference to the existing values to the situations and challenges, then provide the alternatives to solved the issues. Dual process model provides a better perspective to any complex situation. It works towards analyzing the situation from two different angles. This helps the project manager toto make the better and informed decision based on the perspectives. It helps development of the design thinking processes. One of the disadvantages of this model is that it can cause the confusion due to the different perspective delivered as there is lack of scientific approach which can assist in identifying the better perspectives between the alternatives. As noted by Hagger (2016), this model can limit the individual to explore the new ideas and limits the thinking related to the issues. Based on the discussions above, this theory can be helpful in providing critical approach to the issue.
The bounded rationality
Bounded rationality is the decision-making process in which the good possible decision is selected over the best decision. In this kind of model, limited time frame as limited resources are utilised in extracting the solution for any challenge. As noted by De Clippel, and Rozen (2018), this model can be effective in taking the decision for any project as it focusses on the project issues and challenges. It also implements the decision making based on the assumptions. One of the biggest advantages of this model is that it allows project manager to work in restricted time frame and limited resources. It also helps in development of the assumption-based decision-making process using the real-life data which has been extracted from various sources.
As suggested by Jia, and Tsui (2020), this model can make the project vulnerable towards wrong assumption. This can result in taking the wrong decision making or formation of the ideas. There can be issue with the lack of the proper data related to the issue. based on the assumptions derived from this model, solution can be generated.
Key themes
This section of the report works towards analysing the key themes discusses in the module and the impact of these themes in solving the existing issues in a relevant manner.
Module 1
This module provides the information related to the use of data which can help the project manager in differentiating between the relevant or irrelevant data. It must be noted that vast data get collected from various sources related to the issue. It is a duty of the project manager to extract data which either address the issue or help in extracting the relevant solution for the issue.
Module 2
The theme of this module is application of the rational decision-making model which can be helpful in identifying the core reason behind any potential issue. It helps in analysis of the problem using National model and provide the different perspectives on the implication of the solution.
Model 3
The theme of this module is identifying the advantage of the decision-making model. It suggests that decision-making models are effective in highlighting the correct process of making decisions.
Model 4
This theme is related to generation of the strategic idea related to the decision-making situations and handling of these situations using ethics and morals. Ethics and morals refer to the standard principles to explain the importance of decision-making.
Module 5
This theme is related to knowledge management system. This system is known to store and deliver knowledge which can clarify of the project management.
Module 6
This theme is related to the application of the business intelligence system. This includes the use of technology in transforming of raw data into meaning information which can be utilised for the business.
Conclusion
This research proposal provides the perspective related to the application of the decision-making tools in identifying the solution for the project. It uses the example of Torrens University to apply the decision-making tool in addressing the issue of the organisation. This report followed the structure of decision-making model to analyse the data and discuss the challenged faced by the organisation in generation of the revenue. It highlights that reduce in the number of customers for the organisation reduced generation of revenue. The core reason for reduced number is due to customer dissatisfaction. To address the issue, the three decision making models are rationality model, bounded rationality model and the dual process model. It also discusses the key themes of each module.
References
Strandvik, T., Heinonen, K., & Vollmer, S. (2019). Revealing business customers’ hidden value formation in service. Journal of Business & Industrial Marketing.
Dorantes, A. R., & Peterson, J. L. (2020). Business and Finance Staff: Impact and Success at Private Colleges and Universities. New Directions for Higher Education, 2020(189), 57-70.
Kanten, I. K., & Darma, G. S. (2017). Consumer Behaviour, Marketing Strategy, Customer Satisfaction, and Business Performance. Jurnal Manajemen Bisnis, 14(2), 143-165.
Haque, M. (2018). Customer Satisfaction Survey of Business Customers of Epsilon Systems and Solutions Ltd.
Gough, D., & Boaz, A. (2017). Applying the rational model of evidence-informed policy and practice in the real world. Evidence & Policy, 13(1), 3.
Djulbegovic, B., Elqayam, S., & Dale, W. (2018). Rational decision making in medicine: implications for overuse and underuse. Journal of evaluation in clinical practice, 24(3), 655-665.
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological review, 125(2), 270.
Hagger, M. S. (2016). Non-conscious processes and dual-process theories in health psychology.
De Clippel, G., & Rozen, K. (2018). Bounded rationality and limited datasets. Available at SSRN 3527064.
Jia, Y., & Tsui, A. S. (2020). Beyond Bounded Rationality: CEO Reflective Capacity and Firm’s Sustainable Performance. In Academy of Management Proceedings (Vol. 2020, No. 1, p. 18479). Briarcliff Manor, NY 10510: Academy of Management.
IT intrastructure impact yes no 8.1999999999999993 3.2
improvement of services yes no 8.1999999999999993 3.2
Satisfaction no yes 65 35
factors low number of staff untrained staff members poor infrastructure 8.1999999999999993 3.2 2.6
Solutions increased number of skilled staff trained the current staff improved the infrastructure 8.1999999999999993 3.2 2.6