Assessment 2 – Individual Research Essay (OPEN) Submission Deadline: Friday, 9 January 2026, at 11:59 pm (Week 4)
Assessment Weighting: 20%
Purpose of this Assignment
This assessment aims to strengthen students' understanding of how managers use data-driven information in decision-making to solve problems by identifying the steps in the decision-making process.
Demonstrate Achievement of these Learning Outcomes
ULO 1. Communicate awareness of contemporary social and ethical issues in management.
ULO 2. Discuss management functions of planning, organising, leading and controlling with reference to key theoretical models and concepts.
ULO 3. Apply basic management theories and principles to analyse key factors in the external and internal environments that affect management practices.
For this assessment, students are required to write a 1500-word (plus/minus 10%) research essay on a lecturer-selected contemporary management issue. Students are required to research and apply their theoretical knowledge to analyse the assigned management issue. Students are expected to analyse complex data, discern issues and problems, apply relevant theory to interpret facts, and evaluate possible courses of action.
For most people, artificial intelligence (AI) brings to mind replacing jobs with robots. However, research has found that larger performance gains occurred when humans and machines worked together than when either humans or artificial intelligence worked alone. A recent survey of nearly 12,000 employees revealed that employees and AI are already working together - 40 percent of workers said they use AI tools to help them with their work. Surprisingly, 68 percent of employees were using AI tools at work without telling their boss.
What do humans and machines working together look like? At clothing retailer H&M, human buyers and planners use AI to guide their work. They rely on data to figure out what styles will be purchased, by which types of customers, and what their customers might need in future seasons. Buyers and planners then build on that data to make final decisions.
A similar process is used by Nathan Cates, a buyer at Bombfell, an online styling service for men that sends customers boxes of clothing that they can keep or return. However, before buying an item, Cates insists on touching the fabric and testing it for features such as fabric sheerness and fit. These tasks are not currently accomplished well by machines.
If you call your pharmacy to refill a prescription and don't talk to a human, pharmacy employees are freed up to spend their time on customer questions that are more complex. Some companies, like the Swedish bank SEB, use AI to monitor customer calls handled by humans to see how similar problems might be resolved or even prevented in the future.
Although it's difficult to predict exactly how artificial intelligence will affect jobs in the future, there are some aspects of jobs that may be impossible to automate effectively. As CEO Chida Khatua of the asset-management firm EquBot put it, "If I'm the customer explaining what I want, humans need to be involved. Sometimes I don't know what I really want."
Essay Questions:
"As artificial intelligence (AI) technologies increasingly shape organisational decision-making, managers must adapt their approach to planning, organising, leading, managing people, and controlling."
Evaluate the potential benefits and challenges of integrating human workers with AI tools in organisational decision-making.
In your discussion, evaluate how the adoption of AI in workplaces may reshape managerial roles by 2030. Compare how lower-level managers and top managers might experience these changes differently. Analyse the ethical and organisational implications of employees using AI tools at work.
In your discussion, consider:
In your answer, refer to examples such as H&M, Bombfell, pharmacies, and banks, and explain which of the four management functions managers most need AI guidance on.
Support your analysis with examples and relevant management theory.
Citation and Referencing (APA 7) APA 7 referencing style.
The assignment should show evidence of research, with references from relevant academic journals. You should have at least FOUR (4) different peer-reviewed academic articles and use them as the foundation for each part of your report. Do not use Wikipedia as a reference source. Unless it is a generic theory/model, cited publications must be within the past 15 years.
Word Count
Assessment Submission Guidelines
Before the due date, each student is allowed three (3) submission attempts, providing an opportunity to check for unintended plagiarism using text-matching software. Students should review the similarity report and decide on any necessary revisions to ensure their final submission reflects the original work. If the similarity score is above 30%, revise the content before making your final submission, as high similarity may indicate academic misconduct.
Late Submission
Academic Integrity and Misconduct
Students must submit original work and uphold academic integrity at Southern Cross Institute (SCI). The Academic Integrity Policy and Procedure outlines the principles of academic honesty and details the consequences of misconduct, including plagiarism, recycling, fabricating information, collusion, cheating in examinations, contract cheating, artificial intelligence tools, dishonest behaviour etc. SCI utilises Turnitin to encourage proper citation practices and to detect potential academic misconduct.
Ethical Use of Generative Artificial Intelligence (GenAI) Tools
Assessment is OPEN. The ethical use of GenAI tools is permitted for this assessment.
Refer to the Quick Guide for Students created by the Learning Support Team for best practices in using GenAI tools. While GenAI can assist with idea generation, structuring, and drafting, students must carefully review, paraphrase, and properly reference any AI-generated content if used. Overreliance on AI may raise academic integrity concerns, such as fabricating information.
Creating a reference to ChatGPT or other AI models and software
As per the American Psychological Association (APA) (2020), the reference and in-text citations for ChatGPT are formatted as follows:
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat
Note: Although here we focus on ChatGPT, they can be adapted to the use of other large language models (e.g., Bard), algorithms, and similar software.
For further details, please refer to the MGT100 Unit Assessment Guide for additional information or contact your Lecturer. Please refer to the next page for the marking rubric for Assessment 2.
Criteria | Fail (0-49%) | Pass (50-64%) | Credit (65-74%) | Distinction (75-84%) | High Distinction (85-100%)
Understanding on how social and ethical issues influence management functions, such as planning, organising, controlling and leading. (30%)
Fail (0-49%): Provides a superficial or inadequate discussion of social and ethical issues influencing management functions of planning, organising, controlling and leading. Does not articulate the impacts of the ethical issues.
Pass (50-64%): Shows a basic evaluation of ethical issues influencing management functions of planning, organising, controlling and leading. Articulates the impacts of ethical issues with limited analysis.
Credit (65-74%): Offers a sound evaluation of ethical issues influencing management functions of planning, organising, controlling and leading. Adequately articulates the impacts of ethical issues with some analysis.
Distinction (75-84%): Provides a detailed and thoughtful evaluation of ethical issues influencing management functions of planning, organising, controlling and leading. Clearly articulates the impacts of ethical issues with well-supported analysis.
High Distinction (85-100%): Demonstrates an exceptional depth of evaluation of ethical issues influencing management functions of planning, organising, controlling and leading. Clearly articulates the impacts of ethical issues with insightful and comprehensive analysis.
Develops targeted strategies to enhance management functions by applying the principles of the Triple Bottom Line theory (25%)
Fail (0-49%): Does not apply relevant targeted strategies appropriately or accurately. Does not demonstrate an understanding of how the principles of the Triple Bottom Line theory enhances understanding of the case study, with little to no supporting examples.
Pass (50-64%): Applies relevant targeted strategies with limited appropriateness and accuracy. Demonstrates a basic understanding of how the principles of the Triple Bottom Line theory enhances understanding of the case study, with, supported by few examples.
Credit (65-74%): Applies relevant targeted strategies with some appropriateness and accuracy. Demonstrates an adequate understanding of how the principles of the Triple Bottom Line theory enhances understanding of the case study, supported by some examples.
Distinction (75-84%): Applies relevant targeted strategies appropriately and accurately. Demonstrates a good understanding of how the principles of the Triple Bottom Line theory enhances understanding of the case study, supported by relevant examples.
High Distinction (85-100%): Applies relevant targeted strategies appropriately and accurately. Demonstrates a deep understanding of how the principles of the Triple Bottom Line theory enhances understanding of the case study, supported by specific and insightful examples.
Fail (0-49%): Lacks credible sources or does not use evidence to support the argument. Does not acknowledge AI use, or AI-generated content is used unethically (e.g., AI-generated text is presented as original work).
Pass (50-64%): Uses a limited range of sources, some of which may not be credible. Some attempt to integrate AI, but lacks transparency (e.g., AI assistance not cited) or AI use is superficial.
Credit (65-74%): Uses a range of sources. AI tools (if used) are acknowledged but not always critically evaluated. Evidence supports the argument, though integration may be uneven.
Distinction (75-84%): Uses a range of credible sources and transparently integrates AI use (e.g., AI-assisted research, summarization). AI use is appropriately cited and critically evaluated.
High Distinction (85-100%): Utilises a wide range of credible sources and integrates AI ethically and transparently. AI is used as a tool to enhance critical analysis, not replace original thought. AI use is critically evaluated for reliability and bias.
Fail (0-49%): Report is unstructured and disorganised. Sections are disjointed, and transitions are unclear or missing. AI-generated content is misused (e.g., direct AI-generated text without critical engagement).
Pass (50-64%): Report has basic structure and organisation. Some sections may be poorly arranged, and transitions are unclear. AI-generated content (if used) is acknowledged but not meaningfully engaged with.
Credit (65-74%): Report has a sound structure and organisation. Some sections may not flow logically or may have weak transitions. AI-generated content (if used) is critically engaged with but could be better integrated.
Distinction (75-84%): Report is well-organised with a clear structure. Sections generally flow logically. AI-generated content (if used) is transparently cited and well-integrated.
High Distinction (85-100%): Report is exceptionally well-organised with a clear structure. AI-generated content (if used) is transparently cited, critically analysed, and meaningfully enhances the originality and depth of the argument.
Fail (0-49%): Writing is unclear, with frequent grammatical errors. Referencing is inaccurate or absent. AI use (if any) is not cited, or citation is inappropriate.
Pass (50-64%): Writing is often unclear and contains several grammatical errors. Referencing has multiple inconsistencies with APA 7 but has been attempted. AI use is acknowledged but citation is inconsistent.
Credit (65-74%): Writing is generally clear but may contain some grammatical errors. Referencing is mostly accurate but may have minor inconsistencies with APA 7 style. AI use is appropriately cited but lacks critical reflection.
Distinction (75-84%): Writing is mostly clear and concise, with few grammatical errors. Referencing is mostly accurate and consistent with APA 7 style. AI use is cited correctly and includes some critical evaluation.
High Distinction (85-100%): Writing is clear, concise, and free of grammatical errors. Referencing is accurate and follows APA 7 style consistently. AI use is cited correctly, with critical reflection on its role in the research and writing process.
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