Introduction to Management Functions MGM 101 - Vocabulary Flashcards
Session 1 Overview: MGM 101 Introduction to Management Functions
Course: MGM 101 – Introduction to Management Functions
Institution: Arnon University, Toronto Mississauga campus (UToronto, Mississauga) – 2025 class
Session 1 focus: Introduction to the course and management functions
Session sequencing (high level):
Session 1 – Introduction
Session 2 – Economic Challenges
Session 3 – Small Business / Entrepreneurship
Session 4 – Management, Leadership, and the Internal Organization
Session 5 – Human Resource Management / Marketing
Session 6 – International Business and The Financial System
Session 7 – Business Ethics and Social Responsibility
Session 8 – Strategy – International Business, Accounting/Financial Statements, Financial Management
Session 9 – Technology
Session 10 – Performance, Production, and Operations Management
Session 11 – Accounting/Financial Statements
Session 12 – Financial Management
Contact for course inquiries: otto.yung@utoronto.ca
Assessments Overview
In-Class Quizzes/Exercises:
Best 5 of 9 quizzes/exercises (covering Sessions 3–8 and 10–12)
Weight: 10\% of final grade
Individually based
No make-up for missed quizzes/exercises
If you have more than 5, the additional quizzes/exercises can be used as bonus marks for the combined midterm and final exam mark
Access to Quercus (online platform): https://q.utoronto.ca/
Group Assignments:
Group size: 4 to 6 (may use 6 if people drop the course)
All members self-select from the same section (e.g., 9:10 or 11:10)
Business Plan: 25\% of final grade
Based on GenAI – AI Assessment Scale (AIAS) – see next slides
Part 1 – Business Pitch / Marketing Plan (use AIAS – Level 2 and 3)
Part 2 – Business Plan / Presentation
Consultant Report: 15\% – Analyze/recommend a company, process, technology, idea, or execution of a plan
Parts 1 & 2 of Consultant Report
- Peer Feedback: could impact an individual mark (e.g., a group mark of 80% with a potential individual mark below 80%)
GenAI – AI Assessment Scale (AIAS)
Framework cites Perkins, Furze, Roe, MacVaugh (2024) – Journal of University Teaching & Learning Practice
Five-point scale to balance simplicity and clarity
Goals:
Clarify to students how and where GenAI might be used
Support students in completing assessments with academic integrity
Scale levels:
1. No AI
2. AI-Assisted Idea Generation and Structuring
3. AI-Assisted Editing
4. AI Task Completion, Human Evaluation
5. Full AI
Descriptions (consolidated):
Level 1: No AI – Assessment completed entirely without AI assistance. AI must not be used at any point. Typical prompt example: analyze a company's strategic position within a 3-hour exam period without AI. Final submission contains no AI content.
Level 2: AI-Assisted Idea Generation and Structuring – AI can be used for brainstorming, creating structures, and generating ideas to improve work. Final submission should reflect user-generated content with AI used as a co-generator; AI-generated content may be cited where appropriate.
Level 3: AI-Assisted Editing – AI can be used to edit language and improve clarity/quality; content generation by AI not required to be in the final submission. Students should ensure ownership and proper citation where AI contributed ideas.
Level 4: AI Task Completion, Human Evaluation – AI is used to complete certain task elements; students provide discussion or commentary on AI-generated content. Requires critical engagement with AI outputs.
Level 5: Full AI – AI can be used throughout the assessment to generate content; students must provide their own work in an appendix if required, with proper citations for AI-generated content. The AI acts as a "co-pilot" to enhance creativity and meeting assessment requirements.
Note: Examples prompts vary by level (e.g., Level 2 might include generating architectures or structures; Level 4/5 involve more direct AI-driven task completion or collaboration). Templates for different disciplines (business, CS, design, hospitality) are provided to guide usage.
Tools and Resources
FactSet – https://www.factset.com/tour/iam-workstation-tour
Microsoft Office Suite (Excel/PowerPoint/Co-Pilot/PowerBI/Word, etc.) – UToronto shared services portal
UTM Undergraduate Resources – general student resources hub
https://www.utm.utoronto.ca/current-students#undergraduate
Writing resources hub: https://www.utm.utoronto.ca/rgasc/student-resource-hub/writing-resources
Coding/Analytics – Python/R/SQL (Data/AI/ML/DB/Stats)
UToronto data tools: https://datatools.utoronto.ca/
Library / Finance Learning Centre – UT Mississauga library resources
https://utm.library.utoronto.ca/flc
Business Plan Toolkit
Eleven Industries/Sectors overview (as context for market analysis)
1) Communication Services
2) Consumer Discretionary
3) Consumer Staples
4) Energy
5) Financials
6) Health Care
7) Industrials
8) Information Technology
9) Materials
10) Real Estate
11) Utilities
World Economic Forum – The Future of Jobs Report (context for job trends and skills)
Disruptive Technology (including Artificial Intelligence) – broad category of technologies changing business and society
Lean Startup Canvas (Lean Startup planning tool)
Source: Maurya, A. – Why Lean Canvas vs. Business Model Canvas (Practice Trumps Theory) (2012)
Canvas blocks (typical nine):
Problem
Solution
Value Proposition
Unfair Advantage
Customer Segments
Cost Structure
Key Metrics
Revenue Streams
Channels
Purpose: to articulate a one-page view of a business model that enables rapid iteration and testing
Lean Startup Canvas (Key Blocks) – Quick Reference
Problem – What customer pain are you addressing?
Customer Segments – Who experiences the problem?
Solution – How will you solve the problem?
Value Proposition – Why customers should buy from you
Channels – How you reach customers
Revenue Streams – How you monetize
Cost Structure – Major costs to run the business
Key Metrics – Important data to track
Unfair Advantage – What cannot be easily copied by competitors
Business Model Canvas (BM Canvas) – Key Components
Key Partners – Who helps you operate (suppliers, alliances)
Key Activities – What you must do to deliver value
Key Resources – Assets required (physical, intellectual, human, financial)
Value Propositions – The bundle of products/services solving customer problems
Customer Segments – Who you serve; different segments may have different needs
Channels – How you reach and deliver to customers
Customer Relationships – Type of relationship with each segment (personal, automated, etc.)
Cost Structure – Fixed vs. variable costs; economies of scale/scope
Revenue Streams – How money is earned (sales, subscriptions, licensing, etc.)
Channels, Customer Relationships, and Customer Segments – Details
Channel Phases (typical flow):
Awareness
Evaluation
Purchase
Delivery
After-sales support
Customer Relationship Types – Personal assistance, Self-Service, Automated services, Communities, Co-creation
Customer Segments – Market types to target:
Mass Market
Niche Market
Segmented
Diversified
Multi-sided Platform
Building a Business Model – 7 Guiding Questions
How do you plan to engage the market?
Are you creating a new way of doing business?
Are you disruptive?
How do you plan on making money?
Are you selling products directly or through channels?
How do you fit in?
Do you provide services or manufacture product?
Executive Summary – Typical Contents
Eight main sections to cover:
Executive Summary
Company/Product Description
Industry/Competitor Analysis
Market & Customer Analysis (Customer Analysis)
Marketing Plan
Operations & Location
Financial Plan
Development Timeline / Growth Plan
Also include: Team, Timeline, Risks, and Appendix
The Future of Jobs Report – Key Findings (WEF)
Key drivers of change:
Ubiquitous high-speed mobile internet
Artificial intelligence
Big data analytics and cloud technology
Accelerated technology adoption indicators:
85% of companies likely to expand adoption of big data analytics
Widespread adoption of IoT, cloud computing, and AI technologies
Skills gaps: high across many roles; critical thinking, problem solving, active learning are prominent
Need for a comprehensive augmentation strategy: automation to complement human strengths; freeing humans from routinized tasks; agile learning mindset for continuous upskilling
In-demand future roles include data analysts/scientists, software developers, e-commerce & digital strategy specialists, process automation experts, information security analysts, robotics engineers, etc.
Reskilling imperative: significant portions of training needed; unequal access to reskilling across at-risk employees
The Future of Jobs Report – Roles, Skills, and Learning Agendas
Top in-demand job roles (illustrative, across industries):
Data Analysts and Scientists
AI and Machine Learning Specialists
Big Data Specialists
Digital Marketing and Strategy Specialists
Process Automation Specialists
Business Development Professionals
Digital Transformation Specialists
Information Security Analysts
Software & Applications Developers
Data Engineers, etc.
Top skills for 2025 (selected):
Analytical thinking and innovation
Active learning and learning strategies
Complex problem-solving
Reasoning, problem-solving, and ideation
Emotional intelligence; leadership; social influence
Technological design and programming
Learning agenda and mastery timelines are provided (datasets from Coursera and LinkedIn Economic Graph)
Disruptive Technologies – Core Themes
Core categories (listed multiple times across slides):
Robotics and Artificial Intelligence
Internet of Things (IoT)
Data Science
FinTech
Social Media
Augmented & Virtual Reality (AR/VR)
Lithium & Battery Technology
Autonomous & Electric Vehicles
Cloud Computing
Cybersecurity
Artificial Intelligence & Technology (Developers, AIaaS, Hardware, Quantum Computing)
Blockchain
Cryptoassets
NFTs (Non-Fungible Tokens):
Unique digital assets tied to blockchain (often Ethereum-based ERC-721)
Ownership shown via tokens; creator rights may differ from owner
Examples of high-profile NFT events (e.g., high-value digital art sales) illustrate market dynamics
Asset-backed tokens and security tokens:
Tokenized assets backed by equity, debt, or real assets
Examples include tokenized bonds and real estate investments; potential for reduced costs and faster settlement
Artificial Intelligence – Core Types and Evolution
Major AI families (types):
1) Expert Systems
2) Machine Learning (ML)
3) Neural Networks and Deep Learning
4) Genetic Algorithms
5) Natural Language Processing (NLP)
6) Computer Vision Systems
7) Robotics
8) Intelligent Systems
AI Evolution (key drivers):
Big data generation from the internet, IoT, e-commerce, social media
Decreasing costs of computation and advances in processing power
Investment from industry and academia; researchers like Turing laid early foundations
Notable AI approaches (brief descriptions):
Expert Systems – Rule-based decision support (IF-THEN rules); e.g., credit decisions
Machine Learning – Pattern discovery from large data; example: personalization and fraud detection
Neural Networks – Layers of connected nodes; deep learning for pattern recognition
Genetic Algorithms – Evolutionary search for optimization problems
NLP – Understanding and generating human language
Computer Vision – Interpreting images; facial recognition, object detection
Robotics – Physical automation with sensing/actuation
Intelligent Agents – Background automation (Siri, Cortana, Alexa); chatbots
Detailed AI Types – Selected Highlights (with Examples)
1) Expert Systems
Capture expert knowledge as IF-THEN rules
Applications: credit decisions, diagnostics, engineering, education
2) Machine Learning
Learns from large datasets; example: Facebook ad targeting using ML across servers
3) Neural Networks & Deep Learning
Multi-layer processing to detect patterns; used in fraud detection, image/speech tasks
Deep learning involves many layers; self-learning from data
4) Genetic Algorithms
Heuristic search inspired by evolution; used for complex optimization (e.g., jet engine design, production scheduling)
5) NLP
Understanding/acting on natural language; e.g., chatbots, sentiment analysis
6) Computer Vision
Interpreting visual data; facial recognition, drones, industrial inspection
7) Robotics
Physical automation; combined with control systems; still requires human oversight
8) Intelligent Agents
Background automators (scheduling, email triage, travel booking)
Data & Skills – Specific Tables and Figures (Highlights)
Top 20 job roles: increasing vs decreasing demand (illustrative list includes data analysts/scientists; ML specialists; big data specialists; security analysts; software developers; etc.)
Top 15 skills for 2025 (high-demand skills):
Analytical thinking and innovation
Active learning and learning strategies
Complex problem-solving
Reasoning, problem-solving and ideation
Emotional intelligence
Critical thinking and analysis
Troubleshooting and user experience
Creativity, originality and initiative
Service orientation
Leadership and social influence
Systems analysis and evaluation
Technology use, monitoring and control
Persuasion and negotiation
Technology design and programming
Learning agenda (typical mastery timelines) – summarized: learning data analysis, programming, leadership, ML, Python, stats, etc., with days-to-master estimates
First Movers and Competitive Strategy
Success factors for first movers:
Be first (or very early) into the market
Capture a large market share quickly
Create switching costs to retain customers
Reality: First movers rarely win; it is expensive and risky
Strategy to win:
Cheaper, Faster, Better, and one of these must be at least 10x better (i.e., 10x advantage) to justify early entry
Historical examples illustrate that late movers can overtake leaders with superior strategies and execution
Eleven Industries / Sector Descriptions (Recap)
1) Communication Services
2) Consumer Discretionary
3) Consumer Staples
4) Energy
5) Financials
6) Health Care
7) Industrials
8) Information Technology
9) Materials
10) Real Estate
11) Utilities
Each sector includes representative companies and sector-specific drivers (e.g., tech dynamics in Information Technology; energy price cycles in Energy; regulation in Financials)
Industry Examples and Case Snapshots
Example companies cited (for sector context): JAMS, Caterpillar, Fortis Inc., Sherwin-Williams, Netflix, IBM, Brookfield Asset Management, Johnson & Johnson, TD Bank, Nike, Shell, Walmart, etc. These serve as illustrative benchmarks for sector performance and competitive positioning.
The Future of Jobs – Practical Implications for Management
Organizations should develop an augmentation strategy that combines automation with human capabilities
Emphasize agile learning cultures to rapidly adapt to new roles and skill requirements
Prioritize reskilling and upskilling, with attention to equitable access across the workforce
Leverage AI and GenAI tools responsibly in planning, analysis, and decision-making while maintaining academic integrity in assessments
Additional Notes on Disruptive Technology and NFTs
NFTs illustrate ownership and provenance of digital assets on blockchain networks; governance, licensing, and rights around creators vs. owners can vary by asset
Asset-backed tokens demonstrate tokenization of real assets (bonds, real estate, etc.) to increase liquidity and transparency
Cryptoassets and blockchain technologies are transforming how value is stored, transferred, and tracked across various industries
Summary of Key References and Resources from the Slides
Perkins, Furze, Roe, MacVaugh (2024) – Journal of University Teaching & Learning Practice: Framework for GenAI in Educational Assessment (AIAS)
World Economic Forum – The Future of Jobs Report (2020, 2022, 2025 projections)
Maurya, A. – Lean Canvas vs. Business Model Canvas (context for startup planning)
UToronto libraries and data tools for research and analytics
Quick Reference: Useful Formulas and Notations
Assessment weights (as percentages):
Quizzes/Exercises: 10\%
Business Plan: 25\%
Consultant Report: 15\%
Final exam components: combined midterm and final (weight depends on course design)
AIAS levels are ordinal scales from 1 to 5, with Level 1 representing no AI usage and Level 5 representing full AI integration with appropriate citations and appendix requirements
Practical Takeaways for Exam Preparation
Know the session topics and the stated learning progression across Sessions 1–12
Be able to discuss the Lean Startup Canvas and the Business Model Canvas as complementary tools for business planning
Understand the role of the Future of Jobs Report in highlighting skills gaps, in-demand roles, and reskilling imperatives
Be able to describe disruptive technologies and their real-world implications, including NFTs and tokenization
Remember the AIAS framework and the general guidelines for appropriate AI usage in assessments
Recognize the strategic implications of first-mover vs. late-mover dynamics and the rule of 10x in competitive positioning
Appendix: Sample Prompts and Templates (GenAI Use)
Example prompts by AIAS level (illustrative):
Level 1: Analyze strategic positioning without AI support
Level 2: Brainstorm architectures or structures for a project; draft outlines with AI assistance; final submission should include user-authored content
Level 3: Edit language and improve clarity; keep content authored by student
Level 4: Use AI to complete certain task components; provide reflection on AI outputs
Level 5: Use AI as a co-pilot across the task; cite AI-generated content; include final human-authored sections in addition to AI contributions