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IB Syllabus 5.9
IB Syllabus 5.9
The learning outcomes (or assessment objectives) for this section of the IB Business Management syllabus are:
Data analytics (AO1)
Database (AO1)
Cybersecurity and cybercrime (AO1)
Critical infrastructures, including artificial neural networks, data centres, and cloud computing (AO2)
Virtual reality (AO2)
The internet of things (AO2)
Artificial intelligence (AO2)
Big data (AO2)
Customer loyalty programmes (AO3)
The use of data to manage and monitor employees; Digital Taylorism (AO3)
The use of data mining to inform decision-making (AO3)
The benefits, risks and ethical implications of advanced computer technologies (collectively referred to here as “management information systems”) and technological innovation on business decision-making and stakeholders (AO3)
In-Class Activities
In-Class Activities
Day 1: MIS Concept Specialists
Day 1: MIS Concept Specialists
Jigsaw: MIS Specialists
Artificial neural networks, data centres, and cloud computing (AO2)
Virtual reality (AO2)
The internet of things (AO2)
Artificial intelligence (AO2)
Big data (AO2)
Step 1: MIS Specialists
Be Ready to Explain:
What is your assigned MIS Concept?
How businesses use it
What are the advantages, disadvantages (risks), and ethical implications?
Create a exhibition/ showcase/ example to show how it works and how it’s usefulful
Complete the following table: https://docs.google.com/document/d/12dEhh0IZcF5pgD6UBXTxljIecYSlyX-KUsqshX0Aiq0/edit?usp=sharing
Step 2: Sharing of the Wise (15 min)
Form new groups that has at least 1 “specialized person” of each MIS Concept (must have at least one person from group 1, 2, 3, 4).
Teach the rest of the group members
What is your assigned MIS Concept?
How businesses use it
What are the advantages and disadvantages?
Showcase your exhibition showing how it works and how it’s useful
No one leaves the group until everyone understands the other production methods.
Step 3: Returning Home (10 min)
Return to your original numbered groups.
Summarize and discuss what you have learned in the other groups and make sure everybody understands all the MIS Concepts, advantages and disadvantages, and when businesses could use each concept.
Day 2 - 3: MIS Socratic Seminar Ethical Discussions
Day 2 - 3: MIS Socratic Seminar Ethical Discussions
MIS Concept Groups:
Group 1
Artificial neural networks, data centres, and cloud computing
Virtual reality
The internet of things
Group 2
Artificial intelligence
Group 3
Cybersecurity and cybercrime
Customer loyalty programmes
Group 4:
The use of data to manage and monitor employees; Digital Taylorism
Group 5:
Big Data
The use of data mining to inform decision-making
Step 1: Individual Work
Keep your same groups but change to a new topic.
Each member of your groups you will research your allocated MIS Concepts
They will take notes on the benefits, risks, and ethical implications of your allocated MIS Concept on:
Business decision-making
Different Stakeholders
You will then come up with 3 discussions questions individually regarding the possibilities and ethics of your MIS Concept
Step 2: Group Work
You will share your notes with your other team members and discussion questions
You will prepare a 2 minute presentation for the class to introduce your MIS Concept + the benefits, and risks, and ethical implications
business decision making
different stakeholders
You will choose your top 5 best discussion questions to run a discussion for 8 minutes.
Step 3: Socratic Seminar - Student Led (10 minutes each)
Students will run discussions on their topics
2 minute presentation to the class
8 minute discussion
Each group will have 2 representatives to discuss the topic who sit in the inner circle
Other representatives sit in the Outer Circle taking notes
Outer circle representatives can
pass notes to the inner circle
Enter the inner circle by tapping someone out
Every member of the group must be in the discussion circle at least once and must speak at least once.
Switch Groups after 10 minute discussion is over
Other Possible Discussion Questions:
MIS Concept Questions:
In what ways can businesses use computer systems to improve operations?
How may technology improve business productivity?
How may digital technology enables new business models to flourish?
Discussions:
Are there new ethical challenges emerging from the increased use of data analytics in business decision-making?
To what extent do the classification systems we use in data analytics affect the conclusions that we reach?
How might personal prejudices, biases and inequality become “coded into” customer loyalty programmes?
To what extent is big data changing what it means to know your customers?
Does artificial intelligence allow knowledge to reside outside of human knowers?
What are the moral implications of possessing large amounts of information about consumer behaviour?
Discuss the ethics of using digital Taylorism in the workplace.
To what extent should employees and trade unions resist the growth in digital Taylorism?
What you should know
By the end of this subtopic, you should be able to:
define the following terms: (AO1)
management information systems
data analytics
databases
data mining
cybersecurity
cybercrime
artificial neural networks
data centres
cloud computing
virtual reality
internet of things (IoT)
artificial intelligence (AI)
big data
customer loyalty programmes
digital Taylorism
describe data analytics, databases, cybersecurity and cybercrime (AO1)
analyse the importance of critical infrastructures, including artificial neural networks, data centres and cloud computing (AO2)
explain the role of virtual reality (VR) in business (AO2)
comment on the importance of the internet of things, artificial intelligence and big data (AO2)
examine the benefits and drawbacks of customer loyalty programmes (AO3)
discuss the use of data to manage and monitor employees; digital Taylorism (AO3)
evaluate the use of data mining to inform decision-making (AO3)
examine the benefits, risks and ethical implications of advanced computer technologies (management information systems) and technological innovation on business decision-making and stakeholders (AO3)
https://quizlet.com/pa/836577326/59-management-information-systems-hl-flash-cards/?i=4jrhob&x=1jqt
https://www.gimkit.com/view/653735a892a572002b389e63
Artificial intelligence (AI)
This is an area of computer science that develops the ability of smart machines to perform tasks rather than natural or human intelligence, e.g., voice activated commands on smart devices.
Artificial neural networks (ANN)
These are a feature of critical infrastructure and refer to the use of learning algorithms that can learn things, solve problems, and make decisions independently by processing new data as these are received.
Big data
Big data refers to access to extensive amounts of unprocessed (raw) and processed (structured) data from a broad range of sources.
Cloud computing
This is a virtual, computer generated online space that enables businesses to store, organize, manage, process, and retrieve data in safe and efficient ways.
Critical infrastructures
This refers to the crucial computer systems, structures, networks, and facilities required for the effective functioning of an organization in the modern and digital corporate world.
Customer loyalty
This measures the extent to which customers consistently repurchase products from the same business.
Customer loyalty programmes
These are marketing strategies designed to retain customers by using a rewards programme that give loyal customers direct benefits, such as reward points that can be redeemed for purchases at discounted prices.
Customer retention
This is a measure of customer loyalty by determining the extent to which existing customers will stick to the same brand when making future purchasing rather than switching to a rival brand.
Cybercrime
This refers to any form of illegal activity carried out using electronic methods to deliberately and maliciously attack computer hardware or software, including computer networks, devices, and critical infrastructures.
Cybersecurity
This refers to a firm’s policies, processes, and procedures used to safeguard its computer systems and networks from unwarranted attacks, such as information disclosure, data theft, or physical damage.
Data analytics
This is the management process of examining and scrutinizing raw data to find meaningful trends and patterns to support decision making and business planning.
Data centre
This is a physical facility or the location of computer systems with networks and structures that support organizations in accommodating their telecommunications and data storage systems.
Data mining
This is the management process in which large sets of raw data are extracted to gather useful and valuable information. The data can then be used for analysis and predictive purposes.
Data overload
This means there is too much data available for managers to know what to do. This causes inefficiencies and therefore delays management decision making.
Database
This is a computerized system used by businesses to store, organize, search, select, process, and retrieve data and information.
Digital Taylorism
The management approach that relies on management information systems to improve productivity by managing employees and the tasks they perform in the most systematic and methodical ways.
Machine learning
This is the use of computer systems, algorithms, and statistical models to enable electronic devices to memorize and adapt on their own, thereby imitating intelligent human behaviour and decision making.
Management information systems (MIS)
A collective term used to describe the advanced computer technologies and technological innovations that influence business decision-making and stakeholders of a business.
Technological innovation
This refers to the partial or full replacement of an existing technology by one that improves a firm’s productivity, its product quality, and competitiveness in the market.
The Internet of things (IoT)
This refers to any Internet-enabled device that enables people to store, share, and transfer data with other electronic devices that can connect to the Internet.
Virtual reality (VR)
This is an artificial, computer-generated environment or world accessible to the consumer in a seemingly real world way, such as interactive simulations using highly sophisticated computer equipment.
5.9 Powerpoint
5.9 Powerpoint
5.9 Extra Notes
5.9 Extra Notes
Management Information Systems (MIS)
The science of analysing data to establish patterns, trends and behaviours in order to draw conclusions.
Management Information Systems (MIS) combines business and computing to assist organizations in digitizing work and managing an increasingly remote workforce. MIS professionals have specialized knowledge in areas such as data analytics, software development, and project management, allowing them to assess and adopt new technologies to enhance business processes.
Kognity: Systems that collect, collate, coordinate, control and channel information within an organisation.
Data analytics (AO1)
Data analytics (AO1)
The science of analysing data to establish patterns, trends and behaviours in order to draw conclusions.
Data analytics is the process of transforming raw data into usable information for businesses.
It can help determine trends from a mass of data, making information more user friendly to improve the overall efficiency of a business.
Database (AO1)
Database (AO1)
An organised collection of information stored in an electronic system.
Databases are analysed by data mining, a process of searching for and finding patterns and trends within large data sets.
A database is a computerized system that makes it easy to store, search and select data and information.
Cybersecurity and Cybercrime (AO1)
Cybersecurity and Cybercrime (AO1)
Cybersecurity:
Refers to the protection of computer systems and networks from:
Unwarranted information disclosure.
Theft or damage to computer hardware, software or data.
Cybercrime:
Any illegal activity carried out using computers or the internet by deliberately and maliciously targeting computers, computer networks or networked devices.
The Internet: Cybersecurity & Crime (Examples of cybercrime)
More Examples of things to be protected from:
Hacking. This refers to exploiting weaknesses in computer systems and networks to gain access to data.
Ransomware. This is software planted on a computer system designed to block access to those systems until a sum of money is paid. In 2021, the Taiwanese electronics company Acer was attacked, with hackers demanding 50 million USD. Businesses of all kinds, including critical services such as hospitals and government offices, have been hit by ransomware attacks.
Distributed denial of service (DDoS) attacks. This is an attack on a computer system designed to slow down website traffic by overwhelming it with activity. In 2021, Yandex, Russia’s largest search engine and internet service provider was hit with a DDoS attack and Amazon’s web services platform also reported a significant DDoS attack. Both attacks were successfully repelled by cybersecurity.
Example: Opus Victum of Cybercrime
In 2022 Optus, an Australian telecommunications firm, suffered from a cyberattack that led to the theft of personal data of about 10 million customers (about 40% of the population of Australia).
Prime minister issues warning over Optus data breach | 7NEWS
Example: Yahoo Data Breach
In 2016, Yahoo suffered what is still considered to be one of the largest data breaches ever. Over 500 million accounts were stolen in one breach and another 3 billion customer accounts were compromised in another breach shortly afterwards.
Methods of fighting cybercrime with cybersecurity
Keeping computer software, network structures and operating systems updated.
Using anti-virus software to prevent or limit the volume of cyber attacked.
Training staff about the importance of cybersecurity.
Critical infrastructures, including artificial neural networks, data centres, and cloud computing (AO2)
Critical infrastructures, including artificial neural networks, data centres, and cloud computing (AO2)
Critical infrastructures refers to the essential and interrelated physical structures and facilities needed for the effective function of a business.
Examples include:
Artificial neural networks
Data centers
Cloud computing
Longer Definition from InThinking:
Critical infrastructures are the crucial computer systems, structures, networks, and facilities required for the effective functioning of an organization in the modern and digital corporate world. They consist of both physical infrastructures within an organization's management information systems (such as artificial neural networks and data centres) as well as non-physical infrastructures (such as cloud computing) that power modern business operations.
Artificial Neural Networks
Artificial neural networks (ANN) are a form of machine-learning that is designed to simulate how the human brain processes and analyses data and information. ANN relies on the use of learning algorithms that can acquire knowledge, solve problems, and make decisions independently by processing new data as these are received.
This means ANNs can solve problems without having to be explicitly programmed to do so.
ANNs are the basic building blocks of artificial intelligence.
Example:
Chatbots are an example of an ANN. They are frequently used by commercial banks, hotels and airlines to deal with customer queries in real time.
Chatbots and virtual assistants can offer 24/7 customer service automatically. The software simulates a real conversation, as if it were with a real human being.
Example part 1:
Example part 2
Data centers
A data centre is a physical facility or space of networked computers and component resources that support businesses in housing their critical applications and data.
Services provided by data centres:
Data backup and archiving
Email and file sharing
Database systems
Big data, artificial intelligence and machine learning
Virtual communications and collaboration systems
Cloud computing
is similar to data centres except that it is a virtual resource or online space that enables businesses to store, organize and retrieve data in safe and efficient ways.
Kognity: Involves data storage and the networking of computers, software, databases and servers to allow information to be stored and accessed from anywhere in the world.
Example 1
Google Drive
People save files online on their google drive accounts where they can access files on multiple devices and share documents
Example 2
Apple iCloud
People save photos and other files online in Apple iCloud saving space on their phones and having access on multiple devices
Example 3
Dropbox
People save files online on Dropbox rather than on their computers
Virtual reality (AO2)
Virtual reality (AO2)
The use of computer technologies to create a simulated 3D experience.
It allows people to explore and interact with others in a near-reality way.
Users can interact with the simulations using specifically designed hardware and software, such as the headsets below. VR can be used to recreate or distort real world environments, processes or events. VR is not a new concept, but demand for VR-related technologies is increasing.
Applications of VR
Virtual reality provides workers with the practice they need, albeit in a computer-generated world, so they become familiar with different scenarios they are likely to face in the real world. Replicating these situations in VR helps employees to know what to do in reality should these circumstances arise. Employees are also able to react in a safer way than if they were experiencing the situation for the first time in real life.
Students can experience various sport in a safe and controlled virtual environment, without the costs, risks, and physical requirements associated with the sport in the real-world. This is particularly beneficial for students who are unable to participate in physical sports due to injury or other physical limitations.
Airlines use VR technologies in their flight simulators to train and test pilots.
In Thailand, smart factories are using VR and 5G technology for factory tours, allowing investors, business owners, customers, employees and engineers to explore factories safely and securely without risks.
Other Examples:
Education, including virtual educational visits
Entertainment, e.g., virtual attendance at concerts
Gaming
Learning to play musical instruments
Movies and TV shows
Meditation and therapy
Real estate, e.g., virtual tours of property
Retail, i.e., virtual shopping
Social networks
Sports, e.g., virtual attendance at sporting events
Training - VR allows for immersive simulated experiences that can make employee training more effective and engaging. Companies can train workers on complex equipment and processes in a safe, virtual environment. This is especially useful for hazardous tasks.
Sales and Marketing - Using VR, businesses can create immersive demonstrations and visualizations of their products and services to help potential customers understand and experience them before purchase. This improves the buyer's journey. Real estate agents for example are using VR to "tour" property listings.
Collaboration - VR provides new virtual workspaces where globally distributed teams can collaborate in real-time. Users have a sense of "being there" together via their avatars. This can streamline processes like design, planning and brainstorming across distances.
Customer Service - Some companies are experimenting with VR customer support, using virtual reps who can see and interact with the customer's environment to rapidly solve problems. This raises accessibility.
Events and Conferencing - Instead of traditional trade shows and conferences, VR allows for virtual events where users have an embodied presence to network, attend sessions and visit booths all in one digital space. This reduces costs.
VR Advantages
VR helps to reduce wastage and accidents in the workplace. It creates a safer working environment for employees to train and develop their skills to perform better at their jobs. For example, VR can be used to recreate any scenario, such as falling objects in the workplace or other unsafe situations. This helps the employees be more prepared in the event such scenarios arise in reality, rather than experiencing for the first time without knowing how to react.
VR is highly flexible and can be used for a very broad range of training purposes. For example, hotels can use VR for a range of routine and complex hotel operations, such as procedures and processes to check in guests, cleaning a guest room, providing room service, and handling a wide range of guest enquiries.
Training in VR enables employees to be 100% focused on the task at hand. In the real world, training is often disrupted with other interactions and distractions in the workplace. This makes training more efficient and cost-effective.
Virtual training can help to keep costs down without yet still give workers the near-reality experience they need to develop their talents.
VR Disadvantages
The accelerating pace of VR means that it can be challenging for a business to keep up with technological advances. Equipment can also become obsolete quite quickly.
Linked to this, investing in the latest VR hardware and software means the cost can be expensive. At the same time, there is no guarantee that the investment will be successful or whether customers have a desire and willingness to adopt VR technologies.
Research has shown that some employees suffer from motion sickness when putting on VR headsets. This limits the effectiveness and potentially wide-reaching applications of VR for the organization.
Concerns over ethical issues, security-related issues and legal issues associated with VR and the metaverse. There are ethical concerns that people may lose their ability to interact with the physical world if they spend too much time in the metaverse. In addition, as people interact in the digital world and data is collected about what they do there, there are concerns about data/privacy protection and data security, and about meeting legal obligations across real and metaverse country borders.
Personal at Home VR Examples
Although VR is widely used in various industries, such as automotive and healthcare, due to its operational benefits, it is increasingly popular in the gaming industry. According to market research by Grandview Research, the global VR market size was around $59.96 billion in 2022 and is expected to grow exponentially to $435.36 billion in 2030.
Asia Pacific dominates the VR gaming market with a share of 39.9% (2022). This is due to the technological advancements in Southeast Asian countries and favourable initiatives by governments, such as funding and investments to benefit VR companies in the region.
Some key players operating in the VR gaming market include Oculus VR (owned by Facebook), Alphabet Inc. (parent company of Google) ; HTC Corporation, Microsoft Corporation, and Samsung Electronics Co.
The internet of things (AO2)
The internet of things (AO2)
The Internet of things (IoT) refers to any Internet-enabled device that enables people to store, share, and transfer data with other electronic devices that uses embedded sensors. It consists of a giant network of connected devices ("things" or objects) that collect and share the most relevant data with users, based on real-time information, in order to help address specific needs of the consumer. The data are used to detect patterns, make recommendations, and identify possible problems before they occur.
Examples of popular IoT devices found in "smart homes" include Apple's Smartwatch, Amazon's Echo, Google's Fitbit, and Google's Nest smart thermostat system.
More Examples:
Government agencies integrate IoT sensors for air quality monitoring by identifying pollutants.
Smart microwaves that automatically cook food at the right temperature and for the right length of time.
Smart traffic light systems are used to streamline traffic efficiency and public transportation based on variations in traffic conditions and traffic flows.
Self-driving cars that use highly complex sensors to detect objects in their path.
Wearable fitness devices that measure the number of steps the user has taken each day, their sleep patterns, and their heart rate. The data is then used to suggest bespoke exercise plans tailored to the needs of the user.
Farmers use the IoT technologies to improve agricultural output and pest control. Data analytics is used to track soil moisture levels, climatic changes, and the health of plants in order to increase crop yields.
Global Positioning Satellites (GPS) aligned with smartphone apps and computer hardware and software in motor vehicles.
The Ring smart doorbell home security system is linked to the user's smartphone and lets homeowners know, irrespective of their location, when the doorbell is pressed, lets them see who it is, and to speak with them.
Smartphones can be linked to countless apps that enable users to connect to their home appliances, such as smart lights, thermostats for heating (or air conditioning), home entertainment system, and home security systems; all of which can be operated remotely so long as there is an Internet connection.
Advantages
Removes the need to depend on human intervention to collect, process and interpret data.
Works faster and more accurately, in real-time.
Disadvantages
Data security and privacy concerns mean that firms need to operate within the law governing the collection, storage and transfer or sharing of data.
Concerns on the growing dependency on the internet that could render firms unable to function efficiently without it.
Artificial intelligence (AO2)
Artificial intelligence (AO2)
Oxford Defitinition: "The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
It is an area of computer science that develops the ability of smart machines to perform tasks rather than natural or human intelligence, such as motion or voice activated commands on smart devices.
AI enables computers and IoT devices (the Internet of things) to mimic human behaviour and actions, such as becoming familiar with different situations, learning from experiences, processing information to solve problem, and using data to inform decision making. In theory, AI enables businesses to make rational decisions based on data, rather than relying on human emotions and biases that can result in irrational choices and outcomes.
AI Can:
improve decision-making with use of production, stock, sales and customer data
automate processes to ensure speed, consistency and efficiency
develop new markets through innovations and capabilities
provide virtual assistance for customer service and other areas
Examples of AI in Daily Lives
Apps that support commuters with virtual updates and alternative bus and train routes, showing people where to interchange and which platform to use.
Drive assist functions in motor vehicles can break automatically in emergency situations and assist drivers with parking in tight spaces (a feature of self-driving cars).
Facial and voice recognition systems to access online banking, to complete online purchases, and even to open security doors.
Security systems can use aerial drones that are automatically launched when an alarm is triggered, streaming live video to a private security team.
In the UK, some police forces have tested predictive policing tools, i.e., used AI to predict where crimes are likely to happen as well as the probability of people reoffending. The AI technology could help to reduce pressure on police officers and improve public safety.
Online search engines, social media platforms (such as YouTube), streaming service providers (such as Netflix), and e-commerce businesses (such as Amazon) that provide recommendations that users are likely to be interested in, including social media feeds.
Satellite navigation systems, using the global position system (GPS) to provide live travel assistance to motorists and travellers using a smartphone or other satellite navigation device. Examples include Google Maps and Apple Maps, Bing Maps, and Waze (which is owned by Google).
Smart assistants (often referred to as "chatbots") that provide help with enquiries about banking, insurance, healthcare, as well as travel and tourism. This also covers the use of marketing chatbots.
ChatGPT (Chat Generative Pre-Trained Transformer) is a language model developed by OpenAI, designed to generate human-like text responses to questions and prompts.
Smart home appliances, such as a smart fridge freezers (that auto clean and auto defrost when needed) and smart vacuum cleaner (that use sensors to detect when and where rooms need to be cleaned), without human input.
The use of predictive text functions when typing a message on a smartphone, tablet, or computer.
Primary Sector
Increases efficiency of food production through precise monitoring and application of nutrients, reducing pesticide and water use and improving sustainability.
Drones can closely monitor crops and soil and can also track livestock.
Secondary Sector
Minimises waste and identifies defects, improving lean production.
Factory data is stored in the cloud and machine learning corrects problems and improves efficiency.
Factories operate with little human intervention, reducing costs of labour.
Tertiary Sector
Robots and sensors monitor consumer behaviour and engagement, improving sales forecasting, pricing decisions, product placement and customer segmentation.
Sensors and cameras monitor stocks (inventory) so that reorders are made automatically.
Sensors and cameras monitor stores and automatically calculate purchases in a consumer’s cart, charging the customer’s card as they leave the store.
Chatbots provide 24-hour customer service, reducing the need for human labour.
Siri Example
Siri is Apple’s digital assistant, which was launched in 2011 when the company integrated AI into its operating system. It was included as part of the iPhone 4S. Back then, Apple described Siri as the “most private digital assistant.”
Siri personalises AI to help iPhone users with countless tasks, such as setting timers and reminders (alarms), making hands-free phone calls, and completing online searches - all through voice activation. Today, Siri is integrated with all of Apple's products, including the Apple Watch, and with connectivity to cars, smartspeakers, and home appliances.
Are Self-Driving Cars The Future Of Transportation?
Big data (AO2)
Big data (AO2)
Big data is the process of collecting and analyzing large amounts of data sets in order to identify trends and patterns that can be used in strategic planning and business decision making.
Big data refers to access to extensive amounts of unprocessed (raw) and processed (structured) data from a broad range of sources.
The data are often complex, due to the huge volume available, so sophisticated computer systems are used to capture, process, and analyze the data. Such tasks would be beyond the ability of humans without the use of technology to manage the process.
Business decision-making can be improved when there are large amounts of meaningful data available.
Estimated that big data as a service market was valued at $12.74 billion in 2020 but is forecast to increase to $93.52 billion by 2028 (which represents a compound annual growth rate of 28.2%).
Benefits of Big Data:
Making more informed business decisions, based on facts, trends, and logic.
Understanding their customers in better ways, thereby supplying goods and services that meet their changing needs.
Improving business activities and operational efficiency.
Generating additional revenues and profits.
More Examples of Big Data:
Airline companies use big data to determine different prices to charge passengers on each day of the year, using dynamic pricing.
Amusement park operators, such as Walt Disney World Theme Parks, use big data to understand visitor behaviour at its theme parks and hotels, so that it can offer an even more "magical" experience for its guests.
Social media marketers can access large amounts of data for market research and market planning purposes in order to better inform their sales practices and improve promotional techniques.
Banks use big data to deliver improved and more personalised services for their customers. Using data from bank statements and transactions enables the banks to knows a lot more about their customers, from what they like to buy, and how often, to where they go on holiday most frequently. Big data also enables banks to detect fraud.
Car manufacturers use big data such as live GPS data from motor vehicles to improve traffic flow and reduce congestion. It is also used to predict and warn drivers about maintenance needs for their vehicles, such as repair and servicing schedules. Many insurance companies also use big data from a car's black box to make more informed decisions about risk management and insurance premiums.
E-commerce businesses, such as Amazon, and online streaming services, such as Netflix, use big data for product recommendations. Amazon earns about 35% of its sales revenues from product recommendations.
Energy companies use big data to optimize the generation, distribution, and consumption of energy in homes and places of work. This includes analyzing big data from power plants as well as monitoring and examining data from smart metres in residential homes to improve energy efficiency.
Food delivery service providers, such as Uber Eats, use big data to make accurate forecasts of food delivery times for their customers, as well as meal recommendations.
Healthcare providers use big data to track patient information, monitor pandemics, and improve medical research, e.g., big data is used by medical clinics to store and analyze electronic health records to identify patterns and predict health risks.
Wealth managers and financial advisers use big data and data analytics to assess credit risk and inform investment decisions. It can also enable them to create personalized financial products and services for their clients.
Customer loyalty programmes (AO3)
Customer loyalty programmes (AO3)
Customer loyalty programmes (CLP) refers to any customer-retention strategy that incentivizes customers to continue buying the same products and brands of the business, instead of switching to those provided by competitors.
The purpose of CLPs is to retain customers and encourage repeat purchases.
It's a rewards programme that give loyal customers direct benefits.
Examples:
Reward points that can be redeemed for purchases at discounted prices.
Benefits may include: free merchandise, coupons, or priority access to new product launches.
Frequent flyer customer loyalty programmes used in the commercial airline industry.
Dropbox offers additional cloud storage for referrals
Amazon Prime charges a monthly fee for benefits like free shipping
Grocery stores sometimes provide discounts to members
Management information systems are used to gather and process data about customers to provide more appealing customer loyalty programs to attract prospective customers and retain current ones.
Advantages of Customer Loyalty Programs
Improves customer retention by rewarding repeat purchases
Encourages more spending from loyal customers who may feel special and valued
Generates positive word-of-mouth referrals to new customers
Provides a more cost-efficient way to attract and retain customers
May create an additional revenue stream from membership fees
Helps businesses promote relevant products through customer data
Enhances customer engagement through targeted marketing
Increases long-term profitability for the business
Disadvantages of Customer Loyalty Programs
Higher costs - Businesses may need to be wary of the costs of giving away free products and other extras or rewards to customers. Higher costs may lead to higher prices being charged for the firm's goods and services.
Time - Customer loyalty can take a significant amount of time to develop and nurture.
Competition - Highly likely competitors will also offer their own schemes that reward devoted customers. Customer loyalty diminishes as competition increases. To keep the brand or product relevant and desirable in the minds of the buyers, the business must provide authentic value to its customers in order to keep them coming back.
Ethical Issues of Excess expenditure - Customer loyalty programmes can encourage customers to overspend and even raise the level of consumer debt (if they use credit card payments or loans to make their purchases. This raises ethical issues about businesses actively promoting the use of customer reward programmes.
The use of data to manage and monitor employees; Digital Taylorism (AO3)
The use of data to manage and monitor employees; Digital Taylorism (AO3)
Digital Taylorism
A modern version of Taylor’s scientific management that uses digital technologies to monitor every aspect of employee performance.
Firms use staff monitoring systems to track what employees are doing.
The data gathered allows a firm to continuously evaluate and measure the performance of its employees.
Determines whether to keep employees, provide additional training or discipline, or remove employees who fail to hit targets
Digital Taylorism can Monitor:
How long an employee spends on a particular website
What employees search for on their computers
The contents of emails sent by employees
How long a worker takes to complete certain tasks, such as production or delivery times
The duration taken by employees on their rest breaks
Absence and punctuality rates
Example in Gig Economy
Ride hailing apps and food delivery apps use computerised systems to manage and monitor workers.
Managers and owners can analyse worker behaviour in detail, using data to analyse every aspect of performance, such as delivery times.
Use sensors to track performance by tracking locations, timing, driving, delivery success, delivery rates and many other metrics.
May be used to administer piece-rate pay and may act as a monetary incentive to workers, ensuring that they meet targets in order to receive pay or bonuses.
Advantages of Digital Taylorism
Improved coordination, control, and decision making
Training and development needs can be identified easily.
Improved productivity and efficiency: Workers are monitored and incentivised to increase productivity in order to meet targets; workers can increase their pay when they perform efficiently and well.
Improved Appraisal: Management can assess worker performance and productivity using data analytics rather than using less scientific measurements.
Prevention of illegal behaviour in the workplace.
Limitations of Digital Taylorism
Lower Motivation: Employees may feel unmotivated and mistrusted when being constantly monitored
Reduced Creativity: may reduce the scope for workers to find creative solutions to problems, as they fear missing targets.
Ethics: Monitoring employees without their knowledge or consent is considered unethical and/or illegal in most parts of the world.
Thus, employers have to have ethically and legally justifiable reasons to perform such monitoring, surveillance and data collection.
Digital Taylorism enables employers to use data to manage and monitor employees, such as tracking their emails, phone calls, and online activities. This raises ethical questions about the privacy right of employees.
Dehumanisation and overwork: Data could be used in a dehumanising manner, turning workers into robots that complete task after task.
The use of data mining to inform decision-making (AO3)
The use of data mining to inform decision-making (AO3)
Data mining is the process of extracting raw data from large amounts of different data sets and summarizing this into useful (usable) information to improve decision making by finding patterns, relationships (correlations), and trends.
Data mining relies on other aspects of management information systems (MIS), such as databases, data analytics, big data, and machine learning to analyze these large data sets so as to inform business decision making.
Data mining enables managers to make sense of past trends in order to make informed predictions of the future, rather than relying on management decisions and corporate strategies to be based on intuition and guesswork.
Business applications for data mining
Advertising campaigns
Artificial intelligence
Budgeting
Customer loyalty programmes
Crisis management and risk management
Cybersecurity
Fraud detection
Marketing planning
Medical diagnosis
Research and development
Quality management and quality assurance
Sales forecasting
The Internet of things (IoT)
Consumer profiling
Marketing planning
Market research
Customer loyalty schemes
Market basket analysis
Production planning
Advantages of Data Mining
They help manager and decision makers to predict future situations.
Effective use of data allows businesses to understand their customers better, which helps to improve customer relations.
Being able to make more informed decisions enable businesses to increase sales revenue.
Improved risk management as data mining can be used to detect fraudulent activities and unusual financial transactions. It helps firms to identify potential risks and enhance security measures to protect their assets.
Data mining techniques cut wastage and inefficiencies in operations management, thereby helping businesses to reduce costs, e.g., it enables firms to improve sales forecasting and optimize stock (inventory) levels.
Overall, data mining methods enable businesses to reduce risks and exposure to fraudulent behaviour.
Limitations of Data Mining
Privacy issues are a growing concern due to the increasing amount of data about private individuals on platforms, such as social networks, e-commerce, online forums, and smartphone apps.
Security issues surrounding hackers gaining access to big data of customers, including data on personal and financial information, credit card fraud, and identity theft.
Personal data can be collected and misused, including the unethical sale of private information to third parties. The information can be used unethically to take advantage of vulnerable people or to discriminate against a group of people.
Data mining is challenging and complex. Finding the right or required data is a time consuming and difficult task given the huge volume of data present, which are also generated continuously.
It can be highly expensive, including the need to invest in advanced data mining technologies and hiring specialist technicians. Staff training about the use of mined data may also be required, which further increases costs.
The benefits, risks and ethical implications of advanced computer technologies (collectively referred to here as “management information systems”) and technological innovation on business decision-making and stakeholders (AO3)
The benefits, risks and ethical implications of advanced computer technologies (collectively referred to here as “management information systems”) and technological innovation on business decision-making and stakeholders (AO3)
Benefits of Management Information Systems
Improved decision making - Using advanced computer technologies such as artificial intelligence enables a business to automate and improve decision-making. For example, data analytics can provide managers and employees with an in-depth understanding of an organization’s performance, allowing them to make more informed decisions. Furthermore, predictive data analytics can help to provide insights into potential future outcomes, thereby allowing for more confident decision-making.
Better operational efficiency - MIS and technological innovations can be used to streamline processes and reduce costs due to the improved operational efficiency. This has the potential to improve the profits of the business as well as provide better services to its various stakeholders groups.
Improved customer services - MIS and technological innovations can be used to track customer data, such as purchasing habits and preferences, thereby providing vast amounts of data. This provides this business with valuable insights into customer behaviours and their changing needs and wants. Furthermore, MIS and innovative technologies can provide a business with real-time customer feedback, such as through the use of chatbots (or virtual assistants). In turn, this can be used to improve customer services and product offerings, as well as enhancing customer loyalty programmes.
Enhanced competitive advantages - By using the latest MIS and technologies, businesses can gain more insights into their competitors and the markets in which they operate, thereby identify potential threats as well as opportunities for innovation. For example, a business can use predictive data analytics to gain a better understanding of consumer behaviours and trends, which can then be used to develop new products that better meet the changing needs and preferences of customers.
Risks of Management Information Systems
Cybercrime / Security Risks - Cybercrime refers to any form of illegal activity carried out using electronic methods to deliberately and maliciously attack computer hardware or software, including databases, computer devices, and critical infrastructures. With large volumes of big data and sensitive information about customer and employees, there is always the potential risk of unauthorized access, malicious attacks, or data theft. Strong security protocols and staff training are needed to mitigate risks.
Set-up and maintenance costs - It can be extremely costly to install, upgrade, and maintain management information systems, such as artificial intelligence, customer loyalty programmes, cybersecurity, and virtual reality (VR). Additionally, training must be provided to staff to ensure that they are able to use the MIS and technologies correctly and efficiently, which can be costly for the training and the time it takes to transition. Systems may also become obsolete quicker than expected, requiring further investments.
Regulatory compliance - New systems collecting and processing large volumes of customer data raise legal obligations to protect privacy. Breaches can severely damage reputation and trust with stakeholders. Organizations must remain compliant with evolving data privacy regulations.
For example, the General Data Protection Regulation (GDPR) requires all businesses operating in the European Union (EU) to protect the data of their customers. Failure to comply with the regulations can lead to significant financial penalties.
Businesses dependent on technology functioning properly. Risk of glitches, system downtown, bugs or errors during implementation.
Businesses can prevent cyberattacks by:
Using firewalls (computer security systems that monitor and control incoming and outgoing network traffic), intrusion detection systems, anti-virus software, and multi-layered data encryption.
Providing ongoing cybersecurity training for managers and employees on the use of the data security systems and cybersecurity measures.
Creating and maintaining detailed and updated contingency plans (backup plans) in the event of a system failure or data breach. These plans can businesses to recover any lost information as quickly as possible, minimize the risks to their stakeholders, and ensure business continuity.
Ethical Implications of Management Information Systems
Collecting, storing and using customer data can violate privacy and raises ethics issues, especially where customers do not know what data is being gathered about them and how it is being used.
Privacy & Surveillance: With more data collection, there are risks of infringing on employees' and customers' reasonable expectations of privacy. Systems must be implemented transparently with proper consent and use limitations.
Bias in Algorithms: If not developed carefully, algorithms could inadvertently discriminate against certain groups due to biases in data used to train systems. This can disadvantage vulnerable communities and raise issues of fairness.
Manipulation & Persuasion: Rich data gives power to subtly shape behaviours and decisions through micro-targeting, dark patterns, nudges or persuasive technologies. When does influence become too coercive and undermine autonomy?
Job Disruption: Automation brought about by new technologies may disproportionately impact certain roles. While efficiency gains benefit businesses, effects on employment and necessary support/retraining must be responsibly addressed.
Informed Consent: Do people using digital services truly understand what data is being collected about them and how it's being used? Transparency and meaningful consent are crucial to maintain trust in AI-driven systems.
These are complex topics still requiring much research and debate. An ethical approach demands awareness of such issues, proactive risk assessments, and stakeholder interests being considered in system design from the start.
Ethical Questions:
What are the moral implications of possessing large amounts of information about people and their behaviour?
How much data is being collected? At what point does data collection and data mining begin to violate privacy rights?
Do consumers know how much and what data is being collected about them and how it is being used? Have they really given permission for businesses to engage with their data?
Who has access to this data and under what circumstances? Can the data collected by one business be sold or given to another business or organisation?
Is the data secure?
Is the data being used for malicious purposes?
Are there ethical concerns that people may lose their ability to interact with the physical world if they spend too much time in the metaverse or on other digital devices?
Could poor algorithm programming lead to discrimination and bad decisions, privacy violations, and job losses?
What ethical considerations should be considered when using data to manipulate consumer behaviour?
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