Analyse how digital systems change the way people work and exchange goods and services, from platforms, gig work and automation to e-commerce, cryptocurrency, mobile money and global supply chains.
Each is in the Digital Society glossary, with every other term in the subject.
Making a three-dimensional object by building it up layer by layer from a digital design file, commonly called 3D printing.
The use of algorithms and data to assign, monitor, evaluate and discipline workers, for example by allocating jobs, setting pay per task and deactivating accounts with low ratings.
The use of machines, software or robots to carry out tasks that people previously did, with little or no human involvement.
An economy in which most payments are made electronically, by card, phone, app or transfer, and physical cash is rarely used.
Paid work offered through an online platform to a large, open group of workers, any of whom can take it on.
A digital currency that uses cryptography to secure transactions and usually a blockchain to record ownership, without a central bank or company in control.
A person who works remotely using digital systems while travelling or living in different places, often in other countries, for months or years at a time.
Buying and selling goods and services over the internet, including sales from businesses to consumers, between businesses and between consumers.
Buying and selling financial assets such as shares, currencies, bonds or cryptocurrencies through electronic platforms instead of by phone or on a trading floor.
A labour market in which people are paid per task or gig, such as a ride or a delivery, usually found through a digital platform, instead of receiving a regular wage from one employer.
Buying goods, parts, materials or services from suppliers in whichever countries offer the best combination of cost, quality and speed, often coordinated through digital supply-chain systems.
The growing interconnection of economies, cultures and societies across the world through trade, investment, the movement of people and the flow of data.
Moving work to a lower-cost region of a firm's home country instead of abroad. The term is also used for work that foreign firms bring into a country.
Bringing work that had been outsourced to another company back inside the firm, to be done by its own staff.
A small payment made inside a digital product, usually a game or app, for virtual items, extra features, in-game currency or chances to win random rewards.
Crowd work broken into very small, repetitive online tasks, such as labelling images, often paid a few cents each, that together complete a larger job.
A service that lets people store money in an account linked to their mobile phone number and use it to send, receive and pay, with cash paid in and taken out through local agents.
The increase in value of a product or service to each user as more people use it, which tends to make the largest platform in a market grow larger still.
A unique record on a blockchain that represents ownership of a specific item, often a digital image, video or in-game object, and cannot be swapped for another token as if they were the same.
Moving work to another country, either to a firm's own operation abroad or to a foreign contractor.
A platform on which many independent sellers offer goods or services to buyers, with the platform handling search, payment and often delivery in return for fees.
Contracting another company to carry out work that a firm used to do itself, whether in the same country or abroad.
Adapting the content of marketing, such as the products shown, offers, prices and messages, to each individual, based on data about that person.
Economic activity organised through digital platforms that match buyers and sellers of goods, services or labour, with the platform company setting the rules and taking a commission or fee.
Doing paid work away from the employer's premises, usually at home, using digital systems such as laptops, video calls and cloud services to stay connected.
Moving work that had been sent abroad back to a firm's home country.
Choosing which people see an advertisement based on data about their characteristics, location or online behaviour.
An organisation of workers that acts together to improve pay, conditions and job security, mainly by negotiating with employers on behalf of its members.
Describe how digital systems change the operation and organisation of businesses, and the diversity of businesses and corporations.
Explain how working practices such as office design, remote working, digital nomadism and employee organisations are changing.
Distinguish between crowd work, microwork and the gig economy, and analyse their impacts on workers.
Discuss the impacts and implications of automation for employment.
Explain how e-commerce, e-trading, online marketplaces and personalised and targeted marketing work.
Evaluate cryptocurrency, non-fungible tokens, cashless payment, micro-transactions and additive manufacturing as new ways of producing and exchanging value.
Distinguish between offshoring, outsourcing, reshoring, inshoring and insourcing, and explain how digital systems support borderless selling and global sourcing.
Evaluate the impacts of M-Pesa on people and communities in Kenya.
Analyse an unseen real-world example in the economic context, using course concepts and content.
The economic context covers the ways people and communities work and the ways they exchange goods and services. Almost every part of economic life now runs through a digital system: a farmer in Kenya is paid by phone, a designer in Lisbon works for a client in Singapore, and South Korea's factories use more industrial robots per worker than those of any other country.
A context situates inquiry. The economic context draws on content from 3.1 to 3.7 (data, algorithms, networks, artificial intelligence, robots) and is read through the concepts of 2.1 to 2.7, most often change, power, space, and values and ethics. It overlaps with the other contexts: gig work raises social questions (4.7), data centres and 3D printing raise environmental ones (4.3), and the regulation of platforms is political (4.6).
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The map of the context shows the four areas and typical digital systems in each. The areas overlap: a single company such as Alibaba is a business, an employer, a marketplace, a payment provider and a driver of globalization at the same time.
A business turns inputs (materials, labour, information) into goods or services that it sells. Digital systems now support each stage of this process. Enterprise resource planning (ERP) software, such as SAP, links purchasing, stock, production, accounts and payroll in one shared database, so that a sale recorded in one country updates stock levels and accounts everywhere at once. Customer relationship management (CRM) software records every contact with a customer, and cloud computing lets even a very small firm rent servers and software by the month instead of buying them.
Data is the thread that runs through these systems. Businesses collect data on sales, suppliers, staff and customers, then use data analytics to forecast demand, set prices and spot problems. This shift from decisions based on experience to decisions based on data is one of the main changes in how businesses operate.
The same systems lower the cost of starting a business. A craft seller in Ghana or Peru can open an online shop on Shopify or Etsy, take card payments and print shipping labels without employing anyone. Through the concept of change, this transforms who can run a business, but it also creates dependence, because the shop exists on the platform's terms.
Business function | Digital systems used | Example of the change |
|---|---|---|
Planning and operations | ERP software, cloud computing | Stock, accounts and payroll share one database across countries |
Sales and marketing | Websites, online marketplaces, social media, CRM | A firm sells directly to customers abroad and targets advertising by data |
Production | Robots, sensors, 3D printers | Factories run with fewer workers; parts are printed on demand |
Human resources | Recruitment platforms, scheduling and monitoring software | Applications are filtered by algorithms; shifts are set by apps |
Finance | Online banking, mobile payments, accounting software | Payments clear instantly; small firms keep accounts on a phone |
The organisation of a business is the way it divides up work and authority: who does what, who reports to whom and where decisions are made. Digital systems have encouraged flatter structures than the traditional hierarchy, because tools such as Slack and Microsoft Teams let information bypass layers of management.
The most important organisational change is the rise of the platform economy. A platform company does not mainly make products itself. It runs a digital system that connects two or more groups (buyers and sellers, riders and drivers, guests and hosts) and takes a share of each exchange. Uber owns few cars, Airbnb owns few homes and Alibaba's marketplaces hold little stock of their own.
Platforms grow because of network effects. Each extra user makes the platform more useful to other users: more drivers mean shorter waiting times for riders, which attracts more riders, which attracts more drivers. The two-sided platform model shows this loop.
Network effects explain why platform markets often end with one or two dominant firms. They also explain why platform companies can be very valuable with very few staff. Instagram had 13 employees when Facebook bought it for about $1 billion in 2012, and WhatsApp had about 55 when Facebook bought it for $19 billion in 2014. Through the concept of power, this concentration matters: a platform that most sellers or drivers depend on can change fees, rankings and rules with little negotiation, which is the platform power that regulators such as the European Commission now try to limit.
Diversity in businesses has two meanings. The first is the variety of types of business. Digital systems have changed every type, from the market stall to the multinational corporation.
Type of business | Description | Digital example |
|---|---|---|
Micro-business and sole trader | One person or a handful of staff | A tailor in Nairobi who takes orders on WhatsApp and payment by M-Pesa |
Small and medium-sized enterprise (SME) | Usually up to 250 employees | A family firm in Vietnam selling furniture to Europe through Alibaba.com |
Start-up | A new firm aiming to grow fast, often funded by investors | Jumia, founded in Lagos in 2012, which grew into an online marketplace in several African countries |
Multinational corporation | Operates in many countries | Mercado Libre, founded in Argentina in 1999, which runs e-commerce and payments across Latin America |
Platform company | Connects other people and takes a share | Uber, Airbnb, Deliveroo |
Cooperative or social enterprise | Owned by members or run for a social purpose | Driver-owned ride-hailing apps such as the Drivers Cooperative in New York |
Digital systems lower the cost of entry for small firms, but network effects reward size. As a result, millions of very small online sellers depend on a few very large Big Tech corporations that own the platforms they sell through.
The second meaning is diversity within corporations: the range of genders, ethnicities, ages, abilities and backgrounds among staff and leaders. Technology companies have been criticised for workforces dominated by men, particularly in engineering and leadership roles. Diversity matters for two reasons in digital society. One is fairness to the people who are excluded. The other is the quality of the systems built: teams that lack diversity are more likely to miss the ways a product fails for people unlike themselves, such as face-recognition systems that are less accurate for darker-skinned women.
Digital systems can push diversity either way. Remote working lets disabled people and carers take jobs that once required a daily commute. Automated hiring tools can repeat past patterns, as at Amazon, which abandoned an experimental CV-screening tool after it learned to favour men's applications (Reuters reported the case in 2018). Through the concept of values and ethics, the question is whose interests a business's systems serve, as well as how efficient they are.
Working practices are the everyday ways in which work is organised: where, when, with what tools and under what supervision. Digital systems have changed all four.
Offices have been redesigned around digital tools. Open-plan offices remove private rooms so that staff can see and talk to each other. Activity-based working goes further: staff have no fixed desk ("hot-desking") and choose a space for each task, booking it through an app. Laptops, cloud storage and wireless networks make this possible, because a worker's files and tools follow them to any desk.
The claimed benefits of open offices are cooperation and lower costs per worker. The evidence on cooperation is mixed. In a 2018 study at two large US companies that moved to open-plan offices, Ethan Bernstein and Stephen Turban found that face-to-face interaction fell by about 70%, while email and instant messaging rose. Since the COVID-19 pandemic, many firms have redesigned offices again, with fewer desks and more meeting rooms for the days when staff come in.
Remote working became possible for office jobs once broadband, cloud software and video calling were widespread, but it stayed rare until 2020. The pandemic forced a sudden experiment. In the European Union, the share of employed people who usually worked from home more than doubled between 2019 and 2020, as the remote-working chart shows. The share has since fallen back from its peak, while the share who sometimes work from home has kept rising, which suggests that hybrid working has become the settled pattern.
Remote working affects different people in different ways. Workers gain time and money from not commuting, and people who live far from cities, or who have caring duties or disabilities, gain access to jobs. Employers can recruit from a wider area and cut office costs. The risks include isolation, the blurring of work and home life, and fewer chances for young workers to learn by watching colleagues. Only some jobs can be done remotely at all: a nurse, a cleaner or a factory worker cannot work from home, so remote working has divided workers into those who can and those who cannot.
Remote working also brings digital supervision into the home. Employee-monitoring software (sometimes called "bossware") can log keystrokes, take screenshots and track which applications are open. Employers argue that it protects productivity and security; workers and privacy campaigners argue that it measures activity, not results, and intrudes into private space.
Digital nomads are an extreme form of remote work. MBO Partners, a US research firm, estimated that 18.5 million American workers described themselves as digital nomads in 2025, up from about 7.3 million in 2019. Governments compete for them with special visas: Estonia launched a digital nomad visa in 2020, Barbados offered a "Welcome Stamp" the same year, and Portugal, Indonesia and many others have followed.
Through the concept of space, digital nomadism separates where work is done from where it is paid for. A software developer paid a San Francisco salary can live in Lisbon, Medellín or Bali. Host communities gain spending, but some also face rising rents and the conversion of homes to short-term lets. In Mexico City and Lisbon, housing protests have blamed rising rents partly on well-paid foreign remote workers, alongside tourism and short-term lets.
Trade unions and other employee organisations give workers a collective voice. Digital systems have changed both the obstacles and the tools. Workers spread across homes, cities or apps do not meet at a factory gate, so it is harder to organise. On the other hand, messaging groups, social media and online petitions let workers who have never met share information and coordinate action.
Employees at Google's parent company formed the Alphabet Workers Union in 2021. Warehouse workers at Amazon's JFK8 site in New York voted in 2022 to join the Amazon Labor Union, the first successful union vote at an Amazon warehouse in the USA. In Nairobi, content moderators who reviewed violent and abusive material for global platforms through outsourcing firms formed the African Content Moderators Union in 2023. App-based drivers and couriers in India, Indonesia, Brazil and Europe organise mainly through WhatsApp and Telegram groups, sometimes switching off their apps together as a strike.
Platforms have created new forms of work in which a task, not a job, is the unit of employment. Three overlapping terms describe them.
The gig economy includes location-based work, where the task happens in a physical place (Uber, Deliveroo, Gojek, Rappi in Latin America), and online work, where the task is delivered over the internet. Online work divides further into crowd work and microwork.
Form of work | What the task is | Where it is done | Examples |
|---|---|---|---|
Location-based gig work | A service delivered in person | A specific place, set by the customer | Ride-hailing (Uber, Didi), food delivery (Deliveroo, Glovo), cleaning |
Crowd work | A project or task offered to a large online crowd of workers | Anywhere with an internet connection | Design contests, translation and coding on Upwork or Fiverr |
Microwork | A tiny, repetitive task that takes seconds or minutes | Anywhere, usually at a computer or phone | Labelling images to train AI, transcribing receipts, rating search results on Amazon Mechanical Turk |
Amazon Mechanical Turk, launched in 2005, is the best-known microwork platform. Its name refers to an eighteenth-century chess "machine" that secretly hid a human player, which is an accurate picture of microwork: much of what looks like artificial intelligence depends on hidden human labour that labels training data and checks outputs. A 2018 study of almost 2,700 workers on the platform found median earnings of about $2 an hour. In 2023, TIME magazine reported that workers in Kenya employed by the outsourcing firm Sama were paid less than $2 an hour to label violent and abusive text used to make ChatGPT safer.
The International Labour Organization counted 142 online and location-based labour platforms in 2010 and more than 777 in 2020. Most of them manage workers through algorithms instead of human supervisors.
Algorithmic management is the core of how gig platforms work. A delivery app decides which courier receives an order, calculates the fee, times each step and records customer ratings. Workers often do not know how these decisions are made, so they cannot challenge a pay cut or a deactivation easily.
The gig economy creates real opportunities. It offers flexible hours to students, carers and people with second jobs, an entry into paid work for migrants who face barriers in formal employment, and income in countries where formal jobs are scarce. Customers gain cheaper, faster services, and platforms create markets that did not exist before.
The risks fall mainly on workers. Most platforms classify them as independent contractors, not employees, so they usually lack a minimum wage, sick pay, holiday pay and pensions. Pay can change without negotiation. Courts and lawmakers have responded. In Uber BV v Aslam (2021), the UK Supreme Court ruled that Uber drivers are "workers" entitled to the minimum wage and holiday pay, because Uber controls their fares, routes and ratings. The European Union's Platform Work Directive, adopted in 2024, creates a presumption that platform workers are employees when the platform controls their work, and gives them rights to an explanation of automated decisions.
Automation is not new. Mechanised looms replaced hand weavers in the early nineteenth century, and industrial robots arrived on car assembly lines in the 1960s. What has changed is the range of tasks that can be automated. Robots with machine vision can pick items in a warehouse, and artificial intelligence can now draft text, write code, translate and answer customer queries, which are tasks in office jobs that once seemed safe.
Robot use varies greatly between countries. South Korea has the most industrial robots relative to its manufacturing workforce, as the robot-density chart shows, while China has moved quickly up the ranking as wages there have risen.
Estimates of job losses from automation vary widely, because they depend on whether researchers count whole jobs or the tasks within them. In 2013, Carl Benedikt Frey and Michael Osborne at Oxford estimated that 47% of US jobs were at high risk of automation. The OECD, which looked at the tasks within each job, estimated in 2019 that about 14% of jobs in its member countries were at high risk, and that a further 32% were likely to change significantly. The World Economic Forum's Future of Jobs Report 2025 expected 170 million jobs to be created and 92 million to be displaced worldwide between 2025 and 2030.
These figures point to three effects that happen together.
Displacement: some jobs disappear, such as checkout staff replaced by self-service tills or bank tellers by apps.
Transformation: most jobs change, with machines taking over some tasks and workers doing others. A radiologist uses AI to flag suspicious scans but still decides on the diagnosis.
Creation: new jobs appear, such as data analysts, robot technicians and content moderators, often in different places and needing different skills from the jobs that were lost.
Through the concept of change, the speed of change matters as much as its direction. Gradual automation gives workers time to retrain; fast automation can remove a community's main employer at once. In countries with ageing populations, such as Japan and South Korea, automation is often presented as a response to labour shortages, not as a threat to jobs.
E-commerce has grown steadily for twenty-five years and accelerated sharply during the pandemic, when shops closed. In the USA, its share of retail sales rose from under 3% in 2005 to about 17% in 2025, as the e-commerce chart shows. In China, the share is far higher: online sales of physical goods made up about a quarter of all retail sales of consumer goods in 2024.
Online marketplaces such as Amazon Marketplace, Taobao and Tmall (Alibaba), Mercado Libre, Jumia, Shopee in South-East Asia and Flipkart in India are where most e-commerce now happens. They combine much of the course content in one system. Recommendation algorithms decide which products each shopper sees, ratings and reviews build trust between strangers, and data on every click shapes prices and stock.
Alibaba's Singles' Day festival (11 November) shows the scale. In 2021, sales across its platforms over the eleven-day festival reached 540 billion yuan (about $84 billion), after which Alibaba stopped publishing the total. The platform invented the event itself, which shows a marketplace's power to shape when people shop.
Online marketplaces let small sellers reach customers without a shop, and give rural customers products that local shops did not stock. They also raise concerns: small sellers depend on the platform's fees and search rankings, high-street shops close, delivery work is often low-paid gig work, and fake reviews and counterfeit goods are hard to control.
E-trading has two very different faces. For professional traders, most buying and selling on stock exchanges is now done by algorithms, some of which trade thousands of times a second. Speed brings risks: in the "flash crash" of 6 May 2010, the main US share index fell by about 1,000 points within minutes before recovering, partly because automated systems reacted to each other.
For ordinary people, trading apps such as Robinhood in the USA, Trading 212 in Europe and Zerodha in India have made investing possible from a phone with no commission. In January 2021, users coordinating on the Reddit forum r/wallstreetbets pushed up the share price of the struggling retailer GameStop, causing heavy losses for professional funds that had bet against it. Supporters see these apps as opening finance to everyone; critics argue that their game-like design encourages risky trading by inexperienced users.
Personalised marketing and targeted advertising depend on the collection of personal data on a very large scale. Cookies and tracking pixels record which websites a person visits; apps record location; loyalty cards record purchases; and data brokers combine these sources into profiles. When a web page loads, an automated auction decides in a fraction of a second which advertiser will show an advertisement to that particular visitor.
This business model funds much of the free internet. Meta, which owns Facebook, Instagram and WhatsApp, earned more than 95% of its revenue in 2024 from advertising. The model is sometimes called surveillance capitalism, because the product sold is predictions about users' behaviour.
Stakeholder | Claims in favour | Claims against |
|---|---|---|
Businesses, especially small ones | Advertising reaches the right customers cheaply, with measurable results | Dependence on a few ad platforms that set prices and rules |
Consumers | Relevant offers and free services | Loss of privacy; manipulation; sensitive traits (health, religion) inferred from data |
Platforms (Google, Meta, TikTok) | Data improves services and pays for them | Regulation may cut revenue |
In 2012, the New York Times reported that the US retailer Target had built a model that could predict which shoppers were likely to be pregnant from changes in their purchases, so that it could send baby-product offers early. Regulation has started to limit such practices. The EU's General Data Protection Regulation (GDPR) demands a lawful basis, often consent, for processing personal data. In 2021, Apple's App Tracking Transparency feature began asking iPhone users whether each app could track them across other apps, and most users said no; Meta said the change would cost it about $10 billion in 2022. Through the concept of power, this episode shows one platform's design choice reshaping an entire advertising market.
Money is changing form. A currency is anything a community accepts as a means of exchange, a store of value and a unit of account. For most of history it was coins and notes issued by a state; today most money is already digital entries in bank databases. The new forms differ in who issues the money and who keeps the record.
Bitcoin, the first cryptocurrency, began operating in 2009. Thousands of computers keep copies of the same ledger, and new blocks of transactions are added only when the network agrees on them. Supporters claim this removes the need to trust banks or governments, protects savings from inflation in countries with unstable currencies, and makes sending money across borders cheaper. Critics point to volatile prices, the use of crypto in fraud, ransomware and money laundering, and the very high electricity use of Bitcoin "mining".
El Salvador tested these claims at national level. In September 2021, it became the first country to make Bitcoin legal tender, alongside the US dollar, and launched a government wallet app, Chivo, with $30 of Bitcoin for every adult who signed up. Take-up stayed low: research found that most users stopped using the wallet once the bonus was spent, and few businesses took Bitcoin payments. In January 2025, as part of a loan agreement with the International Monetary Fund, the country amended the law so that businesses no longer had to accept Bitcoin. The collapse of the exchange FTX in November 2022, which left more than a million customers unable to withdraw funds, showed that many crypto users rely on centralised companies after all.
A bitcoin is fungible: any one bitcoin is worth the same as any other. An NFT is not, because each token points to one specific item. In March 2021, the auction house Christie's sold an NFT of the digital artwork Everydays: The First 5000 Days by the artist Beeple for $69.3 million. The Bored Ape Yacht Club, a collection of 10,000 cartoon apes launched in April 2021, became a status symbol, with single apes selling for hundreds of thousands of dollars.
NFTs were promoted as a way for digital artists to sell work directly and earn a share every time it was resold. Prices across the market fell sharply in 2022 and 2023, and most collections lost nearly all their value. Through the concept of values and ethics, NFTs raise the question of what is actually owned: in most cases the buyer owns a token that points to a file, not the copyright in the image, which anyone can still copy.
Several countries are moving towards a cashless society, each through a different digital system. In Sweden and Norway, cards and the phone apps Swish and Vipps have replaced most cash. In China, the super-apps Alipay and WeChat Pay let people pay by scanning a QR code at almost any stall. India's Unified Payments Interface (UPI), launched in 2016 by the National Payments Corporation of India (set up by the central bank and the country's banks), processed about 20 billion transactions in the single month of August 2025. Brazil's central bank launched Pix in 2020, an instant payment system that quickly became the country's most-used way to pay. In Kenya, mobile money plays the same role, as the case study shows.
Opportunities | Risks |
|---|---|
Faster, cheaper payments; no need to carry or guard cash | People without phones, bank accounts or digital skills (often older, poorer or rural people) are excluded |
Less robbery and tax evasion; every payment is recorded | Every payment is recorded, so governments and companies can track spending |
Small traders can accept payments and build a credit record | Outages and cyberattacks can stop all payments at once |
Governments can send benefits directly and quickly | Payment companies can charge fees and gain great market power |
Micro-transactions fund "free-to-play" games such as Fortnite and Genshin Impact, in which players buy outfits, battle passes or virtual currency (V-Bucks in Fortnite). Loot boxes, which sell a random reward, have been compared to gambling. In 2018, Belgium's Gaming Commission ruled that paid loot boxes in several popular games broke its gambling law. In 2022, Epic Games, the maker of Fortnite, agreed to pay $520 million in a settlement with the US Federal Trade Commission, including $245 million in refunds for using dark patterns that led players, many of them children, into unwanted purchases.
In additive manufacturing, a design created in computer-aided design (CAD) software is sliced into thin layers, and a printer lays down plastic, metal powder, resin or concrete one layer at a time. Because the design is data, it can be sent anywhere in the world and printed close to where it is needed.
The technology changes how goods are made in three main ways. Complex shapes can be printed in one piece: GE Aviation's fuel nozzle for the LEAP jet engine combines about 20 separate parts into one printed part that is about 25% lighter. Products can be customised cheaply, as with hearing aids and dental aligners printed to fit each patient. And small batches become affordable, because there is no mould to make.
Construction is a growing use. In Mexico, the non-profit New Story worked with the builder ICON to print low-cost homes in the state of Tabasco. The implications reach across the economic context. Printing parts locally could shorten global supply chains and reduce shipping. It could also reduce the need for factory and construction labour. It creates new risks too: in 2013, the US group Defense Distributed published files for a 3D-printable plastic gun, the Liberator, raising questions about controlling weapons that can be made from a download.
Digital systems are among the main drivers of globalization. Submarine cables and satellites carry data between continents in a fraction of a second, so a design made in Milan can reach a factory in Bangladesh instantly, and a customer service call in Toronto can be answered in Manila. Through the concept of space, this is time–space compression: distance matters less for many kinds of economic activity.
Borderless selling means selling directly to customers in other countries, usually online. Before e-commerce, exporting needed agents, distributors and shops abroad. Now a small firm in Morocco can list leather goods on Etsy and post them to buyers in Canada and Japan, and Shopify lets any business accept payment in many currencies.
Chinese platforms Shein and Temu took borderless selling furthest. They shipped low-cost clothes and goods directly from Chinese factories to individual customers abroad, often in small parcels that, in the USA, fell under the "de minimis" rule letting parcels worth under $800 enter without customs duty. The USA ended that exemption for parcels from China in May 2025 and for all countries in August 2025, after domestic retailers argued that it gave foreign sellers an unfair advantage. The case shows that borderless selling still depends on borders, because states can change the rules.
Global sourcing is made practical by digital systems that track orders, shipments and stock across many suppliers. A smartphone is the standard example: its chips, screen, camera, battery and software are designed and made by firms in the USA, South Korea, Japan, Taiwan, China and elsewhere, and then assembled, mostly in China, India or Vietnam. Business-to-business marketplaces such as Alibaba.com let a small importer in Nigeria or Chile find and contact a manufacturer directly.
Global sourcing lowers prices for consumers and brings jobs and investment to manufacturing countries. Its risks became clear during the pandemic, when factory closures in one country stopped production in many others, and in 2021 when a shortage of computer chips held up car production around the world. Long supply chains also make it harder to check working conditions and environmental damage at each step.
Firms make two separate decisions about each part of their work. The first is who does the work: their own staff, or another company. The second is where it is done: in the home country, or abroad. The five terms describe moves between these choices, as the sourcing grid shows.
Term | Meaning | Real-world example |
|---|---|---|
Outsourcing | Another company does the work | Banks and telecom firms outsourcing customer service to specialist call-centre companies |
Offshoring | The work moves abroad | The Philippines' IT and business process outsourcing industry, which employs well over a million people, largely serving clients in the USA |
Reshoring | The work comes back home | TSMC, the Taiwanese chipmaker, building plants in Arizona with support from the US CHIPS and Science Act (2022), which aims to bring chip manufacturing back to the USA |
Inshoring | The work moves to a cheaper part of the home country | US firms using software teams in smaller, lower-cost cities instead of offshore centres, as offered by companies such as Rural Sourcing |
Insourcing | Outsourced work returns inside the firm | General Motors, which from 2012 brought most of its IT work back in-house after years of outsourcing about 90% of it |
Digital systems have pushed in both directions. Fast networks, cloud software and video calls made it possible to offshore services such as accounting, software development and customer support, which once had to be done near the customer. India's IT industry, led by firms such as Tata Consultancy Services and Infosys, grew on this basis. Now automation and AI push the other way: if a robot or an AI system can do the work, low wages abroad matter less, and firms may reshore production to be closer to customers and less exposed to distant risks.
YouTubeThrough the concept of power, these choices affect people far from the firm's headquarters. Offshoring has brought millions of jobs to cities such as Manila, Bangalore and Nairobi, often with better pay than local alternatives. It has also moved jobs away from regions in richer countries, and it can place low-paid, stressful work, such as the content moderation described in the employee organisations section, where labour protections are weaker. Reshoring, in turn, can bring jobs back, but often fewer of them, because reshored factories tend to be heavily automated.
M-Pesa ("pesa" means money in Swahili) is a mobile money service run by Safaricom, Kenya's largest mobile network operator, in which the UK company Vodafone held a large share. It began as a pilot in 2005, partly funded by a challenge fund of the UK's Department for International Development, to let borrowers repay microfinance loans by phone. In the pilot, users kept finding other uses, especially sending money to relatives. Safaricom refocused the service on person-to-person transfers and launched it nationally in March 2007.
At the time, most Kenyans had no bank account. Banks had few rural branches and charged fees that many people could not afford. Mobile phones, by contrast, were spreading fast, including basic phones in rural areas. M-Pesa built a financial service on the network people already had, which is an example of leapfrogging: skipping the bank-branch stage of development altogether.
M-Pesa links four elements: phones and SIM cards, Safaricom's central platform, a network of agents, and bank accounts that hold the real money. The flow diagram shows how they fit together.
A customer registers with an identity card and receives an M-Pesa account tied to their phone number.
To put money in, the customer gives cash to an agent, usually a shopkeeper. The agent's electronic balance falls and the customer's rises by the same amount.
To send money, the customer selects M-Pesa from the SIM menu, enters the recipient's number and the amount, and confirms with a PIN. Both people receive a text message.
To take money out, the recipient visits any agent, who checks their identity and swaps their e-money for cash.
Safaricom holds an amount of real money equal to all customers' balances in trust accounts at several commercial banks, so the e-money is fully backed.
Because it uses text menus and SMS, M-Pesa works on the most basic phone without internet access. Over time, Safaricom added paying shops (Lipa na M-Pesa), paying bills such as electricity and school fees, receiving wages, savings and loans with partner banks (M-Shwari, from 2012) and an overdraft service (Fuliza, from 2019).
The regulator's attitude was a key condition. The Central Bank of Kenya allowed M-Pesa to launch in 2007 by issuing a letter of "no objection", and only later wrote detailed rules, such as the National Payment System Act of 2011. Regulators in many other countries required mobile money to be run by banks, which slowed it down.
In the year to March 2025, Safaricom reported about 36 million customers in Kenya who had used M-Pesa in the previous month. The value of payments passing through M-Pesa in a year is now more than twice the value of Kenya's annual economic output. The share of Kenyan adults with access to formal financial services, which includes mobile money, rose from about a quarter in 2006 to 85% in 2024, as the financial inclusion chart shows.
People and communities | Positive impacts and opportunities | Negative impacts and risks |
|---|---|---|
Urban migrant workers and rural families | Wages earned in Nairobi or Mombasa reach families in villages instantly and safely, replacing bus drivers and risky cash journeys | Transfer and withdrawal fees take a larger share of small payments |
Women | More control over money and savings; research links M-Pesa to women moving from farming into business | Women are still less likely to own a phone than men, so the poorest may be left out |
Small traders and informal businesses | Customers pay by phone; traders build a transaction record that can support a loan | Records also make them visible to tax authorities; dependence on one provider |
Agents | A new business for hundreds of thousands of shopkeepers | Holding cash makes them targets for robbery; thin margins |
Borrowers | Instant small loans and overdrafts for emergencies | Easy digital credit has led to repeated borrowing and debt for some users |
Banks | Partnerships for savings and loans | Lost payments business to a telecom company |
Safaricom and the state | Large profits for Safaricom; the state collects tax on transaction fees and can send payments directly | Concentration of financial power in one company; risks to privacy and of system outages |
The best-known study of M-Pesa's impact, by Tavneet Suri and William Jack, published in Science in 2016, estimated that access to M-Pesa lifted about 194,000 households, or 2% of Kenyan households, out of extreme poverty, with stronger effects for households headed by women. The mechanism was resilience: when a household suffered a shock such as illness or a failed harvest, relatives could send money quickly, so the household did not have to sell livestock or take children out of school. Other researchers have questioned how much of the improvement can be credited to M-Pesa itself, arguing that the study cannot fully separate it from other changes in Kenya, so the size of the effect remains debated.
Change. M-Pesa is an example of transformation more than evolution. It created a new route into finance for people who had no bank account, instead of improving services for those who already had one. It also shows how users shape a technology: the service designed for loan repayments became a national money-transfer system because customers used it that way.
Power. Safaricom's network effects gave it a dominant position, since a payment service is more useful when everyone a user might pay is on it. Critics, including the Gates Foundation in 2013, argued that limited competition kept fees for small transfers high. Since 2018, customers have been able to send money between Kenya's competing mobile money services, which reduced the "lock-in" to one network. The state also gains power: payments that once happened in untraceable cash are now recorded and taxed.
Systems. M-Pesa works only because many elements depend on one another: the mobile network, the platform, the agents' cash, the trust accounts at banks and the Central Bank's oversight. If any element fails, the whole system fails. Outages, though rare, can stop payments across the country, which shows the risk of a single point of failure in a cashless economy.
Values and ethics. Mobile money raises questions about who benefits from data on millions of payments, and about responsible lending when loans can be offered instantly to anyone with a phone. In 2024, about 9 in every 10 Kenyan adults used some formal or informal financial service. The remaining tenth were more likely to be young, rural, or without phones or identity documents, so a cashless economy risks deepening their exclusion.
M-Pesa is widely seen as one of the most successful digital systems in the economic context of a developing country. It has made sending, saving and borrowing money cheaper and safer for tens of millions of people, and the evidence suggests that it has improved resilience to shocks, especially for women. Its success depended on specific conditions: a dominant mobile operator with a large network of agents, a regulator willing to let it start before writing detailed rules, and a large population of internal migrants sending money home.
When the Vodafone group launched M-Pesa in South Africa and India, where banks were stronger and conditions differed, it attracted few customers and closed both services (in 2016 and 2019). The judgement is therefore conditional: mobile money can drive financial inclusion where banking is weak and phones are widespread, but its benefits depend on fair fees, competition, responsible lending and continued access for people without phones.
Every economic example, however unfamiliar, can be analysed with the same five steps. The method begins with the digital system, moves to the people affected, and ends with a judgement, as the analysis steps diagram shows.
Step 2 should include people who are excluded or affected indirectly, not only users. Step 3 keeps impacts (what has happened) apart from implications (what could happen), and step 4 uses the one concept that adds most insight.
Paper 2 questions are based on sources; Source A is a scenario written in the style of the paper.
At HL, Paper 1 Section B asks for an extended response on an HL challenge, such as the future of work.
I can describe how ERP, cloud computing and data analytics change the way businesses operate.
I can explain how a platform company is organised, using the terms platform economy and network effect.
I can discuss diversity in businesses in both senses: types of business and diversity within corporations.
I can explain how office design, remote working, digital nomadism and employee organisations are changing, with an example of each.
I can distinguish between gig work, crowd work and microwork, and explain algorithmic management.
I can discuss the impacts of automation on employment, using evidence from at least two studies.
I can explain e-commerce, online marketplaces and e-trading, with a real-world example of each.
I can distinguish personalised marketing from targeted advertising and evaluate their implications for consumers.
I can explain cryptocurrency and NFTs and evaluate El Salvador's Bitcoin law.
I can evaluate the opportunities and risks of a cashless society and of micro-transactions.
I can explain additive manufacturing and its implications for production and work.
I can distinguish offshoring, outsourcing, reshoring, inshoring and insourcing, with an example of each.
I can explain how digital systems support borderless selling and global sourcing.
I can evaluate the impacts of M-Pesa on specific people and communities in Kenya through at least two concepts.
I can apply the five analysis steps to an unseen real-world example in the economic context.