The Cascading Failure Chain: A Scoping Review of Barriers to Disruptive Digital Technology Adoption in South African Disaster Management

Imagine you are the captain of a massive ship trying to navigate a stormy ocean. You have the best radar, the sharpest maps, and a crew that knows how to read the stars. But there's a catch: the radar is in the captain's cabin, the maps are in the galley, and the crew is shouting instructions to each other through a broken walkie-talkie. Even though you have all the high-tech gear needed to survive the storm, the ship still crashes because the parts of the system can't talk to each other. This is the heart of the problem this paper explores: Disaster Management. It's the science of preparing for, responding to, and recovering from natural disasters like floods and fires. The paper focuses on Disruptive Digital Technologies—fancy new tools like Artificial Intelligence (AI) that can predict storms, and the Internet of Things (IoT), which are smart sensors that can "feel" rising water levels. The big question isn't whether these tools work; it's why they aren't being used everywhere to save lives.

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The Great South African Tech Puzzle

South Africa is facing a stormy future. Climate change is making floods and droughts more intense, and the country's disaster management system is struggling to keep up. For over twenty years, the government has passed laws saying, "We need to work together and share data!" But in reality, the nine different Provincial Disaster Management Centres are like nine islands. They each have their own computer systems, their own data, and their own rules, and none of them can talk to the others. It's a bit like having nine different libraries in one city, but none of them know what books the others have.

The author of this paper, Mosekama Osia Mokhele, decided to investigate why this is happening. They didn't just guess; they looked at 26 different scientific studies from 2010 to 2025 to see what's really going on. They used a special detective method called a "scoping review" to map out the landscape of technology in South Africa's disaster management.

The "Cascading Failure" Chain

The paper's biggest discovery is that the problem isn't just one thing; it's a Cascading Failure Chain. Imagine a line of dominoes. If you knock over the first one, the rest fall in a chain reaction. In South Africa's disaster system, the dominoes fall in this specific order:

1. The First Domino: Broken Roads (Infrastructure). The journey starts with a lack of basic roads. Many areas don't have reliable internet or electricity. Without these "roads," the high-tech tools can't even get to the disaster zone.
2. The Second Domino: Missing Drivers (Capacity). Even if the roads were fixed, there are no drivers. The paper found that there is a shortage of people who know how to use these fancy AI and data tools. It's like having a Ferrari but no one who knows how to drive it. Expensive equipment often sits idle because no one has been trained to use it.
3. The Third Domino: The Broken Steering Wheel (Governance). Finally, even if you have roads and drivers, the steering wheel is broken. The government has laws saying everyone should share data, but there is no "enforcement" or "binding agreement" to make it happen. The different provinces just don't coordinate.

The paper argues that while the chain starts with broken roads, the primary barrier to fixing the whole system is actually the broken steering wheel (governance). The infrastructure deficits create capacity gaps, which in turn enable governance failures. If you try to install a new, high-tech steering wheel on a car with no engine, it won't go anywhere. But even if you fix the engine and train the driver, the car won't move if the steering wheel is broken and the driver can't agree on where to go.

The "Implementation Theatre"

The author uses a funny but sad term to describe the current situation: "Implementation Theatre." This means that the government puts on a show. They pass laws, they buy some computers, and they hold meetings. It looks like they are doing everything right. But behind the curtain, the actors aren't actually talking to each other. The system is "performing" compliance without actually working.

The paper addresses a common excuse: Is it just about money? People often say, "We can't afford these new technologies." But the paper shows that the budget argument is weakened by the evidence. Artificial Intelligence can actually be 2,000 to 4,500 times cheaper than the old radar systems used to predict weather. The problem isn't that the technology is absolutely too expensive; the problem is that the "affordability" depends entirely on which technology you choose. Because the government is stuck in old ways of buying things (procurement frameworks), they keep buying the expensive, outdated tools instead of the cheaper, better ones. It's like saying you can't afford a bicycle because you don't have a garage to park it in, even though the bicycle is cheaper than a car. The real issue is that the rules for buying things are broken, not that there is no money at all.

The Few Success Stories

Despite the gloomy picture, there are two bright spots where the "dominoes" didn't fall.

1. The Western Cape's Flood System: In the Western Cape province, they built a system called the Flood Risk Intelligence System (FRIS). It works because everyone agreed to play by the same rules. The weather service sent data, scientists cleaned it up, and local officials used it to warn people. It's like a well-oiled machine where every part knows what the others are doing.
2. eThekwini's WhatsApp Alerts: In eThekwini (Durban), they used a tool that everyone already had: WhatsApp. Instead of building a new, complicated app, they sent flood warnings through the WhatsApp groups people were already using. This reduced the time it took to decide to evacuate from 6 hours to under 2 hours. It worked because it fit into the community's existing life, rather than trying to force a new system on them.

The Solution: A Four-Step Plan

The paper suggests a specific order of operations to fix the country's disaster management. You can't just jump to the end; you have to follow the steps:

1. Step 1: Make the Rules (Governance First). Before buying more tech, the provinces must sign binding agreements to share data. They need to agree on how to talk to each other.
2. Step 2: Train the People (Capacity). Once the rules are set, train people to use the tools. Teach them AI, data science, and how to manage the systems.
3. Step 3: Fix the Roads (Infrastructure). Upgrade the internet and servers so the data can actually flow.
4. Step 4: Bring in the Community (Integration). Finally, make sure the technology fits with how real people live, like using WhatsApp or local community leaders.

The Bottom Line

The paper concludes that South Africa has the ingredients for a superhero disaster system. They have the AI, the sensors, and the laws. But they are missing the glue that holds it all together. The author suggests that until they fix the "governance architecture"—the way the different parts of the government agree to work together—these amazing technologies will remain stuck in small pilot projects, never reaching the people who need them most.

The paper is careful to say that this is a suggestion based on a review of existing studies, not a magic wand that has already been tested on a national scale. It's a roadmap for what should happen next, based on the evidence that "implementation theatre" isn't working, and that real change requires fixing the chain of failures, one domino at a time.