The extinction of animal species all over the world is a problem that continues to grow. There are over 500 species that are likely to become extinct in the coming 20 years (Nuwer, 2020). A few of the main reasons for the endangerment of species include climate change, overpopulation and poaching. Due to the effect of COVID-19 on tourism in Africa, poaching has become an even more substantial problem nowadays. Specifically, due to fewer tourists and guides in the nature parks, less income is generated for the inhabitants of Africa, leading to an increase in poverty. Moreover, the effect is notable in the poaching business, where more and more elephants and rhinos are being killed for their ivory (Newburger, 2020).  

However the Internet of Things (IoT) may provide a solution that could decrease poaching. In specific, with the help of the Internet of Things smart parks can be created. These are wildlife parks that have implemented smart sensors in the parks. Smart sensors are a simple and cost effective technology that can improve the security of the endangered animal species (Smartparks, 2020).

An example that can be analyzed is the case of the nature and smart park Mkomazi, located in Tanzania. Mkomazi was one of the first wildlife parks that successfully implemented smart sensors in the park. Over the past few years, the technology of smart sensors has been implemented in multiple other wildlife parks and countries. The goals of the smart parks were to expand the security in the parks for the people and wildlife by creating situational awareness. Furthermore, smart parks aim to efficiently improve the parks operations (Smartparks, 2020).

The smart sensors work mainly on solar power, as there is not always full internet coverage within the stretched out parks. The sensors collect real time data. For instance, by implementing smart sensors into the horns of the endangered species of black rhinos, the  sensors provide the ability to accurately monitor the hourly whereabouts of the animals (Smartparks, 2020). As the nature park stretches out to 3,500 square feet, it is difficult to track the whereabouts without these sensors. Smart sensors may help in keeping the rhinos safe from possible poachers.

 Furthermore, the sensors are used for vehicle tracking and ranger tracking by implementing the sensors into vehicles. Additionally, the sensors help with intrusion detection and electric fence monitoring. The smart sensors help track all the movements going on in the park accurately. The park is currently looking into new options to implement the sensors by creating camera traps for poachers and the use of sensors in tracking equipment, for example the parks firearms (Smartparks. 2020).

However, an important aspect to consider is that the technology of smart sensoring may  also improve the security of the parks, and decrease the killings of the animals. Smart sensors provide a solution to control the problem of poaching for ivory. Nevertheless, implementing these measures will only maintain the problem, but not entirely solve the problem. The problem of poaching unfortunately continues to grow, due to the lasting demand and high price of ivory.
References list

Newburger., E. (2020). ´Filthy bloody business´ poachers kill more animals as coronavirus crushes tourism to africa. [Online] Accessed by: https://www.cnbc.com/2020/04/24/coronavirus-poachers-kill-more-animals-as-tourism-to-africa-plummets.html

Nuwer., R. (2020).  Extinction is not inevitable. These species were saved. [Online] Accessed by: https://www.nytimes.com/2020/09/12/science/extinction-species-conservation.html

Smartparks, (2020). Smart park Mkomazi, Tanzania. [Online] Accessed by: https://www.smartparks.org/projects/smart-park-mkomazi-tanzania/ 

The opinion between researchers differs if Uber can be seen as a disruptor in the industry. This article defines Uber as a company that has disrupted the driving Industry. Uber may not have entered the low-end segment of the market first; they have managed to quickly replace the long term incumbent (Horn, 2016).

Uber defines itself as ‘a platform with a business model premised on licensing software that acts as an intermediary between passengers and drivers.’ (Rosenblat, 2016). The drivers are seen as independent contractors that can enjoy freedom, flexibility and independence (Rosenblat,2016). However, research has shown that Uber may present itself as a neutral party, in reality Uber is not just an intermediary that connects demand and supply for riders and drivers (Rosenblat, 2016).

Due to the drivers being independent contractors, they are not bound to fixed hours, leading to minimalized labor costs for Uber (Scheiber, 2017). Uber is therefore able to gain an advantage over the taxi companies who work with fixed contracts. Resulting in lower prices for the Uber customers. A disadvantage however for uber is that the company does not have the power to obligate drivers to show up at a specific time or place. Leading to a lack of control of when the drivers will be active on the platform. This lack of control is a problem for Uber.

To solve this lack of control Uber practices a lot of known and unknown influences on their drivers, stepping away from the promised position as intermediary. Uber uses different algorithmic systems, psychological manipulation and other techniques to influence their drivers, as to where,when and how long they work (Scheiber, 2017).

To name a few examples of these influences: Uber uses the same algorithm that Netflix does. Netflix uses its algorithm to keep people binge watching a show, Uber tries to influence their drivers to keep driving (Rosenblat, 2016). By using psychological influences such as sending notifications with opportunities for more income when the drivers are still in their current ride. Thus trying to nudge the drivers to accept another drive, even if they weren’t planning on driving another ride (Rosenblat,2016). Furthermore drivers are not always aware of the prospected income and driving time before accepting a ride. Next to the fact that Uber sets the driving rate, drivers can only ask less than the set rate, not more. These facts would suggest that Uber might intervene a little more than is expected of an intermediar party.

These influences and designed algorithms can be seen as a constraint to the independence of the drivers. It can therefore be argued if Uber should indeed be seen as an intermediary platform with independent drivers.

References
Horn, M., B. (2016. June 20). Uber, disruptive innovation and Regulated markets. Retrieved September 21, 2020, from: https://www.forbes.com/sites/michaelhorn/2016/06/20/uber-disruptive-innovation-and-regulated-markets/#7fe3db3337fb

Rosenblat, A. (2016, April 6). The truth about how Uber’s app manges drivers. Retrieved September 21, 2020, from: https://hbr.org/2016/04/the-truth-about-how-ubers-app-manages-drivers

Scheiber, N. (2017, April 2). How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons. Retrieved September 21, 2020 from: https://www.nytimes.com/interactive/2017/04/02/technology/uber-drivers-psychological-tricks.html