**5.3 Collaboration with algorithmic management in terms of passenger allocation**

A previous study argues that humans will collaborate less with computer automation activities than human beings. For Uber and Bolt, passengers are allocated to drivers on their applications. There is a guideline on the pace of acceptance and a cut-off period for approval, which motivates drivers to consider as many algorithm assignments as possible. One of the drivers interviewed explained the following: "*passengers may only be refused if the choice can be made within 15 seconds. Both Uber and Bolt find it impossible to deny allocated passengers for different reasons. And if the position on the map where you pick up a passenger is displayed, whether you are unfamiliar with the area, you will not be able to decide within 15 seconds if you want to go to that area*."

Remarkably, one of the reasons that improved drivers' engagement was to figure out whether or not the assignment of passengers made sense to them. While the passengers' assignment was generally based on the similarity between the driver and the passenger location, other considerations led to the assignment. The following variables are the mutual ranking between the driver and the passenger and the driver's login time. This sometimes causes an appeal from remote passengers delegated to drivers who are not closer to passengers. As this situation transpires, several drivers testified that they did not acknowledge the "uncomplimentary" trip task,

*Evaluation of Algorithmic Management of Digital Work Platform in Developing Countries DOI: http://dx.doi.org/10.5772/intechopen.94524*

considering that they must travel a long way (such as more than 10 minutes) to pick up a passenger. Another driver interviewed,

for example, explained: "*I monitor where other drivers are when I'm not with a passenger. So, if I see between two to four drivers in area X and a request is coming from area Y to area X, given that there are at least two to four drivers sitting right there ready to go, then my instinct will tell me that one of two things would have happened. All the drivers transfer the trip request, which is unlikely because they cannot be sitting there and transfer the assigned trip. Or the GPS mapping was not properly coordinated by the system that is assigning the passenger. Then there is a mistake in sending the request to me who is over ten minutes away from the passenger instead of assigning it to a driver who is at least a minute away*."

The explanation above found it difficult to grasp whether the algorithm assigns the passenger to the driver in error or for legitimate purposes. That's because we could not clarify how the driver app algorithm determined the assignment. Another driver interviewed believed that the task is often made by accident and that he opposes it. However, regardless of how far and inconvenient a passenger assignment might be, drivers can consider the assignment as long as it makes sense to them. For instance, one driver explained as follows: "*When it comes to distance, sometimes I've driven as 15 kilometers, and I know that the fault was not the fault of the passenger. It happened that there weren't many drivers out on the day I happened to be the closest driver at that time.*" This means that the explanation for those passenger assignments might have been significant, but at the moment, this was a feat.

#### **5.4 Workaround strategies for algorithmic assignments**

Drivers on the platforms recognize that the classification of passengers is dependent on position proximity. This allowed them to prepare and collaborate to monitor the algorithmic assignment as part of the existing device functionality. They carefully manipulate when and when to work and when to turn to the driver mode on the app so that the kinds of requests and passengers of their choosing can be delegated to them. They limit the location they operate by shutting off the driver feature on the app while returning from a long journey, avoiding low places to prevent dangerous and hazardous conditions, and not going to neighborhoods where the bar is situated to avoid intoxicated riders. They restrict their place to suburban areas to push customers to bars. They get repeat passengers by phone into the travel arrangement, asking passengers to call for a ride while they are in the driver's seat to be allocated.

Some drivers keep away from each other by monitoring other drivers' GPS positions to avoid vying with each other for passenger demands. If drivers take a rest but do not want to turn off their driver app to benefit from the hourly payment promotion, they park their cars in the same place on the GPS, which stops them from receiving any trip order. Both Uber and Bolts express only basic assignment rules, such as "closest drivers are assigned to a ride." This basic understanding allows drivers to maneuver around algorithm assignment strategies. However, the task algorithm's lack of specifics seemed to promote drivers' reluctance to make decisions, often giving them a pessimistic mood to the firms. One of the drivers explained the following.: "*Uber is very tight-lipped on what is going on. What I'm saying is they inform us 'we only assign it to the closest driver,' but we don't understand what is happening behind the scene*."

#### **5.5 More understanding of the algorithm to the benefit of the driver**

Our analysis showed that drivers benefit from a thorough understanding of the algorithmic assignment. Drivers with more experience and knowledge of the method build workarounds to escape unwanted trip assignments. In contrast,

those with less knowledge and understanding reject undesirable trip assignments, reducing their approval rates. One of the drivers, for instance, clarified that Bolt's assignment algorithms weigh the number of hours the drivers have been online, and the proximity of the driver's radius to pick up passengers increases the longer they wait for passenger assignments. As a result, he uses his knowledge of the algorithm to turn on and off his driver program periodically when at some stopping point, to disable the device from assigning remote trip requests to him. However, not all drivers have access to this knowledge. Our findings have shown that Bolt drivers who do not understand how the algorithmic assignment functions have attributed the remote assignment to the machine error. Many assumed that higher-ranking drivers would earn priority passenger assignments. These drivers are unable to establish workaround techniques to prevent the assignment of remote trip requests.
