*5.8.2 Using quantitative data only leads to inaccuracy in quality of service*

Our study indicates that the Passenger Driver Rating System builds faith and operation behaviors in network work schemes. However, there is not sufficiently metric to be used for driver efficiency assessment. Some drivers rely on passenger ratings as indicators to approve travel requests from passengers or not, believe more in higher-rated passengers, and exercise caution on lower rating passengers. Any of the passengers interviewed clarified that while they ignore driver ratings, the fact that the driver rating system is in place gives them a sense of protection.

The ranking system also allows the drives to provide a feeling of standard service on all their trips. One of the drivers interviewed, for example, explained: "*I want to get five stars. So, I make sure that I am friendly. I relate with the passengers. I ask them their preferences immediately; they step in the vehicle. I ask them if they want me to switch on the Aircon or heater. Or they want me to roll the windows down or up depending on the weather. I also ask them if they will prefer that I play the CD or listen to Radio. And to ones that want the music played, I ask them what type of music they would like to listen to? I sometimes offer them sweets and gum."*

Drivers take ratings very seriously. High ratings like 4.95 have become the basis upon which some of the drivers pride themselves. Although scores below 4.0 upset some drivers and cause them to fear losing their work on the network, they fear being tracked, measured, and arbitrated by customers. These seem to have adverse psychological effects on those who have not scored close to 4. One of the drivers interviewed explained the following.: "you are forced by *the rating to be careful and cautious. Because it looks like what you are doing is being monitored, rated, and judged. If your rating is low for some time, you could be asked not to drive for some on the platform and reapply for the job later."*

Some drivers believe that their passenger scores are not a fair representation of their success in terms of operation and driving, as one driver explained: *"It's like in rugby, the line-up does not indicate the specifics of a player. A player could hit three runs, and three tries it doesn't mean that such a player is a productive player."* Some drivers also explained that passengers' psychological and physical conditions, such as being drunk, having a hurry to meet, or catching a late flight, are often reasons they give lower ratings after the trip. They also explained that some passengers interpret the machine anomaly or algorithm functions as bad experiences. This is because they are sometimes unable to manage due to errors (such as GPS errors, traffic jams, rising pricing, etc.) on their side. As a result, they give drivers lower ratings.

#### **5.9 Making sense of the online platform as a forum for discussion**

Drivers on the digital work platform function separately in distributed locations and, when they do, make use of internet networks as a significant avenue for their socialization. They use online communities such as Facebook groups, WhatsApp, Telegram, Line, Hangout, etc. as sites for different forums to address their work on the site and the algorithmic site management. One of the constructive aspects of making sense of the online forum is to explore how driver efficiency in terms of ratings and recognition can be strengthened and sustained.

Experienced drivers are often eager to exchange strategies and suggestions with inexperienced drivers who ask questions about boosting their scores. They're willing to do this, relying on the expertise they have gained over time. One of the new drivers in one of the forums, for example, asked how to raise his scores after 74 trips in four weeks. Approximately 120 comments were made within three hours of the publication of the query. Some drivers sympathize with his feelings; some share their everyday encounters as part of the first challenge of getting to the platforms. However, several commenters expressed the specific tricks they use, such as designing a service information manual for their cars' back seat, heading to the Central Business District (CBD) at lunchtime on several short journeys building genuine connections with travelers, etc. Any seasoned drivers often make it clear to beginner drivers that ratings will be steady over time and warn them not to encourage tension to get too much on ratings.

However, in terms of knowledge utility and practices that use assignment algorithms and rising pricing, they tend to have a lesser impact. Most of the posts that raise questions are concerned with how assignment algorithms and surge pricing work, understand the competitive display of surge pricing, and the fields in which higher pricing is often applied. Most of the real-time questions and conversations are about exasperating events — no travel requests in high-priced places or remote travel requests that entail long driving periods.

Much of the online forums' conversation centered on offering emotional and social help instead of informational support. For instance, the driver's post was a matter in which he was irritated at the trip request by the algorithm assigned to him, which forced him to ride from east to west of the area, even though he could see other drivers in the vicinity of the passenger making the request. Much of the comments in response to the post were worried about giving emotional support. However, there were no remarks to justify why the algorithm allocated such a trip to him. Company representatives usually do not appear on the forums to answer the driver's questions.

## **6. Discussion**

This section discusses how our findings will continue to develop and improve the architecture of algorithmic data-driven management.

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

### **6.1 Designing and enhancing the algorithmic trip assignment**

Myriads of Algorithmic Passenger Trip Requests for Drivers on the Automated Work Network, such as Bolt and Uber, are made automatically within seconds of ride requests. The regular and speedy approval of the drivers' assignments ensures the platforms' operation's reliability and efficacy. Hence, it is the potential to maximize the number of passengers who can make short journeys. Our results indicate that the task is not based purely on the root of the task (i.e., person versus algorithm) in the digital job site's algorithmic management. However, how the assignment is performed and managed, how staff on the site interact and agree with the assignment [7]. According to the study's findings, the details displayed on the computer, the constraint of the time to approve the request for a ride, and the approval rate jointly decides the degree of cooperation between drivers and the assignment algorithm.

Furthermore, our findings indicate that the openness of the algorithmic assignment's method can enable algorithmic management to produce a high degree of coordination and cooperation with drivers. While Uber and Bolt clarify that their algorithmic handling of assignments is based on the proximity of drivers and passengers, our findings suggest that there are other considerations that the algorithm takes into account. This is why passenger demands are often not allocated to drivers who are nearest to travelers. [8] suggests that the art of describing or encouraging staff to ask questions about each trip assignment may help minimize drivers' refusal rate when assigned to a trip that is not close to them, instead of attributing those assignments to a technological glitch to the network work method. This is because clarity and openness will boost drivers' negative emotions or contrasting ideas about businesses that run digital channels. Our results are consistent with previous studies on suggestion networks where exposure has improved users' confidence and adopt suggestions [9]. This study also draws attention to new transparency implications that have gained little attention in previous studies on intelligent systems, i.e., the effect of transparency in algorithmic management and how to open algorithmic assignment leads to improved job strategies address limitations. Drivers who have a thorough understanding of the assignment algorithm can set up workarounds to prevent a less economical journey. In contrast, drivers who only have basic knowledge of proximity-based algorithmic assignment cannot do the same.

It was also discovered that being autonomous contractors on digital work platforms is a crucial factor in the network drivers' preference and stability, which leads to a lack of control over the algorithmic management of trip assignments. Another consideration is the lack of familiarity with such systems. For example, a driver who acts as a driver and a passenger likes a mechanism that allows him to view and select travel requests directly. He assumes that the algorithm is now managing the choices that he will make himself. This is understood as opposition to transition, which often poses versatile, ethical concerns regarding the trend of emerging technologies that are harmful to people's regulation for the sake of overall machine performance and the consequences of learning and growth while at work [10].

#### **6.2 Integrating information support into the algorithm management design**

Supply–demand management algorithms were initially developed to solve statistical optimization problems concerning non-human entities. In Bolt and Uber, however, they are used to inspire and regulate human behavior. This poses issues, as the supply–demand management algorithm does not consider the speed at which drivers run. Consistent with previous studies on a smart agent that sought to promote healthy behavior [5], the algorithm struggled to account for people's feelings of inequity towards higher prices and overlooked drivers' social and altruistic

motives. This highlights the importance of algorithmic management: (a) the speed and manner in which people work, (b) different forms of inspiration rather than just economic ones, and (c) the feelings people have about the choices that algorithms make. Also, some drivers did not trust the high-priced areas as they had more faith in their expertise. Transparency of how the surge priced region was computed in real-time could increase workers' trust in algorithmic knowledge.
