**1. Introduction**

The capacity to work enables human beings to engage in conscious constructive and imaginative practices that alter and define nature so that human beings can create their means of existence to fulfill their needs, which in themselves constitute the creation of material life [1]. An online job is a term that has been a vital ground for debates in the political economy of internet technologies [2]. The Automated Job Network is a framework in which businesses such as Uber, Bolt (formerly Taxify), Takealot, JumiaEat, Otlob, and others use cloud-based technologies to "match" staff on the customer platform [3]. However, the spread of interactive work networks

has been one of the significant revolutions in work over the last decade. Moreover, the Network Capitalism [4] is part of a broader transition from usual job security to variable employment conditions, including contract, contractual and part-time work.

The amount of data generated from several organizations' processes and activities made it possible for software algorithms to accumulate and interpret data, making it possible to contribute to management and decision-making processes [1]. As a result, data-driven algorithms allow digital work systems to handle transactions between thousands of network staff automatically. These algorithms assign, refine, and analyze the jobs of different platform workers [2].

We apply to automated algorithms that perform managerial decision-making and institutional instruments that assist algorithms in the practice of algorithmic management. Algorithmic management helps companies like Uber, Bolt, and many more oversee the platform's multitudes of employees in an improved way, on a wide scale. Algorithmic management is one of the key technologies that facilitate virtual organization management. A variety of ride-sharing systems, such as Uber and Bolt, have helped connect independent, dispersed drivers with customers who need systems within minutes or seconds. Simultaneously, service rates change rapidly, based on how the demand increases from applications installed on their mobile phones.

Algorithmic management allows individual human administrators to oversee drivers at any place where the ride-sharing systems run, including on a global scale. As a result, drivers had little to no prior contact with the company's members. However, they should communicate via online platforms (such forums) to enhance their social awareness of ride-share programs. This scenario helps one research what happens when algorithms delegate jobs, refine work activity through information analysis, and measure or measure job performance.

This leads to some key testing questions: are human workers (i.e., drivers) engaging and agreeing with work that is algorithmically delegated to them? Are human workers inspired or distracted by algorithmic optimization, and if so, by how much? How successful and accurate is the data-driven assessment of this algorithmic administration, and how do human employees feel about it?

The first move in answering these questions was to interview 41 drivers with Bolt and Ubers. We then triangulated their answers, interviewed 19 passengers, and studied the online driver forums' archived discussion.

Our findings highlight difficulties and opportunities in the architecture of human-centered algorithmic job management, evaluation, and knowledge. The results further underscore the value of fostering sensemaking around social algorithmic structures. We use the results to explore how data-driven algorithmic management can create a safer working atmosphere for intelligent machines while providing potential work recommendations.
