Background:  Despite the utilization of algorithms as data management tools, they are increasingly used as people management tools to allocate, optimize and evaluate workers. This is especially popular among digital labor platforms of the gig economy as it is seen as one of the core innovations that enabled such platforms. Usually, these platform workers are self-employed, which results in an apparent autonomy while working under a rigid algorithm. For those workers, proactively shaping the job according to their own needs and abilities, commonly known as job crafting, may be increasingly important. As research suggests that job crafting occurs across professions and industries, how is it possible under the constraints of algorithmic management?  Purpose:  This thesis investigates the abilities of German food delivery riders of the company Lieferando to perform job crafting while being managed by an algorithm.  Method:  To meet the purpose of this study, the authors conducted a qualitative study. The data was collected through technology-mediated interviews with riders of the company Lieferando in Germany. The authors applied an online recruitment strategy through various social media websites to find suitable interviewees. Interview partners were picked with a random sampling strategy. The interviews were semi-structured, and the researchers guided the interviewees through a previously prepared topic guide with open-ended questions.  Conclusion:  The results of this study provide empirical evidence that riders of the food delivery company Lieferando engage in job crafting activities although working under the constraints of algorithmic management. The outcomes further show that all riders performed task crafting and cognitive crafting in various ways, whereas engagement in relational crafting was less developed. Riders not only have the ability to modify their work but also enrich it.  The findings of this study allow to draw several theoretical and managerial implications as well as provide possible research opportunities for future studies.

Introduction: This qualitative case study extends the research of algorithmic management by examining the experiences of food-delivery workers working for the gig company Foodora. As Foodora promotes the job as flexible and autonomous, the utilisation of labour process theory (LPT) in this study helps to uncover if these sentiments hold true in practice by examining what Foodora’s labour process looks like, and how control, autonomy, and individualism take shape in the workplace. Method: Interviews with eight Foodora delivery workers working in Sweden were carried out, where the data was transcribed and coded. Analysis: Using LPT as a framework for the analysis, the material was coded according to the themes of control, transparency, resistance, consent, and individualism. By doing this, the study contextualises algorithmic management within the wider framework of capitalistic management forms and highlights how it impacts worker experiences. Results: The results show that the experiences of the job were largely different among the individual participants of the study. It is proved that Foodora’s employment of algorithmic management impacts almost every aspect of the daily work since it centres around following automated directives which the workers receive through an app. Individualism is accentuated by a self-entrepreneurial discourse promoted by Foodora and the fact that the workforce is dispersed with limited opportunities to interact. Despite being a dispersed workforce, the workers have been able to organise which has led to a growing number of workers joining the union. Conclusion: The impacts of algorithmic management are proved to be evident. The varied experiences of the job could have multiple explanations and needs to be explored further in relation to economical and societal factors. The effects of the newly implemented collective bargaining agreement also need to be examined in future research.