Research On Online Labor Platform Workers’ Work Outcomes And The Influencing Mechanisms

The deep integration of the gig economy with advanced information technologies(e.g.,big data and artificial intelligence algorithms)has spawned the prosperity of online labor platforms.The online labor platform relies on big data and algorithms to match service requesters and service providers on a large scale,and conducts remote,real-time monitoring and guidance of platform workers,forming a set of business modes based on algorithm management.In recent years,the number of laborers engaged in platform work in China has increased year by year,and online labor platforms have brought about profound changes in employment opportunities and employment ways.However,due to the changes in management and employment ways,a series of practical challenges emerged,which are mainly reflected in three aspects:(1)the paradox of work autonomy;(2)the opacity of mandatory algorithmic control;and(3)the task uncertainty and principal-agent risk.Although existing research has conducted relevant research on the motivation and incentives of online labor platform workers,it is still in early stage for research on the deeply understanding of the work characteristics in this new context from the perspective of work,and how the platform workers’ work outcome can be influenced by the work characteristics and management or governance strategies of platforms and service requesters,of which are mainly exploratory qualitative research,and empirical research on individual behavior in this emerging work context is still lacking.Given the practical challenges faced by online labor platforms,this dissertation focused on platform workers’ work outcome and proposes three research questions,including the influence of platform work characteristics on turnover behavior,the influence of algorithmic control transparency on the coping behavior,and the influence of the governance mechanism of the service requesters on the continued participation behavior.Drawing from the relevant literature and theoretical foundations,a series of empirical studies had been carried out on the research questions.Firstly,this dissertation identified the main job demands and job autonomy in the context of algorithmic management.Based on the job strain theory,a research model was proposed to examine the influencing factors and influence of these work characteristics on work outcomes.Food delivery platforms was chosen as the research target because of the strict algorithmic control.This study collected more than 400 survey data and 30 qualitative data of food delivery workers.Through a mixed-method design and analysis,findings suggest that there was significant heterogeneity in platform workers’ perceptions of work characteristics between those with different work experiences.Besides,these job characteristics would further lead to different levels of work exhaustion and turnover.Secondly,in terms of the influence of online labor platform on work outcome,this dissertation focused on the unique feature of platform work,that is,the algorithmic control,and examined how transparency of algorithmic control influence platform workers’ coping behaviors.Combining the technostress framework and technology threat avoidance theory,this study proposed a research model based on the double cognitive evaluation process to reveal the influence of algorithmic control transparency on platform workers’ perceived challenge technostress,perceived technology threat avoidability and the associated coping behaviors.Results indicated that different dimensions of algorithmic control transparency(purpose versus process)have different effects on platform workers’ perceived challenge and threat avoidability and the associated coping behaviors.Specifically,the process transparency of algorithmic control only influences the coping behaviors by influencing the perceived challenge.While the purpose transparency of algorithmic control influences the coping behavior both by influencing the perceived challenge and the perceived threat avoidability.Thirdly,in terms of the influence of the service requesters on platform workers’ work outcome,this dissertation examined the influence of governance mechanisms of the service requesters on platform workers’ continued participation behavior.Vulnerability crowdsourcing platform of which the tasks are complex was chosen as the research target.Through crawling the data of more than 6 000 platform workers of a well-known vulnerability crowdsourcing platform,this study identified four formal and relational governance mechanisms implemented by the service requesters based on the information systems control and governance perspective.Then,a research model was proposed to analyze the influence of the governance mechanisms on platform workers’ continued participation behaviors based on the psychological contract theory.Results found that in complex and unstructured online labor platform tasks,such as vulnerability crowdsourcing,the service requesters would take a certain degree of governance responsibility.Interestingly,formal governance mechanisms from the service requesters do not always exert a positive influence on platform workers’ continued participation,while relational governance mechanisms can consistently promote platform workers’ continued participation.To sum up,this dissertation systematically studied the relationship between different dimensions of work outcomes and the influencing factors by collecting data from multiple sources,including first-hand survey and interview data as well as second-hand real transaction and text data.This dissertation expands the research perspective of prior literature related to online labor platforms and algorithm management,and enriches the related research on work characteristics in online labor platforms,and also expands and supplements the research contexts and conclusions of information system governance and control theory.Besides,this dissertation provides practical guidance for online labor platforms,platform workers and the service requesters.