Controlled by the algorithm, coached by the crowd – how HRM activities take shape on digital work platforms in the gig economy

Job demands-resources on digital gig platforms and counterproductive work behavior

Introduction

With the rapid expansion of digital gig platforms, counterproductive work behavior among gig workers has become increasingly prominent, adversely impacting the platform's reputation, operational efficiency, and user experience. This study aims to explore how job demands and job resources influence counterproductive work behavior among gig workers.

Methods

Grounded in the Job Demands-Resources model and Job Engagement Theory, this study develops a second-order chain mediation structural model to analyze the effects of job demands (Work pace/workload, physical demands, psychological demands, and customer-related social stressors) and job resources (Compensation, job security, learning opportunities, and opportunities for professional development) on counterproductive work behavior. Cognitive, emotional, and behavioral engagement are also examined as mediators.

Results

Job demands positively influence counterproductive work behavior, while job resources have a negative impact. Cognitive, emotional, and behavioral engagement each negatively affect counterproductive work behavior. Additionally, platform formalization moderates the negative influence of gig workers' engagement on counterproductive work behavior.

Discussion

This research provides a comprehensive theoretical framework for digital gig platform managers to understand and predict gig workers' counterproductive work behavior. It also offers practical implications for optimizing the work environment, enhancing job engagement, and mitigating counterproductive work behavior, thus fostering mutual development between gig workers and the platform.

Navigating autonomy: unraveling the dual influence of job autonomy on workplace well-being in the gig economy

Introduction

With the rapid rise of the gig economy globally, its characteristics of promoting employment and facilitating autonomy have supported its rapid growth and development in China. While the flexibility of gig work offers more employment options and income sources for workers, it also caused many problems and uncertainties. Workplace well-being is an important psychological factor that indicates the psychological state of workers and significantly predicts their behavior at work. However, previous studies on the gig economy rarely analyze gig workers' workplace well-being, which is of great significance to improving their individual emotions, promoting their physical and mental health, and maintaining the sustainable development of the gig economy and society in general.

Methods

This study draws on the cognitive-affective processing system framework to construct a moderated dual-mediator model to explore the dual influence mechanism of job autonomy on gig workers' workplace well-being. Based on the data of 442 digital gig workers who were mainly engaged in manual labor.

Results

The survey results show that job autonomy positively affects employees' workplace well-being, and work alienation and positive emotion mediate this relationship. Perceived algorithmic control can moderate not only the influence of job autonomy on work alienation and positive emotion but also the indirect impact of job autonomy on workplace well-being through work alienation and positive emotion.

Discussion

The finding of this research contributes to expand the comprehension of the relationship between gig-worker job autonomy and workplace wellbeing and this relationship's underlying mechanism, holding significant implications for management practice.

Challenge or Hindrance? The Dual Impact of Algorithmic Control on Gig Workers' Prosocial Service Behaviors

Algorithmic technological progress presents both opportunities and challenges for organizational management. The success of online labor platforms hinges on algorithmic control, making it imperative to explore how this control affects gig workers' prosocial service behaviors. Drawing from affective event theory, our study delves into the factors influencing gig workers' prosocial service behaviors in the online labor platform setting. We utilize the challenge-hindrance appraisal framework to highlight the pivotal role of algorithmic control. To rigorously test our hypotheses, we gathered empirical data from an online questionnaire survey of 660 gig workers. Our results indicate that challenge appraisals and hindrance appraisals in regard to platform algorithm control have a nuanced dual impact on gig workers' prosocial service behaviors. This relationship is clarified by the mediating function of work engagement. A challenge appraisal of platform algorithmic control can positively influence gig workers' prosocial service behaviors. However, hindrance appraisal of platform algorithmic control can negatively influence gig workers' prosocial service behaviors. Interestingly, workplace interpersonal capitalization boosts the effect of challenge appraisal on employees' prosocial service behaviors. However, it does not mitigate the adverse effects of hindrance appraisal on such behaviors. This study has multiple theoretical implications, and it also provides valuable practical insights into organizational management.

Enhancing social media engagement using AI-modified background music: examining the roles of event relevance, lyric resonance, AI-singer origins, audience interpretation, emotional resonance, and social media engagement

Introduction

Drawing on the S-O-R model, this study aims to investigate the influence of three stimuli from AI-modified music (i.e., event relevance, lyric resonance, and AI-singer origins), two responses from social media content consumers (i.e., audience interpretation and emotional resonance) on the social media engagement of personalized background music modified by artificial intelligence (AI).

Methods

The structural equation modeling analyses of 467 social media content consumers' responses confirmed the role of those three stimuli and the mediating effect of audience interpretation and emotional resonance in shaping social media engagement.

Results

The findings shed light on the underlying mechanisms that drive social media engagement in the context of AI-modified background music created for non-professional content creators.

Discussion

The theoretical and practical implications of this study advance our understanding of social media engagement with AI-singer-originated background music and provide a basis for future investigations into this rapidly evolving phenomenon in the gig economy.

Time-Based Stress and Procedural Justice: Can Transparency Mitigate the Effects of Algorithmic Compensation in Gig Work?

The gig economy has led to a new management style, using algorithms to automate managerial decisions. Algorithmic management has aroused the interest of researchers, particularly regarding the prevalence of precarious working conditions and the health issues related to gig work. Despite algorithmically driven remuneration mechanisms' influence on work conditions, few studies have focused on the compensation dimension of algorithmic management. We investigate the effects of algorithmic compensation on gig workers in relation to perceptions of procedural justice and time-based stress, two important predictors of work-related health problems. Also, this study examines the moderating effect of algorithmic transparency in these relationships. Survey data were collected from 962 gig workers via a research panel. The results of hierarchical multiple regression analysis show that the degree of exposure to algorithmic compensation is positively related to time-based stress. However, contrary to our expectations, algorithmic compensation is also positively associated with procedural justice perceptions and our results indicate that this relation is enhanced at higher levels of perceived algorithmic transparency. Furthermore, transparency does not play a role in the relationship between algorithmic compensation and time-based stress. These findings suggest that perceived algorithmic transparency makes algorithmic compensation even fairer but does not appear to make it less stressful.

HRM models of online labor platforms: Strategies of market and corporate logics

Studies on online labor platforms (OLPs) have revealed that OLPs can have extensive managerial control over independent workers, which affects their autonomy and precariousness. The permeability of the management makes some OLPs' roles as neutral intermediaries in labor exchanges questionable. While there are several platform work studies on the effects of human resource management (HRM) activities, earlier studies have focused more on certain types of OLP companies. Earlier OLP classifications did not make systematic distinctions between HRM activities either. This paper offers a classification to view how HRM activities manifest in OLPs. The study utilizes terms of service and webpage data from 46 multinational and Finland-based OLPs. Based on these data, OLPs have been classified into six models with five governance principles and institutional logic. The study uses the idea of institutional complexity and claims that OLPs balance their operations between the complexity of two institutional logics, market, and corporation, by using varying strategies with their HRM activities. Differently managed OLPs are also often marketed to different worker groups. This indicates that workers' levels and quality of autonomy differ between OLPs. Hence, could be expected that platform workers' expectations toward OLPs, perceptions of fairness, and experiences of wellbeing may be influenced by the HRM activities in which they engage. The results contribute to the ongoing discussions of power asymmetries between OLPs and platform workers, and thus OLPs' roles as either marketplaces or hierarchical corporations. Formed models can be utilized to enrich studies on key issues of platform workers' autonomy, precariousness, and experiences in different types of OLPs.

Digital platforms and entrepreneurial support: a field experiment in online mentoring

The benefits of entrepreneurial mentorship are well documented, but there is limited research on how entrepreneurs connect with mentors, especially in digital settings. We partnered with an online platform that connects entrepreneurs to potential mentors to conduct a field experiment in online mentoring. Drawing on literature on entrepreneurial mentorship and Social Cognitive Theory, we compared the effects of three interventions on the likelihood of reaching out and making a connection with a mentor in a digital setting. We find that showing entrepreneurs a video of a successful mentor-mentee relationship increases the chances that they will reach out to a potential mentor but does not improve their chances of making a connection. These findings are more pronounced for female entrepreneurs. While not all entrepreneurs adopt the offered interventions, those that make the effort to learn to navigate the online platform and craft a suitable introductory message are successful in establishing a mentoring connection. We discuss these implications for both theory and practice.