Algorithmic management is transforming traditional face-to-face service sectors like hospitality.To understand this phenomenon, we conducted an interview study in a unionized, mid-sized urban hotel on the West Coast of the USA.Through this work, we examine how an algorithmic management (AM) platform mediates work in a housekeeping department.Our analysis highlights the effects of AM on social processes, revealing that despite careful configuration, the tool's implementation still challenges traditional communication and coordination.This study contributes empirical evidence on AM impacts in a collaborative service environment, emphasizing the importance of organizational dynamics in AM design and implementation.We offer design opportunities for flexible workplace technologies that support, rather than frustrate, the relational aspects of service work.

Labor shortages have shaped many industries over the past several years, with hospitality experiencing one of the largest rates of attrition. Workers are leaving their jobs for a variety of reasons, ranging from burnout and work intensification to a lack of meaningful employment. While some literature maintains that labor-replacing automation is poised to bridge the shortages, we argue there is an opportunity for technology design to instead improve job quality and retention. Drawing on interviews with unionized guest room attendants, we report on workers’ perceptions of a widely-used algorithmic room assignment system. We then present worker-generated design ideas that adapt this system toward supporting three key facets of wellbeing: self-efficacy, transparency, and workload. We argue for the need to consider these facets of wellbeing through design across the service landscape, particularly as HCI attends to the impacts of AI and automation on frontline work.

As food delivery robots are spreading onto streets and college campuses worldwide, users' views of these robots will depend on their direct and indirect interactions with the robots and their conversations with other people, such as those with whom they are ordering food via a robot. We examined if being in a group of 2 to 3 people affects the acceptance and trust of the robot compared to being an individual user. First-time users of the food delivery robot service (N = 60) ordered food either as an Individual or in a Group. We measured the acceptance and trust of the robots after three Exposures (pre-exposure, after ordering food on the app, and after the robots delivered the food). Results indicated that Individual users had more acceptance and trust compared to Group users. Further, as hypothesized, groups had more variation in acceptance and trust compared to individual users, consistent with patterns of group polarization i.e., group members influencing each other's perceptions to become more positive or negative. Further analysis demonstrated that group members were highly influenced by their groupmates. Designers and restaurant operators should consider how to enhance group members' experience of delivery robots.

Recent investments in automation and AI are reshaping the hospitality sector. Driven by social and economic forces affecting service delivery, these new technologies have transformed the labor that acts as the backbone to the industry-namely frontline service work performed by housekeepers, front desk staff, line cooks and others. We describe the context for recent technological adoption, with particular emphasis on algorithmic management applications. Through this work, we identify gaps in existing literature and highlight areas in need of further research in the domains of worker-centered technology development. Our analysis highlights how technologies such as algorithmic management shape roles and tasks in the high-touch service sector. We outline how harms produced through automation are often due to a lack of attention to non-management stakeholders. We then describe an opportunity space for researchers and practitioners to elicit worker participation at all stages of technology adoption, and offer methods for centering workers, increasing transparency, and accounting for the context of use through holistic implementation and training strategies.