Continual Support Systems
People in groups and communities are often willing, available, and able to support others’ needs. For example, a graduate student in a research community may have the domain expertise and be willing to offer feedback on a peer’s study design before a major deployment. Similarly, a software engineer who just wrapped up a major task may be available to help a colleague with an architecture diagram for an upcoming presentation. Individuals within these group and community settings often are willing because they are invested in each others’ needs, have moments of availability to offer help, and have the relevant expertise needed to provide effective help. More than ever before, the development of better communication tools, expertise recommenders, matching algorithms, and real-time and volunteer crowd support systems have made it easier to connect individuals who need support to such potential helpers.
Despite many existing interventions for connecting needs to available helpers, individuals who volunteer their help often struggle to balance supporting the emerging needs of others with focusing on their own goals. For example, a graduate student may be generally willing and have the domain expertise to help a peer with their study design, but in the moment may struggle to recognize they do not have the capacity to help that week given their own upcoming conference deadline. As another example, a spectator at a race may be generally willing and available to cheer for other runners at a race, but may hesitate to do so if they are focused on finding their own runner in the crowd, or may be unprepared to cheer on the fly for someone they do not know. In these situations, people who may be generally willing, available, and able to help can still struggle to keep track of emerging needs and the status of their own goals, evaluate when they have the capacity to support others’ needs and when to focus on their own goals, and prepare to support others’ needs on-demand. Consequently, potential helpers may refrain from supporting needs that they have the capacity to support.
While CSCW and crowd computing research have explored ways of connecting needs to helpers in communities and enabling paid real-time support, in settings where needs emerge and the status of goals can change continuously, there exists a gap in approaches that support volunteer helpers in balancing a desire to support community needs with focusing on their own goals. Existing expertise recommendation systems often lack models of how people’s needs and the status of their goals continuously change, which are needed in settings where you may want to align needs with available helpers in a given moment. Similarly, prior work in real-time crowd support often does not consider settings where individuals are volunteering time to support others’ needs between their own goals, which can change their capacity to contribute support in the moment. Addressing these gaps requires integrated approaches that can monitor emerging needs and the changing status of goals in communities, and coordinate support in ways that are cognizant of and directly support the goals of individuals who volunteer support.
To overcome these challenges, we contribute continual support -- a conceptual and technical framework that enables individuals to balance supporting others’ needs with focusing on their own goals. The continual support framework helps individuals:
- maintain awareness of opportunities by monitoring the continuously changing status of individual goals and emerging needs;
- evaluate opportunities by matching individuals to opportune moments to support others given the state of their goals; and
- prepare for opportunities by priming and cueing individuals with the context they need to provide support on-demand.
The continual support framework can shift the focus of individuals to their primary goals as they demand their attention and towards emerging needs when they have the capacity to help. By supporting individuals in focusing on their own goals and identifying and preparing for the moments where they have the capacity to support community needs, the continual support framework has the potential to significantly expand needs that can be met with existing help resources within a group or community. To demonstrate the value of continual support, we study its use for engaging ad-hoc crowds -- people who may not have explicit social ties to those who need help, but while engaged in a primary activity are generally willing, available, and able to provide support to others at opportune moments. We focus on the practical example of an ad-hoc crowd of spectators at a race, and implement the continual support framework in CrowdCheer, a mobile application to help spectators balance opportunities to cheer for other runners and their own runner. Through a pilot study with 5 spectators and 11 runners at a 10k race, we demonstrate that spectators were able to support other runners in addition to their own with ease because the continual support framework helped them maintain awareness of opportunities to support others, evaluate opportune moments to support others while still focusing on their primary goal, and prepare to provide support on-demand. Results show that 80% of spectators used a continual support system to support the needs of others in addition to focusing on their own goals, and that 64% of the runners who needed help were supported by directed cheers from spectators they did not know. These findings provide early evidence for how CSCW technologies capable of continuously monitoring and connecting emerging needs to available helpers can improve and scale how individuals in group and community settings provide support to one another through existing resources.
Figure 1: The Continual Crowd Support Framework continuously monitors the activity space, looking for support opportunities generated from the requester’s status. The framework then matches an opportunity to a given crowdworker, and primes the crowdworker for the sup- port task, which they can then complete. The framework then verifies the completion of the task and updates the status of requesters and workers.
Figure 2: The Dashboard view of CrowdCheer strategically presents opportunities to spectators at the race. The system identifies periods of idle time, and the interface responds by drawing attention to system goals, such as cheering for other runners.
- CrowdCheer: Situational Crowdsourcing of Motivation for Runners, Grace Hopper ACM SRC 2015
- 🎓 Leesha Maliakal Shah
- 🎓 Scott Cambo
Masters and Undergraduate Students
- 🎓 Christina Kim