Experiential Benefits of Interactive Conflict Negotiation Practices in
Computer-Supported Shift Planning
- URL: http://arxiv.org/abs/2209.12568v1
- Date: Mon, 26 Sep 2022 10:36:17 GMT
- Title: Experiential Benefits of Interactive Conflict Negotiation Practices in
Computer-Supported Shift Planning
- Authors: Alarith Uhde and Matthias Laschke and Marc Hassenzahl
- Abstract summary: Shift planning plays a key role for the health and well-being of healthcare workers.
Current computer-support in shift planning is typically designed from a managerial perspective.
This implies automatic resolutions of emotionally charged scheduling conflicts.
- Score: 28.614580329727254
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Shift planning plays a key role for the health and well-being of healthcare
workers. It determines when they work and when they can take time off to
recover or engage in social activities. Current computer-support in shift
planning is typically designed from a managerial perspective and focuses on
process efficiency, with the long-term goal of full automation. This implies
automatic resolutions of emotionally charged scheduling conflicts. In the
present study, we measured the effects of such a fully automated process on
workers' well-being, fairness, and team spirit, and compared them with a more
interactive process that directly involves workers in the decision-making. In
our experimental online study (n = 94), we found positive effects of the more
interactive process on all measures. Our findings indicate that full automation
may not be desirable from the worker perspective. We close with concrete
suggestions to design more worker-centered, hybrid shift planning systems by
optimizing worker control, considering the worker experience, and embedding
shift planning in the broader work context.
Related papers
- Analyzing Operator States and the Impact of AI-Enhanced Decision Support
in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning
Framework for Intervention Strategies [0.9378955659006951]
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety andeffi- ciency.
The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface.
arXiv Detail & Related papers (2024-02-20T18:31:27Z) - The Foundations of Computational Management: A Systematic Approach to
Task Automation for the Integration of Artificial Intelligence into Existing
Workflows [55.2480439325792]
This article introduces Computational Management, a systematic approach to task automation.
The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
arXiv Detail & Related papers (2024-02-07T01:45:14Z) - Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported
Coordination of Mobile Crowdsourcing [0.7865191493201839]
In mobile crowdsourcing, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully.
We propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing.
arXiv Detail & Related papers (2024-01-23T16:00:45Z) - Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
We propose a method for a collaborative system that remains safe by having a human making decisions.
Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
arXiv Detail & Related papers (2023-11-13T16:00:16Z) - Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments [54.39292848359306]
We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks.
The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task.
We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
arXiv Detail & Related papers (2023-03-27T01:50:45Z) - Planning-oriented Autonomous Driving [60.93767791255728]
We argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car.
We introduce Unified Autonomous Driving (UniAD), a comprehensive framework that incorporates full-stack driving tasks in one network.
arXiv Detail & Related papers (2022-12-20T10:47:53Z) - Learning Action Duration and Synergy in Task Planning for Human-Robot
Collaboration [6.373435464104705]
The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot.
This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots.
arXiv Detail & Related papers (2022-10-21T01:08:11Z) - Learning to Coordinate for a Worker-Station Multi-robot System in Planar
Coverage Tasks [16.323122275188354]
We focus on the multi-robot coverage path planning problem in large-scale planar areas with random dynamic interferers.
We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment.
We propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station.
arXiv Detail & Related papers (2022-08-05T05:36:42Z) - Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy:
Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge.
We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
arXiv Detail & Related papers (2021-06-15T22:06:39Z) - Show Me What You Can Do: Capability Calibration on Reachable Workspace
for Human-Robot Collaboration [83.4081612443128]
We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth.
We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
arXiv Detail & Related papers (2021-03-06T09:14:30Z) - Design and Appropriation of Computer-supported Self-scheduling Practices
in Healthcare Shift Work [28.614580329727254]
Shift scheduling impacts healthcare workers' well-being because it sets the frame for their social life and recreational activities.
We designed a social practice-based, worker-centered, and well-being-oriented self-scheduling system which gives healthcare workers more control during shift planning.
arXiv Detail & Related papers (2021-02-03T16:18:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.