No abstract

With rapid progress in machine learning, language technologies and artificial intelligence, conversational agents (CAs) gain rising attention in research and practice as potential non-human teammates, facilitators or experts in collaborative work. However, designers of CAs in collaboration still struggle with a lack of comprehensive understanding of the vast variety of design options in the dynamic field. We address this gap with a taxonomy to help researchers and designers understand the design space and the interrelations of different design options and recognize useful design option combinations for their CAs. We present the iterative development of a taxonomy for the design of CAs grounded in state of the art literature and validated with domain experts. We identify recurring design option combinations and white spots from the classified objects that will inform further research and development efforts.

Abstract

Many governments and organizations recognize the potential of open innovation (OI) models to create value with large numbers of people beyond the organization. It can be challenging, however, to design an effective collaborative process for a massive group. Collaboration engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration engineers. To manage the complexity of the design process, they use a modeling technique called facilitation process models (FPM) to capture high-level design decisions that serve different purposes, such as documenting and communicating a design, etc. FPM, however, was developed to support designs for groups of fewer than 100 people. It does not yet represent design elements that become important when designing for groups of hundreds or thousands of participants, which can be found in many OI settings. We use a design science approach to identify the limitations of the original FPM and derive requirements for extending FPM. This article contributes to the CE and to the OI literature by offering an FPM 2.0 that assists CE designers to design new OI processes, with a special focus on outside-in OI.

In today's race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.