As organizations increasingly adopt automation, innovation practitioners are responsible for selecting, adapting, testing, and implementing externally sourced innovations. However, little is known about how these upstream practices shape worker-automation arrangements, limiting our ability to intervene in innovation practice to address automation adoption challenges. To disentangle this relationship, we interviewed nine innovation practitioners at a major European airport pursuing long-term autonomous operations and analyzed their practices through a co-performance lens. We synthesize five co-performance design principles and examine where current practices align or conflict. Our findings reveal tensions: innovation practitioners prioritize full-automation arrangements while postponing human considerations; contextual constraints shape solutions, but openness to reconfiguration remains limited; and co-learning rarely extends beyond pilot phases. These insights provide HCI research and practice with guidance for reframing the conceptualization of automation, particularly by encouraging earlier consideration of human roles, promoting iterative visions, and recognizing workers as co-designers throughout innovation pipelines.
An integrated human‑automation teaming framework is presented that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers and provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Jelle A Van Dijk, Rosa van Tuijn, Renske Verwaal-Bootsma et al.· AHFE International· 0 citations
A novel holistic theory of requirements engineering (RE) quality is proposed that can serve as a coherent theoretical framework for understanding the success or failure of RE processes and artifacts, and it is envisioned that the theory can serve as a coherent theoretical framework for understanding the success or fail...
Henning Femmer, Julian Frattini· IEEE International Requireme...· 0 citations
Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implement, and sustain RAI work directly shapes the design and deployment of AI systems. As empirical scholarship examining RAI practices in industry has rapidly expanded, findin...
Wesley Hanwen Deng, Agathe Balayn, Andrew D. Selbst et al.· 0 citations
This article synthesizes recent scholarship and practitioner experience into a structured playbook for executives, design leaders, and human resources partners that argues that the value of HAIC depends on three deliberate design choices: who initiates the collaboration, how broad the AI's knowledge scope must be, and...
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
As organizations increasingly rely on automation and AI-enabled technology, technology failures and disruptions can cascade into operational breakdowns when technology-driven skill degradation (TDSD) erodes employees' essential skills for effective workarounds that enable resilient processes. We build on the skill obso...
Atiya Avery, Michael Dinger, Christian Maier· ACM SIGMIS Database the DATA...· 0 citations
Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and applica...
R. Landaeta, A. Zabihollah, R. Jazar· Applied Sciences· 0 citations
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