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 failure of RE processes and artifacts.
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, findings are dispersed across studies that focus on different roles, organizational contexts, and interventions. This work synthesizes current knowledge through a literature review of 161 empirical studies spanning six years, each engaging industry practitioners via interviews, surveys, workshops, ethnographies, and other methods. Our synthesis reveals both meaningful progress and persistent challenges in industry RAI practice. Practitioner awareness has increased, RAI activities have become more professionalized, and interventions such as toolkits and guidelines are more widely adopted. At the same time, practitioners continue to face substantial barriers, including limited training, uneven organizational support, and a lack of interventions tailored to day-to-day work practices. By consolidating and organizing these findings, we provide a more complete account of industry RAI than any single study to date. We conclude by discussing implications for RAI researchers, practitioners seeking to adopt effective practices, and policymakers aiming to ground governance efforts in the realities of industry contexts.
Wesley Hanwen Deng, Agathe Balayn, Andrew D. Selbst et al.· 0 citations
Human-AI collaboration (HAIC) has moved from research curiosity to strategic priority for organizations pursuing faster, more inventive, and more reliable innovation outcomes. Many implementations underperform, however, because leaders treat artificial intelligence as a generic productivity tool rather than as a designed teammate whose role, capabilities, and trust requirements must match the task. This article synthesizes recent scholarship and practitioner experience into a structured playbook for executives, design leaders, and human resources partners. It 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 whether the cognitive mode is analytical or synthetic. Drawing on engineering design, aerospace, industrial product development, hospitality, and mental health contexts, the discussion translates research findings into operating practices, governance structures, and capability investments. The contribution is practical: a clearer way to decide what kind of AI teammate to build, deploy, and trust for any given problem.
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 obsolescence and deskilling literature to delineate TDSD, which we define as the depreciation of still-required essential skills caused by sustained reliance on technology. Although skill degradation has long been studied, its manifestation in progressively technology-enabled environments introduces increasingly complex mechanisms, including substitution, automation bias, and feedback attenuation. These mechanisms drive risks that demand systematic analysis and actionable guidance. We conduct an integrated systematic literature review across disparate research streams to understand the current state of research and to synthesize evidence on drivers, mechanisms, contexts, and outcomes of TDSD in organizational settings. We synthesize a peer-reviewed corpus to integrate fragmented evidence. We use a sociotechnical lens to articulate how technology deployments interact with organizational routines to degrade cognitive, manual, and interpersonal skills. Our study concludes with a research agenda and offers practical guiding principles for organizations to manage the risk of TDSD. We contribute a precise specification of TDSD, a sociotechnical synthesis that integrates fragmented evidence across research streams, and a five-pronged research agenda with practical implications for managing TDSD.
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 application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems.
R. Landaeta, A. Zabihollah, R. Jazar· Applied Sciences· 0 citations
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