The sociomaterial lens within IS research holds that agency should not be considered as a property solely of humans, or of technology, but instead arises from an emergent interaction between the two. This, emergent, account of agency deepens our understanding of unfolding IS practice, but its largely cognitive orientation remains naïve towards affectively-sensed motivations that also form part of this interaction. By implication, a sociomaterial perspective lacking an affective dimension offers an incomplete conceptualisation of information systems. In response, an affectively-informed negative ontology encourages IS researchers to extend their focus beyond the visible, to encompass how actors’ receptiveness towards material objects (discourses, technologies) is shaped by deep, affectively-derived motivations of which they are not focally aware, but which nonetheless acquire agency in contributing to a sociomaterial outcome. A central argument, and illustrative empirical vignette, demonstrate how the concepts of sociomateriality, affect, and negative ontology combine to offer researchers an enhanced understanding of relational agency. A discussion follows, exploring some initial ontological, epistemological and methodological implications of an affectively-informed negative ontology for IS research.

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Robotic warehouses have transformed logistics, prioritizing speed and efficiency. However, traditional static priority systems often leave low-priority customers facing excessive delays, raising concerns about fairness. This research, based on Invia, a robotic warehouse company, proposes a dynamic priority allocation model to balance efficiency and fairness. By adjusting order priorities over time, this approach ensures that both high-priority and long-waiting low-priority orders receive timely fulfillment. Through stochastic modeling and simulations, we demonstrate that dynamic prioritization reduces delays compared to static and first-come, first-served (FCFS) models. Case studies in e-commerce and healthcare logistics illustrate the broader impact of fairness in automation. As industries increasingly rely on AI-driven decision-making, the balance between efficiency and equity becomes critical. This research challenges the assumption that robotic warehouses should optimize for speed alone and advocates for a future where fairness plays a central role in automated commerce.
YUAN Zhe - EMLV |
- Research
- Digital Transformation, Logistics and Supply Chain