Large language models (LLMs) challenge long‐standing assumptions in linguistics and linguistic anthropology by generating human‐like language without relying on rule‐based structures. This introduction to the special issue Language Machines calls for renewed engagement with LLMs as socially embedded language technologies. We trace the intellectual genealogy that led linguistic anthropology to sideline such technologies, highlighting how disciplinary boundaries and language ideologies shaped this absence. Arguing for the field's unique potential to analyze the semiotic, interactional, and ideological dimensions of LLMs, we invite future contributions that expand linguistic anthropology's scope and relevance in the age of machine‐mediated communication.
As seven of nine planetary boundaries are breached, management scholars face an urgent challenge: how can organizations address complex social-ecological crises that transcend traditional organizational boundaries and objectives? Responding to this need, researchers have leveraged a plurality of systems perspectives, yet current approaches remain nascent and fragmented. In this paper, we review 25 years (2000–2024) of empirical sustainability management research across 17 leading journals. We identify core systems properties—interrelatedness, nestedness, non-linearity, and emergence—that collectively illuminate four critical systems-wide dynamics: equilibrium, disequilibrium, adaptation, and organized systems change. This systematic review offers scholars a unified framework for theorizing and addressing critical sustainability challenges facing organizations, society, and the planet.
Large language models (LLMs) are increasingly used to generate data for research. Typical use cases are classifications, annotations, information extraction, and generation of numerical scores. Unlike conventional measurements, LLM outputs can vary across repeated requests even when the prompt and apparent model settings remain unchanged. This variation arises from deliberate sampling, silent model updates, numerical rounding, or expert routing. Setting a dedicated temperature parameter to zero removes deliberate sampling when that option is available, but it does not eliminate the other sources of randomness. Exact reproduction is therefore generally not possible when using proprietary application programming interfaces. Local execution of open-weight models offers greater control, but reproducibility still depends on the complete hardware and software stack. We illustrate these issues through sentiment classifications of corporate filings and examine their consequences for downstream regression results. We then propose a reporting standard for articles and replication packages, as well as guidance for data editors and authors. Together, these findings and recommendations establish that LLM outputs should be treated as draws from a distribution rather than as fixed measurements.
As technological advancements, artificial intelligence (AI), and climate change become increasingly intertwined, energy efficiency has emerged as a crucial issue for organizations and public authorities. This research examines how firms can align financial and environmental goals to attract diverse investor groups, focusing on AI-driven energy efficiency strategies. To do so, we use the Economies of Worth framework and explore how investors respond to energy strategies framed by financial or environmental motivations (i.e., market or green worlds), depending on the type of AI adopted and the nature of compliance. Across four experimental studies with 1,500 investors, we find that environmental motivations can reduce investor willingness to invest, mediated by perceived energy efficiency. However, AI implementation and certification mechanisms act as critical boundary conditions that can legitimize environmental strategies and enable compromise between market and green logics. Specifically, coupling environmental motivations with AI for energy efficiency and third-party certification leads to higher investor willingness to invest. This study contributes to sustainable investment research by highlighting the critical role of AI and compliance in building hybrid justifications that can facilitate alignment between environmental and financial priorities in investor decision-making.
