The extant carbon neutrality (CN) literature largely offers macro- or meso-level analyses, providing limited insights into implementation experiences that could inform granular policymaking and industry strategies. To address this gap, we examine the lived CN experiences of firms in the transportation, energy, manufacturing, and construction sectors. Using multi-wave qualitative data analyzed through Gioia's approach, we identify policy, strategic practices, carbon-offsetting technologies, and emission-reduction approaches as key drivers of CN implementation. Notably, we establish the microfoundations of CN implementation by uncovering the nuanced roles of strategic, managerial, and operational levels as bridging mechanisms that translate policy mandates into firm-level decisions. Furthermore, we extend the theoretical understanding of the dual role of policy at the firm level, acting both as an enabling driver and a constraining factor. Finally, we propose the STEP framework, which conceptualizes CN implementation as a dynamic ecosystem of interacting forces operating within a feedback loop for continuous improvement and recalibration.

Purpose – This study aims to examine clinicians’ perspectives on the diffusion, adoption and routinization of artificial intelligence (AI) use in hearing care organizations. While clinicians act as gatekeepers of technology adoption, they also engage with AI-generated outputs as a form of knowledge that must be evaluated, validated and integrated into clinical practice. By focusing on how clinicians make sense of and use AI-generated knowledge, the study addresses the limited understanding of post-adoption routinization of AI in healthcare settings. Design/methodology/approach – The study adopts a qualitative research design, using open-ended essays to capture hearing care clinicians’ lived professional experiences. The collected textual data were analyzed using an inductive-abductive approach. Guided by diffusion of innovation (DOI) theory, the study examined how clinicians engage with AI-generated knowledge across stages of adoption and sustained use. Findings – The analysis uncovers the drivers of diffusion, adoption and routinization of AI-enabled innovations, and maps them to five DOI innovation attributes. Specifically, it identifies drivers such as diagnostic precision, workflow integration, hands-on experimentation and quantifiable patient outcomes that explain how clinicians evaluate, validate and integrate AI-generated insights into everyday clinical decision-making. Originality/value – This study contributes to the knowledge management literature by showing that AI diffusion, adoption and routinization can be understood as processes of knowledge evaluation, integration and stabilization within professional practice. In addition, by extending DOI through a knowledge lens, the study offers a novel clinician-centered framework that explains how AI-generated knowledge is embedded in routine decision-making in knowledge-intensive settings.