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The paper aims to demonstrate a geographic information system (GIS) based study on environmental impact assessment (EIA), due to air pollution, for a highway project.

An approach has been designed to explore the scope for the combination of EIA and GIS in development for the proposed Allahabad Bypass Project. The air quality in the study area has been quantified in terms of the air pollution index (API). GIS has been exploited to obtain the spatial information for the prediction of air pollution impact at different suburban and rural areas adjacent to the stretch of bypass.

The study has enabled the researchers to understand the variation in air quality along the total stretch of the bypass keeping in view the “with” and “without” project scenarios. The results obtained from the study show considerable increase in air pollution levels from baseline to the projected period of 20 years, due to gradual increase in vehicular traffic along the highway.

The information presented in this paper serves as an example to quantify the negative impacts of countryside air quality associated with highway projects. The approach utilized the spatial evaluation of air pollution and helps to provide a critical insight to the problem, which is not apparent while carrying out such an exercise in the traditional manner.

Hopefully, this study will encourage the highway planners in India to make a wider application of the technique for an indepth assessment of environmental impacts.

The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.