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Despite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.

A graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.

Two Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.

This study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.

This research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making?

An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis.

The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes.

The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers.

To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.