Vol. 8 No. 05 (2026): Volume 08 Issue 05

Clean cooking adoption remains a persistent challenge in low- and middle-income urban contexts despite technological advancements and policy interventions. This study develops a socio-behavioral framework to explain sustainable adoption patterns of clean cooking technologies in urban Kigali households. Drawing on ethnographic insights, the research integrates behavioral, economic, and socio-cultural determinants influencing adoption decisions and long-term usage. The study critically examines the limitations of conventional diffusion models, particularly the energy ladder hypothesis, and proposes a multidimensional framework that accounts for trust, time-use practices, culinary preferences, and institutional dynamics. The findings reveal that adoption is not a linear transition but a complex, iterative process shaped by contextual realities and behavioral inertia. The proposed framework advances theoretical understanding and provides actionable insights for policymakers and practitioners aiming to achieve durable clean cooking transitions.

The fast usage of generative artificial intelligence (AI) in professional services Customer Relationship Management (CRM) systems has increased opportunities for hyper-personalized engagement with clients, but such change creates new risks in regulated practices such as legal services, where client data isn’t just sensitive, it’s protected by attorney–client privilege and a thicket of compliance rules like GDPR and CCPA. Recent breaches of law firms' confidential client files owing to breaches of data privacy have already shown how messy this can get: the Proskauer Rose breach in 2023, for example, exposed sensitive deal documents, and Bryan Cave Leighton Paisner faced a similar crisis in 2024. Cases like these indicate the urgency for a generative AI framework that preserves privacy while synergistically maximizing the benefits of enhanced personalization.

This paper presents a privacy-preserving generative AI framework that is designed specifically for legal CRM scenarios. This idea is a multi-layered framework approach, which is differential privacy baked into the data, federated training so information doesn’t have to leave its source, compliance checkpoints to catch GDPR/CCPA gaps, and audit trails that hold systems accountable. A synthetic set of anonymized legal CRM records were produced to test the application of the framework. The results showed a 59% reduction in the exposure to privacy risk, a 40% improvement in compliance scores, three times more audibility, and acceptable levels of personalization relevance. In addition to the quantitative results, expert validation from legal technologists and compliance specialists for the adoption of frameworks/case study's in practice was obtained.

In summary, this research offers three contributions: (1) this is the first research to focused on generating AI-driven personalization aligned to compliance-driven privacy safeguards for legal CRM; (2) this study offered a hybrid evaluation process that combines synthetic benchmarks with expert input for evaluation of adoption; and (3) this study contributes to shifting the conversation away from maximum personalization, irrespective of regulations/standards and towards transparency, trust, and compliant, future proofed practices in a regulated domain of legal CRM.