arXiv subjects

Rasam Dorri

Publications and source records attributed to Rasam Dorri.

2 recordsLinked to original sources

arXiv2025

Artificial Intelligence Applications in Lean Startup Methodology: A Bibliometric Analysis of Research Trends and Future Directions

This study presents a comprehensive bibliometric analysis of the emerging intersection between artificial intelligence (AI) and lean startup methodology. Using the PRISMA 2020 framework, we systematically analyzed 12 peer-reviewed articles published between 2010 and June 2025, sourced from the Scopus database. The analysis employed VOS viewer software to conduct co-authorship, keyword co-occurrence, and citation network analyses. Results reveal three distinct research clusters: operational integration of AI within startup experimentation processes, AI-enhanced learning systems for entrepreneurial contexts, and strategic implications of AI for uncertainty management in startups. The findings indicate a developing research domain characterized by fragmented authorship networks, limited international collaboration, and geographic concentration in developed economies, particularly the United States and Germany. Key research themes include business model innovation, iterative methods, and machine learning applications, with artificial intelligence serving as a bridging concept across thematic clusters. The analysis identifies significant research gaps in ethical considerations, cross-cultural validation, and empirical testing of AI-enabled lean startup frameworks. While current research demonstrates growing interest in AI integration within entrepreneurial experimentation, the field requires enhanced theoretical consolidation, methodological rigor, and interdisciplinary collaboration to achieve practical relevance and academic maturity. This study contributes to the emerging discourse on digital entrepreneurship by providing a systematic overview of research trends and identifying priority areas for future investigation at the intersection of AI and lean startup methodologies.

cs.DL↗

arXiv2025

Memory Power Asymmetry in Human-AI Relationships: Preserving Mutual Forgetting in the Digital Age

As artificial intelligence (AI) becomes embedded in personal and professional relationships, a new kind of power imbalance emerges from asymmetric memory capabilities. Human relationships have historically relied on mutual forgetting, the natural tendency for both parties to forget details over time, as a foundation for psychological safety, forgiveness, and identity change. By contrast, AI systems can record, store, and recombine interaction histories at scale, often indefinitely. We introduce Memory Power Asymmetry (MPA): a structural power imbalance that arises when one relationship partner (typically an AI-enabled firm) possesses a substantially superior capacity to record, retain, retrieve, and integrate the shared history of the relationship, and can selectively deploy that history in ways the other partner (the human) cannot. Drawing on research in human memory, power-dependence theory, AI architecture, and consumer vulnerability, we develop a conceptual framework with four dimensions of MPA (persistence, accuracy, accessibility, integration) and four mechanisms by which memory asymmetry is translated into power (strategic memory deployment, narrative control, dependence asymmetry, vulnerability accumulation). We theorize downstream consequences at individual, relational/firm, and societal levels, formulate boundary-conditioned propositions, and articulate six design principles for restoring a healthier balance of memory in human-AI relationships (e.g., forgetting by design, contextual containment, symmetric access to records). Our analysis positions MPA as a distinct construct relative to information asymmetry, privacy, surveillance, and customer relationship management, and argues that protecting mutual forgetting, or at least mutual control over memory, should become a central design and policy goal in the AI age.

cs.HC↗