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The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

This paper aims to explore the role of artificial intelligence (AI) in automating library cataloging and classification processes, exploring current applications, challenges and future possibilities. It aims to provide insights into how AI technologies are reshaping traditional library practices and their implications for the future of information organization and access.

The paper presents a comprehensive review, analyzing recent research and developments in AI applications for library cataloging and classification. It covers traditional methods, relevant AI technologies, implementation challenges, impacts on library workflows and future directions.

AI technologies, particularly machine learning and natural language processing, offer significant potential for enhancing efficiency, consistency and depth in metadata creation and classification. However, implementation challenges include data quality issues, integration with legacy systems and the need for new skill sets among library professionals. The impact on library workflows is profound, necessitating a reimagining of traditional librarian responsibilities. Future developments promise more advanced capabilities in personalized discovery, adaptive classification schemes and predictive collection development.

This paper provides a holistic overview of AI’s impact on library cataloging and classification, synthesizing current research and future trends. It highlights the delicate balance required in leveraging AI to enhance library services while upholding core library values. The paper emphasizes the need for ongoing critical engagement with these technologies to shape the future of library services in the AI era.