AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.

What Should a Startup Know About Software Customization?

Due to the expansion of new markets, accessible technologies, and venture funding, software startup growth is amazing. Moreover, startups are frequently required to adapt their software products prior to deployment at a customer's location. The authors collected a data set of 30 software projects retrieved from three software businesses using a case study research approach, with each software startup supplying a data set of 10 of its software projects tagged as failure projects in their project repository. The authors quantify and demonstrate the level of substantial software customization in startup software projects. The authors have also presented a simplified technique for quantifying customization requests for startups. Additionally, they offer tips and best practices to assist entrepreneurs in managing software customization.