Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

This study analyzes in a comparative way the psychological meanings that social science and basic science researchers assign to the term “research”. Using the Natural Semantic Networks technique with 127 participants from a Colombian public university, we sought to unravel the distinctive epistemological and methodological positions between these disciplines. The findings reveal that, although both groups closely associate research with knowledge, they differ in the lexical network and associated terms, reflecting their different epistemological approaches. Basic science researchers emphasize terms such as “innovation” and “experimentation,” while social science researchers lean toward “solving” and “learning.” Despite the variability in the associated words, “knowledge” remains the common core, suggesting a shared basis in the perception of research. These results show the importance of considering disciplinary differences in research training and knowledge generation. The study concludes that research contributes significantly to both the advancement of individual disciplines and social welfare, urging future research to explore these dynamics in broader contexts to enrich interdisciplinary understanding and foster cooperation in knowledge generation.

In this research, we explore the psychological factors that SMB owners who are micro-entrepreneurs and use SNS for entrepreneurial purposes rely on to make their self-employment decisions. Research-based on a merger of the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) deals with how perceived ease of use (PEU), perceived usefulness (PU), attitude, subjective norms (SN), perceived behavioral control (PBC), openness to experience (OTE), and dominance contribute to people’s behavioural intention (BI) to use SNS for Data was collected from 342 SMB micro-entrepreneurs in the Delhi/NCR region of India by the means of a standardized questionnaire. Employing PLS-SEM, a partial least squares structural equation modeling was used to analyze the data. The results point out an impact of PU, attitude, and behavioral intention, and unappealing presentations, unacceptance of an explanation, unclear mechanisms, and domination do not make any difference. The research emphasizes how technophobe’s attitude, and the perception of effectiveness would impact micro-entrepreneurs desire to avail SNS for entrepreneurship efforts. Moreover, research shows the psychological understanding based on the SNS adoption by the small business owners, micro-entrepreneurs as well as for the practitioners and policymakers who are working to enhance the capability of the SMB. More investigations should be conducted on the other personality traits and cover more nations as demographic dividends in comparison to acquire more inclusive data.

This systematic literature review examines the convergence of entrepreneurship and information technology between 2005 and 2024. It investigates how the emergence of information technologies such as social networks, smart devices, big data, and cloud computing have transformed business operations and entrepreneurial approaches. The study use technologies such as Bibliometrix to analyze academic literature and identify research trends, knowledge structures, and their evolutionary routes. During the specified time frame, a grand total of 292 articles were published by 777 writers. These publications have played a key role in redirecting academic focus from traditional entrepreneurship to the field of digital entrepreneurship and the applications of information technology. A thematic analysis uncovers a shift from theoretical investigation to practical implementations and multidisciplinary research, while a co-citation analysis highlights important contributors and influential works. This study emphasizes the crucial importance of information technology in influencing entrepreneurial behaviors and strategic business decisions. It also offers valuable insights for future research and entrepreneurial practice in the information age.