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.

The main objective of this study is to identify the impact of trust on the construction of corporate value in commerce and services microbusinesses. This work is based on identifying the challenges faced by SMEs (Small and Medium-sized Enterprises), which are conditioned by the type of business and the regulatory and incentive variables that exist in the territory, affecting their permanence and stability in the market and their financial and commercial development. A local study is carried out in Bogotá, Colombia, through a descriptive research project, using a quantitative analysis method (SPSS) to process data obtained from local microbusinesses. As a result, it was observed that trust has a discrete impact on the creation of corporate value, which is created from the use of ICT (Information and Communication Technology). This leads to the recognition that it is necessary to strengthen horizontal networks with suppliers, clients, and similar businesses, as well as vertical networks with entities and public associations, to generate lasting and strong links that increase the competitiveness of these business units in the face of exogenous risks shaped by the social, economic, and cultural characteristics of the territory, which are increasingly conditioned to the use of communication technology.

With the continuous development of network has also greatly developed, exploring the role of social network relationships and attachment emotions on consumer intention helps community managers to promote community purchases for more consumer. As another core component of social e-commerce, social media influencer also has a significant influence on consumer intention. This study systematically analyzed the effects of social network relationships and social media influencer characteristics on consumer purchase intentions. Introduced consumer attachment and perceived value as mediating variables to construct the research framework of this study. This article adopts quantitative analysis methods to test the research hypotheses proposed. This article collected 600 first-hand data in the form of a survey questionnaire and analyzed the data using AMOS and SPSS statistical software. The empirical analysis in this article confirms that social network relationships has a significant impact on consumer purchase intentions; social media influencer characteristics has a significant impact on consumer purchase intentions; consumer attachment has a significant impact on perceived value;  consumer attachment plays a mediating role in the effect of social network relationships on consumers purchase intentions; perceived value plays no mediating role in the effect of social media influencer characteristics on consumer purchase intentions; perceived value plays a mediating role in the effect of consumer attachment on consumer purchase intentions; consumer attachment and perceived value have a chain mediating role between social network relationships and consumer purchase intentions.