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The digitization of businesses is a complex process involving a range of technological, human, and economic aspects. In a post-pandemic world, the expectations of digitalization and its prerequisites are becoming a key driver towards increasing the competitiveness of enterprises. This study aims to empirically investigate the structures of digitalization and reveal the existence of latent factors in it. This paper examines 25 aspects of digitalization identified by previous studies. The research is based on processing 443 questionnaires focused on the digitalization of enterprises and collected in the Czech Republic. Exploratory factor analysis identified five factors of digitalization (three are among the predictors, and two are among the outcomes). Personal digital skills, information, and digital capabilities are the three factors that predict successful digital transformation projects. Organizational performance and Operational efficiency are two outputs of digitalization. Our study explores the relationships between these factors and opens the way for a broader scholarly discussion regarding the conditions for enhancing the success of enterprise digital transformation projects.

The era of E-commerce and availability of data in every field of operations in an enormous volume that implies to Big Data is one of the biggest sources of competitive advantage for the organizations in this digital world. It provides useful information to grow businesses by posting the advertisement and help consumers find the relevant product according to their preferences. The focus of this research is on the advertisement strategies analysis under which a Business employs several online advertisement strategies in order to appeal to the consumer. This research work will present a detailed analysis in user behavioral to use for business or Online Behavioral advertising and provide the framework of how Enterprise Resource Planning systems track the targeted audience and show their content. The paper's prime objective is to classify and effectively run targeted advertising using the data that shows user's retail behavior. This is where an Enterprise Resource Planning driven data will give rise to behavioral analytics. In addition to this, various data streaming technologies are also emphasized that will help to create a pipeline for the huge amount of data in Enterprise Resource Planning's database.

The actionable behavioral rules suggest specific actions that may influence certain behavior in the stakeholders' best interest. In mining such rules, it was assumed previously that all attributes are categorical while the numerical attributes have been discretized in advance. However, this assumption significantly reduces the solution space, and thus hinders the potential of mining algorithms, especially when the numerical attributes are prevalent. As the numerical data are ubiquitous in business applications, there is a crucial need for new mining methodologies that can better leverage such data. To meet this need, in this paper, we define a new data mining problem, named behavior action mining, as a problem of continuous variable optimization of expected utility for action. We then develop three approaches to solving this new problem, which uses regression as a technical basis. The experimental results based on a marketing dataset demonstrate the validity and superiority of our proposed approaches.