PurposeFollowing the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes.Design/methodology/approachThe study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively.FindingsThe antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes.Originality/valueFirst, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.

This paper considers the relationship between various social-media activities of a company/brand and its sales. We use quarterly revenue data of 13 retail-food brands, over 4 quarters, as our dependent variable. We use 6 independent variables involving the social-media activity of these companies on Twitter, YouTube, and Instagram. We use descriptive statistics to describe our data, and use simple, multiple, and stepwise regression to perform our analyses. We find that certain social-media activities do, indeed, positively relate to quarterly sales revenue.

The term “Artificial Intelligence” (AI) refers to the simulation of human intelligence on a computer. Higher education can benefit from AI because it is a computationally efficient paradigm. Learning adapted to the changing demands of students is one of the key educational advantages of AI. Students can modify the pace of a course to better competency. Poor faculty and teaching quality and a general lack of motivation and interest among students are among the difficulties facing higher education. An artificial intelligence-assisted integrated teaching–learning framework (AL-ITLF) for higher education is proposed in this research. Multiple tutoring services are also involved in the curriculum, which is skill-based. The extreme learning machine (ELM) technique evaluates designs integrated into the suitable student monitoring model weighted score (WS) and exam results. An educational model that is more efficient, adaptable, and effective than current traditional education has been developed due to AI research in higher education. Higher education’s use of AI has resulted in a more efficient, adaptive, and effective educational model than traditional schooling. High accuracy, higher performance, lower processing costs, and a high prediction and low error rate are advantages of the suggested AI-ITLF approach. The WS and exam results were evaluated using an ELM algorithm as part of a proper student monitoring model.

Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to properly analyze and manage. Various devices are interconnected with each other, which communicate different types of information. This information is used for different purposes. A huge volume of data is produced, and the storage becomes larger. Computational modeling is the tool that helps analyze, process, and manage the data to extract useful information. The modern industry's challenge is to incorporate knowledge into Big Data applications to deal with distinguishing difficulties in computational models. The techniques and models are delivered with guides to help analysts quickly fit models to information insights. The decision support system is a strong system that plays a significant role in shaping Big Data for sustaining efficiency and performance. Decision-making through computational modeling is also a powerful mechanism for supporting efficient tools for managing Big Data for influential use. Keeping in view the issues of modern-day industry, the proposed study has been considered to present decision-making and computational modeling of Big Data for sustaining influential usage. The existing state-of-the-art literature is presented in an organized way to analyze the currently available research.

In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.