Augmented reality and consumer behavior in Jordan’s telecom sector: Cultural and technological determinants

This paper analyzes an emerging form of social media marketing, meme marketing, which has gained attention for its ability to entertain and engage users. Marketers and companies are recognizing the value of using memes as a tool to connect with consumers. To understand the effects of meme marketing activities, this paper aims to examine the impact of meme marketing activities on consumer purchase intentions and concurrently assess the mediating role of consumer engagement. The study encompassed 452 Indian social media users with active social media accounts and familiarity with memes and meme marketing concepts. It employed a quantitative methodology backed by robust statistical techniques. The method used for analysis was Structural Equation Modeling (SEM) through Analysis of Moment Structures (AMOS) software. The results found that meme marketing activities have a direct and significant positive impact (β = 0.257, p < 0.05) on consumer purchase intentions. It further shows that meme marketing has a direct and significant positive impact (β = 0.745, p < 0.05) on consumer engagement. It shows that consumer engagement has a direct and significant positive effect (β = 0.651, p < 0.05) on consumer purchase intention. However, the indirect impact of meme marketing activities on consumer purchase intentions is also significant, resulting in partial mediation. The study findings hold value for marketing managers, agencies, and companies that interact and engage consumers with memes and undertake meme marketing activities.

Information economy, being the newest type, in the course of formation acquires its distinctive features, which include a significant change in the needs of investors, producers, consumers and other economic relation participants. In order to achieve a competitive information economy, state support for high-tech industries is needed. It is crucial to create a clear legal framework, give boost to the formation of intellectual capital based on other countries' experience. Implementation of the strategy for high-tech industries development in Ukraine is a decisive step in creating a platform for information technology dissemination, creation of new competitive products with high added value. The purpose of the article is to investigate the transformation to the information economy, to analyze the industry competitiveness, to define the opportunities for information sphere improvement. The theoretical aspect of the emergence and formation of the information economy category is studied, the approaches to the defining this economic category and its derivatives have been studied and generalized, essential features of the information type of economic relations have been investigated, and their main components have been determined. In the article, the relationship between the level of information economy development and the competitiveness of domestic enterprises' products has been described, the factors influencing high-tech industries development in Ukraine are considered, and suggestions as for increasing the assistance to the development of knowledge-intensive sectors, including information technology, are proposed.

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.