This research aims at providing a meta-analysis of empirical findings of the literature on the characteristics of social media influencers on customer engagement and purchase intention. For this purpose, researchers derived eight social media influencers’ characteristics, i.e., homophily, expertise, trustworthiness, credibility, congruence with the product, entertainment value, informative value, and attractiveness. The current study synthesizes 176 effect sizes derived from 62 individual studies, and 22,554 individuals act as an aggregate sample. Results revealed that these characteristics have a moderate to high correlation with customer engagement and purchase intention. The entertainment value of social media influencers has the strongest association with customer engagement among all the attributes studied in this analysis. It also concluded that the credibility of influencers impacts purchase intention more than any other attribute. This work provides a novel approach to reducing the heterogeneity in influencer marketing research by empirically specifying the directions of relationships and the extent of the effect of these relationships.

The buying behaviors of the consumers are changing very rapidly in the today's consumer-oriented market. New technologies are evolving in the market to attract the customers. Smart phones have become necessities to cope with the changing dynamics of the market and society as a whole. Due to competitive price offer by the various cellular operators, it has become easy for all to reach the internet. Due to ease of use, the young generations are using the application of internet for various uses especially for purchasing goods and services. Today, every company is applying the digital marketing tool to attract customers, especially the young generation. As per a report published in Economics, the internet users in India are expected to reach 627 million by the end 2020. Due to the digital marketing, a new concept of shopping has evolved in the market, which the authors call off-the-shop retailing.

Research has focused on the implementation of E-Learning amidst the COVID-19 pandemic. During COVID-19, the school and educational institutions were closed due to lockdown. During this period the classes of students are taken online. The digital technology used for e-learning during the COVID-19 pandemic has gained popularity in a very short period. Online classes are taken using Microsoft team, any desk, Zoom, WhatsApp applications. Educational contents are transferred frequently over the internet. Research is considering the impact of e-learning amidst COVID-19 and considering issues such as performance and security during transmission of digital content. The education is provided over cloud environment in a more secure manner with better performance. It has been observed that there have been several kinds of research in the area of cloud computing to provide online education. Issues in such research are performance and security of data. There is a need for a high-speed network to transfer educational content from one place to another. The educational contents needed to be secured and compressed at the time of data transfer. Cloud computing applications and the role of the cloud in e-learning are considered during research. The proposed work is supposed to integrate the proposed mechanism in the educational module. The proposed system is supposed to be secure and fast because data is compressed first then data is encrypted on the sender side. On receiving end the data is decrypted and decompressed. Delay in transmission issue is resolved because the size of data is less during transmission. Moreover, the packet dropping ratio gets reduced. The probability of cracking encrypted files also gets reduced as the data is encrypted after compression.

One of the potential top-level goals for 5G heterogeneous networks may be intellectual and perfect network which modifies consumer preferences in a proactive manner in addition to needs of channel. Research provides an interdisciplinary approach to e-health, primarily concern BDA, and radio space management inside a various level fifth generation network in the company of massive data. The growing need for and usage of big data fuelled digital transformation. The research focuses on the effect of Big Data on digital technologies during the 5G era. To carry out digital transformation, three machine learning (ML) algorithms are identified. In addition to decision tree DT the other algorithms used for the classification are NB, LR. These algorithms run on the large data processing engine they work. These algorithms serve as an ensemble tool for examining old records of stroke outpatients (OPs) and body built IOT based sensors [19]. These readings are available as Big Data. In the model which has been proposed here, OP-Centric Network Optimization Framework was presented before evaluating the machine learning algorithm function and all appropriate steps to plan massive data. An ensemble method in the company of NB classification device, decision tree classification device, and logistic classification device was used in this analysis. These entire classification devices are highly controlled and managed classification device. This method is based on the OP data set and feeds the predicted stroke probabilities to an SV classifier.

Very large volumes of data analytics study the uncovering of hidden patterns, interplay, and other discoveries. Today’s technology enables data analyzing and obtaining answers practically quickly- with more conventional solutions for business intelligence, an endeavor that is longer and less effective. The demand for big data analytics has been increasing on regular basis due to the increase in engagement of users. The role of clustering is to make the big data analytic system manageable. This paper has focused on several applications that are based on clustering and big data analytics. The uses of this technology have been increasing rapidly for distance learning, health care, and IoT environment. The issues in the area of clustering and big data are also considered in this research after considering some existing researches in the relevant field. The present research has made use of an advanced mechanism to make dynamic clusters by making use of the K-mean mechanism in order to perform big-data analytics.

Several kinds of research from both global and local perspectives have discussed social entrepreneurship and inclusive development. Accordingly, this article aims to highlight the key contributors (authors, institutions, countries, journals) and knowledge structure (co-authorship analysis and cooccurrence-of-keyword analysis) of this research domain. Utilizing the bibliographic data of 300 articles extracted from the Scopus database, we primarily employed Biblioshiny software, resulting in 673 authors from 69 different countries and 496 different institutions. Furthermore, the yearly publication of the 300 documents rose from 1 in 1996 to 24 in 2022, with an average age of 6.08 years and a citation rate of 13.79 yearly. Remarkably, the number of publications on this subject accelerated in 2014. According to the study’s findings, the most productive country in line with research publishing is the USA. The most productive institution has been recognized as the Amrita School of Engineering. The most active author is reported to be Kadol N. The most prolific journal is Entrepreneurship and Regional Development. In addition to this, the top three most frequent terms are ‘social development’, ‘economic development’ and ‘entrepreneur. ‘Social entrepreneurship’, ‘social and economic effects’, ‘sustainable development’ and ‘social development’ are the latest keywords in this field that predict future trends. Taken collectively, this review is an expedient resource for gaining a thorough grasp of the state of the art and prospective routes for future research.