Analyzing The Strategic Role Of Social Networking In Firm Growth And Productivity
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Analyzing the Strategic Role of Social Networking in Firm Growth and Productivity
Social media platforms have emerged as an influential and popular tool in the digital era. No longer limited to just personal use, the applications of social media have expanded in recent years into the business realm. Analyzing the Strategic Role of Social Networking in Firm Growth and Productivity examines the role of social media technology in organizational settings to promote business development and growth. Highlighting a range of relevant discussions from the public and private sectors, this book is a pivotal reference source for professionals, researchers, upper-level students, and academicians.
Modern Perspectives on Virtual Communications and Social Networking
With the prevalence of social media, businesses and other organizations have a growing need to utilize various online media platforms and sites to engage and interact with their potential consumer base. Virtual communities and social networking can provide an effective escape route from the limits imposed by traditional media. Using optimal strategies can lead to more successful outcomes when using these platforms. Modern Perspectives on Virtual Communications and Social Networking provides innovative insights into connection and conversation through internet media that foster trust, commitment, and transparency in business. The content within this publication represents the potential to create virtual bonds with consumers through the observation of buying behavior, social media best practices, and digital marketing strategies. It is designed for business professionals, academicians, consultants, managers, marketers, and researchers and covers topics centered on the use of online media as a method of reaching a wider population.
Hidden Link Prediction in Stochastic Social Networks
Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.