Workplace technology is evolving at an accelerated pace, driving innovation, productivity, and efficiency to exceedingly high levels. Businesses both small and large must keep up with these changes in order to compete effectively with fellow enterprises. The Handbook of Research on Enterprise 2.0: Technological, Social, and Organizational Dimensions collects the most recent developments in evaluating the technological, organizational, and social dimensions of modern business practices in order to better foster advances in information exchange and collaboration among networks of partners and customers. This crucial reference supports managers and business professionals, as well as members of academia, IT specialists, and network developers in enhancing business practices and obtaining competitive advantage.

Innovation in higher education is a process of institutional adaptation to changes in the environment that enables higher education institutions to improve their existing practice and to be innovative at different levels and in different forms. Moreover, innovativeness is also related to internal characteristics of higher education institutions. Innovation in higher education can be observed as a result of the changing contexts in which higher education institutions function. Adjacently, a comprehensive approach to considering innovativeness is needed in order to enable the examination of different elements of innovativeness in higher education, that is, to identify the key factors that (de)stimulate innovations and affect their interactions with other relevant stakeholders at the national level and beyond. The Handbook of Research on Enhancing Innovation in Higher Education Institutions is a critical scholarly book that examines innovativeness in higher education and its complications and diversity. Starting from the view that higher education is currently confronted by global forces that require new research ideas, the publication suggests that comprehensive understanding of innovativeness is imperative for higher education’s institutions in the 21st century. Analyzing the recognized trends within the publication and concluding which aspects should be taken to improve innovativeness in higher education, this reference book outlines quality and innovation in teaching, innovative university-business cooperation, institutional framework and governance of higher education institutions, knowledge management, and leadership and organizational culture. It is ideal for curriculum designers, administrators, researchers, policymakers, academicians, professionals, and students.

Given the widespread, frequent use of social networks as a means for people to communicate and share their interests, it comes as no surprise that they have become an important tool for businesses and business networking. The Handbook of Research on Business Social Networking: Organizational, Managerial, and Technological Dimensions investigates the beginning of social networks and provides perspectives on how they can enhance business. This two-volume reference discusses the main issues, challenges, opportunities, and trends related to the range of new developments and applications in business social networking. Social networks and their integration in businesses are be addressed using technological, organizational, managerial, and social perspectives with the aim of disseminating current developments, case studies, new integrated approaches, and practical solutions and applications.

"This book discusses the new technologies of semantic Web, transforming the way we use information and knowledge"--Provided by publisher.

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.