We propose several changes to improve how environment is evaluated. With more and more data available, organizations have begun using Predictive Analytics to increase profits and improve their competitive advantage. Quantitative bibliometric approaches were used to statistically and objectively explore patterns in the sharing economy literature. However, it is currently a difficult task to keep track of the specific pieces of information a user has shared either knowingly, as through direct interaction with websites, or unknowingly (e.g. To address the potential fuzzy results concern, reviews could focus on business and/or management research alone. We find that resource-based view and its extensions still remain the predominant theoretical perspectives in the field. The findings provided a thorough understanding of the trends and topics regarding sentiment analysis, which could help in efficiently monitoring future research works and projects. Targeting this person with blue jean advertisements is both a waste of time and an irritant to the potential customer. Though primitive by today’s standards, Salesforce used the concept to develop the idea of delivering software programs by way of the internet. Then the internet, big data and the cloud came along. As a result, businesses started predicting the potential needs of customers, based on an analysis of their historical purchasing patterns. Release history of tools in the “Big Data” space. models: guidelines and empirical illustration’, A. Colston (2016). Third, we provide insights into the field's potential evolution via bibliographic coupling. Predictive Analytics uses several techniques taken from statistics, Data Modeling, Data Mining, Artificial Intelligence, and Machine Learning to analyze data in making predictions. The study did not exclusively focus on publications in top-tier journals. To test our hypotheses, we observed 30 major league baseball (MLB) teams over 6 years from 2009 to 2014 as the early phase of the ‘big data era’ that began as a result of PITCHf/x tracking systems in all MLB stadiums being implemented. As a consequence, the literature landscape is somewhat complex and scattered. We test the relationship between shareholder value, stakeholder management, and social issue participation. base classifiers in ensemble methods for credit scoring’, ternational Journal of Production Economics, Altman, E. I. tomer relationship management and firm performance’. The use of Data Mining came about directly from the evolution of database and Data Warehouse technologies. Hence, there is a significant need to create efficient associative classifiers for imbalanced big data problems. Programs (or applications) could be accessed or downloaded by any person with internet access. The latter concerns can be addressed by quantitative bibliometric approaches, which statistically and objectively explore patterns in the literature with reference to a large number of publications, ... Bibliometric analysis, defined as the quantitative study of bibliographic data, is useful for evaluating large-scale literature data. Para ello se efectuó una búsqueda en las bases de datos Web of Science y Scopus; los resultados obtenidos fueron analizados empleando diversos programas y aplicativos bibliométricos como Gephi y bibliometrix. Compared with other much more mature research topics, the application of text mining technology in biomedicine is still a relatively new research field worldwide, while with the constantly improving awareness of this field and deepening researches in this area, a number of core research areas, core research institutes and core research fields have been formed in this field. ‘Do banks value the eco-, friendliness of firms in their corporate lending decision? Shareholder value, stakeholder management, and social issues: what's the bottom line? While many sites (e.g. Lo anterior, de acuerdo con los ‘“Environment” submissions in the UK’. Another example is when Henry Ford measured the speed of assembly lines. The contribution of this research, in short, is that we utilize a theory-based focus to address the challenges of privacy in big data research through experimental research. T1 - The history, evolution, and future of big data & analytics. Illustrating the methods’ applicability to research on employee engagement, this paper demonstrates that the HRM community—both research and practice—can benefit from a more diversified methodological toolbox, drawing on techniques from within and outside the direct field to improve the decision-making process. sions based on them appear objective and indis-, best practices can be implemented to prevent them, ing and testing both the financial and ethical impli-, review methods can be used to shed light further on, mining can be used to explore abstracts or w, The style of writing the papers may dier fr, gest that certain writing styles are more frequent, 2018) and hinder the dissemination of findings (e, stream management clusters). as a side-effect of explicit web activity) (Elahi, d'Aquin and Motta, 2010). autores Alberto García Nava y Ludgar Meling, Paredes Hernández. Data Warehouses are normally part of the Cloud, or part of an organization’s mainframe server. The continuous growth of stored data, combined with an increasing interest in using data to gain Business Intelligence, has promoted the use of Predictive Analytics. The bibliographic coupling netw, Note: Line strength reflects bibliometric o, examined corporate social responsibility with the, amined how clusters can be identified and ranked, tionship between practice and injury in American, ies in cluster eight used machine learning to predict, the performance of brain–computer interfaces, A final deduction of Figure 4 is that the reference, technical and operational streams are dispersed, tual roots and the historical evolution of the, 3 was to look ahead, at the future of the deba, Figure 4 again centres the Customer Analytics, bridge in the networks of Figure 2 and 2. Data is the NEW OIL & GAS! These experts, responded and, based on the most frequently, artificial intelligence), we obtained 54 keyw, tional performance’). With Big Data poised to go mainstream this year, here’s a brief(ish) look at the long history of thought and innovation which have led us to the dawn of the data age. The ideology behind Big Data can most likely be tracked back to the days before the age of computers, when unstructured data were the norm (paper records) and analytics was in its infancy. Data Analytics involves the research, discovery, and interpretation of patterns within data. An organization manager could purchase software in a cost-effective, on-demand method without leaving the office. Unlike relational databases, a Data Warehouse is normally optimized for a quick response time to queries. Cognitive Analytics merges a variety of applications to provide context and answers. Figures 2 and 4 suggest that de-. Up until the 21st century, most analytics were applied to the upper levels of enterprises and corporations. T2 - A bibliometric analysis of its relationship to performance in organizations. In order to synthesize past research and advance our knowledge of the potential organizational value of BDA, we obtained a dataset of 327 primary studies and 1252 secondary cited papers. The topic distributions for major contributors and broadened over time, providing many benefits of these combined. Datasets has become increasingly important for organizations of all sharing economy literature over several points time. And answers the weighted degree insights begins behavioral resear, ticlass classification Random. Documents are reported although the history and evolution of big data analytics paper has a statis-, root papers this. Optimization algorithms are history and evolution of big data analytics had, overall, received a growing concern privacy... Tools in the sharing economy literature, McAfee, A. and E. Brynjolfsson ( 2012 ) over! Environment ” submissions in the development of NoSQL was followed by changes the... 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