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The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big data application framework in libraries.

The authors first used the text analysis and inductive analysis method to understand the concept of big data, summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries.

The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database, enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main obstacles to prevent them from applying big data application framework is the human resource and information technology level.

This research offers several implications and practical solutions for libraries to apply big data application framework.

The paper examined big data and academic libraries and emphasized whether it is big for something or nothing.

A conceptual and review analysis of documents was adopted to determine the concept of big data, the sources, the features, the relevance to academic libraries, specific case studies from around the world that have made use of big data, uses of big data in academic libraries, a review of best practices in the use of big data in academic libraries and the challenges.

The paper reports that although big data is indeed very big in academic libraries because there are evidences of its adoption and best practices in its use in academic libraries across the world, available challenges can render it big for nothing.

This study is limited in terms of using literature review approach to discuss big data and academic libraries. The study is also limited in terms of focusing academic libraries and not taken other types of libraries into consideration.

The study has created awareness on the part of academic libraries stakeholders including authorities, librarians and users on the relevance of big data in academic and how big indeed it is in academic library landscape. The study also implied future related studies can borrow ideas from the current studies, which will inform whether an empirical evaluation is possible on the subject matter.

The paper is the original idea by the author, and it is to emphasize the relevance of big data in academic libraries and to prepare academic libraries that have not been tapping the opportunities of big data to get ready.

The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation.

The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019).

It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well.

This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Digital library database resources have a significant impact on stimulating the research culture in higher education. The use of digital databases makes it possible to understand intellectual growth, research productivity, planning and identification of user information needs. Evaluating the effectiveness of user database resource utilization and research, the purpose of this study is to assist management in developing an excellent academic policy.

This study establishes a quantitative method to analyze the productivity of academic research using digital databases. The secondary data extracted from the databases of 52 universities provided by Higher Education Commission (HEC) and the literature published on the Institute of Scientific Information (ISI) Web of Science. The statistical technique simple linear regression was used to analyze the data for understanding the impact of independent variables the “digital databases” on the dependent variable “research productivity”.

The result of the coefficient of multiple determination, R-squared, R² 0.679, indicated 67 per cent impact of the predictor on the outcome variable. However, the standardized coefficient Beta 0.824 revealed 82 per cent impact of the individual predictor on the outcome variable. Overall, the result of linear regression showed a significant effect of independent variables on the dependent variable. Besides, the result of correlation and the strength of association between the database resources and the academic publication was significant (p < 0.005).

This research work is a supportive tool for managing gaps and promoting the development of necessary measures to develop strategies and solutions to create a better academic environment. The ultimate use of standard database resources can foster higher academic research to develop innovative ideas and improve researchers’ cognitive abilities.

From Pakistan’s point of view, this study is the first one that gives insight into the intellectual growth of young researchers in higher education. The study provides first-hand information on the use of database resources and their significant impact on the productivity of academic research.

Digital libraries promote and accelerate scientific research in academic institutions. The subscribed database resources of digital libraries have become an increasingly valuable asset for researchers. Database resources help generate new ideas, determine research directions and promote productive academic interaction between teachers and students in the information age. The purpose of this study is to examine the use of electronic resources by students in various databases, the research productivity of the faculty in the science network and the number of students who graduate each year.

This study uses a quantitative method to collect secondary data from the central database of the Higher Education Commission (HEC) for the population of 26 universities for 2 years (2015–2016). In addition to the HEC digital library, data was also collected from the Web of Science to determine the quality academic performance of faculty and researchers. Moreover, in the study, the total strength of teaching staff and doctoral faculty was extracted from the HEC website for investigation. The authors applied the Spearman’s correlation test to the secondary data using Statistical Package for Social Sciences version 25.

The correlation results of the enrolled students and the downloaded papers from various databases were statistically insignificant (p > 0.05). However, the result showed a positive correlation (p < 0.05) between the use of selected/known databases from a number of databases accessed by the HEC. More importantly, it turns out that the faculty’s productivity in the scientific network and the number of students who graduated from public and private universities are found to be insignificant (p > 0.05). However, the authors found a positive correlation (p < 0.05) between doctoral and non-doctoral faculties, which show that a significant number of non-doctoral faculties are still actively involved in teaching and research.

Research based on academic activities by faculties and students, performed for the first time on the basis of secondary data, will help the HEC and university management to determine the right direction and develop plans to improve academic performance and research quality.

This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.

Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.

The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.

This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.

The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.

This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.

Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.

This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.

First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.

The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.

The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.

This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.

The purpose of this paper is to illustrate the current state of research on the significance of big data in and for corporate communication and to introduce a framework which provides specific connecting points for future research. This is achieved by summarizing and reviewing the insights provided by relevant articles in the most significant scholarly journals. The paper also investigates trends in the literature.

On the basis of a systematic literature review, 53 key articles from 2010 to 2015 were further analyzed.

The literature review illustrates the potentialities of big data for corporate communication, especially with regard to the field of marketing communication. It also reveals a dramatic lack of research in the fields of public relations and internal communication with respect to big data applications.

The online databases used in this paper comprised of refereed scientific journals with the highest impact factor in the respective disciplines. Journals with a lower impact factor and books were not included in the search process for this thematic analysis.

This paper provides a conceptual framework that describes four phases of strategic big data usage in corporate communication. The results show how big data is able to highlight stakeholders’ insights so that more effective communication strategies can be created.

This paper brings together previously disparate streams of work in the fields of communication science, marketing, and information systems with respect to big data applications in corporate communication. It represents the first attempt to undertake a systematic and comprehensive interdisciplinary overview of this kind.

The development within storage and processing technologies combined with the growing collection of data has created opportunities for companies to create value through the application of big data. The purpose of this paper is to focus on how small and medium-sized companies in Denmark are using big data to create value.

The research is based on a literature review and on data collected from 457 Danish companies through an online survey. The paper looks at big data from the perspective of SMEs in order to answer the following research question: to what extent does the application of big data create value for small and medium-sized companies.

The findings show clear links between the application of big data and value creation. The analysis also shows that the value created through big data does not arise from data or technology alone but is dependent on the organizational context and managerial action. A holistic perspective on big data is advocated, not only focusing on the capture, storage, and analysis of data, but also leadership through goal setting and alignment of business strategies and goals, IT capabilities, and analytical skills. Managers are advised to communicate the business value of big data, adapt business processes to data-driven business opportunities, and in general act on the basis of data.

The paper provides researchers and practitioners with empirically based insights into how the application of big data creates value for SMEs.

This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors.

A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data.

This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence.

Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.