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This paper aims to introduce dataspace into digital libraries in order to address its emerging data management challenges which stem from cross-domain, heterogeneous, and uncertain nature of data resources, based on looking into the fundamental principles, significant features, and research directions of dataspace.

This research mainly employs three types of research methodologies: a literature study was conducted for revealing the fundamental principles, analyzing the significant features, and discussing the new research topics; knowledge engineering methodology is used to design the data model; software engineering methodology is applied to develop the reference framework and the digital library.

This paper for the first time proposes a motion to build a Dataspace Support Digital Library (DSDL), and provides its data model, data management policies and a reference framework. Further, its implementation is described and some implications learned from the case study are also discussed.

Introducing dataspace technologies into developing a digital library frees the developer to just focus on solving business challenges, rather than addressing non-business related, data management level tasks. In addition, the data model and the reference framework presented in this paper lay foundations for constructing Dataspace Support Digital Libraries.

This is the first paper to introduce dataspaces into the design of digital libraries and is also the first paper to propose a novel data model, data management and reference framework for Dataspace Support Digital Libraries.

The purpose of this chapter is to situate how the digitalized research environment is changing the roles of researchers and participants, and how these changes lead to more complex and less discrete ethics challenges. Incorporating contemporary examples from the social sciences, we outline the core challenges of the changing research landscape that embrace both research actors (researcher, participant, and research users) and data issues. The ethical implications related to research actors’ roles are discussed by considering how data is accessed, how people can now participate in research, and issues related to accessing participants. Digital data and associated ethical issues are explored through examining authorship and ownership, how digital data is produced, and how research transparency can be achieved. Following on from this consideration of research actors and data issues, we suggest which challenges have been re-contextualized by the digital environment, and which are novel to the digital research context, outlining six practical yet reflective questions for researchers to ask as a way to navigate ethics in the digital research territory.

The purpose of this paper is to explore how positive organizational behaviors, specifically team proactivity, can be captured through digital data and what determines content validity of these data. The aim is to enable scientifically rigorous HR analytics projects for measuring and managing organizational behavior.

Results are derived from interview data (N = 24) with team members, HR professionals and consultants of HR software.

Based on inductive qualitative content analysis, the authors clustered six data types generated/recorded by 13 different technological applications that were proposed to be informative of team proactivity. Four determinants of content validity were derived.

The overview of technological applications and resulting data types can stimulate diverse HR analytics projects, which can contribute to organizational performance. The authors suggest ways to control for the threats to content validity in the design of HR analytics or research projects.

HR analytics projects in the application field of managing organizational behavior are rare. This paper provides starting points for choosing data to measure team proactivity as one form of organizational behavior and guidelines for ensuring their validity.

With the rapid development of digital humanities, some digital humanities platforms have been successfully developed to support digital humanities research for humanists. However, most of them have still not provided a friendly digital reading environment and practicable social network analysis tool to support humanists on interpreting texts and exploring characters’ social network relationships. Moreover, the advancement of digitization technologies for the retrieval and use of Chinese ancient books is arising an unprecedented challenge and opportunity. For these reasons, this paper aims to present a Chinese ancient books digital humanities research platform (CABDHRP) to support historical China studies. In addition to providing digital archives, digital reading, basic search and advanced search functions for Chinese ancient books, this platform still provides two novel functions that can more effectively support digital humanities research, including an automatic text annotation system (ATAS) for interpreting texts and a character social network relationship map tool (CSNRMT) for exploring characters’ social network relationships.

This study adopted DSpace, an open-source institutional repository system, to serve as a digital archives system for archiving scanned images, metadata, and full texts to develop the CABDHRP for supporting digital humanities (DH) research. Moreover, the ATAS developed in the CABDHRP used the Node.js framework to implement the system’s front- and back-end services, as well as application programming interfaces (APIs) provided by different databases, such as China Biographical Database (CBDB) and TGAZ, used to retrieve the useful linked data (LD) sources for interpreting ancient texts. Also, Neo4j which is an open-source graph database management system was used to implement the CSNRMT of the CABDHRP. Finally, JavaScript and jQuery were applied to develop a monitoring program embedded in the CABDHRP to record the use processes from humanists based on xAPI (experience API). To understand the research participants’ perception when interpreting the historical texts and characters’ social network relationships with the support of ATAS and CSNRMT, semi-structured interviews with 21 research participants were conducted.

An ATAS embedded in the reading interface of CABDHRP can collect resources from different databases through LD for automatically annotating ancient texts to support digital humanities research. It allows the humanists to refer to resources from diverse databases when interpreting ancient texts, as well as provides a friendly text annotation reader for humanists to interpret ancient text through reading. Additionally, the CSNRMT provided by the CABDHRP can semi-automatically identify characters’ names based on Chinese word segmentation technology and humanists’ support to confirm and analyze characters’ social network relationships from Chinese ancient books based on visualizing characters’ social networks as a knowledge graph. The CABDHRP not only can stimulate humanists to explore new viewpoints in a humanistic research, but also can promote the public to emerge the learning interest and awareness of Chinese ancient books.

This study proposed a novel CABDHRP that provides the advanced features, including the automatic word segmentation of Chinese text, automatic Chinese text annotation, semi-automatic character social network analysis and user behavior analysis, that are different from other existed digital humanities platforms. Currently, there is no this kind of digital humanities platform developed for humanists to support digital humanities research.

The purpose of this paper is to provide insights into the benefits of data‐driven services marketing and provide a conceptual framework for how to link traditional and new sources of customer data and their metrics. Linking data and metrics to strategic and tactical business insights and integrating a variety of metrics into a forward‐looking dashboard to measure marketing ROI and guide future marketing spend is explored.

A detailed synthesis of the literature is conducted and contemporary sources of marketing data are categorized into traditional, digital and neurophysiological. The benefits and drawbacks of each data type are described and advantages of integrating different sources of data are proposed.

The findings point to the importance and untapped potential of data in its ability to inform tactical and strategic marketing decisions. Future challenges, including top management support, ethical considerations and developing data and analytic capabilities, are discussed.

The results demonstrate the need for executive service marketing dashboards that include key metrics that are service‐relevant, complementary and forward‐looking, with proven linkages to business outcomes.

This paper provides a synthesis of data‐driven services marketing and the value of traditional and contemporary metrics. Since the true potential of data‐driven service management in a connected world is still largely unexplored, this paper also delineates fruitful avenues for future research.

At the end of a building’s lifecycle, there are several limitations to the decision-making process (DMP). There is a lack of data available from the building’s history, the difficulty in assessing the condition of a building and the variety of stakeholders’ needs that have to be satisfied. The purpose of this paper is to answer the question: how would end-of-life (EOL) DMP change when buildings will have been digitally built? The answer will be illustrated through a conceptual framework.

A qualitative analysis of the existing literature has been performed to identify the elements within building information modelling (BIM) and advanced digital technologies that could be of support to the DMP. The findings have been collected and summarised in a conceptual framework that has been validated and enhanced through online interviews with industry experts.

The enhanced framework has identified that BIM technology would bring the benefit of providing the initial digital data source, from which machine learning and data analytics would then extract the relevant data needed to measure accurately the criteria during the analysis of the EOL options put on the table.

The findings of this research could contribute to developing the software modules making the bridge between BIM and machine learning technologies, to implement them in the EOL DMP.

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research.

The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology.

The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem.

The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked.

The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology.

This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.

Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.

The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.

The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.

This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.

To obtain first‐hand data on the main challenges in preserving digital resources in libraries, archives and information centres in China.

The data in this paper have been acquired by e‐mail questionnaire. The conclusions are based on feedback from 57 respondents, distributed in 14 provinces in China, who work in libraries, archives and information centres with rich digital collections and long‐term experience in preserving digital resources.

The authors consider that the long‐term preservation of digital resources in China faces two critical difficulties: firstly, the pressure on preserving digital resources has not been fully recognised and so the relevant training in the management and operation of digital preservation is absent; secondly, government departments attach little importance to this field and lack uniform guidelines. Therefore, the paper suggests that relevant international conferences or workshops should be held in China, and that a working group led by the National Development and Reform Commission of China should be set up to plan the construction, sharing and maintenance of Chinese digital resources.

This research is limited to Chinese administration systems. Although the data in this paper are authentic and representative, nevertheless it is a small sample of the numerous document collection organisations in the country.

The data and analysis in this paper should greatly help international colleagues understand the status regarding the preservation of digital resources in China.

This research is the first of its kind undertaken in China and will provide a new vision for international peers and organisations involved in digital documentary heritage around the world.

This paper aims to propose an integrating model for creating virtual libraries in Iranian universities of medical sciences.

This study was conducted with an analytic survey method. The statistical population comprised 66 Iranian universities of medical sciences, of which 59 libraries participated in the study. A researcher-made checklist was used for data collection. To ensure the accuracy of data, interviews and, in some cases, observations were also performed. Statistical estimates, including frequency, percentage, cumulative frequency and diagrams, were used for data analysis, and the system analysis method was used for modeling.

Results demonstrated that the library software programs of the studied universities of medical sciences do not have desirable interoperability capabilities. Only Azarsa program can exchange information with other systems. In terms of metadata and its standards, the studied libraries use programs with various standards, with MARC and Dublin Core standards being the most frequently used ones in the studied sample.

The model proposed here for integration is a hybrid model which can translate metadata standards and use the Z39.50 and OEI protocol to transfer data.