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Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

Geotechnologies have a long tradition in several areas of society and research. The recent development of the ‘Internet of Everything’ (IoE) and Geographic Information Systems (GIS) technologies opened several doors to the contribution of tourism. Emergent technologies contributions to tourism and planning such as web mapping, augmented reality (AR), crowdsourcing and crowdsensing are relatively recent, and there is a lack of research around their potential for Creative Tourism enhancement. For example, combining web mapping with AR or storytelling can be an excellent contribution to operators, planners and tourists. For research purposes, new opportunities are open, particularly by integrating community-shared data. It is well known for the popularity of social networks, the exponential growth of photo sharing, but few studies have been implemented to understand their contribution to research. This chapter focuses on emerging geotechnologies concerning cultural mapping, Creative Tourism and sustainability. Since it is a new growing niche, more research is needed to develop and understand the potential of new approaches. Besides traditional techniques such as quantitative (e.g. surveys) and qualitative ones (e.g. interviews, focus groups and world café), it revises the role of geotechnologies on Creative Tourism development and growing activities. Results from case studies from Europe are analysed.

This paper aims to provide a roadmap for organizations to build big data projects and reap the most rewards out of their data. It covers all aspects of big data project implementation, from data collection to final project evaluation.

In each stage of the proposed roadmap, we introduce different sets of information and communications technology platforms and tools to assist IT professionals and managers in gaining a comprehensive understanding of the methods and technologies involved and in making the best use of them. The authors also complete the picture by illustrating the process through different real-world big data projects implementations.

By adopting the proposed roadmap, companies and organizations willing to establish an effective and rewarding big data solution can tackle all implementation challenges in each stage of their big data project setup: from strategy elaboration to final project evaluation. Their expectations of privacy and security are also baked, in advance, into the big data project design.

While technologies to build and run big data projects have started to mature and proliferate over the last couple of years, exploiting all potentials of big data is still at a relatively early stage. The value of this paper consists in providing a clear and systematic methodology to move businesses and organizations from an opinion-operated era where humans’ skills are a necessity to a data-driven and smart era where big data analytics plays a major role in discovering unexpected insights in the oceans of data routinely generated or collected.

Big data analytics has emerged as one of the most used keywords in the digital world. The hype surrounding the buzz has led everyone to believe that big data analytics is the panacea for all evils. As the insights into this new field are growing and the world is discovering novel ways to apply big data, the need for caution has become increasingly important. The purpose of this paper is to conduct a literature review in the field of big data application for humanitarian relief and highlight the challenges of using big data for humanitarian relief missions.

This paper conducts a review of the literature of the application of big data in disaster relief operations. The methodology of literature review adopted in the paper was proposed by Mayring (2004) and is conducted in four steps, namely, material collection, descriptive analysis, category selection and material evaluation.

This paper summarizes the challenges that can affect the humanitarian logistical missions in case of over dependence on the big data tools. The paper emphasizes the need to exercise caution in applying digital humanitarianism for relief operations.

Most published research is focused on the benefits of big data describing the ways it will change the humanitarian relief horizon. This is an original paper that puts together the wisdom of the numerous published works about the negative effects of big data in humanitarian missions.

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

Purpose – For decades, archivists have been appraising, preserving, and providing access to digital records by using archival theories and methods developed for paper records. However, production and consumption of digital records are informed by social and industrial trends and by computer and data methods that show little or no connection to archival methods. The purpose of this chapter is to reexamine the theories and methods that dominate records practices. The authors believe that this situation calls for a formal articulation of a new transdiscipline, which they call computational archival science (CAS).

Design/Methodology/Approach – After making a case for CAS, the authors present motivating case studies: (1) evolutionary prototyping and computational linguistics; (2) graph analytics, digital humanities, and archival representation; (3) computational finding aids; (4) digital curation; (5) public engagement with (archival) content; (6) authenticity; (7) confluences between archival theory and computational methods: cyberinfrastructure and the records continuum; and (8) spatial and temporal analytics.

Findings – Each case study includes suggestions for incorporating CAS into Master of Library Science (MLS) education in order to better address the needs of today’s MLS graduates looking to employ “traditional” archival principles in conjunction with computational methods. A CAS agenda will require transdisciplinary iSchools and extensive hands-on experience working with cyberinfrastructure to implement archival functions.

Originality/Value – We expect that archival practice will benefit from the development of new tools and techniques that support records and archives professionals in managing and preserving records at scale and that, conversely, computational science will benefit from the consideration and application of archival principles.

The awareness of probability was observed in ancient cultures through the discovery of primitive dice games made with animal bones. The history of analytics in the workplace, as it is currently known (defined as predictive analytics), probably started in ancient Roman times, when the concept of insurance was first created. While the previous example showed that analytics for business had been around for some time, it is only relatively recently that there is an increased emphasis on the use of analytics in the modern firm. Credit card firms and retail catalog companies relied on analytics to drive their business models, for most of the latter half of the twentieth century. The use of advanced analytics for business also grew around the Millennium since the widespread use of data warehousing and relational databases on client servers. Moreover, Machine Learning and Artificial Intelligence Techniques, which have been around for many decades, have had very few breakthrough successful applications up until recently when cloud computing and being able to take advantage of the infrastructure of companies, such as Amazon and Google, with their Cloud Services enabled these algorithms to be used to their full extent in firms. This powerful infrastructure availability coupled with BIG DATA is creating breakthrough applications across many business models on a consistent basis. This chapter explores the use of advanced analytics across different business functional areas. It also introduces some breakthrough models, which include Netflix, Pandora, eHarmony, Zillow, and Amazon, and explores how these are not only changing the lives of consumers but also changing the nature of the workplace and creating new issues for firms such as data protection and liabilities for the actions of automated algorithms.

The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.