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Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.

A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.

Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.

The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

Local governments face increasingly complex challenges related to their internal operations as well as the provision of public services. However, research on how they embrace evidence-based approaches such as data analytics practices, which could help them face some of those challenges, is still scarce. This study aims to contribute to existing knowledge by examining the data analytics practices in Kansas City, Missouri (KCMO), a city that has become prominent for engaging in data analytics use through the Bloomberg’s What Works Cities (WWC) initiative with the purpose of improving efficiency and enhancing response to local constituents.

This research conducted semistructured interviews with public servants who had data analytics experience at KCMO. Analysis looked for common and emerging patterns across transcripts. A conceptual framework based on related studies is built and used as the theoretical basis to assess the evidence observed in the case.

Findings suggest that data analytics practices are sponsored by organizational leadership, but fostered by data stewards who engage other stakeholders and incorporate data resources in their analytical initiatives as they tackle important questions. Those stewards collaborate to nurture inclusive networks that leverage knowledge from previous experiences to orient current analytical endeavors.

This study explores the experience of a single city, so it does not account for successes and failures of similar local governments that were also part of Bloomberg's WWC. Furthermore, the fact that selected interviewees were involved in data analytics at least to some extent increases the likelihood that their experience with data analytics is relatively more positive than the experience of other local government employees.

Results suggest that data analytics benefits from leadership support and steering initiatives such as WWC, but also from leveraging stakeholder knowledge through collaborative networks to have access to data and organizational resources. The interplay of data analytics sponsored activities and organizational knowledge could be used as means of assessing local governments’ existing data analytics capability.

This study suggests that data analytics practices in local governments that are implementing a smart city agenda are knowledge-driven and developed incrementally through inclusive networks that leverage stakeholder knowledge and data resources. The incrementality identified suggests that data analytics initiatives should not be considered a “blank slate” practice, but an endeavor driven and sustained by data stewards who leverage stakeholder knowledge and data resources through collaborative networks.

Based on an organizational capability perspective, this paper aims to propose a development model for social media analytics (SMA) capability that can be applied to business-to-business (B2B) marketing, with the aim of facilitating the use and integration of SMA in B2B marketing and maximizing the benefits of business networks in the age of social media.

This is a critical interpretive synthesis of SMA publications collected from academic journals, business magazines and the SMA service industry. In addition, an inter-disciplinary approach was adopted by drawing upon both marketing and information systems literature. In total, 123 academic papers, 106 industry case studies and 141 magazine papers were identified and analyzed. The findings were synthesized and compiled to address the predefined research question.

An SMA capability development model is proposed. The proposed model consists of four inter-dependent levels (technological, operational, managed and strategic) that collectively transfer the technological capability of SMA to the dynamic organizational capability. Each level of SMA capability is detailed. SMA-in-B2B marketing is highlighted as a socio-technical phenomenon, in which a technological level SMA capability is emphasized as the foundation for developing organizational level SMA capabilities and organizational capabilities, in turn, supporting and managing SMA activities and practices (e.g. strategic planning, social and cultural changes, skills and resources, measurements and values).

The proposed research framework may have implications for the operational level SMA development and the investigations on the direct and/or indirect measurements to help firms see the impact of SMA on their business.

This study may have implications for the adoption, use, integration and management of SMA in B2B marketing. The proposed model is grounded on the integrated insights from academia and industry. It is particularly relevant to B2B firms that have engaged in or plan to engage in applying SMA to extract insights from their online networks and is relevant to B2B researchers who are interested in SMA, big data and information technology organization integration studies.

The purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research, contributing authors and countries. It is required to understand technical concepts like information technology, big data analytics, Internet of Things and blockchain needed to implement smart city models successfully.

The data were collected from the Scopus database, and analysis techniques like bibliometric analysis, network analysis and content analysis were used to obtain research trends, publications growth, top contributing authors and nations in the domain of smart cities. Also, these analytical techniques identified various fields within the literature on smart cities and supported to design a conceptual framework for Industry 4.0 adoption in a smart city.

The bibliometric analysis shows that research publications have increased significantly over the last couple of years. It has found that developing countries like China is leading the research on smart cities. The network analytics and article classification identified six domains within the literature on smart cities. A conceptual framework for the smart city has proposed for the successful implementation of Industry 4.0 technologies.

This paper explores the role of Industry 4.0 technologies in smart cities. The bibliometric data on publications from the year 2013 to 2018 were used and investigated by using advanced analytical techniques. The paper reviewS key technical concepts for the successful execution of a smart city model. It also gives an idea about various technical considerations required for the implementation of the smart city model through a conceptual framework.

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

This chapter examines the ways social media, analytics, and disruptive technologies are combined and leveraged by political campaigns to increase the probability of victory through micro-targeting, voter engagement, and public relations. More specifically, the importance of community detection, social influence, natural language processing and text analytics, machine learning, and predictive analytics is assessed and reviewed in relation to political campaigns. In this context, data processing is examined through the lens of the General Data Protection Regulation (GDPR) effective as of May 25, 2018. It is concluded that while data processing during political campaigns does not violate the GDPR, electoral campaigns engage in surveillance, thereby violating Articles 12 and 19, in respect to private life, and freedom of expression accordingly, as stated in the 1948 Universal Declaration of Human Rights.

Introduction: In recent years, fresh big data ideas and concepts have emerged to address the massive increase in data volumes in several commercial areas. Meanwhile, the phenomenal development of internet use and social media has not only added to the enormous volumes of data available but has also posed new hurdles to traditional data processing methods. For example, the insurance industry is known for being data-driven, as it generates massive volumes of accumulated material, both structured and unstructured, that typical data processing techniques can’t handle.

Purpose: In this study, the authors compare the benefits of big data technologies to the needs for insurance data processing and decision-making. There is also a case study evaluation concentrating on the primary use cases of big data in the insurance business.

Methodology: This chapter examines the essential big data technologies and tools from the insurance industry’s perspective. The study also included an analytical analysis that supported several gains made by insurance companies, such as more efficient processing of large, heterogeneous data sets or better decision-making support. In addition, the study examines in depth the top seven use cases of big data in insurance and justifying their use and adding value. Finally, it also reviewed contemporary big data technologies and tools, concentrating on their key concepts and recommended applications in the insurance business through examples.

Findings: The study has demonstrated the value of implementing big data technologies and tools, which enable the development of powerful new business models, allowing insurance to advance from ‘understand and protect’ to ‘predict and prevent’.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.