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Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

The main objective of this study is to investigate the factors that influence the adoption intention of cloud computing services among individual users using the extended theory of planned behavior.

A purposive sampling technique was used to collect a total of 339 data points, which were analyzed using SmartPLS to derive variance-based structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA).

The results obtained from PLS-SEM indicate that attitude towards cloud computing, subjective norms, perceived behavioral control, perceived security, cost-effectiveness, and performance expectancy all have a positive and significant impact on the adoption intention of cloud computing services among individual users. On the other hand, the findings from fsQCA provide a clear interpretation and deeper insights into the adoption intention of individual users of cloud computing services by revealing the complex relationships between multiple combinations of antecedents. This helps to understand the reasons for individual users' adoption intention in emerging countries.

This study offers valuable insights to cloud service providers and cyber entrepreneurs on how to promote cloud computing services to individual users in developing countries. It helps these organizations understand their priorities for encouraging cloud computing adoption among individual users from emerging countries. Additionally, policymakers can also understand their role in creating a comfortable and flexible cloud computing access environment for individual users.

This study has contributed to the increasingly growing empirical literature on cloud computing adoption and demonstrates the effectiveness of the proposed theoretical framework in identifying the potential reasons for the slow growth of cloud computing services adoption in the developing world.

The purpose of this paper is to describe how the authors gained a better understanding of the variety of library users' data needs, and how gradually some new data services were established based on current capabilities.

This paper uses a case study of the new data services at the John Cotton Dana Library, at Rutgers, The State University of New Jersey, Newark campus, to demonstrate the possible ways to extend data reference services and provide data computing services. A content analysis of services records shows how each user group falls into the multiple data services levels and subcategories.

Library users can be classified into many different categories, and each of these may have different needs. Research centers might have big projects involving data gathering and applications where a librarian can mainly provide consultation; while an individual faculty member or student might need the librarians as research partners, with help for their specific problems. Computing data services can involve group training and statistical analysis assistance, where researchers need emergent help. Data librarians can take various opportunities for data management education, thereby gradually raising awareness and cultivating better research habits among researchers.

Library data computing services can make unique contributions to faculty and students' research and study. Institution, library and users' interaction determines the levels and extent of data services and is generalized from the description and analysis of typical data service examples. Classic concept of data services levels is applied to a concrete case of data services program, and sub‐categories of each data services level and user types are developed based on the authors' services record.

This study aims to identify the level of security from existing work, analyze categories of security as a service (SECaaS) and classify them into a meaningful set of groups. Further, the report will advise commercial applications and advice of SECaaS as an extended context to help firms make decisions.

This paper compares the SECaaS categories in Cloud Security Alliance (CSA) with the security clauses in ISO/IEC 27002:2013 to give a comprehensive analysis of those SECaaS categories. Reviewed from a number of related literature, this paper analyzes and categorizes SECaaS into three major groups including protective, detective and reactive based on security control perspectives. This study has discussed the three groups and their interplay to identify the key characteristics and problems that they aim to address.

This paper also adds new evidence to support a better understanding of the current and future challenges and directions for SECaaS. Also, the study reveals both the positive and negative aspects of SECaaS along with business cases. It advises on various sizes and domains of organizations to consider SECaaS as one of their potential security approaches.

SECaaS has been demonstrated to be one of the increasingly popular ways to address security problems in Cloud computing. As a new concept, SECaaS could be treated as integrated security means and delivered as a service module in the Cloud. However, it is still in infancy and not very widely investigated. Recent studies suggest that SECaaS is an efficient solution for Cloud and real industries. However, shortcomings of SECaaS have not been well-studied and documented. Moreover, reviewing the existing research, researchers did not classify the SECaaS-related categories.

The purpose of this paper is to examine the impact of cloud computing (CC) use on collaboration and its ultimate impact on the agility of humanitarian supply chains. Further, this paper aims to analyze the moderating role of inter-organizational trust in the relationship between CC use and collaboration.

This paper provides an empirical assessment of CC use based on an interview analysis of 19 individuals from humanitarian organizations. A survey questionnaire is later used with 107 participants from US relief organizations. Partial least squares test is used to examine the relationships depicted in the conceptual model.

The results provide an account of how CC is used in a humanitarian context. Further, the results indicate that CC use has a positive and significant impact on collaboration between humanitarian organizations and their suppliers. Collaboration is found to be significantly positively associated with agility in humanitarian organizations.

No study, to the best of the authors’ knowledge, has empirically assessed the impact of CC use on humanitarian supply chain collaboration. This will be the first study to empirically analyze the relationships between CC use, inter-organizational trust, collaboration and agility in a humanitarian context.

This study provides a theoretically and empirically validated model depicting the relationships between CC use, collaboration, agility and inter-organizational trust in humanitarian supply chains. Humanitarian organizations can use these findings to optimize agility.

This study contributes to supply chain management research, particularly humanitarian supply chain management knowledge, by empirically examining the usefulness of CC use on collaboration and agility in the supply chain.

The University of Minnesota Libraries have established a full‐service information center to facilitate end‐user access to machine‐readable datafiles, particularly U.S. government datafiles such as the Census. The Machine Readable Data Center (MRDC), funded through a three‐year, $240,000 grant from the College Library Technology and Cooperation Grants Program (HEA Title II‐D), presents an alternative, library‐centered model for providing students, faculty, and independent researchers with direct access to machine‐readable data.

Improved integration, centralized databases, access through Web browsers and application service providers (ASPs) are some of the current trends impacting on organizational decisions regarding IS/IT outsourcing. Web‐based technology liberated the client/server IT model from the limits of geographical boundaries delimited by a local area network. Partnering with the right ASPs could provide organizations with the new synergy required for competitive advantage. This paper outlines the development of a “solutions” innovation business model of rental‐based sourcing, charters current ASPs and the differences in IS/IT provision and offers an audit of the benefits and costs of ASPs to businesses and other stakeholders. Future challenges are mapped out for consideration as are strategic choices associated with continued outsourcing, as distinct from in‐sourcing, of IS/IT.

Enterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.

The study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.

Based on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.

The study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.

This study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

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’.

Electric mobility as a service (eMaaS) is suggested as a possible solution to ease transportation and lessen environmental issues by providing a collaborative transport sharing infrastructure that is based on electric vehicles (EVs) such as electric cars, electric bicycles and so on. Accordingly, this study aims to propose a multi-tier architecture to support the collection, processing, analytics and usage of mobility data in providing eMaaS within smart cities. The architecture uses application programming interfaces to enable interoperability between different infrastructures required for eMaaS and allow multiple partners to exchange and share data for making decision regarding electric mobility services.

Design science methodology based on a case study by interview was used to collect data from an infrastructure company in Norway to verify the applicability of the proposed multi-tier architecture.

Findings suggest that the architecture offers an approach for collecting, aggregating, processing and provisioning of data originating from sources to improve electric mobility in smart cities. More importantly, findings from this study provide guidance for municipalities and policymakers in improving electric mobility services. Moreover, the author’s findings provide a practical data-driven mobility use case that can be used by transport companies in deploying eMaaS in smart cities.

Data was collected from a single company in Norway, hence, it is required to further verify the architecture with data collected from other companies.

eMaaS operates on heterogeneous data, which are generated from EVs and used by citizens and stakeholders such as city administration, municipality transport providers, charging station providers and so on. Therefore, the proposed architecture enables the sharing and usage of generated data as openly available data to be used in creating value-added services to improve citizen’s quality of life and viability of businesses.

This study proposes the deployment of electric mobility to address increased usage of vehicles, which contributes to pollution of the environment that has a serious effect on citizen’s quality of life.

This study proposes a multi-tier architecture that stores, processes, analyze and provides data and related services to improve electric mobility within smart cities. The multi-tier architecture aims to support and increase eMaaS operation of EVs toward improving transportation services for city transport operators and citizens for sustainable transport and mobility system.