Abstract
The systematic review of the literature is a fundamental methodology for analyzing critically the existing literature on a given research theme. They are designed to be methodical, replicable and guide the author in identifying the main lines of investigation and conclusions in each scientific domain and, in addition, help them in the identification of new directions of research. However, the systematic review process is typically viewed as too heterogeneous, complex and time-consuming. In this sense, it is pertinent to propose a new approach for conducting systematic reviews that may be more agile, not only in terms of development, but also in the analysis of the results of a systematic review process. This article presents a canvas framework for conducting a systematic review composed of nine blocks and based on a set of identified good practices found in the literature, in which it is possible to easily identify all the steps of the process, options taken, and main results.

Abstract
The design of web and mobile applications is one of the most challenging fields of the current information technology landscape. Increasingly, companies intend to have a strong presence in the information society, which allows them to advertise their products, services, make online business, interact with customers, among others. However, the development and design of web and mobile solutions have numerous challenges and best practices that should be known and applied. In this chapter, the authors adopt a qualitative methodology based on multiple case studies that allow them to identify a total of six challenges and best practices that are later confronted and compared with the recent findings on the coverage of the topic. © 2018, IGI Global.

Abstract
Information Technology (IT) plays an increasingly important role for small and medium-sized enterprises. It has become fundamental for these companies to protect information and IT assets in relation to risks and threats that have grown in recent years. This study aims to understand the importance and structure of an information security policy, using a quantitative study that intends to identify the most important and least relevant elements of an information security policy document. The findings of this study reveal that the top three most important elements in the structure of a security policy are the asset management, security risk management and define the scope of the policy. On the other side, the three least relevant elements include the executive summary, contacts and manual inspection. Additionally, the study reveals that the importance given to each element of the security policy is slightly changed according to the sectors of activity. The elements that show the greatest variability are the review process, executive summary and penalties. On the other side, the purpose of the policy and the asset management present a stable importance for all sectors of activity.

Abstract

Abstract
A. Campbell, M. Gutierrez and M. Lancelott, Operating Model Canvas, 2017, Zaltbommel, Netherlands: Van Haren Publishing, 224 pp., $41.00. ISBN: 078-0401800716.

Abstract
The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.