Cloud Service Brokerage: A systematic literature review using a software development lifecycle

Cloud Service Brokerage (CSB) is an emerging technology that has become popular with cloud computing. CSB is a middleman providing value added services, developed using standard software development lifecycle, from cloud providers to consumers. This paper provides a systematic literature review on this topic, covering 41 publications from 2009 to 2015. The paper aims to provide an overview of CSB research status, and give suggestions on how CSB research should proceed. A descriptive analysis reveals a lack of contributions from the Information Systems discipline. A software development lifecycle analysis uncovers a severe imbalance of research contributions across the four stages of software development: design, develop, deploy, and manage. The majority of research contributions are geared toward the design stage with a minimal contribution in the remaining stages. As such, we call for a balanced research endeavor across the cycle given the equal importance of each stage within the CSB paradigm

Smart Technology Adoption’s Impact on the Value of Logistics Service Providers’ Firms

Although it took a pandemic to raise awareness about supply chain issues in the minds of the public at large, industry players have long understood supply chain complexities—particularly in the face of continually evolving technologies and ever-more interconnected global enterprises. With Logistics 4.0 and the rapid developments in smart technologies, these complexities make the ongoing need for technology adoption even more complicated for logistics providers. While the literature regularly reports on the adoption of specific technologies, there is little research on the adoption process and even less that might guide providers in prioritizing their technology targets. This research examined the literature for drivers and consequences of technology adoption among providers, then tested those concepts through in-depth interviews with 40 senior-level executives at global logistics provider firms. Among the study’s findings are that the drivers and consequences of smart technology adoption are similar among logistics providers. However, firm size, business tenure, and client relationships moderate the adoption of these innovations. The study identifies incumbent people, processes, and systems as “excess baggage” that slows adoption because of adjustments needed to accommodate new technologies and creates bottlenecks for these firms. However, when combined with new competencies, streamlined processes, and proper change management, this baggage may improve firm performance because of the legacy processes integrated with customers’ supply chains. The study also developed a framework to inform practitioners’ adoption efforts. The framework addresses the research questions. It also recommends that to realize quicker revenue gains when adopting smart technology. Providers focus on two key drivers: customer relationships and market demands. This research also suggests that providers adopting smart technology leverage their incumbent human resources, processes, and technologies to deliver customer value and improve firm performance

Science advice to governments: diverse systems, common challenges

This briefing paper formed the basis of discussions at the 'Science Advice to Governments' summit, which took place in Auckland, New Zealand from 28-29 August 2014, and was attended by science advisors and policymakers from 48 countries

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Towards a Service-Oriented Enterprise: The Design of a Cloud Business Integration Platform in a Medium-Sized Manufacturing Enterprise

This case study research followed the two-year transition of a medium-sized manufacturing firm towards a service-oriented enterprise. A service-oriented enterprise is an emerging architecture of the firm that leverages the paradigm of services computing to integrate the capabilities of the firm with the complementary competencies of business partners to offer customers with value-added products and services. Design science research in information systems was employed to pursue the primary design of a cloud business integration platform to enable the secondary design of multi-enterprise business processes to enable the dynamic and effective integration of business partner capabilities with those of the enterprise. The results from the study received industry acclaim for the designed solutions innovativeness and business results in the case study environment. The research makes contributions to the IT practitioner and scholarly knowledge base by providing insight into key constructs associated with service-oriented design and deployment of a cloud enterprise architecture and cloud intermediation model to achieve business results. The study demonstrated how an outside-in service-oriented architecture adoption pattern and cloud computing model enabled a medium-sized manufacturing enterprise to focus on a comprehensive approach to business partner integration and collaboration. The cloud integration platform has enabled a range of secondary designs that leveraged business services to orchestrate inter-enterprise business processes for choreography into service systems and networks for the purposes of value creation. The study results demonstrated enhanced levels of business process agility enabled by the cloud platform leading to secondary designs of transactional, differentiated, innovative, and improvisational business processes. The study provides a foundation for future scholarly research on the role of cloud integration platforms in enterprise computing and the increased importance of service-oriented secondary designs to exploit cloud platforms for sustained business performance

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical