A Literature Review on Cloud Computing Adoption Issues in Enterprises

Part 3: Creating Value through ApplicationsInternational audienceCloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 51 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: internal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information systems (IS) research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprises

DECAS:a modern data-driven decision theory for big data and analytics

Abstract Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions

Ex-post evaluation of data-driven decisions:conceptualizing design objectives

Abstract This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting from collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting the development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation

Back-Propagation Artificial Neural Network for ERP Adoption Cost Estimation

Published version of a chapter in the book: Enterprise information systems, vol 220, part 2, 180-187. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-24355-4_19Small and medium size enterprises (SMEs) are greatly affected by cost escalations and overruns Reliable cost factors estimation and management is a key for the success of Enterprise Resource Planning (ERP) systems adoptions in enterprises generally and SMEs specifically. This research area is still immature and needs a considerable amount of research to seek solid and realistic cost factors estimation. Majority of research in this area targets the enhancement of estimates calculated by COCOMO family models. This research is the beginning of a series of models that would try to replace COCOMO with other models that could be more adequate and focused on ERP adoptions. This paper introduces a feed-forward back propagation artificial neural network model for cost factors estimation. We comment on results, merits and limitations of the model proposed. Although the model addresses SMEs, however, it could be extended and applied in various environments and contexts