Over the past few years, emergence of Cloud Computing has notably made an evolution in the IT industry by putting forward an 'everything as a service' idea .Cloud Computing is of growing interest to companies throughout the world, but there are many barriers associated with its adoption which should be eliminated. This paper aims to investigate Cloud Computing and discusses the drivers and inhibitors of its adoption. Moreover, an attempt has been made to identify the key stakeholders of Cloud Computing and outline the current security challenges. A SWOT analysis which consists of strengths, weaknesses, opportunities and threats has also carried out in which Cloud Computing adoption for SMEs (Small and Medium-sized Enterprises) is evaluated. Finally, the paper concludes with some further research areas in the field of Cloud Computing.

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Sustainable competitiveness (SC) encourages nations not only to meet the needs of the current generation but also to sustain or even expand national wealth in the future without depleting natural and social capital. Drawing on complexity theory, we used a configurational approach to identify under what necessary and sufficient conditions, digitalisation contributes to achieve higher SC. Shifting attention from net effects to configuration analysis improves our understanding of cross-national differences in sustainability by exploring how the digitalisation factors combine to strengthen SC power across countries. To address the complexity of this configuration, we have incorporated fsQCA and NCA techniques in the modelling of high and low levels of sustainable competitiveness recipes. Analysis of data from 127 countries advanced our perception of how access to ICT infrastructures and capabilities, combined with the adoption and usage of ICT could result in different degrees of sustainable competitiveness. Theoretically, this study contributes to the literature on digitalisation and national sustainability; and it can practically act as a guideline for policymakers to understand the complex interactions and causal configurations of digitalisation factors on sustainability.

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PurposeBig data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.Design/methodology/approachWe use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.FindingsOur findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.Originality/valueThis study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

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PurposeIn today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks.Design/methodology/approachTo take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data.FindingsThe results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.”Originality/valueIn addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers.