Purpose This study aims to examine the current state of literature on structural equation modeling (SEM) studies in “cloud computing” domain with respect to study domains of research studies, theories and frameworks they use and SEM models they design. Design/methodology/approach Systematic literature review (SLR) protocol is followed. In total, 96 cloud computing studies from 2009 to June 2018 that used SEM obtained from four databases are selected, and relevant data are extracted to answer the research questions. Findings A trend of increasing SEM usage over years in cloud studies is observed, where technology adoption studies are found to be more common than the use studies. Articles appear under four main domains, namely, business, personal use, education and health care. Technology acceptance model (TAM) is found to be the most commonly used theory. Adoption, intention to use and actual usage are the most common selections for dependent variables in SEM models, whereas security and privacy concerns, costs, ease of use, risks and usefulness are the most common selections for causal factors. Originality/value Previous cloud computing SLR studies did not focus on statistical analysis method used in primary studies. This review will display the current state of SEM studies in cloud domain for all future academics and practical professionals.

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Projects are, by definition, risky and uncertain ventures. Therefore, the performance and risk of major projects should be carefully controlled in order to increase their probability of success. Quantitative project control techniques assist project managers in detecting problems, thus responding to them early on, by comparing the baseline plan with the project progress. However, project risk and uncertainty are rarely considered by these techniques. This paper proposes a project control framework that integrates the project uncertainty and associated risk factors into project control. Our framework is based on earned value management (EVM), which is an effective and widely used quantitative project control technique. The framework uses hybrid Bayesian Networks (BNs) to enhance EVM with the ability to compute the uncertainty associated with its parameters and risk factors. The framework can be applied to projects from different domains, and we illustrate its use with a simple example and a case study of a construction project.

Cloud-based solutions for software development activities have been emerging in the last decade. This study aims to develop a hybrid technology adoption model for cloud use in software development activities. It is based on Technology Acceptance Model (TAM), Technology–Organization–Environment (TOE) framework, and the proposed extension Personal–Organization–Project (POP) structure. The methodology selected is a questionnaire-based survey and data are collected through personally administered questionnaire sessions with developers and managers, resulting in 268 responses regarding 84 software development projects from 30 organizations in Turkey, selected by considering company and project sizes and geographical proximity to allow face-to-face response collection. Structural Equation Modeling (SEM) is used for statistical evaluation and hypothesis testing. The final model was reached upon modifications and it was found to explain the intention to adopt and use the cloud for software development meaningfully. To the best of our knowledge, this is the first study to identify and understand factors that affect the intention of developing software on the cloud. The developed hybrid model was validated to be used in further technology adoption studies. Upon modifying the conceptual model and discovering new relations, a novel model is proposed to draw the relationships between the identified factors and the actual use, intention to use and perceived suitability. Practical and social implications are drawn from the results to help organizations and individuals make decisions on cloud adoption for software development.