The Relationship Between Technology Adoption Determinants and the Intention to Use Software-Defined Networking

AbstractThe advent of distributed cloud computing and the exponential growth and demands of the internet of things and big data have strained traditional network technologies\u27 capabilities and have given rise to software-defined networking\u27s (SDN\u27s) revolutionary approach. Some information technology (IT) cloud services leaders who do not intend to adopt SDN technology may be unable to meet increasing performance and flexibility demands and may risk financial loss compared to those who adopt SDN technology. Grounded in the unified theory of acceptance and use of technology (UTAUT), the purpose of this quantitative correlational study was to examine the relationship between IT cloud system integrators\u27 perceptions of performance expectancy, effort expectancy, social influence, facilitating conditions, and their intention to use SDN technology. The participants (n = 167) were cloud system integrators who were at least 18 years old with a minimum of three months\u27 experience and used SDN technology in the United States. Data were collected using the UTAUT authors\u27 validated survey instrument. The multiple regression findings were significant, F(4, 162) = 40.44, p \u3c .001, R2 = .50. In the final model, social influence (ß = .236, t = 2.662, p \u3c .01) and facilitating conditions (ß = .327, t = 5.018, p \u3c .001) were statistically significant; performance expectancy and effort expectancy were not statistically significant. A recommendation is for IT managers to champion SDN adoption by ensuring the availability of support resources and promoting its use in the organization\u27s goals. The implications for positive social change include the potential to enhance cloud security, quality of experience, and improved reliability, strengthening safety control systems

Transparency and reproducibility in artificial intelligence

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field

Transparency and reproducibility in artificial intelligence

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field

Reporting guidelines for human microbiome research: the STORMS checklist

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results. The STORMS tool provides guidance for concise and complete reporting of microbiome studies to facilitate manuscript preparation, peer review, reader comprehension of publications, and comparative analysis of published results