Assessing Cybersecurity service quality in corporate environments

This study assess the quality of Cybersecurity as a service provided by IT department in corporate network and provides analysis about the service quality impact on the user, seen as a consumer of the service, and on the organization as well. In order to evaluate the quality of this service, multi-item instrument “SERVQUAL” was used for measuring consumer perceptions of service quality. To provide insights about Cybersecurity service quality impact, DeLone and McLean information systems success model was used. To test this approach, data was collected from over one hundred users from different industries and partial least square (PLS) was used to estimate the research model. This study found that SERVQUAL is adequate to assess Cybersecurity service quality and also found that Cybersecurity service quality positively influences the Cybersecurity use and individual impact in Cybersecurity.

Effects of selection and drift on G matrix evolution in a heterogeneous environment: a multivariate Qst-Fst Test with the freshwater snail Galba truncatula.

Unraveling the effect of selection vs. drift on the evolution of quantitative traits is commonly achieved by one of two methods. Either one contrasts population differentiation estimates for genetic markers and quantitative traits (the Q(st)-F(st) contrast) or multivariate methods are used to study the covariance between sets of traits. In particular, many studies have focused on the genetic variance-covariance matrix (the G matrix). However, both drift and selection can cause changes in G. To understand their joint effects, we recently combined the two methods into a single test (accompanying article by Martin et al.), which we apply here to a network of 16 natural populations of the freshwater snail Galba truncatula. Using this new neutrality test, extended to hierarchical population structures, we studied the multivariate equivalent of the Q(st)-F(st) contrast for several life-history traits of G. truncatula. We found strong evidence of selection acting on multivariate phenotypes. Selection was homogeneous among populations within each habitat and heterogeneous between habitats. We found that the G matrices were relatively stable within each habitat, with proportionality between the among-populations (D) and the within-populations (G) covariance matrices. The effect of habitat heterogeneity is to break this proportionality because of selection for habitat-dependent optima. Individual-based simulations mimicking our empirical system confirmed that these patterns are expected under the selective regime inferred. We show that homogenizing selection can mimic some effect of drift on the G matrix (G and D almost proportional), but that incorporating information from molecular markers (multivariate Q(st)-F(st)) allows disentangling the two effects.