Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research

Exploring the Determinants of IoT Adoption: Findings from a Systematic Literature Review

The Internet of Things (IoT) heralds a new era of disruptive technologies that provide organizations with both benefits and challenges. However, organizational adoption of IoT is not yet widespread and greater understanding of the phenomenon is required. This study examines the existing literature on the key determinants (drivers, benefits, barriers, and challenges) that influence the adoption of IoT by organizations. Therefore, this paper presents findings from a Systematic Literature Review (SLR) and concept matrix approach to identify these IoT adoption determinants at the organizational level. The key constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) were examined in relation to the determinants identified to understand applicability of this theory in the IoT context. Future research will complement these findings through an empirical investigation. Therefore, the overall aim of this research is 1) to generate a model that outlines the determinants influencing organizational IoT adoption and 2) to ascertain the applicability of UTAUT in understanding IoT adoption and to further enrich UTAUT by contextualizing its constructs to the IoT phenomenon