2 research outputs found
Small firms and internationalisation: learning to manage and managing to learn
Small firms contribute significantly to the UK economy, but most research into
learning and work features the experience of large organisations. This paper focuses
on learning and work in small organisations. An interpretive framework relating to
organisational learning is derived from the literature. Data on learning in small
organisations that internationalise are analysed to assess the extent to which models
of organisational learning are applicable to the context and challenges they face.
This paper suggests that the large firm model of learning is inappropriate. The
distinctive culture and communication systems of small firms require different
approaches to the acquisition, transmission and interpretation of knowledge. Tacit
knowledge, developed through informal learning, is a priority and learning through
local business networks is more important than participation in formal programmes.
Advocacy of HRD practices based on conventional theories of organisational
learning, therefore, may hinder rather than encourage performance in small
organisations
Additional file 1 of Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes
Additional file 1: Table S1. An overview of the assays used for this study. BDNF: brain-derived neurotrophic factor; CCL27: CC motif chemokine ligand 27; CRP: C-reactive protein; CXCL: C-X-C motif chemokine ligand; GFAP: glial fibrillary acidic protein; IL: interleukin; LIF: leukaemia inhibitory factor; MCP1: monocyte chemoattractant protein 1; TGF-β: transforming growth factor beta; TNFα: tumour necrosis factor alpha; TNFR1: tumour necrosis factor receptor 1; TCC: terminal complement complex, VDBP: vitamin D binding protein. Table S2. Rates of missing or imputed data in assay results. Table S3. Characteristics of 4 test/ train cohorts. Table S4. Comparison of CSF biomarkers between multiple sclerosis and non-multiple sclerosis groups. Mann–Whitney comparisons, corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S5. Comparison of serum biomarkers between multiple sclerosis and non-multiple sclerosis groups Mann–Whitney comparisons corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S6. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S7. Breakdown of the Train / Test results for the combined CSF & serum modelling of MS versus non-MS status. Mean AUC values were ordered from lowest to highest, and the optimum model was selected when addition of a further analyte resulted in an AUC increase < 0.01. Table S8. Sensitivity analysis: all biomarker concentrations were corrected for age and sex according to a linear model generated in control samples. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S9. Concordance of biomarkers in predicting time to next relapse in univariate analysis (adjusted for sex and age). Table S10. A progression from single through combinations of multiple biomarkers to predict time to relapse and time to disability, adjusted for age and sex. Table S11. Concordance of biomarkers in predicting time to EDSS 6 in univariate analysis (adjusted for sex, age and disease modifying therapy). Fig. S1. The plots show the 3 models below, run on 1000 random selection of (approx 75%) Train and (approx. 25%) Test data. The left hand plot is the range of AUC’s from the Train data when used also as Test and the right-hand plot shows the range of AUC’s when Test data are used as test