A normative model for assessing SME IT

Information technology (IT) is a key enabler of modern small businesses, yet fostering reliably effective IT systems remains a significant challenge. This paper presents a light weight IT effectiveness model for small businesses to assess their IT and formulate strategies for improvement. Employing an action research approach we investigate a mixed method analysis of 120 survey responses from small family businesses and user participation in 10 semi-structured interviews. We then conduct critical reflection to identify refinements which are validated using 72 survey responses from university students. The results present compelling evidence that employees’ normative patterns (norms) are a significant driver of IT effectiveness in a second order PLS predictive model able to explain 26% of observed variance. A norms-based approach to IT effectiveness helps fill a significant research and managerial gap for organizations unable or unwilling to adopt IT best practice frameworks used by large organizations. Our findings imply that comparing norms to IT best practices may offer a less technical approach to assessing IT operations, which may be well suited to small businesses. Although further investigation cycles are needed to systematically test this model, we encourage small business managers to: 1) anticipate IT risks and mitigate them; 2) identify measures of IT performance, and monitor them, and 3) review/synchronize business and IT goals

Bias values and associated 95% confidence intervals (CI) for Bland-Altman comparisons shown in Fig 3A–3D.

<p>n/a, not applicable.</p><p>Bias values and associated 95% confidence intervals (CI) for Bland-Altman comparisons shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128118#pone.0128118.g003" target="_blank">Fig 3A–3D</a>.</p

Remote vs real-time reading of DFA paper ALT test results (for fingerstick samples).

<p>A. Plot of real-time reads vs reads of 2 MP images. B. Plot of real-time reads vs reads of 8 MP images. For both A and B, the diagonal solid line represents the line of equality. C. Bland-Altman plot of differences between real-time reads and reads of 2 MP images. D. Bland-Altman plot of differences between real-time reads and reads of 8 MP images. For both C and D, the blue line represents the bias/average difference between results obtained by the two reading methods. The red lines represent the 95% limits of agreement. The green line represents the line of no bias.</p

Bland-Altman plots to evaluate agreement between ALT results generated by different platforms for fingerstick or serum samples.

<p>The blue line in each plot represents the “bias” or average difference between the two methods. The red lines represent the 95% limits of agreement. The green line represents the line of no bias. A. Difference between DFA paper test results for fingerstick blood vs paired serum. B. Difference between DFA paper test results for fingerstick blood and Abaxis Piccolo test results for paired serum. C. Difference between DFA paper test results for serum and Abaxis Piccolo test results for serum. D. Difference between the log transformation of the results of the two automated platforms (Abaxis Piccolo and Roche/Hitachi) for serum samples.</p

Schematic of the 3-Zone DFA paper-based alanine aminotransferase (ALT) test.

<p>A. The DFA ALT test is constructed by assembling two patterned paper layers, a plasma separation membrane disc, and protective lamination films to create a 3-dimensional device. A cover film is applied to devices to be used with fingerstick whole blood samples; this cover film is not applied to devices to be used with serum samples. All layers are adhered together using patterned, pressure-sensitive adhesive films. B. The test utilizes a peroxidase-based colorimetric assay to provide a semi-quantitative ALT result determined through visual comparison with a reference color chart. The color chart also allows the user to place the ALT result in one of three categorical bins: <3X upper limit of normal (ULN), 3-5X ULN, and >5X ULN. C. A fingerstick blood sample is applied to the sample port on the “sample application side” of the test. Blood cells are separated by the plasma separation membrane, allowing plasma to wick into the device and react with reagents dried onto individual detection zones; results are viewed on the “read side.” D. The results are read after an incubation time that corresponds to the ambient temperature. E. Two control zones on the test are used to determine the validity of the test results. Examples of valid devices are shown in the two images on the left. Four invalid examples are shown on the right, as follows: i. Insufficient sample volume, evident by the lack of a yellow color in the negative control zone. ii. Insufficient sample volume (as in i) and positive control failure, latter indicating inactive reagents at the time of testing. iii. Positive control failure. iv. Hemolyzed sample, indicated by the presence of a red color in the negative control zone.</p

Plots of ALT results generated by different platforms for fingerstick or serum samples.

<p>The diagonal black line represents the line of equality. A. Comparison of DFA paper test results for fingerstick blood vs paired serum. B. Comparison of DFA paper test results for fingerstick blood to Abaxis Piccolo test results for paired serum. C. Comparison of DFA paper test results for serum to Abaxis Piccolo test results for serum. D. Comparison of the results of two automated platforms (Abaxis Piccolo and Roche/Hitachi) for serum samples.</p

Precision of the DFA paper-based ALT test and the Abaxis Piccolo ALT test, as performed on serum standards.

<p>(DFA, Diagnostics For All; ALT, alanine aminotransferase; SD, standard deviation; CV, coefficient of variation)</p><p>Precision of the DFA paper-based ALT test and the Abaxis Piccolo ALT test, as performed on serum standards.</p

Supplementary Material from Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting

Three adaptive hypotheses have been forwarded to explain the distinctive Neanderthal face: (i) an improved ability to accommodate high anterior bite forces, (ii) more effective conditioning of cold and/or dry air, and, (iii) adaptation to facilitate greater ventilatory demands. We test these hypotheses using three-dimensional models of Neanderthals, modern humans, and a close outgroup (<i>H. heidelbergensis</i>), applying finite-element analysis (FEA) and computational fluid dynamics (CFD). This is the most comprehensive application of either approach applied to date and the first to include both. FEA reveals few differences between <i>H. heidelbergensis</i>, modern humans and Neanderthals in their capacities to sustain high anterior tooth loadings. CFD shows that the nasal cavities of Neanderthals and especially modern humans condition air more efficiently than does that of <i>H. heidelbergensis</i>, suggesting that both evolved to better withstand cold and/or dry climates than less derived <i>Homo</i>. We further find that Neanderthals could move considerably more air through the nasal pathway than could <i>H. heidelbergensis</i> or modern humans, consistent with the propositions that, relative to our outgroup <i>Homo</i>, Neanderthal facial morphology evolved to reflect improved capacities to better condition cold, dry air, and, to move greater air volumes in response to higher energetic requirements