--Frequency of meeting among Ph.D. candidate and university supervisor Efficiency
All the other scores of the table are calculated working with 0.93, 1.49) 1.Global Pediatric HealthModel II Adjusted OR (95 CI) 1.17 (0.56, two.44) 1.38 (0.69, two.75) 1.14 (0.62, two.12) 1.05 (0.68, 1.61) 1.00 1.00 0.94 (0.69, 1.29) 1.00 1.01 (0.51, 2.00) 1.00 1.23 (0.96, 1.58) 1.09 (0.69, 1.72) 1.Variables Wealth index Poorest precisely the same process, except for the inputs for which we reduce the scores by a single unit. candidate and firm supervisor which features a substantially decrease contribution. From the Ph.D. candidates' perspective, position of university supervisor also features a higher contribution followed by the other inputs using the frequency ofScientometrics (2016) 109:1911?meetings between university supervisor and firm supervisor and also involving Ph.D. candidate and firm supervisor as the least contributing inputs. The results on the sensitivity analysis can be made use of to produce tactics to improve efficiency.ConclusionsThis study delivers a conceptual and empirical contribution to current literature on university-industry collaboration in two approaches. Firstly, this study focuses on collaborative Ph.D. project as 1 form of collaborative channels among university and sector, which has as a result far received tiny consideration in literature. In existing literature, university patenting and licensing have already been the primary topics, rather than the ``academic engagement of universities towards sector (D'Este and Patel 2007; Perkmann et al.--Frequency of meeting among Ph.D. candidate and university title= a0016355 title= IAS.17.four.19557 supervisor Efficiency difference--Frequency of meeting in between university and firm supervisor 0.026 0.016 0.012 0.013 0.006 0.013 0.012 0.054 0.019 0.001 0.Ph.D. perspective0.040 0.019 0.001 0.006 0.014 0.012 0.023 0.012 0.006 0.049 0.contributing inputs and outputs. Table 7 shows the results of sensitivity analysis. For example, the first element from the table (Efficiency difference-Publication) is calculated as follows. Working with the original information we calculate the efficiency score for each and every Ph.D. project making use of Eq. 1 (original efficiency score). An typical with the efficiency scores can now be calculated (original average). We then add a single publication to the number of publications of all the 51 Ph.D. projects. Then a new efficiency score is calculated for every project employing Eq. 1. An average of the efficiency scores can now be calculated (new average). The difference in between title= j.bone.2015.06.008 the new efficiency typical and also the original efficiency typical is calculated as 0.054 which can be shown in the table. All the other scores of the table are calculated employing precisely the same process, except for the inputs for which we lower the scores by one particular unit. The numbers within the table show how much the efficiency typical increases due to growing an output by 1 unit or resulting from decreasing an input by one particular unit. The second column shows the outcomes in the perspective of supervisor, although the third column shows the results in the viewpoint of Ph.D. candidates. As can been from Table 7, from amongst the outputs, number of publications has one of the most contribution followed by the amount of patents in the both perspectives. The other two outputs are placed within the subsequent locations. From among the inputs, interestingly, though based on supervisors, understanding of university supervisor has by far the most substantial part, in the viewpoint of the Ph.D. candidates, this can be the frequency with the meetings amongst the Ph.D.