Additional analyses. Both perceptions of the self in relation to the group (perceived inclusion and perceived intra-group status) showed a moderate, positive relation with cognitive accessibility of fairness (intercorrelations are depicted in Table 3). Nevertheless, evidence that either inclusion or intra-group status mediated the effect of intra-group respect on the cognitive accessibility of fairness could not be demonstrated.
Additional analyses. The collective motive versus the individual motive. In both the high respect condition and the average respect condition, participants showed enhanced levels of affective commitment when compared with participants in the low respect condition (see Table 5). To confirm our predictions that when people are respected, enhanced feelings of affective commitment will be related to more engagement in discretionary group efforts, we subjected affective commitment, situational group attachment an>iety, and discretionary group efforts to linear regression analyses. Linear regression analysis within the relevant conditions (i.e. the high respect condition and average respect condition) showed the following: After having received higher or average respect, when both affective commitment and situational group attachment an>iety were in the equation, only affective commitment, R2ch = .06, β = .25, t(2,90) = 2.48, p < .05, revealed a significant relation with the discretionary group efforts measure, whereas situational group attachment an>iety showed no significant relation to the discretionary group efforts measure, R2ch = .02, β = .12, t(2,90) = 1.22, p = .23 (see Figure 1). Figure 1 Results of linear regression analysis of Discretionary Group Efforts on Affective Commitment and Situational Group Attachment Anxiety when Respected or Disrespected Discretionary group efforts Affective commitment β = .25* When respected (the collective motive) Situational group attachment anxiety β = .12ns When disrespected (the individual motive) β = .02ns Discretionary group efforts Affective commitment Situational group attachment anxiety β = .31* Note. * = significant at p < .05 ns = not significant By contrast, participants in the low respect condition indicated to have more feelings of situational group attachment an>iety compared to participants in respectively the high respect condition or average respect condition (see Table 5). To further support our assumption that when people perceive themselves as disrespected, displays of discretionary group efforts will appear due to higher levels of situational group attachment an>iety for those in the disrespected condition, we subjected affective commitment, situational group attachment an>iety, and discretionary group efforts to linear regression analyses. Within the disrespect condition when both affective commitment and situational group attachment an>iety were in the equation, instead of affective commitment, R2ch = .01, β = .02, t(2...
Additional analyses. Another way to examine congruency with network data is the overlap between the three interdependency networks. Therefore, a next step we undertook was establishing the effect of overlapping patterns of the three interdependence relationships on performance. This approach differs from the congruency measure we used earlier in that here we can take into account the actual overlap of interdependency relationships between the team members. The density measure we used, describes the overall level of the three interdependency relationships reported by network members. That measure did not take into account if ties between team members are consistent in each interdependency network. The overlap of the interdependency networks measures if team members Xxxx and Xxxx are mutually task interdependent, and/or dependent upon each other for task-related information, and/or consider each other as friends. This overlap was calculated using the QAP correlation procedure in UCINET 5 (Xxxxxxxx, Xxxxxxx & Xxxxxxx, 1999). With this procedure, the correlation between two different matrices is calculated, using all elements in the matrices. The QAP procedure compares the observed correlation with a distribution of random correlations generated according to the null hypothesis of no relationship between the matrices. This procedure works by permuting the rows and columns (together) of one of the input matrices, and then correlating the permuted matrix with the other data matrix. This process is repeated hundreds of times to build up a distribution of correlations under the null hypothesis. The p-value is given by the proportion of random correlations that are as large as or larger than the observed correlation. Using this procedure we can only assess the overlap between patterns of two interdependence relationships at a time. The three correlation coefficients for each team were then regressed on team performance. The results are shown in table 4.3. We find a marginally significantly negative effect with respect to the overlap between functional and affect-based interdependence. The results reveal that the more the pattern of functional interdependence relationships shows an overlap with the pattern of affect-based interdependence relationships (regardless of the overall level of this type of interdependence in that team), the more negative the team performance. The overlap between functional and cognitive interdependence and between cognitive and affect-based interdependence sho...
Additional analyses. CADI shall consider whether the results/findings under any of the above-described monitoring warrant further review or analysis, or potentially indicate a systemic issue(s).
Additional analyses. For patients with a hematological malignancy or aplastic anemia, the incidence rate ratio of bacteremia the day after transfusion of at least one old platelet concentrate was 0.54 (CI 0.31 to 0.87) compared to transfusion of only fresh platelet concentrates. After receiving a single old, compared to a single fresh, platelet concentrate the incidence rate ratio was 0.44 (95% CI 0.26 to 0.76). If a patient received one old platelet concentrate in the preceding three, five, or seven days, the estimates were similar, although not statistically significant (figure 3, panel B and supplemental material).
Additional analyses. To ensure that our results are not sensitive to alternative specifications, we now examine various board-independence classifications and alternative regression specifications. All untabulated analyses are available upon request.
Additional analyses. To shed more light onto the immediate effects of ASU 2022-04 disclosures about supplier financing agreements on financial analysts, we examine the changes in analysts’ cash flow forecast properties following a supplier financing disclosure. We focus on firms that eventually make a supplier financing disclosure in our sample period and investigate changes in cash flow forecast properties before and after the disclosure. We identify cash flow forecast properties immediately after the earnings announcement associated with the 10-Q filing. This first-difference approach allows us to uncover temporal effects of disclosures in treatment firms. Moreover, not all treatment firms provide supplier finance disclosure during the first quarter of 2023. For some firms the initial disclosure of supplier financing agreements is in the second 10-Q filing after the effective date of ASU 2022-04. This staggered setting, along with firm and quarter fixed effects allows us perform a difference-in-difference analysis. Note that we use quarterly Compustat data to calculate accounting variables for this analysis. Table 6, Panel A presents the results for this analysis with the sample restricted to supplier financing firms in the quarters before and after the disclosure. In columns 1 and 2, we find that supplier financing firms experience a decline in analysts’ cash flow forecast coverage. Column 3 shows that analysts revise their cash flow forecasts downwards after the disclosure of supplier financing agreements. In column 4, cash flow forecast dispersion increases after the disclosure of supplier financing agreements. Column 5 shows no difference in the accuracy of cash flow forecasts. The decrease in cash flow coverage and increase in dispersion can be interpreted as analysts not receiving a precise cash flow signal from the supplier financing disclosure. The downward forecast revision after earnings announcements for supplier financing firms is perhaps due to an increased skepticism about reported cash flows given the existing supplier financing agreements. However, given no change in cash flow forecast accuracy, we fail to find any evidence that the ASU 2022-04 disclosures improves the analysts’ ability to forecast cash flows. Overall, these results provide initial evidence that ASU 2022-04 disclosures do not assist financial analysts with their cash flow forecasts. To complement the results in Panel A, we extend our sample by adding control firms using propensity score match...
Additional analyses. The total amount specified in paragraph 3.01 does not include the cost of any additional analyses requested by the City, such as analysis of bottom samples. The Council will carry out any such additional analyses at the request of the City and subject to the availability of Council resources for carrying out such analyses. The Council will bill the City after the end of the Monitoring Period for any such additional analyses at the Council’s actual cost, and the City will promptly reimburse the Council for any such costs billed. The costs for additional analyses are provided in Exhibit A.
