Organization-level risky decision making was assessed
using an objective, global financial measure labeled percent of
funds taken. As part of implementing the focal legislation, CMHBs
could elect to assume varying levels of responsibility for the
care of a high risk population. The level of responsibility assumed
by CMHBs is reflected by percent of funds taken and could range
from 0% (i.e., no responsibility) to 20% (i.e., maximum responsibility)
during the study period. While positive outcomes were possible
for CMHBs who chose to take greater responsibility (i.e., greater
percent of funds taken), the magnitude of potential financial
loss to CMHBs also was directly related to percent of funds taken.
As a result, percent of funds taken served as our organizational
level measure or risky decision making behavior.

##### RESULTS

The
response rate at the CMHB/organization level was 98% (52/53),
with an average of 3.6 responses per Board.

The response rate for the top 4 executive positions (e.g., Executive
Director, Associate Director) was 56% (100/179). Responses were
received from 36 CEOs or 68% of CMHBs. For 94% (50/53) CMHBs,
at least one of the four top executives responded. These rates
compare favorably with other research in this area and suggest
that respondents are representative of TMTs.

Although
hypotheses are tested at the organizational level, the individual
was the unit of analysis for assessing the internal consistency
of the problem framing (opportunity/threat) scale, perceived risk
scale, and the risk propensity scale. Cronbach alphas ranged from
.85 to .88 suggesting that scales are internally consistency.

Convergence
among key informants was assessed with the inter-rater agreement
index (IRR; James, Demaree & Wolf, 1984, 1993) because IRR
was developed to compute agreement when judges are nested within
firm. Values ranged from a high of .92 for the opportunity/threat
scale, to a low of .81 for the inertia measure, suggesting good
convergence among respondents within CMHB and providing justification
for averaging ratings within CMHB..

Organizational-level
correlations indicated all 4 perceptual measures were significantly
correlated in the expected directions with the objective indicator
of risky decision making. Yet, only 2 of 6 possible correlations
among the 4 perceptual measures were significant (and these 2
were expected), reducing concerns about shared method variance.
However, contrary to predictions, the link from risk propensity
to risk perception was not significant.

The
full model (Figure 1) and specific linkages (Hypotheses 1-9) were
tested using RAMONA, a structural equation modeling program within
SYSTAT (Wilkinson & Hill, 1994). Evaluation of the model followed
Medsker, Williams and Holahan's (1994) guidelines. The a priori
model and a revised model were compared to a null model with no
causal links. Four fit indices were examined to allow for comparisons
with the null model: the normed and nonnormed fit indices (NFI,
NNFI), the comparative fit index (CFI), which is resistant to
errors associated with sample size, and the parsimonious model
fit index (PFI), which adjusts for degrees of freedom in the target
model. Values of .90 and greater indicate adequate model fit for
the first 3 indices; .60 and above has been suggested as the guideline
for PFI.

The
a priori model demonstrated good fit according to 3 of 4 fit indices.
The values of NFI (1.07), NNFI (.97) and CFI (1.00) exceeded the
target value of .90. Only, the value of PFI (.32) fell below the
guideline of .60. Review of the output from the a priori model
indicated that all hypothesized direct paths in the model were
significant with the exception of the path from risk propensity
to perceived risk. This path was removed from the model and a
revised model was run. The values of 3 of the fit indices were
essentially unchanged in the revised model and continued to suggest
good fit ( NFI =.96; NNFI = 1.1; and CFI = 1.00). The value of
PFI (.43) increased somewhat, but was still below the guideline
of .60. (Our modest sample size deflates this index). The parameter
estimates which are significant at the .05 level for the revised
model are shown in Figure 1 and are used to test the 9 hypotheses.

##### Tests
of Hypotheses

Five
of seven direct-effect hypotheses were supported. Support was
found for H1 (r = 0.21, p < .05), H2 (r = .32, p < .05),
H4 (r = 0.71, p < .05), H5 (r = -0.68, p < .05), and H8
(r = 0.22, p < .05). Support was not found for H7 and H9. Hypothesis
7 (H7) predicted a negative correlation between risk propensity
and risk perception. Neither the bivariate correlation (r = -0.09,
ns) nor the path coefficient (r = -0.08) between these variables
were significant. Hypothesis 9 (H9) which predicted a negative
association between risk perception and risky decision making
also was not supported. The zero-order correlation provided preliminary
support for this hypothesis (r = -0.30, P < .05) and the association
remained significant but the sign changed from the predicted negative
association to a positive association (r = 0.24, p < .05) suggesting
the existence of a suppressor effect.

Support was found for H3 which predicted that risk propensity
partially mediates the effect of inertia on risky decision making
as indicated by the product of the path coefficients (i.e., 0.32
X 0.22 = .06) being considerably lower than the direct path coefficient
linking inertia to risky decision making (r = .21, p < .05).
H6 predicted that risk perception partially mediates the effect
of problem framing on risky decision making. Because the product
of path coefficients is much smaller and negative (i.e., -0.68
x 0.24 = -14) compared to the direct path from problem framing
to risky decision making, we conclude that risk perception acts
as a suppressor, explaining unique variance in risky decision
making behavior beyond that explained by problem framing.

Research
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