Mediating role of formalization of RM methods among the perceived business risk and organization performance

Figures & data

short-legendFigure 1.

Table 1. Sector-wise survey responses

Sector	Selected companies	Response received
Bank	35	25
Insurance	45	20
Modarabah	10	05
Sugar	45	32
Pharmaceutical	08	03
Cement	22	12
Automobile	20	8
Textile	140	89
Oil & Gas	20	7
Telecommunication	06	3
Total	351	204

Table 2. Convergent validity

Economic factor		Formalization of RM methods		Organizations performance
Items	Std. Est. (λ > 0.50)	Items	Std. Est. (λ > 0.50)	Items	Std. Est. (λ > 0.50)
EF-1	0.71	FRMM-1	0.73	PER-1	0.67
EF −2	0.64	FRMM-2	0.65	PER-2	0.67
EF −3	0.76	FRMM-3	0.64	PER-3	0.64
EF-4	0.74	FRMM-4	0.77	PER-4	0.61
AVE = 2.04 /4 = 0.51		FRMM-5	0.69	PER-5	0.86
CR = 8.12/(8.12 + 1.96) = 0.80		FRMM-6	0.77	PER-6	0.73
Financial market indicator		FRMM-7	0.75	PER-7	0.66
Items	Std. Est. (λ > 0.50)	FRMM-8	0.64	PER-8	0.77
FI-1	0.83	FRMM-9	0.79	PER-9	0.70
FI-2	0.76	FRMM-10	0.75	PER-10	0.82
FI-3	0.59	FRMM-11	0.75	PER-11	0.79
FI-4	0.81	FRMM-12	0.67	PER-12	0.66
FI-5	0.71	FRMM-13	0.76	PER-13	0.79
FI-6	0.60	FRMM-14	0.76	PER-14	0.75
AVE = 3.13/6 = 0.52		FRMM-15	0.79	AVE = 7.38 /14 = 0.53
CR = 18.49/(18.49 + 2.86) = 0.86		AVE = 7.76 /15 = 0.52		CR = 311.87 /(311.87 + 12.42) = 0.96
Technological change		CR = 102.3/(102.3 + 6.62) = 0.94		Political Uncertainty
Items Std. Est. (λ > 0.50)		Market competition		Items Std. Est. (λ > 0.50)
TC-1	0.82	Items Std. Est. (λ > 0.50)		PU-1	0.70
TC-2	0.72	MC-1	0.71	PU-2	0.69
TC-3	0.78	MC-2	0.79	PU-3	0.73
TC-4	0.65	MC-3	0.66	PU-4	0.79
TC-5	0.71	MC-4	0.73	PU-5	0.67
TC-6	0.68	AVE = 2.09 /4 = 0.52		AVE = 2.58/5 = 0.51
AVE = 3.13/6 = 0.53		CR = 8.3/(8.3 + 1.90) = 0.81		CR = 12.88/(12.88 + 2.41) = 0.84
CR = 18.49/(18.49 + 2.86) = 0.86

EF = Market Uncertainty, FI = Financial Uncertainty, TC = Technological Change, MC = Regularity Uncertainty, PU = Political Uncertainty, IC = Formalization of RM Methods, PER = Performance.

Table 3. Cronbach’s alpha

Variables	Source	Items	Valid Items	Cronbach’s Alpha
Economic factor	Hammad, Jusoh, and Ghozali (2013) Miles et al., (1978)	6	4	0.78
Financial indicator		9	6	0.86
Technological change		10	6	0.81
Political uncertainty		8	5	0.82
Market competition		8	4	0.80
Formalization of RM methods	(Miles et al., 1978; Subramaniam et al., 2011)	15	15	0.96
Organization performance	Govindarajan (1985)	14	14	0.94

Table 4. Model fit index

Model fit criteria	Measurement model	Acceptable range*
Χ^2	1.39	-
Χ^2/Df	1.39	1–3
GFI	0.99	>0.90
AGFI	0.96	>0.80
CFI	0.99	>0.95
TLI	0.98	>0.90
NFI	0.99	>0.90
RMR	0.01	<0.09
RMSEA	0.04	<0.08
PCLOSE	0.35	>0.05

Figure 2. Structural equation models: Indirect effects with mediating variables.

Table 5. Structural equation models: Indirect effects with mediating variables

Variables			Estimates	P value
Formalization of RM methods	←	Economic factors	0.22	0.005
Formalization of RM methods	←	Financial indicators	0.16	0.000
Formalization of RM methods	←	Technological change	0.20	0.021
Formalization of RM methods	←	Political uncertainty	0.25	0.010
Formalization of RM methods	←	Market competition	0.18	0.003
Organization performance	←	Formalization of RM methods	0.28	0.001

Table 6. Comparison of direct and indirect effects (Formalization of RM methods)

Variables			Direct effects				Indirect effects				Hypothesis Support
			Estimate		P-value		Estimate		P-value
PER	←	Economic factors		0.19		0.005		0.14		0.006		H1 Accepted
PER	←	Financial indicators		0.33		***		0.09		0.020		H2 Accepted
PER	←	Technological change		0.23		***		0.08		0.011		H3 Accepted
PER	←	Political uncertainty		0.16		0.005		0.11		0.004		H4 Accepted
PER	←	Market competition		0.12		0.019		0.06		0.001		H5 Accepted

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