Performance Measurement System Design as Link Between Strategy Formulation and Performance Information Use in Public Sector Organizations

Figures & data

Table 1. Man-Years and Population by Non-Responding and Responding Organizations (N = 498).

Variable and tier	N	Mean	Median	SD	Min	Max	Test of differ-rence
Man-years 2020
Municipalities	356	1126.81	491.00	2807.14	38.00	43395.40	−4.40
Non-responding	271	819.88	435.60	2676.13	38.00	43395.40	df = 86.51
Responding	85	2105.35	882.20	3001.67	74.20	17448.00	p<.001
Counties	10	4257.83	3748.80	2184.77	2452.60	9969.70	1.19
Non-responding	3	5619.97	3740.30	3778.53	3149.90	9969.70	df = 3.01
Responding	7	3674.06	3757.30	1037.03	2452.60	5584.00	p=.320
Agencies	111	500.27	178.00	988.69	8.00	6375.00	−0.84
Non-responding	71	434.99	141.00	833.90	8.00	5087.00	df = 59.93
Responding	40	616.15	238.50	1219.60	13.00	6375.00	p=.407
Higher education institutions	21	1844.24	1383.00	2172.71	116.00	8013.00	−1.09
Non-responding	10	1316.80	431.50	1537.97	116.00	4148.00	df = 16.45
Responding	11	2323.73	1559.00	2604.11	232.00	8013.00	p=.291
Population 2020
Municipalities	356	15077.47	5162.50	44325.37	198	693,494	−4.32
Non-responding	271	10449.76	4454	42635.17	198	693,494	df = 86.05
Responding	85	29831.69	11,074	46596.10	461	283,929	p<.001
Counties	10	467408.60	395390.50	299423.27	241,235	1,241,165	1.13
Non-responding	3	634624.00	419,396	532607.24	243,311	1,241,165	df = 3.36
Responding	7	395744.86	371,385	141253.26	241,235	636,531	p=.332

Welch’s t-test of independent samples difference.

Table 2. Survey Questions Used in the Measurement Models.

Formal strategic planning (FSP)
Var8	We develop the strategic plan through a systematic planning process.
Var10*	Our organization’s strategic plan is a formal strategic plan or an update of a formal strategic plan.
Var12	During plan development, we conducted situational analyses of our organization’s strengths and weaknesses.
Var14	During plan development, we conducted situational analyses of our environment’s opportunities and threats.
Var16	During plan development, we established strategic goals and used them to drive decisions and actions throughout our organization.
Logical incrementalism (LI)
var9	We continually work on designing strategy in our organization.
Var11	Periodically, we reassess our own performance in light of changing circumstances and adjust our strategies accordingly.
Var13	We have tried to maintain flexibility for future options and made changes in strategy when suggested by newly emerging information.
Var15	We have used forecasts of future conditions as the basis of our strategy but also have developed contingency plans to deal with uncertainties in those forecasts.
Performance measurement system design (PMS design)
Var45	The organization reports performance associated with goals in the strategic plan to the government ministry/the board/the county council/the municipal council on a regular basis.
Var49*	The organization benchmarks performance measures against other organizations to gauge the effectiveness of strategic actions (for example by participating in benchmarking networks).
Var50	The organization tracks performances measures over time to determine whether its performances improve.
Var53	A new strategic plan is always followed up by changes in performance measures to support the strategic plan.
Performance information use (PIU)
Var97	It contributes to maintaining a good reputation and public support.
Var99	It contributes to the delivery of high-quality organizational services.
Var101	It leads to defining better organizational priorities.
Var105	It contributes to making sound decisions regarding programs, systems, and resources.
Var106	It provides direction and control over the employee’s activities.

* Indicator excluded from the final measurement model.

Table 3. Principal Component Analysis (N = 135).

	Component loadings
	1	2	3	Uniqueness
Var8		0.61		0.39
Var12		0.88		0.26
Var14		0.83		0.25
Var16		0.67		0.30
Var9			0.81	0.30
Var11			0.75	0.30
Var13			0.53	0.33
Var15			0.80	0.38
Var97	0.55		0.43	0.46
Var99	0.72			0.38
Var101	0.89			0.18
Var105	0.82			0.30
Var106	0.87			0.26

“Oblimin” rotation with 3 fixed components, only showing factor loadings > =0.4. Bartlett’s Test of Sphericity Chi²=952.89 (df = 78, p<.0.001). Mean Measure of Sampling Adequacy (MSA) 0.89, min. MSA 0.85.

Table 4. Assessment of Measurement Models (N = 135).

Variable	Measurement model and indicators	Min	Max	Mean	Variance	Skew-ness	Kurto-sis	Dijkstra-Henseler’s rho (ρA)	Jöreskog’s rho (ρC)	Cron-bach’s alfa (α)	AVE	VIF	Weight	Loading
PIU	Latent variable (mode A consistent)							0.87	0.87	0.87	0.57
Var97	Maintain public support	3.00	7.00	5.39	0.96	−0.28	−0.54						0.245***	0.750***
Var99	Deliver high-quality public services	2.00	7.00	5.21	1.14	−0.73	0.26						0.253***	0.774***
Var101	Define organizational priorities	2.00	7.00	5.64	1.10	−1.10	1.71						0.260***	0.795***
Var105	Make sound decisions regarding programs, systems, and resources	2.00	7.00	5.16	1.27	−0.74	0.57						0.242***	0.741***
Var106	Provide direction and control over employees’ activities	1.00	7.00	5.46	1.28	−0.95	1.55						0.233***	0.712***
FSP	Latent variable (mode A consistent)							0.86	0.85	0.85	0.59
Var8	Systematic planning process	1.00	7.00	5.84	1.40	−1.28	1.75						0.290***	0.744***
Var12	Analysis of strengths and weaknesses	1.00	7.00	5.47	1.61	−1.04	0.99						0.276***	0.708***
Var14	Analysis of opportunities and threats	1.00	7.00	5.41	1.68	−0.88	0.68						0.288***	0.738***
Var16	Establish strategic goals to drive decisions and actions	1.00	7.00	5.64	1.68	−1.11	1.10						0.344***	0.882***
LI	Latent variable (mode A consistent)							0.84	0.84	0.84	0.56
Var9	Continuous work on strategy design	1.00	7.00	5.33	1.91	−1.02	0.88						0.320***	0.786***
Var11	Periodically reassess performance and adjust the strategies accordingly	1.00	7.00	5.02	2.42	−0.72	−0.35						0.319***	0.784***
Var13	Maintain flexibility for future options and make changes in strategy when suggested by newly emerging information	1.00	7.00	5.26	1.76	−0.94	0.81						0.299***	0.735***
Var15	Use forecasts and develop contingency plans	1.00	7.00	5.47	1.65	−1.25	1.93						0.282***	0.693***
PMS design	Emergent variable (mode B)
Var45	Reports performance regularly	1.00	7.00	5.07	2.61	−0.93	0.18					1.99	0.403***	0.863***
Var50	Track performances measures over time	1.00	7.00	5.09	2.28	−1.11	0.90					2.24	0.433**	0.869***
Var53	New strategic plan always followed up by changes in performance measures	1.00	7.00	4.16	3.09	−0.31	−0.85					1.69	0.403***	0.854***
	Single indicator emergent variables
Sector	Central government	0.00	1.00	0.36	0.23	0.58	−1.69						1.00	1.00
Org. size	Manyears_LN	2.56	9.77	6.52	2.28	−0.08	−0.53						1.00	1.00

AVE: average variance extracted. Note: **t-values > 2.576 (p < 0.01); ***t-values > 3.090 (p < 0.001); two-tailed p-values.

Table 5. Performance Information Use by Sector (N = 135).

	N	Mean	SD
Local government organizations	86	5.26	0.88
Central government organizations including higher education institutions	49	5.57	0.81

Welch’s independent samples t-test −2.07 (df = 106.97), p = 0.041.

Figure 1. Main results from the measurement models and the structural model (N = 135). PLS-PM results, 300 iterations, 5,000 bootstrap samples. SRMR = 0.06 (HI95 = 0.05, HI99 = 0.06). Highest HTMT = 0.81. *p < 0.05; **p < 0.01; ***p < 0.001, two-tailed p-values. Path-coefficients are standardized (beta) coefficients.

Table 6. Spearman Correlations (N = 135).

	1	2	3	4	5
1. Performance information use
2. Performance measurement system design	0.62***
3. Formal strategic planning	0.59***	0.75***
4. Logical incrementalism	0.57***	0.74***	0.81
5. Man-years LN transformed	0.08	0.20	0.23	0.22
6. Central government sector	0.19*	0.13	0.22	0.05	−0.36

^* t-values > 1.960 (p < 0.05); ***t-values > 3.090 (p < 0.001); two-tailed p-values.

Table 7. Assessment of the Structural Model (N = 135).

	Beta	p-Value		95-Percent confidence interval	Effect size Cohen’s f^2
Performance measurement system design -> Performance information use	0.36*	0.03		[0.03, 0.67]	0.09
Formal strategic planning -> Performance information use	0.18	0.65		[−0.34, 1.08]	0.01
Formal strategic planning -> Performance measurement system design	0.43*	0.02		[0.08, 0.79]	0.17
Logical incrementalism -> Performance information use	0.17	0.62		[−0.64, 0.59]	0.02
Logical incrementalism -> Performance measurement system design	0.39*	0.04		[−0.01, 0.75]	0.13
Organizational size -> Performance information use	−0.03	0.69		[−0.22, 0.12]	0.00
Central government sector -> Performance information use	0.08	0.54		[−0.22, 0.30]	0.01
Endogenous variables	Coefficient of determination (R^2)			Adjusted R^2
Performance information use	0.44			0.41
Performance measurement system design	0.61			0.61
Goodness of model fit	Saturated model			Estimated model
	Value	HI95	HI99	Value	HI95	HI99
SRMR	0.06	0.05	0.06	0.06	0.05	0.06
dULS	0.54	0.49	0.60	0.54	0.51	0.62
dG	0.35	0.36	0.44	0.35	0.37	0.44

*t-values > 1.960 (p < 0.05); SRMR: standardized root mean squared residual; d_ULS: squared Euclidean distance; d_G: geodesic distance. Estimation with 5,000 bootstrap samples.