Top executives’ discretion and organizational performance: Analysis of quasi-governmental organizations in Korean government

Abstract

This study has examined the impacts of top executives’ discretion on organizational performance in public organizations. We analyzed longitudinally collected data from quasi-governmental organizations in Korea. The results show that top executives’ managerial discretion has an inverted U-shape relationship with the overall performance of organizations. The marginal effect of top executives’ discretion on organizational performance was negative. This suggests that the benefits from top executives’ discretion decrease as the level of managerial discretion increases. The greater discretion of top executives in project management was more likely to yield desirable outcomes for organizations. Top executives’ discretion did not significantly affect customer satisfaction and transparency of organizations.

Points for practitioners

This study has empirically shown that there exists an inverted U-shape relationship between managerial discretion and organizational performance. It is necessary to let public managers exercise their discretion in managing their businesses given that flexibility and innovativeness in decision making serve key roles in effective organizational management in highly turbulent organizational contexts. It will also be important to properly monitor public managers’ discretionary authority to avoid unintended negative consequences of discretion.

Keywords

Managerial discretion organizational performance top executives

Introduction

Public management scholars have highlighted discretion as the essence of management for results (e.g. Moynihan, 2006; Nielsen, 2014). Kettl (1997: 449) argued that “letting managers manage” will bring better results for organizations. Managing for results assumes that granting managers high managerial authority and letting them focus on results will enhance organizational performance (Moynihan 2006). However, scholars in economics and others in public management have made contrasting arguments on the relationship between managerial discretion and organizational performance (e.g. Karlsson, 2019; Williamson, 1963). Discretion is a “double-edged sword” (Kwon, 2014: 769). More discretion can increase the returns to public managers’ strategic actions of seeking to promote their interests, consequently incentivizing them to prioritize their interests before principals’ (e.g. corruption; Kaufmann and Siegelbaum, 1997; Kwon, 2014). Uncontrolled managerial discretion can harm organizational performance (Martins et al., 2020).

A considerable body of research on managerial discretion in public management has tested a simple linear relationship with organizational performance. However, the relationship may be more complicated than has been known. We thus attempt to answer a question, “Does the effect of managerial discretion on organizational performance change by different levels of discretion?” or “Is the relationship between discretion and organizational performance nonlinear?” Indeed, recent research in business management (e.g. Ponomareva et al., 2019) suggested a curvilinear relationship between managerial discretion and organizational outcomes. A higher level of managerial discretion with little strategic control may lead to higher costs of managerial opportunism at the expense of shareholders’ interests, but decreasing managerial discretion beyond a level which is not acceptable to managers may rather increase strategic opportunity costs, thereby lowering organizational performance (Ponomareva et al., 2019). Given that public organizations operate in organizational environment imposing greater restriction on managerial discretion (e.g. political and legal controls, multiple stakeholders) than private organizations, we might find potentially different dynamics in the relationship between managerial discretion and the performance of public organizations.

We analyzed a longitudinal dataset collected from 131 Korean state-owned enterprises and quasi-governmental organizations over multiple time periods (2017–2019). We focus on chief executives’ discretion and its influence on organizational performance because chief executives’ managerial discretion can shape the fates of organizations through purposive strategic actions (Andrews, 1971; Hambrick and Finkelstein, 1987).

Our research setting is unique because our data possess different organizational characteristics (e.g. industry type, size, structure, publicness) and operate in highly politicized but various external contexts. Such heterogeneity among organizations helps us obtain highly externally valid results, while our longitudinal analysis also contributes to the establishment of the direction and magnitude of causal relationships (Menard, 2002). We adopted objective measures of organizational performance, which is rare in public management research. First, we review the relevant literature and develop hypotheses based upon this. The hypotheses are then tested statistically. Finally, the findings and implications are discussed.

Theoretical backgrounds

Managerial discretion and organizational performance

Hambrick and Finkelstein (1987) defined managerial discretion as “the latitude of managerial action”, which allows managers the freedom to make decisions, lets them manage in a way that they desire, and holds them accountable for the results. When Hambrick and Finkelstein (1987) theorized the concept of managerial discretion, they focused on top managers or chief executives. Chief executives play significant roles in hosting both substantive and symbolic domains of organizational management, and therefore it is obvious that they affect organizational outcomes in substantial ways if their managerial discretion allows them to do so (Romanelli and Tushman, 1983). Chief executives’ decisions in the substantive domains of organizational management, which include resource management, business decisions, administrative choices (e.g. staffing and personnel management systems, organizational structure), and competitive initiatives, are likely to be directly connected to organizational outcomes.

Three conflicting paradigms account for the relationship between managerial discretion and performance: population ecology, agency, and strategic choice theory. Population ecology theorists note that management does not affect organizational outcomes because the dynamic interactions of internal and external pressures on organizations construct organizational results (Hannan and Freeman, 1977). Conversely, both agency and strategic choice theory highlight the important role of management and managers’ ability in shaping organizational outcomes. The major difference between them lies in managers’ motivation. Agency theory argues that managers are self-motivated and can possibly influence organizational performance in a negative way. In contrast, strategic choice theory sees managers as those who have organization-oriented motivations and make strategic choices to pursue organizational goals and produce higher organizational performance (Andrews, 1971).

The underlying logic of strategic choice theory notes that higher managerial discretion will improve organizational performance. Given that chief executives are selected to fulfill organizational goals and make organizations better off, they are held accountable for organizational outcomes. It is thus highly likely that they endeavor to produce better outcomes for organizations and stakeholders. Higher compensation, which is contingent on organizational outcomes, will also work as a strong incentive for them to make extra efforts to produce better results. Some empirical research has reported that chief executives who are granted more discretion in management have exercised more influence on organizational consequences and have received higher compensation (Wangrow et al., 2015). Organizations’ strategic behavior also turned out to be significantly associated with chief executives’ discretion. Chief executives with higher discretion have been more likely to involve organizational risk-taking strategies, innovation, and proactive behavior (McClelland et al., 2010; Quigley and Hambrick 2012). If not specifically focused on top executives, there are some similar findings from public managers. When perceiving higher discretion, they are more likely to resist political and regulative control and focus on goal accomplishment (Bjørnholt et al., 2022; Grøn et al. 2022; Karlsson 2019).¹ Additional evidence from public organizations shows that higher managerial authority is positively correlated with higher organizational performance and effectiveness (Pandey et al., 2007; Verhoest et al. 2004).

In contrast, along the line of agency theory, scholars in economics have warned that increasing discretionary power associated with economic rents can increase unintended consequences (e.g. corruption), in turn having harmful effects on the productivity and performance of firms (Kaufmann and Siegelbaum 1997; Kwon 2014). The perspective of transaction cost economics assumes that agents think and behave rationally and maximize their utility (Williamson, 1985). Higher discretion with a lack of political control can offer more opportunities for public managers as agents to increase the returns to their self-serving behaviors (e.g. moral hazards, shirking, bureaucratic drifting) when their interests differ from those of the principal (Grøn et al., 2022; Moe, 1984). Such mis-behaviors can hinder productive and innovative activities of an organization by imposing supplementary costs, increasing uncertainty, and producing low-quality products and public services (Martins et al., 2020). In particular, public rent-seeking activities could distort resource allocation by increasing the returns to unproductive rent-seeking activities rather than innovative and strategic activities for higher productivity (Baumol, 1990). More recent research (Ponomareva et al., 2019) has shown that while an increase in strategic control by the principal may reduce the costs of managerial opportunism which seeks to promote agents’ interest at the expense of shareholders, the increasing constraints on managerial discretion beyond a certain level can significantly reduce a range of managers’ strategic decisions and behaviors, rather increasing strategic opportunity costs and leading to decreased organizational performance.

We assume that each of these contrasting arguments reflects some aspects of the relationship between managerial discretion and organizational performance. Thus, integrating these competing perspectives, we predict that the relationship will be nonlinear, and more specifically, an inverted curvilinear relationship. Although higher managerial discretion can have a positive effect on organizational performance to a certain level, the relationship will turn negative since too much discretion will incentivize a public manager to take opportunistic behaviors, which may have harmful effects on organizational performance.

There still exists uncertainty in defining the relationship between managerial discretion and organizational performance in different organizational contexts. Tighter governmental regulations and procedural controls over management are likely to inhibit public managers from taking more entrepreneurial and strategic actions that might foster organizational performance and make them be more risk-averse and stick to input controls. Thus, public managers are less likely to exercise the proper level of managerial discretion that can make differences in organizational performance. Recent studies (e.g. Bjørnholt et al., 2022; Grøn et al., 2022; Karlsson, 2019) analyzed the relationship between managerial discretion and organizational outcomes in public organizations. Nevertheless, still more efforts are needed to understand the relationship, especially at the top executive level. To fill in gaps, we seek to examine if higher managerial discretion of top executives contributes to improvement of organizational performance in public organizations.

Data and methods

Data sources

The data were collected from Korean state-owned enterprises and quasi-governmental organizations over a three-year period (2017–2019).² They have delivered social services for the public since the 1960s. They vary by size, financial dependence on public sources, and marketization of goods and services. They employ a minimum of 50 employees, make more than 2.5 million (USD) profits, and possess 0.8 million net property value. In the case of state-owned enterprises, over 50% of the revenue is generated from their profits from the market. However, less than 50% of the revenue of quasi-governmental organizations is from their own profits. Their total expenditure in 2019 was about 142% of that of the Korean government in the same year. Given their crucial roles in government, top executives tend to hold substantial levels of managerial authority and accountability for results.

We extracted our data from multiple sources: (1) the Korean Public Service Organizations Survey (KPSOS) from the Center for Organizational Diagnosis and Evaluation Research at Seoul National University; (2) archival data posted to the online system of All Public Information In-One (ALIO, hereafter); (3) results from the Customer's Satisfaction Survey; and (4) results from the Transparency and Integrity Survey. KPSOS data were collected through face-to-face interviews with two middle-level managers (informants) from each agency: one from the department of human resources and the other from finance. The interview was conducted with a structured questionnaire, which inquired about general information on the organization (size, age), multiple dimensions of organizational effectiveness, top executives’ managerial behaviors and traits (managerial discretion), and the organization's structural characteristics (centralization, procedural stability, red tape). The survey items used in our research are found in the Online Appendix. The average age of the informants was 37.5 years, while the average length of service was 9.2 years. Given that the term of CEOs is limited to three years by law, we assume that the informants had experienced working under at least three different CEOs on average. Because the informants had held the managerial positions for an average of 3.9 years in the department of human resources or finance, we believe that they had enough institutional knowledge to provide valid answers about their organizations, top executives, and the overall organizational management.

We pooled the three-year data and obtained 290 observations, which include organizational-level data from 77 state-owned enterprises and 213 quasi-governmental organizations.³ Our research focuses only on those that are subject to annual performance evaluations administered by the Ministry of Economy and Finance. To test if our sample is representative of the population, we compared the key characteristics of the sample with those of the population. The results shown in Table 1 show that our sample is properly representative of the population.

Table 1.

Comparison of the characteristics of the population and the sample.

		Observations	Mean	Standard deviation		Observations	Mean	Standard deviation
State-owned enterprises (population)	Number of employees	106	5688.5	8271	State-owned enterprises (sample)	77	5612	8352
	Total revenue (trillion KRW)	106	6701.2	13,200		77	7649	14,800
	Total assets (trillion KRW)	106	24,700	50,600		77	27,900	55,000
	Organizational age	106	28.8	19.3		77	29.7	19.6
	Financial publicness (%)	106	6	16.9		77	5.9	16.8
Quasi-governmental organization (population)	Number of employees	274	1317.3	2320.6	Quasi-governmental organization (Sample)	213	1321.1	2435.1
	Total revenue (trillion KRW)	274	3027.6	10,100		213	2536.9	9875.5
	Total assets (trillion KRW)	274	11,200	70,800		213	5529.4	44,100
	Organizational age	274	24.2	17		213	23.8	16
	Financial publicness (%)	274	54.3	37.1		213	54.2	36.9

Measurements

Dependent variables

Given that public organizations follow multiple goals and values, measuring performance also requires a multidimensional approach that involves multiple performance indicators (Meier and O'Toole, 2002). We, thus, developed three dependent variables.

Annual performance evaluations

The measures were developed using archival data from ALIO. The performance of Korean state-owned enterprises and quasi-governmental organizations is annually evaluated by Ministry of Economy and Finance and the results are posted to ALIO. An independent committee of external experts and professionals including professors, accountants, and consultants evaluates the performance of each organization and the results will be both in six grades (S, A–E) and in numeric values (0–100). We employed the numeric values of the evaluations to reflect greater variations among organizations. The annual performance evaluation comprises two parts: organizational management and project management. The key measures in organizational management include: (1) managerial strategies and leadership; (2) public values-oriented; (3) task efficiency; (4) organizational, personnel, and budgetary management; (5) employee welfare; and (6) innovation and communication with the public. The performance of managing projects is assessed by looking at strategic plans, management, and outcomes of the key projects of an agency.

Customer satisfaction

The measure was developed based on responses from Customer's Satisfaction Survey, which the Ministry of Economy and Finance conducts annually to evaluate customer satisfaction with state-owned enterprises and quasi-governmental organizations. The customer satisfaction with each agency is evaluated in four grades: outstanding (4), good (3), satisfactory (2) and needs improvement (1). The archival data were extracted from ALIO.

Transparency

The measure was from the Transparency and Integrity Survey, which was conducted by the Anti-Corruption and Civil Rights Commission. The survey annually evaluates the transparency of each agency in five grades. The higher grades indicate that the agency has a higher level of transparency and a lower level of corruption than others.

Independent variables

Managerial discretion

The discretion measures were developed based upon data from KPSOS. We measured two dimensions of a chief executive's discretion—discretion on organizational management and discretion on project management along with the aggregated measure of them. Responses to four relevant questions were used: the levels of discretion the chief executive can exercise in managing human and financial resources and those in setting goals and selecting strategies of managing key projects of the agency. To create an aggregated measure of discretion, we combined responses to all these questions using factor analysis.

We compared the level of managerial discretion on organizational management and that on project management and found that top executives are likely to have significantly higher discretion on project management than on organizational management. This might be because internal management is strictly subject to the law.⁴ The t-test results in Table 2 support this argument (t = −5.05, p < 0.001).

Table 2.

Comparison of two dimensions of top executives’ discretion.

Variables	Observations	Mean	Standard deviation	t-Value
Discretion on organizational management	290	3.27	0.04	−5.05***
Discretion on project management	290	3.42	0.04	−5.05***

Note: For the t-test analysis, the average value was used when measuring two variables. *** Significant at 0.001 level.

Little research has attempted to directly measure the managerial discretion construct. The meta-analysis by Wangrow et al. (2015) showed that most prior studies measured managerial discretion indirectly, adopting proxy measures such as task environment, internal organization, and managerial characteristics to examine the relationship between discretion and organizational performance. There are some exceptions, but they relied on survey methods (e.g. Hambrick and Abrahamson, 1995).⁵ Although our measures also rely on responses to survey questions, they are from key informants’ evaluations, thus reducing potential subjective biases that could result from self-evaluation.

Control variables

Governmental control

The variable measures the level of governmental control over organizational management and project management. Five questions from KPSOS inquired how much other governmental entities control and monitor the organization's personnel and financial management, and goal setting and the selection of strategies for its projects. Responses to the questions were combined by factor analysis.

Organizational characteristics

We controlled organizational characteristics—procedural stability, red tape, financial publicness, organizational size and age, type of quasi-governmental organizations, and industrial type. Procedural stability refers to the level of stability of work procedures, positively correlated with organizational performance (Ryu and Kim, 2022). The variable was developed combining three KPSOS questions about whether to have the formal documents of work schedules, goals, and work descriptions. Red tape refers to extreme formality, which causes delays and decreased efficiency in administration. Responses to the four relevant questions were combined through factor analysis. The questions inquired whether formal rules and procedures and also regulations impede penalties for poor performance or rewards for high performance, the procurement of resources, and budgetary adjustment. Financial publicness indicates the organization's financial dependence on public funding. It was measured through the proportion of funding from public sources among the organization's entire financial resources. Size and age are likely to correlate with more institutional resources (e.g. maturation, experience, stability, reputation) as well as possessing more personnel and financial resources, which may contribute to organizational performance. The organizational size was measured by calculating the natural logarithm of the number of full-time employees. The age of the organization was also measured by calculating the natural logarithm of the number of years since the founding year. A dummy variable was created to distinguish the type of quasi-governmental organizations. State-owned enterprises were coded as “1”, while quasi-governmental organizations were coded as “0”. To control the type of industry, eight dummy variables were generated—social overhead capital (SOC), employment and labor, public safety, education and research, finance, arts and culture, industry, and energy. Table 3 displays the descriptive statistics.

Table 3.

Descriptive statistics.

Variables	Observations	Mean	Standard deviation	Minimum	Maximum
Dependent variables
Aggregated measure of performance	290	76.64	4.56	55.95	85.31
Performance of organizational management	290	72.80	5.31	55.77	86.29
Performance of project management	290	79.95	5.56	56.14	91.85
Customer satisfaction	260	3.04	0.83	1	4
Transparency	266	3.04	0.89	1	5
Independent variables
Managerial discretion	290	0.01	1.00	−3.07	3.00
Discretion on organizational management	290	0.01	0.99	−2.89	2.85
Discretion on project management	290	0	1.01	−3.94	2.56
Control variables
Governmental control	290	0.02	1.00	−3.56	2.24
Procedural stability	290	−0.02	1.02	−3.37	1.51
Red tape	290	0.03	1.04	−2.14	2.73
Financial publicness	290	41.40	39.10	0	100
Size of staff	290	6.78	1.34	4.37	10.57
Organizational age	290	3.03	0.73	0.69	4.28

Note: The proportion of each type in the total sample is as follows: (1) type of quasi-governmental organization: state-owned enterprises (26.6%), quasi-governmental organizations (73.4%); (2) type of industry: SOC (10.3%), employment and labor (8.0%), public safety (14.1%), education and research (18.0%), finance (7.2%), arts and culture (12.4), industry (15.2), and energy (14.8%).

Results

The impacts of managerial discretion on each of the performance measures were analyzed in separate models. Model 1 tests the effects of managerial discretion on the results of annual performance evaluations. We then disaggregated the performance evaluation results into two categories—performance of organizational management and performance of project management. Model 2 tests the effect of managerial discretion on organizational management, while Model 3 examines the managerial discretion impact on project management.

We also tested the potential disparate impacts of top executives’ managerial discretion in different areas of organizational performance. To do so, we disaggregated the measure of managerial discretion into two dimensions—organizational management (personnel and budgetary management) and decision making associated with project management. Model 4 separately tests the effect of each dimension of managerial discretion on the aggregated measure of performance evaluation, while Models 5 and 6 examine those on the disaggregated measures of performance evaluation.

Model 7 tests the effect of managerial discretion on customer satisfaction, while Model 8 investigates that on organizational transparency. Models 9 and 10 investigate the effects of two discretion measures on customer satisfaction and transparency, respectively.

Because the performance evaluation measures have numeric values, we analyzed Models 1–6 using the pooled ordinary least squares regression methods. All six models are statistically significant and the R² values range between 0.44 and 0.58. Models 7–10 were analyzed through the pooled ordered logistic regression method because the dependent variables of the models were measured on an ordinal scale. All four models were statistically significant.

Table 4 displays the findings from Model 1 to Model 3. We postulate the potential nonlinear relationship between managerial discretion and organizational performance. Top executives’ managerial discretion and the overall organizational performance turned out to have an inverted curvilinear relationship. The negative marginal effect of discretion suggests that the positive effect of managerial discretion decreases as the level of discretion becomes higher. At the highest point of the curve, it levels off and becomes negative. If top executives’ managerial discretion increases beyond a certain level, it can rather bring harmful effects on organizational performance. However, the relationship between discretion and project management was linear, although the positive effect of discretion appears to be marginal. As top executives have stronger authority on setting goals and selecting strategies to manage key projects, organizational performance will be higher. Figure S1 (see online) visualizes the inverted curvilinear relationships between managerial discretion and organizational performance measures.

Table 4.

The results of the impact of managerial discretion on organizational performance.

		DV: Aggregated measure of performance			DV: Performance of organizational management			DV: Performance of project management
		Model 1-1	Model 1-2	Model 1-3	Model 2-1	Model 2-2	Model 2-3	Model 3-1	Model 3-2	Model 3-3
		Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)
Lagged DV (t − 1)		0.49*** (0.05)	0.49*** (0.05)	0.48*** (0.05)	0.43*** (0.04)	0.43*** (0.04)	0.42*** (0.05)	0.39*** (0.06)	0.39*** (0.06)	0.38*** (0.06)
Managerial discretion			0.43* (0.18)	0.63** (0.20)		0.46* (0.24)	0.75* (0.29)		0.38 (0.27)	0.55^† (0.30)
Squared managerial discretion^a				−0.19* (0.10)			−0.25^† (0.15)			−0.18 (0.15)
Governmental control		−0.18 (0.18)	−0.18 (0.18)	−0.19 (0.18)	0.004 (0.24)	0.003 (0.23)	−0.01 (0.23)	−0.36 (0.26)	−0.36 (0.26)	−0.37 (0.26)
Procedural stability		0.37*(0.18)	0.30^† (0.18)	0.32^† (0.18)	0.49* (0.23)	0.42^† (0.24)	0.44^† (0.24)	0.35 (0.23)	0.29 (0.24)	0.31 (0.25)
Red tape		0.18 (0.20)	0.21 (0.20)	0.23 (0.20)	−0.17 (0.25)	−0.14 (0.25)	−0.10 (0.25)	0.45^† (0.28)	0.48^† (0.28)	0.50^† (0.28)
Financial publicness		0.006 (0.005)	0.006 (0.006)	0.007 (0.006)	−0.007 (0.007)	−0.008 (0.007)	−0.008 (0.007)	0.02* (0.01)	0.02* (0.01)	0.02* (0.01)
Size of staff		0.68** (0.23)	0.67** (0.23)	0.68** (0.24)	0.84*** (0.25)	0.84*** (0.25)	0.85*** (0.25)	0.67* (0.31)	0.67* (0.31)	0.68* (0.31)
Organizational age		−0.11 (0.29)	−0.11 (0.29)	−0.07 (0.29)	−0.36 (0.39)	−0.35 (0.39)	−0.30 (0.40)	0.09 (0.39)	0.10 (0.38)	0.13 (0.38)
Public corporation		−3.08*** (0.82)	−3.06*** (0.81)	−3.15*** (0.81)	−1.03 (0.97)	−1.01 (0.98)	−1.11 (0.97)	−5.47*** (1.23)	−5.45*** (1.23)	−5.52*** (1.22)
Industry type	Employment and labor	−1.95 (1.19)	−1.83 (1.19)	−2.05^† (1.22)	−0.61 (1.24)	−0.47 (1.26)	−0.74 (1.28)	−3.35^† (1.79)	−3.24^† (1.78)	−3.44^† (1.82)
	Public security	−2.26* (1.11)	−2.16^† (1.11)	−2.27* (1.12)	−1.67 (1.21)	−1.57 (1.22)	−1.70 (1.23)	−3.23* (1.61)	−3.15* (1.60)	−3.25* (1.61)
	Research and education	−3.00** (1.10)	−2.93** (1.09)	−2.98** (1.09)	−2.14^† (1.19)	−2.06^† (1.20)	−2.13^† (1.20)	−4.18* (1.67)	−4.11* (1.67)	−4.16* (1.67)
	Finance	−1.45 (1.15)	−1.22 (1.13)	−1.26 (1.14)	−008 (1.33)	0.18 (1.36)	0.12 (1.37)	−2.90^† (1.74)	−2.69 (1.73)	−2.73 (1.72)
	Arts and culture	−3.44** (1.13)	−3.48** (1.12)	−3.63** (1.13)	−3.31** (1.21)	−3.36** (1.21)	−3.54** (1.21)	4.59** (1.69)	−4.62** (1.68)	−4.75** (1.69)
	Industry	−2.72* (1.14)	−2.64* (1.13)	−2.82* (1.14)	−2.84* (1.24)	−2.74* (1.25)	−2.97* (1.27)	−2.97^† (1.74)	−2.89^† (1.71)	−3.05^† (1.73)
	Energy	−1.26 (0.99)	−1.17 (0.98)	−1.24 (0.99)	−1.99^† (1.08)	−1.89^† (1.08)	−2.00^† (1.09)	−1.15 (1.33)	−1.06 (1.32)	−1.12 (1.33)
Year (2018)		1.57** (0.56)	1.64** (0.55)	1.71** (0.55)	2.55*** (0.75)	2.62*** (0.75)	2.69*** (0.75)	−0.07 (0.70)	−0.001 (0.70)	0.08 (0.69)
Year (2019)		1.68*** (0.49)	1.65*** (0.49)	1.67*** (0.49)	2.29*** (0.67)	2.24*** (0.67)	2.25*** (0.66)	0.56 (0.61)	0.53 (0.61)	0.57 (0.62)
Constant		36.16*** (4.04)	36.15*** (4.02)	36.63*** (4.07)	36.37*** (4.04)	36.38*** (4.00)	36.87*** (4.05)	48.10*** (5.02)	48.04*** (5.03)	48.35*** (5.05)
Observations		290	290	290	290	290	290	290	290	290
R ²		0.5313	0.5595	0.5639	0.4662	0.4941	0.4992	0.4105	0.4349	0.4374
ΔR²			0.0282	0.0044		0.0279	0.0051		0.0244	0.0025
Wald F−test		21.09***	20.87***	20.31***	17.71***	17.18***	17.23***	11.51***	11.69***	11.47***
Mean VIF		2.35	2.29	2.28	2.41	2.35	2.34	2.31	2.24	2.24

Note: Robust standard errors in parentheses. DV, Dependent variable.

The square term of the variable managerial discretion.

†Significant at the 0.1 level; *significant at the 0.05 level; **significant at the 0.01 level; ***significant at the 0.001 level.

Table 5 demonstrates the findings from Model 4 to Model 6. We examined the effects of two attributes of managerial discretion on performance evaluation results. Only the effects of managerial discretion on project management on performance measures turned out to be significant. Top executives’ managerial discretion on personnel and budgetary management is substantially restricted by the law, and therefore less likely to bring significant impacts on organizational performance. On the other hand, the desirable effects of top executives’ discretion on project management can spill over into organizational management, given that the performance measure of organizational management assesses top executives’ strategic leadership and innovativeness, as well as networking efforts. Top executives who hold higher discretion on project management are also likely to exercise stronger leadership in fulfilling innovation, public value accomplishment, and better communication with the public when managing the organization.

Table 5.

The results of two dimensions of managerial discretion impact on organizational performance.

		DV: Aggregated measure of performance		DV: Performance of organizational management		DV: Performance of project management
		Model 4-1	Model 4-2	Model 5-1	Model 5-2	Model 6-1	Model 6-2
		Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)
Lagged DV (t − 1)		0.48*** (0.05)	0.48*** (0.05)	0.42*** (0.05)	0.42*** (0.05)	0.39*** (0.06)	0.38*** (0.06)
Discretion on organizational management		−0.18 (0.22)	−0.09 (0.23)	−0.12 (0.27)	−0.01 (0.28)	−0.32 (0.33)	−0.23 (0.33)
Squared discretion on organizational management^a			0.04 (0.13)		0.04 (0.16)		0.04 (0.19)
Discretion on project management		0.64** (0.23)	0.69** (0.24)	0.64* (0.29)	0.71* (0.28)	0.72* (0.35)	0.77* (0.38)
Squared discretion on project management			−0.24* (0.12)		−0.28* (0.14)		−0.25 (0.24)
Governmental control		−0.18 (0.18)	−0.21 (0.17)	0.002 (0.23)	−0.02 (0.22)	−0.36 (0.25)	−0.38 (0.26)
Procedural stability		0.31^† (0.18)	0.32^† (0.18)	0.42^† (0.24)	0.44^† (0.24)	0.30 (0.24)	0.31 (0.24)
Red tape		0.24 (0.20)	0.25 (0.20)	−0.11 (0.25)	−0.10 (0.25)	0.52^† (0.28)	0.53^† (0.28)
Financial publicness		0.01 (0.01)	0.01 (0.01)	−0.01 (0.01)	−0.01 (0.01)	0.02* (0.01)	0.02* (0.01)
Size of staff		0.69** (0.23)	0.72** (0.23)	0.86*** (0.24)	0.89*** (0.25)	0.69* (0.30)	0.72* (0.31)
Organizational age		−0.10 (0.29)	−0.08 (0.29)	−0.34 (0.39)	−0.32 (0.40)	0.11 (0.38)	0.12 (0.38)
Public corporation		−3.07*** (0.82)	−3.17*** (0.82)	−1.02 (0.99)	−1.12 (0.98)	−5.45*** (1.24)	−5.54*** (1.23)
Industry type	Employment and labor	−1.87 (1.19)	−2.03^† (1.21)	−0.51 (1.27)	−0.70 (1.28)	−3.29^† (1.78)	−3.44^† (1.80)
	Public security	−2.18^† (1.11)	−2.20* (1.12)	−1.58 (1.23)	−1.61 (1.24)	−3.16* (1.60)	−3.18* (1.61)
	Research and education	−3.01** (1.09)	−3.04** (1.08)	−2.13^† (1.21)	−2.17^† (1.19)	−4.20* (1.67)	−4.23* (1.66)
	Finance	−1.12 (1.13)	−1.13 (1.13)	0.27 (1.37)	0.26 (1.37)	−2.56 (1.70)	−2.56 (1.71)
	Arts and culture	−3.51** (1.13)	−3.62** (1.13)	−3.39** (1.21)	−3.51** (1.21)	−4.64** (1.69)	−4.75** (1.70)
	Industry	−2.70* (1.13)	−2.77* (1.14)	−2.81* (1.26)	−2.89* (1.28)	−2.97^† (1.72)	−3.03^† (1.73)
	Energy	−1.17 (0.98)	−1.18 (0.98)	−1.89^† (1.08)	−1.90^† (1.09)	−1.05 (1.31)	−1.05 (1.31)
Year (2018)		1.64** (0.55)	1.17** (0.55)	2.61*** (0.76)	2.64*** (0.76)	0.01 (0.69)	0.06 (0.69)
Year (2019)		1.62*** (0.49)	1.65*** (0.50)	2.19*** (0.68)	2.22*** (0.67)	0.50 (0.61)	0.55 (0.62)
Constant		36.25*** (4.13)	36.59*** (4.17)	36.48*** (4.05)	36.74*** (4.08)	47.95*** (5.10)	48.23*** (5.12)
Observations		290	290	290	290	290	290
R ²		0.5649	0.5705	0.4974	0.5033	0.4407	0.4447
ΔR²			0.0056		0.0059		0.0040
Wald F-test		19.75***	18.73***	16.89***	16.14***	11.40***	10.84***
Mean VIF		2.29	2.29	2.35	2.34	2.25	2.25

Note: Robust standard errors in parentheses. VIF = variance inflation factors.

The square term of the variable discretion on organizational management.

The square term of the variable discretion on project management.

†Significant at the 0.1 level; * significant at the 0.05 level; ** significant at the 0.01 level; *** significant at the 0.001 level.

Similar to the findings from Model 1 to Model 3, the relationships between discretion on project management and the overall performance and organizational management measures were nonlinear. The relationship between discretion on project management and performance of project management was linear. Figure S2 (see online) shows the relationships.

Table 6 shows the findings from Model 7 to Model 10. The effect of top executives’ managerial discretion on customer satisfaction was not statistically significant. Neither discretion on organizational management nor discretion on project management significantly influenced customer satisfaction. In a similar line, top executives’ discretion did not significantly affect transparency of the organization. Discretion on organizational management and on project management similarly was not significantly associated with the transparency measure of the organization.

Table 6.

The results of two dimensions of managerial discretion's impact on customer satisfaction and transparency.

		DV: Customer Satisfaction			DV: Transparency			DV: Customer Satisfaction		DV: Transparency
		Model 7-1	Model 7-2	Model 7-3	Model 8-1	Model 8-2	Model 8-3	Model 9-1	Model 9-2	Model 10-1	Model 10-2
		Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)	Coefficient (SE)
Lagged DV (t − 1)		2.33*** (0.24)	2.34*** (0.24)	2.33*** (0.24)	0.65*** (0.13)	0.65*** (0.13)	0.64*** (0.13)	2.33*** (0.24)	2.34*** (0.25)	0.65*** (0.13)	0.64*** (0.13)
Managerial discretion			−0.04 (0.13)	0.02 (0.18)		0.001 (0.13)	−0.08 *0.14(
Squared managerial discretion^a				−0.06 (0.08)			0..08 (0.07)
Discretion on organizational management								−0.13 (0.15)	−0.11 (0.16)	−0.01 (0.17)	−0.05 (0.18)
Squared discretion on organizational management^b									−0.10 (0.10)		0.05 (0.11)
Discretion on project management								0.08 (0.17)	0.14 (0.18)	0.01 (0.15)	−0.03 (0.16)
Squared discretion on project management^c									0.06 (0.09)		0.05 (0.08)
Governmental control		−0.04 (0.14)	−0.04 (0.14)	−0.04 (0.14)	−0.22^† (0.12)	−0.22^† (0.12)	−0.22^† (0.12)	−0.05 (0.14)	−0.03 (0.14)	−0.22^† (0.12)	−0.22 (0.12)
Procedural stability		−0.06 (0.15)	−0.05 (0.15)	−0.05 (0.15)	0.17 (0.14)	0.17 (0.14)	0.17 (0.14)	−0.05 (0.15)	−0.05 (0.15)	0.17 (0.14)	0.17 (0.17)
Red tape		0.13 (0.13)	0.13 (0.13)	−0.13 (0.13)	−0.06 (0.12)	−0.06 (0.12)	−0.07 (0.12)	0.14 (0.13)	0.15 (0.13)	−0.06 (0.12)	−0.07 (0.12)
Financial publicness		−0.001 (0.004)	−0.001 (0.004)	−0.0003 (0.004)	−0.01 (0.004)	−0.01 (0.004)	−0.01 (0.004)	−0.0004 (0.004)	−0.0002 (0.004)	−0.01 (0.004)	−0.01 (0.005)
Size of staff		0.32* (0.15)	0.32* (0.15)	0.33* (0.15)	−0.14 (0.12)	−0.14 (0.12)	−0.14 (0.12)	0.32* (0.15)	0.32* (0.15)	−0.13 (0.13)	−0.14 (0.13)
Organizational age		0.36 (0.23)	0.36 (0.23)	0.37 (0.23)	−0.22 (0.19)	−0.22 (0.19)	−0.24 (0.19)	0.36 (0.23)	0.39^† (0.23)	−0.22 (0.190)	−0.24 (0.19)
Public corporation		0.12 (0.68)	0.12 (0.68)	0.11 (0.68)	0.15 (0.50)	0.15 (0.50)	0.18 (0.50)	0.11 (0.68)	0.11 (0.68)	0.16 (0.50)	0.17 (0.50)
Industry type	Employment and labor	0.18 (0.87)	0.17 (0.87)	0.10 (0.86)	0.81 (0.74)	0.81 (0.74)	0.90 (0.74)	0.15 (0.86)	0.10 (0.86)	0.81 (0.74)	0.89 (0.74)
	Public security	−0.62 (0.81)	−0.63 (0.82)	−0.65 (0.81)	0.50 (0.61)	0.50 (0.61)	0.53 (0.61)	−0.64 (0.81)	−0.69 (0.81)	0.50 (0.61)	0.53 (0.61)
	Research and education	0.51 (0.83)	0.51 (0.83)	0.50 (0.82)	−0.17 (0.69)	−0.17 (0.69)	−0.17 (0.69)	0.48 (0.82)	0.51 (0.82)	−0.17 (0.69)	−0.19 (0.69)
	Finance	0.02 (0.85)	−0.01 (0.85)	0.002 (0.85)	0.05 (0.77)	0.05 (0.77)	0.05 (0.77)	0.02 (0.85)	0.01 (0.85)	0.05 (0.78)	0.05 (0.77)
	Arts and culture	0.70 (0.98)	0.71 (0.98)	0.68 (0.98)	−0.19 (0.63)	−0.19 (0.64)	−0.14 (0.64)	0.69 (0.98)	0.68 (0.98)	−0.19 (0.64)	−0.15 (0.64)
	Industry	0.51 (0.85)	0.50 (0.85)	0.45 (0.84)	0.17 (0.70)	0.17 (0.70)	0.25 (0.70)	0.48 (0.85)	0.42 (0.84)	0.17 (0.70)	0.23 (0.71)
	Energy	−0.28 (0.72)	−0.29 (0.71)	−0.30 (0.71)	0.20 (0.46)	0.20 (0.46)	0.21 (0.46)	−0.29 (0.72)	−0.32 (0.73)	0.20 (0.46)	0.02 (0.46)
Year (2018)		−0.60^† (0.35)	0.60^† (0.35)	−0.58^† (0.35)	−0.07 (0.32)	−0.07 (0.32)	−0.12 (0.33)	−0.61^† (0.35)	−0.58 (0.35)	−0.07 (0.32)	−0.11 (0.33)
Year (2019)		−0.21(0.32)	−0.20 (0.32)	−0.19 (0.32)	0.16 (0.27)	0.16 (0.27)	0.14 (0.27)	−0.22 (0.32)	−0.21 (0.32)	0.16 (0.27)	0.14 (0.27)
Observations		260	260	260	266	266	266	260	260	266	266
Pseudo R²		0.3314	0.3415	0.3424	0.0670	0.0671	0.0689	0.3423	0.3437	0.0671	0.0687
ΔR²			0.0101	0.0009		0.0001	0.0018		0.0014		0.0016
Wald χ²-test		114.74***	114.06***	113.60***	47.18***	47.33***	50.59***	115.34***	115.32***	48.08***	55.51***
Log likelihood		−201.48	−201.42	−201.16	−319.03	−319.03	−318.42	−201.19	−200.76	−319.03	−318.50
Mean VIF		2.51	2.43	2.42	2.30	2.24	2.23	2.44	2.43	2.25	2.25

Note: Robust standard errors in parentheses.

The square term of the variable managerial discretion.

The square term of the variable discretion on organizational management.

The square term of the variable discretion on project management.

†Significant at the 0.1 level; * significant at the 0.05 level; ** significant at the 0.01 level; *** significant at the 0.001 level.

We controlled the impacts of an organization's internal contexts and external environment on organizational performance. Governmental or external control over quasi-governmental organizations did not significantly influence organizational performance. Unlike our expectation, the effect of governmental control on transparency was marginally significant and negative. We suspect that higher governmental control will lead to higher standards of regulations and tighter oversight imposed on an organization, consequently yielding lower ratings of transparency of the organization.

The size of an organization showed a consistently positive effect on the results of the performance evaluation. Bureaucratic characteristics such as red tape, despite having marginal effects, turned out to have positive impacts on higher performance in project management. When top executives perceive higher restrictions on internal management and resource management, they may make extra efforts to overcome such limitations through activities such as strategic planning and networking with external parties. This might produce higher performance at least in project management. Dependence on public funding can offer stability and consistency in proceeding projects and is thus more likely to yield better outcomes. The effect of financial publicness on project management was small, but still significant. Regarding the type of quasi-governmental organizations, state-owned enterprises are inclined to have lower performance evaluations, particularly in project management, than quasi-governmental organizations. Larger organizations are more likely to obtain higher customer satisfaction than others.

Discussion and conclusion

This study has some important implications. First, it empirically supports the combined model of strategic choice theory and agency theory. This study extended our understanding of the relationship, showing that, depending on the level of managerial discretion, the impacts on organizational performance may be different. Second, we highlight the uniqueness of our research setting. State-owned enterprises and quasi-governmental organizations have various internal characteristics and operate under different external environments. O’Toole and Meier (2014) urged scholars to test the model of public management in different research settings, accumulate empirical evidence, and build up a generic theory of public management. We tested our hypotheses by analyzing data from organizations that operate in widely varying contexts and from different cultural backgrounds, and this will accordingly enhance the generalizing ability of the public management model.

The key finding of this research is that managerial discretion and organizational performance have an inverted U-shaped curvilinear relationship. Top managers’ discretionary power will enhance organizational performance, but the marginal return of the positive effect decreases, eventually leading to negative impacts on organizational performance. Both strategic choice theory and agency theory explain some aspects of our finding. Up to a certain level of managerial discretion, public managers with higher discretion were more likely to invest their time and efforts towards accomplishing organizational goals (Grøn et al., 2022; Karlsson, 2019). However, beyond that level too much discretion with a lack of political control will increase self-serving opportunities for public managers (or agents) to pursue their own interests and involve corruption, which could pose significant challenges to organizations (Grøn et al., 2022; Moe, 1984). The finding is consistent with evidence from business management (Ponomareva et al., 2019), suggesting that the curvilinear relationship between managerial discretion and organizational outcomes could also be found in public organizations.

Another interesting finding is that the positive impact of top executives’ discretion on project management is not likely to yield diminishing marginal return on organizational performance. The benefit of top executives’ freedom to manage organizations’ key projects strategically may be marginal, but will linearly increase as their discretion rises. Managers with high-level discretion tend to become a more proactive and braver manager to overcome organizational or environmental challenges than those with low-level discretion who should accept and obey restraints (Hambrick and Finkelstein, 1987; Karlsson, 2019). This might indicate that, at least in managing projects, top executives who are appointed based on their experiences and expertise in the area should be allowed to exercise as high discretion as they can to fulfill their goals and keep them accountable for results. Greater accomplishments in project management are more visible and contribute more to enhancing the top executive's reputation in the industry, which will also increase the executive's chances of serving as a top executive in another institution. Thus, top executives may pay more attention to achieving better outcomes in project management than in organizational management, which is strictly restricted by the law.

In sum, the practical implications from our findings are as follows. Allowing managers to manage will be helpful to accomplish higher effectiveness of public organizations. At minimum, public managers should be able to exercise their discretion in managing their businesses with external parties, given that administrative environments are increasingly turbulent and unpredictable since the experience of the global pandemic and the fourth industrial revolution. Flexibility and innovativeness in decision making will be necessary to manage public organizations more effectively under constantly changing organizational contexts. Furthermore, it will be important to pay more attention to finding an optimal level of discretion that can be offered to public managers, which might vary by different organizational contexts. In addition to providing sufficient discretionary authority for public managers, monitoring and supervising them properly will be required to avoid unintended negative consequences of discretion.

Supplemental Material

sj-docx-1-ras-10.1177_00208523231197943 - Supplemental material for Top executives’ discretion and organizational performance: Analysis of quasi-governmental organizations in Korean government

Supplemental material, sj-docx-1-ras-10.1177_00208523231197943 for Top executives’ discretion and organizational performance: Analysis of quasi-governmental organizations in Korean government by Sungjoo Choi and Yeongjun Ko in International Review of Administrative Sciences

Footnotes

Acknowledgment

We appreciate helpful comments from anonymous reviewers. We also acknowledge scholarly support from Professor Young Han Chun who significantly contributed to development of our research. This research was supported by the Center for Organizational Diagnosis and Evaluation Research (CODE), Graduate School of Public Administration (GSPA), Seoul National University (SNU).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was supported by the Center for Organizational Diagnosis and Evaluation Research (CODE), Graduate School of Public Administration (GSPA), Seoul National University (SNU).

ORCID iD

Sungjoo Choi

Supplemental material

Supplemental material for this article is available online.

Notes

Sungjoo Choi is a professor in the Department of Public Administration at Kyung Hee University in Seoul, South Korea. Her research interests include innovative work arrangements, diversity management and representative bureaucracy, and gender issues in government. She has published her articles in Journal of Public Administration Research and Theory, Public Administration Review, International Public Management Journal, and Review of Public Personnel Administration. [ORCID: 0000-0001-8459-6195]

Yeongjun Ko is a visiting researcher at the Korea Institute of Public Affairs in the Graduate School of Public Administration at Seoul National University, Republic of Korea. His research focuses on organizational autonomy, public management, local policy and management, public finance, and accountability mechanism.

References

Andrews

(1971) The Concept of Corporate Strategy. Homewood, IL: Dow Jones-Irwin.

Baumol

(1990) Entrepreneurship: productive, unproductive and destructive. Journal of Political Economy 98(3): 893–921.

Bjørnholt

Boye

Hansen

(2022) The influence of regulative contents, stakeholders, and formalization on managerial autonomy perceived at the front line. Public Management Review 24(10): 1569–1590.

Grøn

Opstrup

Salomonsen

, et al. (2022) Managing in all the right directions? The relationship between public managers’ perceived autonomy and leading upwards, sidewards, outwards and downwards. Public Management Review: 1–23. DOI: 10.1080/14719037.2022.2126882.

Hambrick

Abrahamson

(1995) Assessing managerial discretion across industries: A multimethod approach. Academy of Management Journal 38(5): 1427–1441.

Hambrick

Finkelstein

(1987) Managerial discretion: A bridge between polar views of organizational outcomes. In: Cummings

Staw

(eds) Research in Organizational Behavior. Greenwich, CT: JAI Press, Vol. 9, 369–406.

Hannan

Freeman

(1977) The population ecology of organizations. American Journal of Sociology 82(5): 929–964.

Karlsson

(2019) Searching for managerial discretion: How public managers engage managerialism as a rationalization for increased latitude of action. Public Management Review 21(3): 315–333.

Kaufmann

Siegelbaum

(1997) Privatization and corruption in transition economies. Journal of International Affairs 50(2): 519–558.

10.

Kettl

(1997) The global revolution in public management: Driving themes, missing links. Journal of Policy Analysis and Management 16(3): 446–462.

11.

Kwon

(2014) Motivation, discretion, and corruption. Journal of Public Administration Research and Theory 24(3): 765–794.

12.

Martins

Cerdeira

Teixeira

ACA

(2020) Does corruption boost or harm firms’ performance in developing and emerging economies? A firm-level study. The World Economy 43(8): 2119–2152.

13.

McClelland

Liang

Barker

III (2010) CEO Commitment to the status quo: Replication and extension using content analysis. Journal of Management 36(5): 1251–1277.

14.

Meier

O'Toole

Jr (2002). Public management and organizational performance: The effect of managerial quality. Journal of Policy Analysis and Management 21(4): 629–643.

15.

Menard

(2002) Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.

16.

Moe

(1984) The new economics of organization. American Journal of Political Science 28(4): 739–777.

17.

Moynihan

(2006) Managing for results in state government: Evaluating a decade of reform. Public Administration Review 66(1): 77–89.

18.

Nielsen

(2014) Performance management, managerial authority, and public service performance. Journal of Public Administration Research and Theory 24(2): 431–458.

19.

O’Toole

Jr Meier

(2014) Public management, context, and performance: In quest of a more general theory. Journal of Public Administration Research and Theory 25(1): 237–256.

20.

Pandey

Coursey

Moynihan

(2007) Organizational effectiveness and bureaucratic red tape: A multimethod study. Public Performance & Management Review 30(3): 398–425.

21.

Ponomareva

Shen

Umans

(2019) Organizational discretion, board control, and shareholder wealth: A contingency perspective. Corporate Governance: An International Review 27(4): 248–260.

22.

Quigley

Hambrick

(2012) When the former CEO stays on as board chair: Effects on successor discretion, strategic change, and performance. Strategic Management Journal 33(7): 834–859.

23.

Romanelli

Tushman

(1983) Executive Leadership and Organizational Outcomes: An Evolutionary Perspective. New York: Graduate School of Business, Columbia University.

24.

Ryu

Kim

(2022) Understanding the role of public management and organizational stability in an uncertain environment: Evidence from Korean Public Corporations. Public Administration Review 82(2): 303–313.

25.

Verhoest

Peters

Bouckaert

, et al. (2004) The study of organisational autonomy: A conceptual review. Public Administration and Development 24(2): 101–118.

26.

Wangrow

Schepker

Barker

III (2015) Managerial discretion: An empirical review and focus on future research directions. Journal of Management 41(1): 99–135.

27.

Williamson

(1963) Managerial discretion and business behavior. The American Economic Review 53(5): 1032–1057.

28.

Williamson

(1985) The Economic Institutions of Capitalism: Firms, Markets, Relational Contracting. New York: Free Press.

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