Navigating a Rough Terrain of Public Management

:663-686

Navigating a Rough Terrain of Public Management: Examining the Relationship between Organizational Size and Effectiveness Chan Su Jung City University of Hong Kong

ABSTRAa This study examines the relationship between size and effectiveness of 97 US federal governnnent organizations in combination with relevant mediating variables. The dependent variable, actual goal attainment, is considered as a measure of organizational effectiveness. The nunriber of full-time employees and budget size, the independent variables, are considered as measures of organizational size. Mediating variables considered are goal ambiguity measures, the number of programs, the proportion of professionals in the organization, and institutional location (independent agencies versus agencies inside executive departments). The path analysis corroborates an inverted U-shaped relationship between the number of employees and organizational effectiveness. Furthermore, the relationship between budget size and organizational effectiveness is found to be negative and log-linear, and the relationship is partially mediated by target-specification ambiguity and timeline ambiguity.

INTRODUCTION

The issue of organization size, in terms of its impact on organizational effectiveness, is a long-standing debate in the field of organization theory (Armandi and Mills 1982; Blau and Schoenherr 1971; Daft 2010; Dalton et al. 1980; Kimberly 1976; Van de Ven 1976). Is effectiveness within an organization determined by its size? The debate brings conñicting perspectives to light. Some scholars considered small organizations to be more effective; Weber (1947) recognized large organization size as one of the main causes for the development of bureaucracy. Simultaneously, he emphasized that "only by reversion in every field—political, religious, economic, etc.—to small-scale organization would it be possible tc escape its [bureaucracy's] objectionable influence" (Weber 1947, 334). In addition, small organizations can be more flexible and able to adapt and improve and encounter fewer difficulties in accepting and implementing change (Damanpour 1992). On the other hand, as Gouldner (1955) pointed out, I am grateful to three anonymous reviewers, Richard M. Walker, and Hal G. Rainey for their thoughtful and insightful comments. Address correspondence to the author at [email protected]. doi:l 0.1093/jopart/mus040 Advance Access publication October 15, 2012 © The Author 2012. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. For permissions, please e-mail: [email protected]

664

Journal of Public Administration Research and Theory

some people believed that large organizations are more effective than smaller counterparts. Economists and organization theorists often assumed a positive net relationship between organizational size and economies of scale (e.g., Gooding and Wagner 1985; Katz and Kahn 1978; Mintzberg 1979; Sheperd 1979). Additionally, larger organizations are more likely than their smaller counterparts to harness the financial resources necessary for increasing financial rewards and advancement opportunities, which, in turn, can assist in attracting and retaining competent employees (e.g., Stanford 1980; Williamson 1975). Similarly, Damanpour (1992) suggested that large organizations can have greater technical knowledge and potential than their smaller counterparts, as a result of employing a greater number of professional and skilled workers. Based on these different perspectives, organizational size has conventionally been targeted by reformers as a way to improve public organizations' effectiveness (Boyne 2003b). That is, changes in the number of employees or the budget size of governmental agencies have been one of the most common levers employed by reformers to achieve their desired policy missions and goals. Furthermore, the actual goal achievement rate, which is considered the core concept of organizational effectiveness, has "a powerful resonance with current debates about the effectiveness improvement" of public organizations (Boyne 2003a, 216). Hence, this study aims to advance research on the relationship between organization size and actual effectiveness by assessing and comparing linear and nonlinear relationships between the number of employees and budget size with organizational goal achievement rates simultaneously in 97 US federal agencies. Furthermore, it evaluates the direct as well as indirect relationships between organization size and effectiveness by considering mediating variables; three goal ambiguity measures (target-specification ambiguity, timeline ambiguity, and evaluation ambiguity), the number of programs, the proportion of professionals, and institutional location (independent agencies versus agencies inside executive departments). The data on actual goal attainment rates as a measure of organizational effectiveness originates from the Program Assessment Rating Tool (PART) of 2006, which was a performance audit database provided by the US Office of Management and Budget (OMB). The results of path analyses show an inverted U-shaped relationship between personnel size and organizational effectiveness, and a log-linear relationship between budget size and effectiveness. Moreover, for the latter relationship, target-specification ambiguity and timeline ambiguity are found to partially mediate. The next three sections provide a review of organizational effectiveness and organizational size and present hypotheses for the relationships between the two size dimensions and organizational effectiveness. Subsequently, the article offers theoretical arguments about variables mediating the relationship between size and effectiveness. The following sections present descriptions of data, an organizational effectiveness measure, and independent and mediating variables. Following the analyses and results, a discussion of implications is provided. ORGANIZATIONAL EFFEaiVENESS

Evaluating organizational effectiveness' has been an important topic and challenge for scholars as well as managers in public administration (Likert 1958; Rainey 2009). I "Scholars often use the terminology 'effectiveness' and 'performance' interchangeably to describe the same phenomenon, the overall ability of organizations to perform weil or eíTectiveiy pursue their mission" (Seiden and Sowa 2004, 396).

Jung

Navigating a Rough Terrain of Public Management

Scholars have focused on the problems of effectiveness measurement and monitoring (Berman and West 1995; Cohen 1993; Hatry 1999; Kettl et al. 1996; Kopczynski and Lombardo 1999). Public management scholars and administrations of many countries, including the United States, United Kingdom, Australia, and New Zealand, which accepted new public management reforms, have put a focus on how to measure organizational effectiveness. The Government Performance and Results Act (GPRA) and the PART reflect such efforts in the United States. Although organization theorists agree on the importance of assessing and measuring organizational effectiveness, they have not arrived at an agreement on a decisive approach (Boyne 2003a); most organizations are large, diverse, and fragmented, thus generating some intended and some unintended outcomes of the attempts to be effective (Daft 2010). Public organizations in particular face multiple and conflicting internal stakeholders and environmental constraints, including external stakeholders, and have multiple goals (Hall and Tolbert 2009). Therefore, various approaches to organizational effectiveness have different criteria: productivity (e.g., Georgopoulos and Tannenbaum 1957; Mott 1972; Price 1968), goal attainment (e.g.. Steers 1975), staff/employee (e.g., Boyne 2002; Gibson, Ivancevich, and Donnelly 1973) and customer/citizen satisfaction (e.g., Boyne 2002; Kanter and Brinkerhoff 1981; Katz et al. 1975),2 profitability (e.g.. Child 1972; Friedlander and Pickle 1968), resource acquisition (e.g., Yuchtman and Seashore 1967), efficiency (e.g., Webb 1974), and open communication (e.g., Schein 1970). Organizational effectiveness evaluates the extent to which an organization attains its multiple goals (Daft 2010; Etzioni 1964). Based on this definition, this study measures the core concept of organizational effectiveness—that is, actual attainment rates of multiple goals of US federal agencies. Rainey (2009, 153) suggested the conceptual closeness of this goal approach to organizational effectiveness by maintaining that "when scholars in organization theory first began to develop frameworks for assessing organizational effectiveness, they tried to focus on organizational goals and to evaluate whether the goals are achieved." Furthermore, Daft (2010, 65) argued that "this is a logical approach because organizations do try to attain certain levels of output, profit, or client satisfaction." This approach focuses on operative goals organizations have to pursue (Steers 1975) and measures progress toward attainment of those goals (Daft 2010). Furthermore, the goal approach is also practically useful; without the existence of secondary data/effectiveness indicators, researchers are left with the question of how to record organizational effectiveness. Some studies use perceptual data,^ but this is likely to suffer from methodological issues, of which common-method bias 2 The relationship between the two types of satisfaction was expressed as a satisfaction mirror, since customer satisfaction has been recognized as reflecting employee satisfaction (Heskett, Sasser, and Schlesinger 1997). However, they do not necessarily go hand in hand; "measures of staff satisfaction need to be placed alongside customer satisfaction, not least because the former may be a precondition of the latter" (Boyne 2002, 18); the factors that are most important to customers, such as service price, convenience, and service availability, are not the service aspects directly connected with the customers' interface with the staff (Silvestro and Cross 2000). 3 Regarding the relationship between organization size and effectiveness, Dalton et al. (1980) pointed out the dependence on perceptual data of organizational effectiveness as one of the main reasons for inconsistent results in studies of the relationship.

665

666


is regarded the most serious (Andrews, Boyne, and Walker 2006). An alternative way to overcome this methodological issue is to examine the goals organizations set for themselves and to establish actual attainment of these goals. In addition, the goal approach has the advantage of allowing for comparison between government agencies with very different purposes and goals, as the measure of effectiveness is standardized to the issue of whether the agencies met their own specified goals (Steers 1975). Thus, Boyne (2003a) argued that the goal approach resounds in recent debates about the improvement of organizational effectiveness in the public sector, in spite of some problems.** In order to apply the goal approach, it is necessary to consider Price's (1971) assertion that without standardized measures it is very difficult to assess and compare the effectiveness of public programs or agencies. This study uses the standardized goals of federal agencies in the PART^ provided by OMB as the data source for goal attainment rates. The PART^ is used to operationalize organizational effectiveness because the performance audit data identify organizational goals in a broad array of government agencies and describe their program goals and performance measures based on statutory mandates (Chun and Rainey 2005a; OMB 2006; Van de Ven and Ferry 1980). In particular, the PART provides various measurable types of goals, including outcome-oriented, output-oriented, and efficiency goals, which reflect diverse dimensions of organizational effectiveness (Cyert and March 1963) (a summary of list of performance goals is presented in Appendix 1).

4 This study recognizes a methodological limitation of the goal approach suggested by Pollitt (1995) and Boyne (2003a): Although operative goals are clearly stated and standardized, this approach would still face an attribution problem; effectiveness improvement can only be validly inferred from a positive shift in effectiveness, once all relevant influences have been considered. In order to relieve this problem, therefore, this study takes into account past effectiveness scores and mediating variables. 5 A literature review suggests that the PART is now well known and established in the academic and policy literature rather than a new measure of performance. According to the OMB (2009) Web site (http:// georgewbush-whitehouse-archives-gov/omb/organization/role-html), "OMB's predominant mission is to assist the President in overseeing the preparation of the federal budget and to supervise its administration in Executive Branch agencies. In helping to formulate the President's spending plans, OMB evaluates the effectiveness of agency programs, policies, and procedures, assesses competing funding demands among agencies, and sets funding priorities. OMB ensures that agency reports, rules, testimony, and proposed legislation are consistent with the President's Budget and with Administration policies." 6 Provided here is a short description of how the PART has matured and worked. OMB organized the Performance Evaluation Team early in 2002 and the team released an initial draft of the questionnaire for public comment in May 2002. The draft of the questionnaire was reviewed by many independent groups, including the Performance Measurement Advisory Council, chaired by former Deputy Secretary of Transportation, Mortimer Downey, and a group from the President's Council on Integrity and Efficiency (Dull 2006; OMB 2004). Then, the PART was the subject of a congressional hearing as well as a workshop convened by the National Academy of Public Administration (Dull 2006; OMB 2004). In 2002, the PART was released for use on July 16 through the approval of a final version of the PART by the President's Management Council on July 10 (OMB 2004). OMB rated approximately 234 federal programs for fiscal year 2004 (Gilmour and Lewis 2006b) and planned to increase the number of reviewed federal programs by approximately 250 each year (OMB 2004). The number of reviewed programs for the 2007 PART was 794. The PART, which includes approximately 30 standard questions and in which the number of questions varies depending on the lype of program being evaluated, asks for information that responsible federal managers should provide for performance assessment. Ratings of the PART proceed through four areas of performance assessment: design, strategic planning, management, and results/accountability.

Jung


ORGANIZATIONAL SIZE

Of the various factors utilized to predict organizational effectiveness, the importance of organizational structure has been highlighted by numerous scholars (e.g., Boyne 2003b; Dalton et al. 1980; Gooding and Wagner 1985; Hall and Tolbert 2009; Kimberly 1976; Mintzberg 1979; Revans 1959; Van de Ven 1976). Organizational structuring focuses on dividing the labor of the organizational mission into a number of distinct goals and tasks and then coordinating all the goals and tasks to achieve the mission of the organization (Mintzberg 1979). Within the organizational structure, organization members exercise power, make decisions, and fulfill the organization's activities (Hall and Tolbert 2009). One can find consensus on the argument that organizational size is one of the structural qualities, which are physical characteristics of organizations (Boyne 2003b; Dalton et al. 1980). Scholars have provided conceptual definitions of organizational size: Blau (1972,3) conceptualized size as "the scope of an organization and its responsibilities," whereas Aldrich (1972, 33) contended that in some cases "size refers to the scale of an organization's operations and not necessarily to the size of the labor force." Some scholars argued that the number of organization members is not size per se but is rather the implications for patterns of control and affiliation that are of theoretical importance for gaining an understanding of organizational phenomena (Kimberly 1976; Weick 1979).' However, the number of employees has been recognized as a good measure of organization size, in terms of operational definition: "since it is people who are organized," this size measure will more closely relate to structure than will other aspects of size (Child 1973, 170). Although organizational size seems to be a simple variable—the number of people in an organization, different conceptual definitions and perspectives suggest that size measurement is much more complicated than it appears to be (Hall and Tolbert 2009). Thus, from the various operational definitions in the relevant literature, a variety of measures have been derived: the number of employees, sections, subunits, and supervisory levels, and budget size (Blau 1972; Gooding and Wagner 1985; Kimberly 1976; Van de Ven 1976). Among these dimensions, this study considers the number of full-time employees and budget size in US federal agencies as measures of organizational size. The former takes into consideration the fact that at any given time an agency has a finite number of members available to do its work, and this has been the most commonly used measure in the literature (Caplow 1957; Kimberly 1976). Budget size, on the other hand, is the amount of money that an agency can spend at a given time to do its work and is important insofar as it represents both the performance of past activity and the potential for future activity (Kimberly 1976; Yuchtman and Seashore 1967). Regarding these two dimensions, Kimberly (1976) argued that they are conceptually independent from each other. Whereas they may be correlated to some degree—and even to a large degree in some cases—"the magnitude of the correlations is neither consistent enough nor high enough to justify their being considered essentially interchangeable measures of the

7 This argument can support some situations in which the number of employees may not be a reliable measure of people working on activities of the organization—for example, use of contractors, students, or temporary employees (Hall and Tolbert 2009).

667

668


same concept" (Kimberly 1976, 588). Supporting Kimberly's argument, the data show a weak correlation (/• = 0.108,;? = .2943) between personnel size and budget size.

ORGANIZATIONAL SIZE AND EFFEaiVENESS Number of Employees and Organizational Effectiveness

Organizational theorists have long studied and debated the effect of organizational personnel size, which increases with organizational age, on organizational effectiveness.^ However, conflicting perspectives and findings exist regarding the relationship between personnel size and organizational effectiveness (Boyne 2003b; Gooding and Wagner 1985). A number of researchers have argued or reported a negative relationship; with the irxrease in the number of employees or subunits, organizations become more complex, more conflicts can occur, communication can be distorted, and coordination costs can increase (e.g., Blau and Schoenherr 1971 ; Fiedler and Gillo 1974; Scherer and Ross 1990). On the other hand, organizational members have been recognized as important assets for achieving organizational goals (Glisson and Martin 1980; Gooding and Wagner 1985; Pfeffer and Salancik 1978). Gooding and Wagner (1985) concluded in their literature review that personnel size was positively related to organizational productivity. Recently, Brewer (2005) also argued that US federal agencies with more staffing would perform better and that excessive workforce reductions would decrease organizational effectiveness. Rather than these linear relationships, this study considers an inverted U-shaped relationship based on two theoretical arguments.' First, Blau ( 1970) theoretically contended that the marginal influence of an organization's size on performance declines with increasing size because differentiation intensifies administrative problems. He observed that larger government bureaus tend to subdivide their broad responsibilities to enhance organizational performance, and the bureaus become differentiated along diverse lines. In turn, the process of differentiation can cause problems of coordination and communication, which have a negative effect on organizational performance (Blau 1970). Second, from the perspective of economics, marginal productivity or performance of individual employees decreases as the number of employees increases. That is, employees' capacity to work rises to a maximum once the number of employees reaches a certain point and declines thereafter (Revans 1959). On the basis of these two rationales and given the lack of systematic empirical analysis of government agencies, this study hypothesizes the inverted U-shaped relationship between the number of employees and organizational effectiveness.'^ 8 Given the many arguments ihal relate to size or size as a proxy, the relationships hypothesized here are limited, based on selective literature. The range of factors organizational size might represent—for example, the importance of the program to politicians in Congress or the executive branch, formalization, and specialization—suggest the range of hypothesized relationships. In this section, the suggested benefits related to institutional size are a few among many examples. 9 With regard to the inverted U-shaped relationship, Boyne (2003b, 385) suggested, based on his literature review, that "further tests should include quadratic terms (or other equivalent procedures)." 10 A positive coefficient for personnel size and a negative coefficient for the squared value of personnel size depict the inverted U-shaped relationship. If the relationship does not have an inverted U-curve, the direction of the relationship between the coefficient for the squared value of personnel size and organizational efTectiveness will be either negative or statistically insignificant (Meier and Hicklin 2008).

Jung


Budget Size and Organizational Performance

Many scholars have also paid attention to the relationship between budget size and organizational performance. A review reveals that the arguments about this relationship have been inconsistent (e.g., Bohte 2001; Bradley, Johnes, and Millington 2001). Regarding the positive relationship, Boyne (2003b, 369) provided two theories: A strong version of the theory suggests that a larger budget size can be a sufficient condition for better performance, since this must lead to a higher quantity and/or quality of public services; a weak version suggests that a greater amount of money is not a sufficient but necessary condition, and thus, the budget must be effectively managed in order to improve performance. Furthermore, organizations with greater financial resources are more likely to acquire better control over environmental entities that mediate critical resources and will be more likely to possess a level of resource certainty to ensure continued productive viability (Gooding and Wagner 1985; Pfeffer and Salancik 1978). However, this perspective has been criticized by public choice theorists. Observing the great increase in public spending, which resulted in a big increase in taxes with few tangible benefits, these theorists claimed that self-interested and inefficient bureaucrats waste extra money and result in inefficient public organizations (Downs 1967; Niskanen 1971; Tullock 1965). Accordingly, this view postulates a negative or insignificant relationship between budget size of public organizations and their performance. Hanushek's (1997) review of empirical studies examining the relationship between school resources and student performance reported that there is very weak evidence supporting the idea that simply providing greater overall spending will result in improved organizational performance; approximately 70% of the empirical studies reported an insignificant relationship between financial resources and performance. Against these contradictory perspectives, it is hypothesized that federal agencies with larger budgets are likely to have lower levels of organizational effectiveness. In addition, this study also assumes the possibility of curvilinearity in the relationship, for at least two reasons. First, we can expect that, similarly to the number of full-time employees, marginal productivity of a certain budget also declines as budget size increases. The second is related to a technical rationale: the values of budget size—like those of the number of employees—exhibit a skewed distribution, in which large or extreme values can strongly influence the sizes of correlation coefficients with organizational effectiveness (Hickson, Pugh, and Pheysey 1969; Kimberly 1976). Accordingly, both negative-linear—included in the quadratic relationship—and log-linear relationships are examined to find a better fit in the data between budget size and effectiveness.

MEDIATING VARIABLES BETWEEN ORGANIZATIONAL SIZE A N D EFFECTIVENESS

Although the aforementioned issues concerning the relationship between organizational size and effectiveness can be relevant contributions to the literature of public administration, in order to theoretically enhance the contribution, it is necessary to consider various conditions that can influence the relationships. Organizational

669

670


size has been expected to have both direct and indirect relationships with organizational effectiveness. Concerning the causal relationship between personnel size and effectiveness, some authors argued that empirical tests need to consider intervening or mediating variables, such as complexity, centralization, formalization, and specialization (e.g., Blau 1972; Child 1972; Damanpour 1991; Glisson and Martin 1980; Imai, Keele, and Tingley 2010). On the other hand, Boyne (2003b) contended that the insignificant results might be caused by the indirectness of the relationship between financial resources and organizational effectiveness rather than the nonlinearity of the relationship. Thus, this section provides theoretical arguments about variables that can mediate the relationship between organizational size and efTectiveness. Organizational goal ambiguity is an important factor that should be considered in the relationship between organizational size and effectiveness. This concept refers to "the extent to which an organizational goal or set of goals allows leeway for interpretation, when the organizational goal represents the desired future state of the organization" (Chun and Rainey 2005a, 2). Classical organization theories, including the principles of chain of command and unity of command, and role theory imply that larger public organizations are likely to have higher levels of goal ambiguity Classical organization theory argues that each position in a formal organizational structure should have a specified set of goals, which is intended to allow managers to hold subordinates accountable for specific performance (Rizzo, House, and Lirtzman 1970). However, if the organizational size increases and there are multiple lines of authority or a wider span of control, employees' orientation to their organization or to their profession can be disrupted and goals are perceived as more ambiguous (Blau 1970; Etzioni 1959; Gouldner 1958; Rizzo et al. 1970). Likewise, role theory states that the probability of there being a lack of necessary information or ambiguity in that information available to an organizational position can increase with organizational size in a positive way; as the number of organizational members increases, the members may use defense mechanisms, which distort the reality of the situation, causing them to perform less effectively (Kahn et al. 1964; Rizzo et al. 1970). Chun and Rainey (2005a) hypothesized that larger agencies will have higher levels of organizational goal ambiguity since they should have more goals and thus have more difficulty in listing goals in order of priority as compared to smaller agencies. Furthermore, Chun and Rainey (2005b) empirically demonstrated that organizational goal ambiguity relates negatively to organizational effectiveness. This study provides the concepts and measures of three dimensions: target-specification ambiguity, timeline ambiguity, and evaluation ambiguity. Regarding organizational complexity, Armandi and Mills (1982) provided two propositions. The first is that large size promotes structural complexity, and the second is that the higher the complexity, the lower the organizational performance. More complex organizations are likely to have higher costs for coordination and communication among members and teams, which can harm organizational effectiveness (Blau 1970). A literature review shows that organizational complexity includes the number of job titles, branch offices, and levels of authority, and the proportion of employees included in the "professional" job category to the number of full-time employees (Blau 1970; Chun and Rainey 2005a; Meier 2000). Of these dimensions, this article

Jung


includes the number of programs and the proportion of professionals to full-time employees in each federal agency as measures of organizational complexity. We can expect that management-related characteristics can also influence the relationship between organizational size and effectiveness. For this reason, this study takes note of the institutional location of federal agencies. Institutional location refers to "whether a federal agency is inside an executive department or is an independent establishment"(Chun and Rainey 2005a, 10). Independent agencies may need higher managerial autonomy to avoid the clientele pressure in departments with strong clientele ties, to serve other government agencies, to try to focus on relatively narrow functions, and to be independent from presidential influence than do agencies inside the executive departments (Chun and Rainey 2005a; Meier 2000). On the basis of the above theoretical arguments, this study hypothesizes that these variables—target-specification ambiguity, timeline ambiguity, evaluation ambiguity, the number of programs, professionalization, and institutional location—will mediate the relationships between organizational size—the number of full-time employees and budget size—and effectiveness."

DATA A N D VARIABLES

The units of analysis in this study are US federal agencies. The sample size of this study is 97 federal agencies, including executive departments, sub-departmental agencies, and independent agencies. Sub-agencies in the same department consist of agencies that perform different functions and have "an individual identity apart from any umbrella organizations" (Wolf 1993, 162). The full list of studied agencies is provided in Appendix 2. The validity of the PART data has been open to conflicting arguments. The OMB Watch, which monitored the PART process, questioned whether the PART measured the outcomes of federal programs in an accurate and value-neutral way (Radin 2006). Lewis (2008) pointed out issues, including variation in the expertise of OMB evaluators. Moreover, PART scores were generated by only OMB, a presidential agency, and applied unevenly across federal agencies, with the result that some programs that took the PART more seriously and were more familiar with the process received higher scores (Gallo and Lewis 2012; Gilmour 2006; Moynihan 2008).

11 In terms of the relationship between organizational size and effectiveness, in this study, we could consider the possibility that organizational size is a function of organizational effectiveness, as opposed to the other way around. The history of administrations' integration initiative of budget and performance reveals the significant effort to implement performance budgeting (Gilmour and Lewis 2006b).The US OMB has aimed to make budget decisions by evaluating the performance of federal programs and agencies. In the research on OMB's implementation of performance budgeting, Gilmour and Lewis (2006b) empirically showEd that a dimension of federal program performance had a positive effect on the budget size. Although politics invariably influences budget decisions and makes the relationship between performance and budget size more complicated, decisions regarding budget size would be made based at least in part on performance because a small budget could be a cause of poor performance (Gilmour and Lewis 2006b; GAO 2004). Thus, the relationship between performance and organizational size, at least budget size, might be simultaneous. However, this study focuses, in a defensible way, on the influence of organizational size on effectiveness by taking size variables from a time period preceding that of organizational effectiveness.

671

672


On the other hand, a degree of outside involvement and transparency was involved in the development of the PART (Frederickson and Frederickson 2006; Moynihan 2008). For example, some independent groups, such as the Performance Measurement Advisory Council and a group from the President's Council on Integrity and Efficiency, brought in experts on performance management to review the PART questionnaire (Moynihan 2008; OMB 2004). Furthermore, OMB solicited comments from scholars and agencies (Moynihan 2008). Throughout various examinations, the PART has been recognized as systematic, evidence-based, and transparent (Frederickson and Frederickson 2006; Lewis 2008; Moynihan 2008). Recently, further support for the validity of the PART has come from Bertelli et al. (2008) and Gallo and Lewis (2012), who provide evidence indicating that career professionals familiar with the PART believe it to be an appropriate measure of program performance. Therefore, the PART is valuable as a source of data on performance goals and scores for a sample of programs that is representative enough and large enough to make statistically reliable claims (Lewis 2008). Thus, the criticisms of the PART do not diminish its usefulness in measuring organizational goal ambiguity and effectiveness in this article.

Dependent Variable

The organizational effectiveness measure is calculated by averaging the actual goal attainment rates of program goals in 97 agencies included in the PART of 2006, which assessed performance activities of 2006 and published it in early 2007. The values of organizational effectiveness are expressed as ratios. The current analysis employs the data on "target" and "actual achievement" in the "Program Performance Measure" section of the PART worksheet. Following the guidelines of OMB, US federal agencies must build off a reliable baseline and set achievable targets, once they define goals or measures (OMB 2006). In the process, oversight and peer review by OMB representatives help federal agencies to set more reasonable goals and targets (OMB 2006). The data are provided in worksheets for more than 99% of the federal programs. In addition, this study excludes program goals that fail to provide adequate effectiveness measures, the percentage of which is approximately 20%. The following example shows a method of measuring actual attainment rates of program goals in federal agencies. The case is the performance goal "Increase accuracy rate for application of USDA grading and certification services" of the program "Agricultural Commodity Grading and Certification" in the Agricultural Marketing Service. This goal set as its target 90% and reported 88% as its actual attainment for 2006; thus, 0.978 is the actual goal attainment rate. This goal exemplifies OMB directives, encouraging federal agencies to develop quantifiable targets. Yet, it is only in a very few cases that qualitative measures and targets can be established after peer review (OMB 2006). An example of qualitative measure is the attainment of a target assessed with a multi-categorical scale, the categories of which are "excellent," "adequate," and "needs improvement." This study assigns the "excellent" rating 1.00, the "adequate" assessment 0.66, and the "needs improvement" category 0.33 as the goal attainment

Jung


rates. Furthermore, this study checks the validity'^ and the reliability'^ for this effectiveness measure.

Main Independent Variables

Main independent variables are taken from a time period preceding those of mediating variables and organizational effectiveness, as described below. The first dimension of organizational size is the number of full-time employees of US federal agencies in 2005. The data are taken from the "Employment" section of September 2005 on the US Office of Personnel Management Web site, www.fedscope.opm.gov. Further, to investigate the inverted U-shaped relationship of personnel size with organizational effectiveness, the squared value of the full-time employee number is considered. Another dimension of organizational size is the total budget size of all the programs housed in each federal agency in the PART of 2006. This variable aggregates the actual budgets set in 2005 for all the programs of 2006 in each federal agency.'" For better presentation, this study rescales personnel size and budget size by dividing by 100,000 and 1,000,000, respectively.

Mediating and Control Variables

The data for mediating variables are taken from time period 2005-2006, which corresponds to the time period between the data collection for the independent and dependent variables. The first three mediating variables are dimensions of organizational goal ambiguity: target-specification ambiguity, timeline ambiguity, and 12 It is quite challenging to show the validity since no alternative measure of effectiveness that can be compared with this effectiveness measure is systematically available. In this situation, "the most appropriate tests of validity are face validity and content validity" (Carmines and Zeller 1979; Gilmour and Lewis 2006a, 27). Face validity refers to the degree to which evaluators judge that the measure or the assessment process is appropriate or reasonable to the targeted concept or construct on its face value (Haynes, Richard, and Kubany 1995). Content validity is the degree to which the measure is relevant and representative of the targeted concept or construct (Haynes et al. 1995). The organizational effectiveness measure is calculated as the ratios of actual goal attainment to the targets, based on the definition of the goal approach. Furthermore, the performance goals in this research represent reasonably appropriate aspects for organizational effectiveness based on the goal approach. Thus, the effectiveness measure can be considered to have passed, at a minimum, the tests of both face and content validity (Gilmour and Lewis 2006a). 13 This study chooses the inter-rater reliability method, which refers to the degree of agreement between raters, to measure organizational effectiveness through the same method (Campbell and Fiske 1959). The author instructed a doctoral student and a master's student majoring in public administration in how to read targets and achievements and calculate the goal attainment rates. Then the three persons, including the author, independently calculated and coded the goal attainment rates for 35 randomly selected goals. Between the three raters' ratings, the lowest correlation coefficient was 0.9498 {p < .001). Thus, the results provide evidence of the reliability of the organizational effectiveness measure. M This study gathered these size variables in a time period preceding that of the dependent variable for several reasons. First, it helps to relieve endogeneity problems between organizational size and effectiveness; second, there was not a big change but rather an incremental change in both size variables, and thus the current results were similar to those generated by using size variables of 2006; third, Yuchtman and Seashore (1967) argued that resources, including personnel and financial resources, are important, since they reflect and predict the effectiveness of past and future activities.

673

674


evaluation ambiguity, as measured from the PART of 2006. Target-specification ambiguity refers to the overall lack of clarity in deciding on the quantity and/or quality of work toward the attainment of all the program goals of each federal agency (Jung 2011). For some of the goals reported in the PART reports, such target information is stated. For other goals, it is not. Thus, target-specification ambiguity is measured by the proportion of performance objectives without concrete targets to the total number of performance objectives in all the programs of each federal agency (Jung 2011). Next, timeline ambiguity refers to the overall lack of clarity in deciding on the distinction between annual goals and long-term goals in each federal agency (Jung 2011). With regard to time frame, in some instances, federal programs' PART reports specify annual performance objectives, and in other instances, they specify long-term performance objectives. Sometimes the reports state the same objective as both annual and long-term without any explanation and any progressive steps to attain the final targets (Jung 2011). In terms of timeline, these goals can be differently interpreted and thus recognized as ambiguous. Thus, timeline ambiguity is calculated as the proportion of performance objectives with an ambiguous timeline to the total number of performance objectives in all programs of each federal agency (Jung 2011). Based on Chun and Rainey's (2005a, 4) definition of evaluative goal ambiguity, referring to the level of interpretive leeway that a program goal allows in evaluating the progress toward the achievement of the goal, the evaluation ambiguity concept is related to the external evaluation or distinction between output-oriented and outcome-oriented goals, which are more directly connected with public benefits and have a higher possibility of external evaluation (Jung and Rainey 2011). The PART includes four types of performance indicators in relation to performance evaluation: outcome, output, outcome-oriented efficiency, and output-oriented efficiency measures. The more the output-oriented performance goals, rather than outcome-oriented ones with a higher possibility of external evaluation, the higher the goal ambiguity (Chun and Rainey 2005a). Thus, evaluation ambiguity is measured by the proportion of output and output-oriented efficiency measures, among all performance indicators for each federal agency (Jung and Rainey 2011). For one dimension of organizational complexity, this study uses the number of programs that each federal agency has in the PART of 2006. Another dimension of organizational complexity, which has been called professionalization, is measured by the proportion of employees contained in the "professional" job category to the number of full-time employees of early 2006, using data from the US Office of Personnel Management Web site (www.fedscope.opm.gov). For institutional location, independent agencies are coded as one and agencies inside the authority of executive departments as zero. As a measure of past organizational effectiveness, this analysis also controls for aggregated past PART performance scores: design, strategic planning, management, and results scores of 2005 or before. Current performance is infiuenced by past performance, since public management can be considered an inertial system (O'Toole and Meier 1999). The inclusion of past performance is important for two reasons. First, it can help render the coefficients for the variables less biased; second, it can help capture

Jung


Table 1 Descriptive Statistics of the Dependent and Independent Variables

Variables Dependent variable Organizational effectiveness Organizational size Full-time employees Budget size Potential mediators Target-specification ambiguity Timeline ambiguity Evaluation ambiguity Number of programs Professionalization Institutional location Previous PART scores Purpose and design Strategic planning Management Results and accountability

Explanation

Mean

SD

Min

Max

0.774

0.096

0.506

1

9274.87 12028.52

25233.71 45141.59

49 17

216732 412385

Proportion

0.218

0.200

0

0.733

Proportion Proportion Natural number Proportion 1, independent agency; 0, inside department

0.124 0.273 6.155 0.271 0.196

0.212 0.172 8.144 0.213 0.398

0 0 1 0 0

0.905 0.846 50 0.781 1

Percentage/100 Percentage/100 Percentage/100 Percentage/100

0.865 0.788 0.837 0.543

0.133 0.162 0.127 0.230

0.4 0.2875 0.5225 0

1 1 1 1

Ratio Natural number Dollars in millions

the potential effects of budget and environmental constraints (Andrews et al. 2009). Table 1 provides the descriptive statistics of all the variables.

ANALYSES A N D RESULTS

This study conducted path analyses using AMOS 19, in order to examine the direct relationships between organizational size (full-time employees and budget) and effectiveness, and the effects of mediators between them. In order to find a better fit between budget size and effectiveness, two path analyses were conducted for two nonlinear relationships: one (Model 1) includes a log-transformation measure of budget; the other (Model 2) includes both raw and squared measures of budget. This is the only difference between the two models; both include raw and squared measures of personnel size and the same set of mediating variables and past PART scores. The two path models fit the data statistically well, since fit indices of both are above the recommended levels: for Model 1, x^(30) = 32.461, p = .346, standardized root mean square residual (SRMR) = 0.073, root mean square error of approximation (RMSEA) = 0.029, Tucker-Lewis index (TLI) = 0.988, incremental fit index (IFI) = 0.993, comparative fit index (CFI) = 0.992; for Model 2, ^{3,9) = 44.231, p = .260, SRMR = 0.074, RMSEA = 0.037, TLI = 0.982, IFI = 0.990, CFI = 0.990. However, based on the comparisons of AIC (Akaike information criterion) and BIC (Bayesian information criterion), which are used to compare which one of non-nested models is better (the smaller value indicates the better fit between model and data).

675

676


Figure 1 Organizational Effectiveness Model Results 1

Target-Specification Ambiguity / Timeline Ambiguity ,,...

/

Previous PART Design Score (.016)

/

(Squared) /

Full-Time Employees

/

h

'

Evaluation Ambiguity

1 /.m\

Organizational Effectiveness

.253* ••

,

. (-.001)

Budget Size

Previous PART Planning Score (.046) Pre\'ious PART Management Score Previous PART Results Score (.010)

Professioaalization (.047) Institutional Location

jVo/iV *••/) < .001; **p < .001; *p < .05; \p < .10; unstandardized path coefficients statistically insignificant for organizational efTectiveness are presented in the parentheses.

Figure 2 Organizational Effectiveness Model Results 2

Target-Spccificat ion Ambiguity

Fuli-Time Employees - . 1 3 3 " (Squared)

^ " •

Timeline Ambiguity Evahmtion Ambiguity

Previous PART .

.

.

^

^

Previous PAR'!' Planning Score (.018)

.289" Effecliveness

Employees •

Programs Budget Size (-.897) Budget Size (Squared) (1.725)

Professionalization (.101)

-.003»

—

'

Previous PART Management Score Previous PART Results Seore (.012)

Institutional Location

Sote: ***p< .001; **p< .001; *p< .05; unstandardized path coefficients statistically insignificant for organizational effectiveness are presented in the parentheses. The difference between Model 1 and Model 2 is the measure of budget size.

Model 1 (AIC = 104.46, BIC = 197.15) is evaluated to have a betterfitwith the data than Model 2 (AIC = 122.23, BIC = 222.65) (Langbaum 2009). Hence, the interpretation of the results is mostly based on Model 1. The results of path analyses connecting organization size, the number of employees, and budget size to organizational effectiveness for US federal agencies are presented in figures 1 and 2. The article presents unstandardized path coefficients.

Jung


The two models corroborate the inverted U-shaped relationship between the number of full-time employees and actual goal achievements. That is, both path models show a positive linear term (unstandardized path coefficient = 0.253, p < .001 in Model 1) and a negative squared term (unstandardized path coefficient = -0.188, p = .004 in Model 1) of personnel size for organizational effectiveness. In the data, the tipping point at which the benefits of size turn negative is 0.67287, and only two agencies exceeded the tipping point.'^ Thus, in the data, organizational effectiveness increases for personnel size up to 67,287 employees, but after that point effectiveness declines. The negative relationship between budget size and organizational goal achievements is also corroborated in Model 1, which includes a logged measure of budget size (unstandardized path coefficient = -0.010,/? = .017). As the budget size of federal agencies increases, goal achievement rates incrementally decrease. Based on the results of Model 2, we can see from the data that neither the linear relationship nor another nonlinear (squared value of budget size) relationship between budget size and organizational goal achievement rates is corroborated. Turning to mediating variables, the personnel size of federal agencies does not have any mediating variables in the data. Instead, this size variable has a direct influence on organizational goal achievement. However, budget size has two partial mediators: target-specification ambiguity and timeline ambiguity. That is, budget size exerts a direct infiuence on organizational effectiveness as well as an indirect influence through target-specification ambiguity (0.017 x -0.134 = 0.0023) and timeline ambiguity (0.039 X -0.109 = 0.0043). On the other hand, another goal ambiguity dimension, program evaluation ambiguity, does not have the expected mediating effect between organization size and effectiveness but shows a negative influence on organizational effectiveness. In terms of institutional location, organizational effectiveness is higher in independent agencies than in agencies inside executive departments. This suggests that managers in federal agencies may relatively benefit from the independent locus, in terms of performance management: intervening and changing organizational direction when problems occur, reducing the likely detrimental effects of internal conflict, and supporting flexibility to respond to environmental changes (Chun and Rainey 2005b; Walker, Damanpour, and Devece 2011). Regarding the influence of past PART performance scores, although all of them (design, strategic planning, management, and result/accountability) have the expected positive signs, only the past PART management score is statistically significant in both models. This result is unexpected, since the past PART result/accountability score seems conceptually closer to, and thus is expected to be more closely related to, goal achievement rates than the other three PART performance scores. Different measured performance dimensions and different measuring methods between the PART system and this analysis may have caused this unexpected result. 15 In order to check whether the inverted U-shaped relationship could be due to these two large agencies, this study conducted an analysis without them for a sensitivity test. In the analysis, which met recommended levels of model fit indices, a tipping point—that is, an inverted U-shaped relationship—was still found (unstandardized path coefficient of linear term = 0.660,/?< .001; unstandardized path coefficient of squared term = -0.919,/; = .016). Thus, this study did not exclude the two agencies.

677

678


DISCUSSION AND CONCLUSION

Whether large or small organizations are more effective has been a lasting issue in public administration. Change in governmental agency size has been an important lever to achieve desired objectives in public management reforms. However, in public administration, the relationship between organizational size and performance is not well established, and empirical studies of mediators between the two have been scant. This study reports that the number of full-time employees and budget size have an inverted U-shaped and negative log-linear relationship, respectively, with organizational effectiveness, which is measured as goal achievement rate, based on the goal approach. Moreover, this study finds two dimensions of goal ambiguity—target-specification ambiguity and timeline ambiguity—that have a negative mediating effect between budget size and organizational goal achievement rates. Regarding the use of organizational goal achievement rate as a measure of organizational effectiveness, this study explains its variation among 97 US federal agencies with organization size and mediating variables. However, it is not suggested here that this analysis addresses the complications inherent in using the goal approach to assess organizational effectiveness. Some organizations have clear, tangible goals, whereas other organizations or programs may deal with more complex, intangible, and difficult-to-measure goals. Moreover, some governmental managers might try to game the focused strategies for a higher efTectiveness score and increased budget size (Kelman and Friedman 2009). Thus, caution is recommended when interpreting the results. In order to improve the applicability of the goal approach to the public sector, practitioners and scholars need at least to discuss how to consider the types of organizational goals and effectively oversee the process of setting the targets. Furthermore, this study shows that there is an optimal, "Goldilocks" organizational size—not too large, not too small, but just right—in the US federal government, in terms of the number of full-time employees. Supporting a decrease in the marginal effect of personnel size on organizational effectiveness or the marginal effectiveness of individual employees, the result suggests that as the number of employees increases, the costs of control, delegation, and cooperation for effective performance management go up (Walker et al. 2011); the negative influences of internal conflict increase (Blau 1970); or the flexibility to respond to environmental changes is reduced (Damanpour 1992). Therefore, different managerial approaches need to be considered according to the personnel size of an organization for better organizational effectiveness. For example, strategies to improve coordination and communication among organizational members should be considered, and the importance of these strategies can vary according to the number of personnel. In addition, administrative innovation will be effective to a greater or lesser degree according to the size of the government agency (Damanpour 1992; Gremillion 1984). The message suggested here is that it is unclear whether government reformers are better advised simply to break up large public organizations or to amalgamate small ones (Boyne 2003b). Public organizations will benefit from the accumulation of more empirical studies of the inverted U-shaped relationship between personnel size and organizational effectiveness and the consideration of significant mediating.

Jung


moderating, or control variables, in discussions about the right sizing of government agencies. With regard to budget size or financial resources, Boyne (2003b, 376) argued that a majority of the empirical evidence reveals no significant relationship between financial resources and organizational effectiveness. However, this study reveals a direct but negative influence on organizational effectiveness. Taken at face value, this seems consistent with the claim of public choice theorists that bureaucrats inefficiently manage financial resources (Boyne 2003b). The more meaningful finding, however, seems to be that the negative relationship is partially and negatively mediated by target-specification ambiguity and timeline ambiguity in the data. These mediators are associated with arguments of Downs (1967) and Boyne (2003b) that, since budget size itself is just a necessary condition, effective management can be required to enhance organizational effectiveness. This is because unambiguous performance indicators and targets considering timeframe are critical elements of managing for results and new public management reforms (Moynihan 2006). The assumption is that public organizations cannot successfully plan better performance without information regarding their starting point and the subsequent progress (Boyne and Chen 2007). Thus, clear targets and their timelines are critical for effective performance management (Behn 2003; Boyne and Chen 2007; Jung 2011). In the data, these are the important levers for the constructive relationship between budget size and organizational effectiveness. For theoretical improvement and practical implications of performance management, more confounding factors between budget size and organizational performance need to be explored. Last, some limitations of this study are recognized. One clear caveat is that this empirical analysis does not prove causation, although it shows significant relationships between the two dimensions of organizational size and effectiveness, considering theoretical arguments, past performance scores, and time-point orderings among the independent, mediating, and dependent variables. In relation to this issue, a crosssectional design is of limited help in addressing issues of unobserved heterogeneity between governmental organizations, which are likely correlated with independent variables. For example, task difficulty could be related to size, making very large organizations appear to perform worse due to their large size. Another concern is that several variables included as mediators, such as professionalization and the number of programs, could reasonably be expected from theoretical arguments and prior research to be correlated with the size measures. Hence, the direction of the causation is not clear and the variables do not necessarily constitute mediators. For more convincing analysis and arguments, future research needs to gather more data covering a period of several years and to use a panel data analysis. With these theoretical and practical implications and methodological limitations, this study offers a short journey over a rough terrain of organizational effectiveness in the public sector.

679

680


APPENDIX 1 Summary of the Performance Measures for Organizational Effectiveness Measure Outcome Measure 1. Service quality (from review) Accuracy of loan guaranty activities (Statistical Quality Index)—from Veterans Benefits Administration Explanation: Evaluates the quality of services performed by housing field stations. Calculated by conducting two-tiered review of a sample of loan files; review conducted in light of published guidelines. 2. Customer satisfaction (from survey) Percent of respondents who rate the quality of service provided by the national cemeteries as excellent—from National Cemetery Administration Explanation: VA headquarters' stafT oversees the data collection process for the annual Survey of Satisfaction with National Cemeteries. The survey collects data from family members and funeral directors who have recently received services from a national cemetery. Output Measure: Increase in Quantity or Participants 1. Number of vocational rehabilitation placements with new employers. Explanation: Number of placements—from Employment Standards Administration 2. The number of foreign exchange participants by region to reflect current US foreign policy objectives commensurate with funding. Percentage annual increase over FY 2002 Baseline—from Department of State Efficiency Measure: Administrative Efficiency (Cost-saving) 1. E-FATS (Efficiency measure of Foreclosure Avoidance Through Servicing)—from Veterans Benefits Administration Explanation: The ratio of dollars saved through successful interventions to dollars spent by VA on Loan Administration FTE who perform that work. 2. Percent of administrative costs in relation to program costs—from Department of State Explanation: The efficiency measure assures that the highest percentage of funds go to direct program costs and program beneficiaries. A'oie: Ail the goals and explanations are drawn from the 2007 PART.

Jung Navigating a Rough Terrain of Public Management

APPENDIX 2 US Federal Agencies in this Study (/V = 97) Department of Agriculture (A^ = 13) Rural Development Farm Service Agency Food and Nutrition Service Economic Research Service (R) Foreign Agriculture Service Grain Inspection, Packers and Stockyards Administration Forest Service Department of Commerce {N = 8) Bureau of Economic Analysis (R) Bureau of Industry and Security US Patent and Trademark Office National Telecommunications and Information Administration Department of Education (A' = 6) Office of Safe and Drug-Free Schools (R) Institute of Education Sciences (R) Office of Special Education and Rehabilitative Services (R) Department of Energy (R) Department of Health and Human Services National Institutes of Health (R) Food and Drug Administration Administration for Children and Families Centers for Disease Control and Prevention (R) Substance Abuse and Mental Health Services Administration (R)

Agriculture Marketing Service Agricultural Research Service Animal and Plant Health Inspection Service Natural Resources Conservation Service Food Safety and Inspection Service National Agricultural Statistics Service (R)

Bureau of the Census (R) National Oceanic and Atmospheric Administration Economic Development Administration International Trade Administration

Office of Postsecondary Education (R) Office of Elementary and Secondary Education Office of Vocational and Adult Education (R)

(A' = 9) Agency for Healthcare Research and Quality (R) Health Resources and Services Administration (R) Centers for Medicare and Medicaid Services Indian Health Services (R)

Department of Housing and Urban Development {N = 5) Public and Indian Housing Office of Housing Healthy Homes and Lead Hazard Control Fair Housing and Equal Opportunity Community Planning and Development Department of Homeland Security (A'^= 1) Transportation Security Administration Department of Justice (A' = 5) Bureau of Prisons US Marshals Service (R) Federal Bureau of Investigation (R) Department of Labor (JV = 7) Bureau of Labor Statistics (R) Bureau of International Labor Affairs (R) Employment Standards Administration Employment and Training Administration Department of State (R)

Drug Enforcement Administration Office of Justice Programs

Occupational Safety and Health Administration Veterans' Employment and Training Service Mine Safety and Health Administration

Continued

68t

682


APPENDIX 2

{Continued)

Department of the Interior (^V = 6) National Park Service Bureau of Reclamation Office of Surface Mining Reclamation and Enforcement

Minerals Management Service United States Geological Survey Bureau of Land Management

Department of the Treasury {N = 8) United States Mint (R) Bureau of the Public Debt (R) Internal Revenue Service Bureau of Financial Crimes Enforcement Network (R)

Office of Thrift Supervision Comptroller of the Currency Bureau of Engraving and Printing (R) Financial Management Service (R)

Department of Transportation (A^ = 7) Federal Aviation Administration Maritime Administration Pipeline and Hazardous Materials Safety Administration Federal Transit Administration Department of Veterans Affairs (A^ = 3) National Cemetery Administration Veterans Benefits Administration Environmental Protection Agency Commodity Futures Trading Commission National Aeronautics and Space Administration (R) National Science Foundation (R) Office of National Drug Control Policy Overseas Private Investment Corporation (R) Securities and Exchange Commission Social Security Administration US Agency for International Development (R)

Federal Highway Administration Federal Railroad Administration National Highway Traffic Safety Administration

Veterans Health Administration General Services Administration (R) Export-Import Bank of the United States (R) National Archives and Records Administration (R) Nuclear Regulatory Commission Office of Personnel Management (R) Peace Corps (R) Small Business Administration US Trade and Development Agency (R)

Note: Based on the Federal Regulatory Directory (Congressional Quarterly 2008), (R) indicates regulatory agencies and the others are non-regulatory agencies.

REFERENCES

Aldrich, Howard E. 1972. Technology and organizational structure: A reexamination of the findings of the Aston Group. Administrative Science Quarterly 17:26-43. Andrews, Rhys, George A. Boyne, Jennifer Law, and Richard M. Walker. 2009. Strategy formulation, strategy content, and performance. Public Management Review 11:1-22. Andrews, Rhys, George A. Boyne, and Richard M. Walker. 2006. Subjective and objective measures of organizational performance: An empirical exploration. In Public service performance: Perspectives on measurement and management, ed. G. A. Boyne, K. J. Meier, L. J. O'Toole, and R. M. Walker, 14-34. West Nyack, NY: Cambridge Univ. Press. Armandi, Barry R., and Edgar W Mills Jr. 1982. Organizational size, structure, and efficiency: A test of a Blau-Hage model. American Journal of Economics and Sociology 4\A'i-60.

Jung


Behn, Robert D. 2003. Why measure performance? Different purposes require different measures. Public Administration Review 63:586-606. Berman, Evan M., and Jonathan P. West. 1995. TQM in American cities: Hypotheses regarding commitment and impact. Journal of Public Administration Research and Theory 5:213-30. Bertelli, Anthony M., Joshua D. Clinton, Christian Grose, David E. Lewis, and David C. Nixon. 2008. The ideology of bureaucrats, presidents, and legislators. Presented at the Annual Meeting of the American Political Science Association, Boston, MA, April 28-31. Blau, Peter M. 1970. A formal theory of differentiation in organizations. American Sociological /?mew 35:201-18. . 1972. Interdependence and hierarchy in organizations. Social Science Research 1:1-24. Blau, Peter M., and Richard Schoenherr. 1971. The structure of organizations.Nev/York,NY: Basic Books. Bohte, John. 2001. School bureaucracy and student performance at the local level. Public Administration Review 61:92-99. Boyne, George A. 2002. Concepts and indicators of local authority performance: An evaluation of the statutory frameworks in england and wales. Public Money and Management 22:17. . 2003a. What is public service improvement? Public Administration 81:211-27. . 2003b. Sources of public service improvement: A critical review and research agenda. Journal of Public Administration Research and Theory 13:367-94. Boyne, George A., and Alex A. Chen. 2007. Performance targets and public service improvement. Journal of Public Administration Research and Theory 17:455-77. Bradley, Steve, Geraint Johnes, and Jim Millington. 2001. The effect of competition on the efficiency of secondary schools in England. European Journal of Operational Research 135:545-68. Brewer, Gene A. 2005. In the eye of the storm: Frontline supervisors and federal agency performance. Journal of Public Administration Research and Theory 15:505-27. Campbell, Donald T., and Donald W. Fiske. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin 56:81-105. Caplow, Theodore. 1957. Organizational size. Administrative Science Quarterly 1:484—505. Carmines, Edward G., and Richard A. Zeller. 1979. Reliability and validity assessment. Beverly Hills, CA: Sage Publications. Child, John. 1972. Organizational structure, environment and performance: The role of strategic choice. Sociology 6:1-22. . 1973. Predicting and understanding organization structure. Administrative Science Quarterly 18:168-85. Chun, Young Han, and Hal G. Rainey. 2005a. Goal ambiguity in US federal agencies. Journal of Public Administration Research and Theory 15:1-30. . 2005b. Goal ambiguity and organizational performance in US federal agencies. Journal of Public Administration Research and Theory 15:529-57. Cohen, Steven A. 1993. Defining and measuring effectiveness in public management. Public ' Productivity and Management Review 17:45-57. Cyert, Richard M., and James G. March. 1963. Behavioral theory of theflrm. Englewood Cliffs, NJ: Prentice Hall. Daft, Richard L. 2010. Organization theory and design. Cincinnati, OH: South-Western College. Dalton, Dan R., William D. Todor, Michael J. Spendolini, Gordon J. Fielding, and Lyman W. Porter. 1980. Organization structure and performance: A critical review. Academy of Management Review 5:49-64. Damanpour, Fariborz. 1991. Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal 34:555-90. . 1992. Organizational size and innovation. Organization Studies 13:375-402. Downs, Anthony. 1967. Inside bureaucracy. Boston, MA: Little, Brown. Dull, Matthew. 2006. Why part? The institutional politics of presidential budget reform, iournal of Public Administration and Research Theory 16:187-215.

683

684


Etzioni, Amitai. 1959. Authority structure and organizational effectiveness. Administrative Science Quarterly 4:43-67. . 1964. Modern organizations. Englevvood Cliffs, NJ: Prentice Hall. Fiedler, Fred E., and Martin W. Gillo. 1974. Correlates of performance in community colleges. Journal of Higher Education 45:672-81. Frederickson, David G., and George H. Frederickson. 2006. Measuring the performance of the hollow state. Washington, DC: Georgetown Univ. Press. Friedlander, Frank, and Hal Pickle. 1968. Components of effectiveness in small organizations. Administrative Science Quarterly 13:289-304. Gallo, Nick, and David E. Lewis. 2012. The consequences of presidential patronage for federal agency performance. Journal of Public Administration Research and Theory 22:219-43. Georgopoulos, Basil S., and Arnold S. Tannenbaum. 1957. A study of organizational effectiveness. American Sociological Review 22:534-40. Gibson, James L., John M. Ivancevich, and James H. Donnelly Jr. 1973. Organizations: Structure, process, behavior. Dallas, TX: Business Publishing. Gilmour, John B. 2006. Implementing OMB's program assessment rating tool (part): Meeting the challenges of integrating budget and performance. Washington, DC: IBM Center for the Business of Government. http://www.businessofgovernment.org/sites/defauIt/filcs/GilmourReport.pdf (accessed June 1, 2011). Gilmour, John B., and David E. Lewis. 2006a. Political appointees and the competence of federal program management. American Politics Research 34:22-50. . 2006b. Assessing performance budgeting at OMB: The infiuence of politics, performance, and program size. Journal of Public Administration Research and Theory 16:169-86. Glisson, Charles A., and Patricia Yancey Martin. 1980. Productivity and efficiency in human service organizations as related to structure, size, and age. Academy of Management Journal 23:21-37. Gooding, Richard Z., and John A. Wagner. 1985. A meta-analytic review of the relationship between size and performance: The productivity and efficiency of organizations and their subunits. Administrative Science Quarterly 30:462-81. Gouldner, Alvin W. 1955. Metaphysical pathos and the theory of bureaucracy. American Political Science Review 49:496-507. . 1958. Cosmopolitans and locals: Toward an analysis of latent social roles II.

Administrative

Science Quarterly 2:444-80. Gremillion, Lee L. 1984. Organization size and information system use: An empirical study. Journal of Management Information Systems 1:4-17. Hall, Richard H., and Pamela S. Tolbert. 2009. Organizations: Structures, processes, and outcomes, 10th ed. Upper Saddle River, NJ: Pearson Prentice Hall. Hanushek, Eric A. 1997. Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis 19:141-64. Hatry, Harry P 1999. Performance measurement: Getting results. Washington, DC: Urban Institute Press. Haynes, Stephen N., David C. S. Richard, and Edward S. Kubany. 1995. Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment 7:238-47. Heskett, James L., W. Earl Sasser Jr., and Leonard A. Schlesinger. 1997. The service profit chain: How leading companies link profit and growth to loyalty, satisfaction, and value. New York, NY: Free Press. Hickson, David J., D. S. Pugh, and Diana C. Pheysey. 1969. Operations technology and organization structure: An empirical reappraisal. Administrative Science Quarterly 14:378-97. Imai, Kosuke, Luke Keele, and Dustin Tingley. 2010. A general approach to causal mediation analysis. Psychological Methods 15:309-34. Jung, Chan Su. 2011. Organizational goal ambiguity and performance: Conceptualization, measurement, and relationships. International Public Management Journal 14:193-217. Jung, Chan Su, and Hal G. Rainey. 2011. Organizational goal characteristics and public duty motivation in US federal agencies. Review of Public Personnel Administration 31:28-47.

Jung


Kahn, Robert L., Donald M. Wolfe, Robert R Quinn, J. Diedrick Snoek, and Robert A. Rosenthal. 1964. Organizational stress: Studies in role conflict and ambiguity. New York, NY: Wiley. Kanter, Rosabeth Moss, and Derick Brinkerhoff. 1981. Organizational performance: Recent developments in measurement. Annual Review of Sociology 7:321-49. Katz, Daniel, Barbara A. Gutek, Robert L. Kahn, and Eugenia Barton. 1975. Bureaucratic encounters: A pilot study in the evaluation of government services. Ann Arbor: Survey Research Center, Institute for Social Research, Univ. of Michigan. Katz, Daniel, and Robert L. Kahn. 1978. The social psychology of organizations., 2nd ed. New York, NY: Wiley Kelman, Steven, and John N. Friedman. 2009. Performance improvement and performance dysfunction: An empirical examination of distortionary impacts of the emergency room wait-time target in the English national health service. Journal of Public Administration Research and Theory 19:917-46. Kettl, Donald F., Patricia W. Ingraham, Ronald P. Sanders, and Constance Homer. 1996. Civil service reform: Building a government that works. Washington, DC: Brookings Institution Press. Kimberly, John R. 1976. Organizational size and the structuralist perspective: A review, critique, and proposal. Administrative Science Quarterly 21:571-97. Kopczynski, Mary, and Michael Lombardo. 1999. Comparative performance measurement: Insights and lessons learned from a consortium effort. Public Administration Review 59:124-34. Langbaum, J. 2009. Predicting memory training response patterns: Results from ACTIVE. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences 64:14. Lewis, David E. 2008. The politics of presidential appointments: Political control and bureaucratic performance. Princeton, NJ: Princeton Univ. Press. Likert, Rensis. 1958. Measuring organizational performance. Harvard Business Review 36:41-50. Meier, Kenneth J. 2000. Politics and the bureaucracy. Fort Worth, TX: Harcourt College Publishing. Meier, Kenneth J., and Alisa Hicklin. 2008. Employee turnover and organizational performance: Testing a hypothesis from classical public administration. Journal of Public Administration Research and Theory 18:573-90. Mintzberg, Henry. 1979. The structuring of organizations. Englewood Cliffs, NJ: Prentice Hall. Mott, Paul E. 1972. The characteristics of effective organizations. New York, NY: Harper & Row. Moynihan, Donald P. 2006. Managing for results in state government: Evaluating a decade of reform. Public Administration Review 66:77-89. . 2008. The dynamics of performance management: Constructing information and reform. Washington, DC: Georgetown Univ. Press. Niskanen, William A. 1971. Bureaucracy andrepresentative government. Chicago, IL: Aldine-Atherton. O'Toole, Laurence J., and Kenneth J. Meier. 1999. Modeling the impact of public management: Implications of structural context. Journal of Public Administration Research and Theory 9:505-26. Pfeffer, Jeffrey, and Gerald R. Salancik. 1978. The external control of organizations. New York, NY: Harper & Row. Pollitt, Christopher. 1995. Justification by works or by faith? Evaluation 1:133-54. Price, James L. 1968. Organizational effectiveness: An inventory of propositions. Homewood, IL: Richard D. Irwin. . 1971. The study of organizational effectiveness. Sociological Quarterly 13:3-15. Radin, Beryl A. 2006. Challenging the performance movement: Accountability, complexity, and democratic values. Washington, DC: Georgetown Univ. Press. Rainey, Hal G. 2009. Understanding and managing public organizations, 4th ed. San Francisco, CA: Jossey-Bass. Revans, R. W 1959. Human relations, management and size. In Human relations and modern management, ed. E. M. Hugh-Jones, 177-220. Chicago, IL: Quadrangle Books Rizzo, John R., Robert J. House, and Sidney I. Lirtzman. 1970. Role conflict and ambiguity in complex organizations. Administrative Science Quarterly 15:150-63. Schein, Edgar H. 1970. Organizational psychology. Englewood Cliffs, NJ: Prentice-Hall.

685

686


Scherer, Frederic M., and David Ross. 1990. Industrial market slructure and economic performance. Boston, MA: Houghton Mifflin Company. Seiden, Sally Coleman, and Jessica E. Sowa. 2004. Testing a multi-dimensional model of organizational performance; Prospects and problems. Journal of Public Administration Research and Theory 14:395-416. Sheperd, William G. 1979. The economics of Industrial organization. Englewood ClifTs, NJ: Prentice Hall. SJIvestro, Rhian, and Stuart Cross. 2000. Applying the service profit chain in a retail environment challenging the "satisfaction mirror." Journal of Service Management 11:244~68. Stanford, Robert E. 1980. The effects of promotion by seniority in growth-constrained organizations. Management Science 26:680-93. Steers, Richard M. 1975. Problems in the measurement of organizational effectiveness. Administrative Science Quarterly 20:546-68. Tullock, Gordon. 1965. The politics of bureaucracy. Washington, DC: Public Affairs Press. US General Accounting Office (GAO). 2004. Performance budgeting: Observations on the use of OMB's program assessment rating tool for the fiscal year 2004 budget, http://www.gao.gov. (accessed April 6, 2009) US Office of Management and Budget (OMB). 2004. FY 2004 budget chapter introducing the part: Rating the performance of federal programs, http://georgewbush-whitehouse.archives.gov/ omb/performance/fy2006/2006_guidance_final.pdf (accessed September 6, 2012). . 2006. Guidance for completing 2006 parts, http://www.whitehouse.gov/omb/part/index. html (accessed February 28, 2010). -. 2009. http://georgewbush-whitehouse.archives.gov/omb/organization/role.html (accessed September 6, 2012). Van de Ven, Andrew H. 1976. A framework for organization assessment. Academy of Management Review 1:64-78. Van de Ven, Andrew H., and Diane L. Ferry. 1980. Measuring and assessing organizations. New York; NY: John Wiley. V/alker, Richard M., Fariborz Damanpour, and Carlos A. Devece. 2011. Management innovation and organizational performance: The mediating effect of performance management. Journal of Public Administration Research and Theory 2\:367-S6. V/ebb, Ronald J. 1974. Organizational effectiveness and the voluntary organization. Academy of Management Journal 17:663-77. V/eber, Max. 1947. The theory of social and economic organization, trans. A. M. Henderson and T. Parsons. New York, NY: Free Press. V/eick, Karl E. 1979. The social psychology of organizing, 2nd ed. Reading, MA: Addison-Wesley Publishing. V/illiamson, Oliver E. 1975. Markets and hierarchies: Analysis and antitrust implications. New York, NY: Free Press. V/olf, Patrick J. 1993. Acasesurvey of bureaucratic effectiveness in US cabinet agencies: Preliminary results. Journal of Public Administration Research and Theory 2:\6\-B]. Yuchtman, Ephraim, and Stanley E. Seashore. 1967. A system resource approach to organizational effectiveness. American Sociological Review 32:891-903.

Copyright of Journal of Public Administration Research & Theory is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

Navigating a Rough Terrain of Public Management

Navigating a Rough Terrain of Public Management

Suggest Documents

Navigating a Rough Terrain of Public Management ...

Navigating a Rough Terrain of Public Management ...

ROUGH-TERRAIN SCISSOR LIFTS

ROUGH TERRAIN CRANE‵

ROUGH TERRAIN - AJ Access Platforms

ROUGH TERRAIN CRANE TR-250M

Autonomous Traversal of Rough Terrain Using ... - CiteSeerX

Dynamics of a Tethered Rover on Rough Terrain

BigDog, the Rough-Terrain Quadruped Robot

Learning to Drive Over Rough Terrain

GVF Rough Terrain Forklift Parts Manual

Compliant Quadruped Locomotion Over Rough Terrain - CiteSeerX

An Optimization Approach to Rough Terrain Locomotion

Tadano Rough-Terrain Crane TR-250M-6

A Method for Mobile Robot Navigation on Rough Terrain - CiteSeerX

A tradeoff analysis for rough terrain cargo handlers ... - IEEE Xplore

Jumping robots: a biomimetic solution to locomotion across rough terrain

Rough Terrain 3D Mapping and Navigation using a Continuously

Wheel torque control for a rough terrain rover - Infoscience - EPFL

Navigating the Human Terrain: Development of Cross ...

Navigating the Human Terrain: Development of Cross-Cultural ... - DTIC

Navigating Two-Dimensional and Perspective Views of Terrain

Development of Rough Terrain Mobile Robot using Connected ...

Probabilistic Modeling and Analysis of High-Speed Rough-Terrain ...