Performance Gaps and Managerial Decisions

Performance Gaps and Managerial Decisions: A Bayesian Decision Theory of Managerial ActionKenneth J. Meier,*,† Nathan Favero,* Ling Zhu‡ *Texas A&M University; †Cardiff University; ‡University of Houston

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An extensive literature finds that managerial decisions matter for the performance of public organizations, yet little attention has been devoted to why managers make the decisions that they do. This article builds a theory of public management decision making based on the simple assumption that managers are concerned with performance and the perfor-mance gaps of their organization. Using a logic borrowed from bounded rationality and Bayesian decision theory, we theorize a set of prior expectations. Whether the organiza-tion meets these expectations or fails to do so is then used to specify a series of precise hypotheses about when managers make a variety of decisions including when to seek additional information, take risks, decentralize the organization, determine goals, or select a managerial strategy as well as other managerial actions. The logic of the theory can easily be extended to decisions about selecting goals or managerial strategy. We then extend the basic theory by considering multiple goals, hierarchy, and alternative theoretical approaches.

The study of public1 management is characterized by a strong belief and substantial evidence that what managers do matters for the performance of public organizations (Ingraham, Joyce, and Donahue 2003; O’Toole and Meier 2011; Pollitt and Bouckaert 2000; Simon 1947). Although a growing literature documents the impact of manage-ment on performance (Boyne 2003; Rainey 2014), there is substantially less focus on why managers make the decisions that they do. This absence of attention is surprising because there is a massive prescriptive theory on how managers should decide (Blake

Address correspondence to the author at [email protected].

We would like to thank Lotte Bøgh Andersen, Simon Calmar Andersen, Rhys Andrews, Jens Blom-Hansen, Ohbet Cheon, Carla Flink, Nehemia Geva, Erik Godwin, David Lewis, George Krause, Martin Lodge, Steve Martin, Angel Molina, Poul Aaes Nielsen, Laurence J. O’Toole, Nicolai Petrovsky, Hal G. Rainey, James Rogers, Amanda Rutherford, Brian Shreck, Søren Serritzlew, Camilla Denager Staniok, Andrew Whitford, Søren Winter, Sam Workman, and seminar participants at the Danish Institute for Social Research, the Cardiff School of Business, the London School of Economics, and the LBJ School of Public Affairs for comments on previous versions of this article.1 The theory presented here is very general and could easily be applied to managers in nonprofit and private sector organizations. Specific studies using this theory could actually probe whether public and private managers make the same decisions given similar situations.

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doi:10.1093/jopart/muu054© The Author 2015. 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].

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and Mouton 1968; McGregor 2006; Ouchi 1981; Simon 1947). This article is a modest effort to unify the prescriptive literature on management with the empirical approach of the management-performance literature and provide a theoretical basis for system-atically analyzing the Bayesian logic of managerial behavior. Bayesian decision theory is concerned with the making of decisions under conditions of uncertainty (Berger 1985). Prior information is used to define expectations, which are updated when new information becomes available. A subsequent decision, then, is adjusted based on the updated expectations.

The purpose of this article is to generate a parsimonious theory of the decisions that managers make. It starts from a simple assumption that managers are concerned about the performance of their organizations and that performance, or lack of per-formance, is the primary motivator behind management decisions. One might gen-erate a theory of management decisions from a wide variety of other assumptions (e.g., isomorphic rationality [IR], professional norms, hierarchy), and it might even be possible to create an integrated theory from all of these assumptions (see below). At the present time, however, the lack of precision in existing theory requires improve-ments to ensure that theories generate clear testable hypotheses and can be falsified by empirical work. This is the first step for one such theory.

The focus of the theory will be on decisions made by public managers.2 A con-cern with decision making has always been a fundamental part of public administra-tion. The historical functions of public administration enumerated by POSDCORB were essentially a list of areas of managerial decision making—planning, organizing, staffing, directing, coordinating, reporting, and budgeting. The early study of deci-sion making was dominated by the notion of synoptic rationality (see Braybrooke and Lindblom 1963), which required that managers when faced with a decision start by listing all alternatives, evaluate each of the alternatives in terms of benefits and costs, and select the alternative that should provide the greatest relative benefit. Simon (1947) effectively challenged the notion of synoptic rationality and offered the more practical bounded rationality whereby managers satisfice rather than maximize (see Cyert and March 1963; March and Simon 1958). Building on Simon’s work, scholars proposed instrumentalism in decision making, which describes allocation decisions as a function of some base (often defined by previous year’s allocation outcomes, Lindblom 1959; Padgett 1980). Extending the bounded rationality literature, recent research stylizes Bayesian models to describe how a rational agency learns from the success and failure of its own past decisions and uses that prior information to opti-mize future decisions (Carpenter 2004; Krause 2003). Whether maximizing, satisfic-ing, or pursuing the incremental style of decision making, all decisions start with the recognition of a performance gap, that is a comparison of current performance with some standard of performance. These performance standards might be externally imposed by stakeholders or be internal standards set by management. They might be existing standards or aspirational standards. However, the standards are set or what-ever type of standards they are, they play a key role in indicating a performance gap and thus a need for a decision.

2 We adopt the decision-theoretic approach instead of the game-theoretic approach. We focus on unpacking managers’ decision logics rather than analyzing equilibrium outcomes rising from strategic interactions between managers. Krause (2003) provides a more detailed discussion on the difference between these two approaches.

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Meier et al. Performance Gaps and Managerial Decisions 3

This article concentrates on patterns of decisions rather than individual decisions, although the theoretical logic applies whether the manager is making one decision or a meta decision to approach problems in a given way. Patterns of decisions include decisions about how to deal with the organization’s environment, that is, the deci-sion to exploit the environment or buffer it, informational decisions including what information to seek and from whom via networking or monitoring, strategic decisions such as the priority placed on one goal versus another (see Nielsen 2013), the deci-sion to delegate and whether the organization should be centralized or decentralized, the decision to invest in the organization either through the development of human resources or technology, and quite likely a variety of other dimensions.3

In the following sections, we outline our Bayesian decision theory. We use for-mal notation to aid in expressing portions of our theory precisely and efficiently. Our model looks quite different from many formal models; our purpose is not to demon-strate that a certain set of assumptions implies a specific set of results. Instead, our model identifies a number of key variables that we expect to affect managers’ deci-sions and uses formal notation to aid in expressing the manner in which each variable should affect decisions.

THE BAYEsIAN LOGIc OF A PErFOrMANcE GAP

Our first assumption is that public managers make decisions based on concerns about organizational performance. Putting performance at the center of a theory of mana-gerial decisions is a promising place to start for two reasons. First, although con-cerns with performance have been expressed in public administration since the start of the field as a conscious enterprise (Wilson 1887; White 1926),4 the rise of the New Public Management (Hood 1991) on top of such previous reforms as management by objectives, zero base-budgeting, and program budgeting and performance systems has created a political concern with performance and performance management that is unprecedented. Elaborate performance appraisal systems have been created at all levels of government in the United States and a variety of other countries (Pollitt and Bouckaert 2000). Although the implementation of performance management has been somewhat irregular (see Moynihan 2008), the concern appears to be fairly univer-sal. Second, unlike other basic assumptions that could be used, a concern for perfor-mance has both empirical and normative face validity. Politicians, citizens, journalists, and bureaucrats all express concerns about performance and how best to improve it. Although these expressions might be strategic—that is, cheap talk—there appear to be consequences of poor performance for politicians (Ferejohn 1986), bureaucra-cies (Carpenter 2001; Meier and Bohte 2003; Rourke 1984; Rutherford 2014), and individual managers (Behn 2003; Boyne et al. 2010). Rather than assuming that the entire concern with performance is a charade, it is reasonable to build a theory around

3 Although we focus on managerial decisions concerning information seeking, risk taking, decentralization, and organizational investment, out theoretical framework applies broadly to other managerial decisions that seek to meet performance expectations and are made under uncertainty.4 Although Wilson’s essay had little influence on the development of public administration until it was reprinted more than 50 years later (see Van Riper 1983), it does make the link of administration to performance nicely.

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the notion that performance is central to public management and determine what the implications of that theory are.5

The emphasis on performance does not rule out other theories of managerial decision making; they could well be compatible with the present approach as we illus-trate later in this article. Four prominent alternative theories are included. First, pro-fessional norms are frequently proposed as a guiding principle for studying the actions of individuals within an organization (Adolph 2013; Teodoro 2011).6 Second, IR sug-gests that managers pattern their actions after the behaviors taken by other manag-ers in similar or exemplary organizations (DiMaggio and Powell 1983; Frumkin and Galaskiewicz 2004). Third, resource dependency theory holds that managers make decisions based on the need for resources and the constraints on those resources (Pfeffer and Salancik 2003). Fourth, population ecology theories essentially hold that environments determine organizational outcomes and that managerial decisions play little role in the process (Belton and Dess 1985; Hannan and Freeman 1977). The first three of these theories can be integrated into the model presented here (see below) and the fourth essentially serves as an alternative view of organizations and manage-ment. On their own, none of these four existing theories acknowledges performance concerns as a central determinant of managerial decision making.

Formally, we assume that management decisions (D) are a function of perfor-mance gaps (G):

D f G= ( ),or

D G 1= +β ε, (1)

where β1 is a parameter that can be estimated and ε is an error term. We further assume a standard of performance Ptʹ and a level of existing performance, Pt.7 A per-formance gap (G) is essentially the difference between existing performance and the standard of performance or:

G P Pt t= −( )′ . (2)

Theoretically, it is useful to think of the standard of performance as a Bayesian prior, that is, as the manager’s prior expectations regarding the level of performance.8 In

5 This theory could be considered a normative theory, that is, much like the normative theory of regulation that specifies that one should regulate in the presence of a set of market failures, this theory postulates what managers should do if performance is driving their decisions. To the extent that the actual behavior of managers differs from this normative theory, one might seek to explain these deviations by other theories or hypotheses that bring nonperformance elements into the model.6 Focusing on individual agency (i.e., managers’ behavior) opens a great deal of recent literature as relevant. In addition to examining values and norms managers bring to the decision-making process, recent research also examined how managers’ risk orientations shape strategic managerial decisions (see Bozeman and Kingsley 1998; Nutt 2006).7 This article will not define performance as what performance is might well be contested among the various stakeholders of the organization. Similarly, the definition of performance might be very precise (e.g., performance on standardized tests) or vague (e.g., producing democratic citizens). These definitional issues will be incorporated in later sections on goal ambiguity and multiple goals.8 The Bayesian updating process uses the information gathered on the prior as well as the current performance and subjectively weights the reliability of the information. Depending on their judgments about the information, managers might be more or less confident that a gap actually does or does not exist and thus their behavior might be more or less sensitive to changes in the estimated gap. The model as presented can also be seen as analogous to an error correction model in econometrics.

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other words, the prior, in the context of Bayesian decision theory, is defined as a man-ager’s subjective evaluation (belief) of the unknown performance outcome. It reflects a manager’s knowledge of how likely a certain performance outcome would occur before she can actually observe it. This prior can be informed by a wide variety of information available to the manager (see below). Modeling when a manager will per-ceive a performance gap (and therefore when a decision or set of decisions will be made) requires an understanding of both the prior and what factors influence the prior.9

The first step in a Bayesian logic of decision making, therefore, is to theoretically define the prior. We will start from the simplest types of priors and move to more com-plex specifications. The objective of the theorization process is to specify a range of possible priors and then use these different priors in conjunction with current perfor-mance to determine which might influence the decisions that managers make. In short, the purpose is not to determine exactly which prior a manager holds but rather to infer from decisions made by the manager which prior might best predict the actual results. Each prior will be based on information available to the manager and will be discussed in order of the complexity of the thinking in estimating the prior. We will illustrate these priors with examples from one type of public manager, school superintendents, to provide grounding in the world of practice. Many other managers might be used in lieu of superintendents for illustration purposes. Following the discussion of how priors are estimated, the article will incorporate performance gaps into a model of decision making and generate a set of hypotheses.

Organizational History as the Prior

At the simplest level, mangers might hold a naive prior such as simply expecting the organization to do as well as it did last year,10 that is a school superintendent expect the district to score as well on standardized tests as it did last year, or:

P Pt t 1′ = − . (3)

Although this prior contains little information except last year’s performance, it could be an acceptable prior in specific situations such as organizations operating with highly stable procedures such as federal budgetary decision making. For example, Padgett (1980, 354) analyzed the Office of Management and Budget program allocation data and found that “most programs most of time received budgetary allocations which are only marginally different from the [their own] historical base.”

9 One can conceptualize the prior as a combination of subjective expectations for the organization as well as aspirations for the organization (what managers want to achieve). The former suggests that managers are somewhat passive and accepting of the priors, whereas the latter suggest that they actively seek to shape them. Managers and stakeholders might use either criteria and this will generate some of the goal conflict and ambiguity discussed later in the theory.10 The use of the time period of 1 year is arbitrary. Essentially, the logic deals with decision cycles, that is, the time between one decision and another. Decision cycles could be of varying lengths and could vary both across and within organizations. Because our concern is public organizations and a variety processes are set up for an annual decision cycle in such organizations, we will use the term “year” for simplicity sake.

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Another sort of naive prior makes sense for what LaPorte and Consolini (1991) calls high-reliability organizations, organizations where failure is catastrophic. A nuclear regulatory agency would have an expectation of no accidents, and this prior would hold from year to year. Priors that are not based on past performance can also occur in organizations with precise performance targets. Unemployment insur-ance agencies have a defined target of paying 87% of claims within 3 weeks (Wenger, O’Toole, and Meier 2008, 178). This target remains constant from year to year, and beating the standard does not create a new standard among organizational stakehold-ers (although it might create a new prior in the minds of managers).11

One logical step after considering past performance in setting a prior is to consider the trend in past performance or what might be termed an autoregressive trend model:

P P P P

or substituting P P Pt t 1 t 1 t 2

t 1 t 1 t 2

′ = + −

= −− − −

− − −

( )

( )∆PP P Pt t 1 t 1′ = +− −∆ .

(4)

Equation (4) predicts that managers will establish a prior that is based on last year’s performance plus the incremental gain of last year’s performance over the previous year.12 A superintendent in a district with an average test score of 80 last year and 76 the year before would have a prior of 84. Although equation (4) is a simple autoregres-sive trend model, one needs to recognize that more complex versions of this model could be considered. As an example, the manager might apply weighting parameters (β1 and β2) to past performance and to the change in the performance as follows:

P P Pt 1 t 1 2 t 1′ = +− −β β ( ).∆ (5)

The weighting factors result from a Bayesian updating process that subjectively weights each piece of information. Several factors influence the weighting factor. A pessimistic manager might assume that last year was an exceptionally good year for a variety of reasons and set the value of β2 at less than 1.0. An optimistic manager who perceived that last year was exceptionally difficult might set a value of β2 at some-what more than 1.0.13 Changes in resources and task difficulty might also matter (see below). An alternative approach might have the manager consider more than the past 2 years in estimating a trend. Such a calculation might consider some type of moving average or even a more sophisticated modeling of the performance patterns over time.

Much of management deals with the collection of information so that decisions can be informed. Equation (5) includes information only about the organization itself and its pattern of performance. Clearly, organizations collect a variety of information

11 Priors are subjective; they deal with expectations. If one consistently beats an established standard, the organization might well have priors that require performance well above the standard.12 Private sector research in the tradition of Cyert and March (1963, 34) terms this historical aspirations (see Greve 1998, 2008; applied to the public sector Salge 2011).13 This formulation clearly holds when the change in performance is more than 0. Whether it also holds or whether political sovereigns will allow it to hold, when the change in performance is negative is an interesting question and needs additional theorizing.

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both from within the organization and from the environment. Let us first deal with the collection of information from the environment. Terms like performance and efficiency are relative rather than absolute terms for most organizations (what are the best schools in the area?). That implies that how well organizations do or are expected to do is in comparison to competitors (private business), peers (comparable organizations), or some other group (Horn 1995).14 To the extent that public managers care about reputa-tions (Carpenter 2001; Carpenter and Krause 2012; Krause and Douglas 2005), they will be concerned with how their organizations compare to those around them.

considering Peer Effects in Prior Formation15

The most logical adjustment to a prior from equation (5) would be to add in informa-tion about how other organizations are performing (P*)16 over the same 2-year time period. In order to compare the gains of other organizations to the gains of the man-ager’s own organization, we subtract ΔPt−1 from ΔP*t−1:

P P P P Pt t 1 t 1 t 1 t 1′ = + + −− − − −∆ ∆ ∆[ * ]. (6)17

Using a hypothetical example, let us say that the past performance of a school district (Pt−1) is and overall pass rate of 75 and its change over the prior 2 years is 5, which would generate a prior (Ptʹ) of 80 for the current year. If the average change in all school districts’ performance for the 2 years (ΔP*t−1) is +7, this indicates that the trend for the organization is less positive than the comparison organizations. The term in brackets suggests that the manager should adjust the prior by 2 (or 7−5) for a new prior (Ptʹ) of 82.

What constitutes a comparable peer organization when a manager is subjectively learning looking to other organizations to set his own performance expectations? The simple way a manager defines P*t−1 is to compare her own organization (O1) to all the other peer organizations (Oi, i > 1):

Pi,t

i

N

N1

P* .t− =

−=

−

∑ 12

1 (7)

Equation (7) denotes a world of N organizations, indexed by i. The manager for an organization (O1) will use the average of the past performance (Pi, t−1) of all other peer organizations’ (Oi, i > 1) in determining her prior. In reality, when a manager

14 The private sector literature terms this social aspirations (Greve 2008).15 We thank one anonymous reviewer for the helpful discussion on how a manager looks at the performance of his peer organizations in defining the appropriate subjective performance expectation. We also thank this anonymous reviewer for suggesting that we include equations (7) and (8). 16 For simplicity, this is incorporated as a single value, for example, the average performance of peer organizations. It is possible to treat this as a vector of the performance of all other relevant organizations.17 Equation (6) can be reduced to a simpler form, but we leave it as written in order to clearly show that equation (6) is an unweighted version of equation (9).

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looks to her peer organizations, she is most likely to look for similarly situated peer organizations and weigh their performance accordingly. A school superintendent in a poor rural area, for example, would not expect his organization to perform exactly the same as a wealthy suburban school district, but would want to compare how his organization performs relative to other poor rural districts. This general mechanism of peer effects and learning requires additional consideration of how a manager would weigh his peer organizations’ past performance differently. Hence, equation (7) can be adjusted by adding a weighting factor πi for each peer organization:

P * .t

i,ti

N

ii

N

P

−

−=

=

=∑

∑1

2

2

π

π

1

(8)

In equation (8), the weight applied to each peer relationship πi is defined between 0 and 1; it should assume a value near 0 if there is little or no valid comparison to be made between two different organizations and near 1 if the two organizations are very comparable. The relationships implied by the weighting scheme used in equation (8) can be understood in terms of a network peer effect. An organization is more embed-ded in its learning networks as the number of peer organizations with nonzero weight-ing values (πi) increases and as the number of organizations with near-one weighting values (πi) increases. The learning network is strong if an organization is taking per-formance cues from a large number of peer organizations.

Just as the organization’s past performance could be discounted or augmented by some factors β1 and β2, information about peer performance might also be interpreted in light of management’s perception of the situations surrounding the performance of other organizations with a weighting factor of β3 as follows:

P P P P Pt 1 t 1 2 t 1 3 t 1 t 1′ = + + −− − − −β β β∆ ∆ ∆[ * ]. (9)

Comparing the β’s provides some information about management’s relative assess-ment of the value of information about the organization itself in contrast to the value of information about the performance of other organizations. A manager who heav-ily discounted the performance of other organizations would essentially reinforce the general autoregressive aspects of the organization and be less likely to take cues for performance from how others are doing. This discounting might approach its extreme in cases where the tasks of the government organization are unique, that is, there are no competitors or peer organizations. Similarly, a manager might give greater cre-dence to the performance of other organizations and might even use this as a strategy to spur the organization and its members on to higher performance.

Before tracing out the nuances of incorporating information from the environ-ment, discussing why managers are unlikely to establish a prior using just a simple comparison of their organization to other organizations based on current or past per-formance, that is, based on (P*t − Pt) or (P*t−1 − Pt−1), rather than changes in perfor-mance is necessary. Such a comparison is to take a static view of performance and

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does not consider any differences in resources or task difficulty across the organiza-tions and risks comparing, say, an inner-city school with its suburban counterpart. Although policy makers and journalists frequently make such naive comparisons, managers are expected to have an understanding of the production function of their organization and take this into consideration when proposing an action. Using a dif-ference in performance comparison (equations (6) and (9)) nets out the initial differ-ences in resources and task difficulty that various organizations face.

Although the comparison of the organization’s trend performance to that of other organizations is rational, the actual comparison group can be subject to consid-erable variation. The default option is a comparison to the universe of organizations, that is, schools in a state might be compared to all schools; but a more sophisticated use of information would be to compare to a set of peers, that is, all schools with simi-lar characteristics. One might even consider comparisons that focus on value added by the organization using some type of residuals from a regression analysis.18

The External Environment and Prior Formation

A second source of information that can be used to adjust a manager’s prior would be changes in the environment of the organization, that is, changes in the resources, constraints, or task environment. Public organizations face changes in all these fac-tors that in turn make task performance easier or more difficult. Budget reductions, for example, would normally imply a reduction in the expected prior as would the addition of constraints such as new laws or reporting requirements, or a more difficult task environment (any type of negative change in the environment). We might think of a vector of all such factors all coded so that larger numbers indicate items that will make task performance more difficult (T).19 As managers would generally know most of these factors at the start of a new performance year, the difference in tasks from the previous year (ΔTt) should come into the calculations as a negative factor with the appropriate vector of weighting factors (βʹ4)20:

P P P P P t t 1 2 t 1 3 t 1 t 1 4 t′ ′= + + − −− − − −β β∆ ∆ ∆ ∆[ * ] .ββ T (10)21

Additional information can be incorporated via considering the changes taking place inside the organization. Such factors might include investments in technology

18 With the example of schools, a value-added measure might be created by taking the residuals from a regression of students’ current performance on their past performance and/or their demographic characteristics. As the independent variables in this regression would explain much of the variance in student ability, the variation in the residuals would more precisely reflect what each school added to the students’ existing ability.19 Throughout this article, we use boldface letters to indicate column vectors. A prime denotes the transposition of a vector, and each row vector (e.g., βʹ4) is represented as the transposition of a column vector.20 This means if tasks get easier, perhaps through positive changes in the environment or new technologies, that the values of ΔTt would be less than 0 and these factors would increase the size of the prior.21 One might complicate this further by considering the changes in tasks for peer organizations just as a baseball team might consider both its resources and those of its competitors. To do this, one might modify this term in the model to take the same format as the previous term where one considers the change in the organization’s performance relative to the changes in the performance of peer organizations.

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that promise higher levels of performance, the predicted improvements from greater employee development, the contracting out or contracting in of some functions, turno-ver of management or street-level personnel, and a wide variety of other factors. If we designate a vector of these factors, I, all coded in a direction positively linked to per-formance, then a manager might logically consider the change in this vector of internal factors in adjusting a prior up or down with appropriate weighting, as in equation (9):

P P P P P t 1 t 1 2 t 1 3 t 1 t 1 4 t 5 t′ ′ ′= + + − − +− − − −β β β∆ ∆ ∆ ∆ ∆[ * ] .ββ ββT I (11)

The above argument essentially incorporates the calculus of estimating a prior before the start of a decision cycle (a year, a program term, etc.), but priors are subject to updating and can change both during a decision cycle and at the end of a decision cycle. A within-decision-cycle change in prior can best be illustrated by a negative shock to the organization, an unanticipated event that has negative ramifications for the organization. There is an existing literature on unanticipated budget shocks (Meier and O’Toole 2009; O’Toole and Meier 2010) as well as shocks generated by a massive influx of new clientele (either via severe weather events or immigration see Andrews et al. 2011; Hill and Hawes 2011; Meier, O’Toole, and Hicklin 2010).22 Such events create stress for the organization and may lead to a reassessment of what the organi-zation can accomplish in this decision cycle. This can easily be incorporated into the model by simply designating a shock variable (S) with a negative sign and some level of weighting into the equation:

P P P P P St 1 t 1 2 t 1 3 t 1 t 1 4 t 5 t 6 t′ ′ ′= + + − − + −− − − −β β β∆ ∆ ∆ ∆ ∆[ * ] .ββ ββ ββT I (12)

The shock does not have a time dimension, but it might be thought of as t minus a, where a is less than the length of the decision cycle.23

Shocks, of course, can be positive as well as negative. The nature of positive shocks has not been studied empirically nor has it been subject to extensive theorizing. Organizations always have the option of ignoring a positive shock. More interesting are the organizations that seek out events than can be turned into positive benefits for the organization. Although the subject of positive shocks has great potential for the study of public management, at the present time, it has been under theorized and under studied making it premature to model how such shocks might affect perfor-mance priors.

Performance gaps can widen or narrow as the result of updating the prior at the con-clusion of a decision cycle as new information comes into play. Perhaps the best example might be based on the comparison between how much the organization’s performance actually changed versus that of the peer organizations. A school district might meet its previously established prior (exam pass rate), but discover that it gained say four points in

22 Changes in the top manager might also be a shock if it did not result from a voluntary retirement (see Boyne and Dahya 2002; Hill 2005).23 Although the β parameters are subjective, it is also possible that some managers choose weighting factors that more accurately predict actual performance.

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overall performance during the cycle that the peer districts on average gained six points. A rational manager might conclude that the organization’s performance should have improved more than it did and adjust the prior by two points. This can easily result in a testing regime such as that for public schools as the content of tests varies from year to year and the process of norming tests does not always work perfectly (i.e., a test may end up easier or harder in practice than was predicted). Similarly, the introduction of a new technology that is fairly widespread might increase performance for everyone, or the implementation of a new law could reduce performance for every organization.

HYPOTHEsEs FOr MANAGEMENT: GIVEN A PErFOrMANcE GAP

The previous section outlined the Bayesian logic for determining when a performance gap (G) exists. It specified several possible estimates of a performance gap based on the level of information that is incorporated into the prior. An individual manager might use all of the criteria specified or might only use one or two criteria. The model, however, allows one to estimate these different performance gaps and then empirically determine which one best predicts the behavior of management in decision making. Several key management decisions provide promising areas to examine, including the strategic decisions of how much risk to take, the decision to gather additional informa-tion, and the decision to delegate (or to centralize). We focus on information gathering, risk taking, and the decision to delegate (or to centralize) because these managerial decisions are most likely to trigger substantial organizational changes. The same theo-retical logic applies to any other managerial decisions that are performance related.

As conceptualized here, a performance gap is an interval variable. It can take on nega-tive values, that is, actual performance can be better than expected performance. The inter-val nature of the gap generates some false precision at some levels. If gaps are relatively small, either positive or negative, they likely fall into the manager’s zone of indifference (Barnard 1938). Within this zone, it is rational for the manager to take no action based on the gap. First, the gap is small and, in fact, might be a function of the precision of measur-ing performance or the basic random fluctuations that one would expect in organizational outcomes. This uncertainty suggests waiting rather than acting might be more prudent. Second, managers have to consider decision costs and transactions costs in making deci-sions. If the potential payoff for a decision is small, then it makes little sense to invest in making a decision where the costs of decision making exceed the potential benefits to be gained.

Negative Performance Gaps

As noted above, a performance gap is an interval variable. Performance gaps can be both negative (exceeding the performance target) and positive (falling short of the performance target). Prospect theory suggests that managers will treat negative and positive gaps asymmetrically, that is, they will tend to over value losses relative to gains (Kahneman and Tversky 1979). One option is that public managers will simply ignore negative gaps—after all the organization exceeded its prior (i.e., expected per-formance)—and therefore, scholars should be concerned only with organizations that have positive gaps (i.e., performance decline). Such a philosophy, however, creates a

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selection bias (Konisky and Reenock 2013) in terms of examining only decisions that are forced on the manager (via the failure to perform) and eliminates the decisions where managers have the flexibility to respond either immediately or at some future point. One would expect that strategic rather than reactive managers would deal with both positive and negative performance gaps, albeit in different ways.

The private sector literature suggests that negative performance gaps are an essen-tial part of managing such organizations (Rainey 2014; Teece 2009).24 In the private sector, a negative performance gap is the equivalent of gambling with house money; that is, it allows a great deal of discretion in terms of management action. When performance exceeds the expectation, managers could shift their attentions from per-formance-related concerns to slack-related concerns (Greve 2003; Singh 1986) and thus decide to pass these gains along to shareholders via dividends or other payments. Alternatively, they might decide to divert these gains for their own benefit via bonuses to management or stock options. Managers might also use the positive gains for con-quest, that is, to seize greater market share or expand into new markets. In short, negative performance gaps free private sector managers to consider a large range of management strategies and options and likely provide the resources for such actions.

Public sector managers are more constrained in that they cannot shift markets unilaterally and cannot take profits in a normal sense. Work on the politics of bureau-cracy, however, suggests that exceeding performance expectations can be translated into both additional resources and autonomy in the use of those resources. Francis Rourke (1984) argues that organizational performance and the reputation for per-formance can be used to expand the mandates of an agency such as the National Institutes of Health did through its sponsorship of research.

Effective performance can also be a strong argument to make to legislatures and budgeting officials that resources are being spent effectively and that additional resources would yield even greater benefits (Fenno 1966). The entire logic of perfor-mance systems is that managers will be rewarded for higher performance; a process that works at the individual level and also has some analogies to the agency level. At the least, a public organization that exceeds expectations should have less difficulty in attaining future resources than one that fails to meet expectations.25

The logic on how performance can lead to greater agency autonomy is best devel-oped by the work of Carpenter (2001) who examined several historical cases with substantial attention to what becomes the US Postal Service. Carpenter argued that the reputation for expertise and performance generates trust on the part of political actors who are in return more likely to grant discretion and autonomy to the agency (see also Krause and Douglas 2005). This process applies not just to the implementa-tion of public programs, but to the actual design of policy as legislators rely on the

24 Beating expectations in terms of earnings is a central element in driving the price of a corporation’s stock. Corporations and analysts provide estimates (the equivalent of performance priors) for firms and these priors are reflected in stock prices. Beating expectations, as a result, generates a gain in stock price; failing to meet the expectations results in losses.25 One might adopt the Niskanen (1971) assumption that bureaucrats are budget maximizers because such behavior allows a bureaucrat to extract rents linked to power, prestige, and other nonincome factors. Our argument appears plausible without such an assumption, and empirical tests of Niskanen have not been promising.

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expertise of the agency to draft new legislation. As Carpenter and Krause (2012) con-tend, the ability to meet performance standards determines a bureaucratic agency’s “performative reputation.” Thus, managerial behavior in performance targeting is one important dimension of the efforts to build a reputation.

There could also be some nonlinearities in relation to negative performance gaps. If the organization is exceeding expectations by a modest amount, it suggests it might be rational for the manager to engage in incremental activities to improve the organizations. Such actions might include actions to improve the human capital of the organization with additional training or via seeking newer sets of skills, efforts to engage in more long-term strategic planning, or evaluation projects that can provide management with greater information on how programs work in different contexts. Negative gaps might also generate decisions that impose short-term costs (e.g., invest-ment in new technologies or physical facilities) that will lower performance initially but pay off greater in future years (Chen 2008; Greve 2003). Efforts to increase overall resources and to generate autonomy for the organization appear to require a consist-ent level of high performance rather than simply the modest exceeding of expectations (Rourke 1984).

The issue of negative performance gaps is clearly a promising area for additional work both on the theoretical level and the empirical level.26 We have few efforts to develop a political theory of management that rely on organizational effectiveness to gain political support or to solve conflict with political actors. Similarly, the types of decisions that public managers make when they have exceeded expectations or when they are awarded greater resources need systematic empirical investigation.

Positive Performance Gaps

The relationship between a positive performance gap and managerial decisions such as the efforts to acquire more information or to adopt a more innovative (read risky) strategy is unlikely to be strictly linear. When performance gaps are small but outside the zone of indifference, managers might decide to take modest incremental actions or simply to wait to see if the gap occurred by random fluctuation. Larger gaps are likely to encourage managers to take larger risks to compensate for what is clearly inadequate performance.27 The larger the gap, the more things the organization will need to change. This generates the first two hypotheses:

H1 The greater the positive performance gap, the more likely the manager will adopt a prospecting strategy (Miles and Snow 1978), that is, seek to be innovative and seek to exploit the environment.

26 Negative performance gaps, or consistently negative performance gaps, might be taken as a measure of managerial quality.27 The bivariate relationship between positive performance gaps and innovation may be confounded by the effect of resources if organizations with larger (positive) gaps tend to have fewer resources (which probably would not be the case if resources are adequately accounted for in calculating the prior; see equation (10)). Thus, it may be necessary to control for resources when estimating the effect of performance gaps on managerial decisions to innovate.

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H2 The relationship between performance gaps and prospecting strategies will increase at an increasing rate.

Note that risk-taking behavior such as innovation will introduce real organizational changes. With large positive performance gaps, a manager might also desperately take other risk-bearing activities without necessarily making any change to his organiza-tion. Cheating on performance records (see, for example, Bohte and Meier 2000) is one of these risk-taking decisions that does not introduce any real organizational change. The similar decision logic, however, could apply to cheating (as a risky behavior)—the motivation for cheating increases as a positive performance gap increases. Managers in successful organizations rarely need to cheat to improve their performance reports; and a manager is unlikely to cheat on his performance record when his organization exceeds the performance expectation.

In contrast, relatively small performance gaps might bring about efforts to buffer the organization from the environment so that the organization can focus on generat-ing greater internal efficiencies or what might be called a defender strategy (Miles and Snow 1978).

H3 The relationship between performance gaps and defending strategies will have an inverted U shape.

H4 The relationship between performance gaps and decisions to augment the human capital of the organization via training and similar efforts will have an inverted U shape.

The inverted U shape occurs because such strategies are targeted at incremental gains rather than bridging a large performance gap. With small gaps that remain in the manager’s zone of indifference, no action will be taken. As the gap increases, efforts to focus on improvements in efficiency have the potential to generate the gains necessary to close the gap. When the gap becomes very large, perhaps because the function is no longer viable (snail mail versus email), greater efficiencies can only postpone dealing with the underlying threat to the organization, not eliminate it. In such situations, a rational manager eschews defending for more risky strategies.

Managers also make decisions on acquiring information. Information might be linked to determining exactly what the cause of the performance gap is or alternatively trying to determine what possible solutions might be available. One would expect that performance gaps would increase the effort to gather informa-tion; but at larger and larger performance gaps, the need might be to take action quickly to rectify the situation which is likely to cut short the information-seeking process.

H5 The relationship between networking and other information gathering actions will be positively related to the size of the performance gap.

H6 The relationship between networking and the size of the performance gap will experience diminishing marginal returns and possibly even an inverted U shape.

The size of the performance gap might also influence which network nodes receive greater attention. Each of the potential network partners brings different information

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and perhaps different resources; similarly each of the nodes will make different demands on the organization in any exchanges. Modest performance gaps might be addressable by greater interaction with political sovereigns or organizational superi-ors in order to communicate the specific reasons for a performance gap or to provide assurances that it is being addressed. Larger performance gaps are likely to focus on professional nodes and peer organizations that can supply information that might be used to craft new policies and programs.

H7 The size of the performance gap will affect the contact with some network nodes more than others depending on the type of information needed.

A performance gap could also be a trigger for a manager to reconsider the delegation decision, that is, how centralized or decentralized organization decisions are. The gen-eral management literature frequently advocates decentralization as a general pana-cea, but the effectiveness of decentralization clearly is contingent on the location of skills and talent in the organization. Moving decisions downward in an organization, a logical response to environmental variation, works only when lower level managers are capable of using the knowledge gained in their positions and making decisions that are superior to those made centrally.

Separate from the potential for improved performance, the incentives created by a performance gap should be considered. A large positive performance gap or persis-tent performance gaps increase the probability that the top manager will be replaced. Given this risk to the manager, the logic suggests that positive performance gaps should result in greater centralization in the organization (i.e., the manager will match responsibility with authority and seek to control his or her own destiny by centraliz-ing decisions, Singh 1986). Managers respond to organizational decline by tightening control because they perceive performance loss as a threat to the legitimacy of power and leadership under crisis. Although decentralization may provide a mechanism for scapegoating, it is unlikely to happen under organizational decline. Besides the risk of losing managerial control, decentralization inevitably increases transaction costs and the time associated with making decision because there are more internal stakehold-ers in a decentralized system than those in a centralized system (Smart and Vertinsky 1977). Decentralization, moreover, requires resource commitment to low-level units as decisions are passed downward. During organizational decline, managers are more likely to cutback and contain transaction cost (Levine 1978; Pfeffer 1978), which makes centralization a more attractive strategy for managers in failing organizations. In sum, as the positive performance gap gets larger, the incentive to centralize should increase proportionately.

H8 A larger positive performance gap will be positively related to decisions to cen-tralize the organization.

rELAXING THE AssUMPTION OF ONE GOAL

The basic logic thus far is that a managerial decision (D) is a function of a perfor-mance gap (G) as expressed in equation (1):

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D G 1= +β ε. (1)

The theory presented above assumes a single performance indicator and thus a single performance gap. In many ways, the study of public organizations is more interesting and more complex because they often have multiple goals that are characterized by conflict or ambiguity in content (Chun and Rainey 2005; Lan and Rainey 1992). The theory can easily be generalized to situations with multiple goals and in the process generates some interesting empirical implications. Rather than having a single goal or performance indicator, the model can be extended by making the managerial decision (D) a function of two, three, or n goals:

D f G G G G1 2 3 n= ( ), , . . . . (11)

In this process, the manager faces not one potential performance gap but several. Because a performance gap is essentially a measure of distance between desired per-formance and actual performance, multiple goals merely move the theory into mul-tidimensional space rather than its current one-dimensional space. The simplest operationalization of this multidimensional space would be to interpret performance gaps as the Euclidian distance measure in this space between the priors and the actual performance.28 All existing hypotheses should apply.

Although the Euclidian distance solution is elegant, it is probably not practical for three reasons. First, a Euclidian distance treats positive and negative gaps as equal, which is clearly incorrect. This might be resolved by creating two distance measures—one for positive gaps and one for negative gaps. Second, as the number of dimensions increases, the calculation of the priors becomes more burdensome. This generates an auxiliary hypothesis:

H9 As the number of performance dimensions increases, the calculation of priors and the adjustment of priors will move toward less complex estimates of the priors.

In short, the bounded rationality of individuals and organizations will move in the direction of equation (3) (the pure past performance estimate) and away from the more complex estimation in equation (10).29

Third, the Euclidian distance measure does not consider the reality that not all organizational goals are equally important. Some of the goals have stronger stake-holder support or have greater professional justification. This difference in the impor-tance of goals, and thus the importance of different performance gaps, suggest that models should not aggregate performance gaps but rather treat them as individual items that each enter into the manager’s decision calculus.30 Equation (1) can be modi-fied to reflect multiple goals, each with its own weight, by modifying β1 and G to be the vectors βʹ1 and G, respectively:

28 Using the Euclidian distance would produce a functional form like the following: D G G G G1

22

23

2n

2 5= + + + +( . . . ) .0 .29 This argument assumes that managers actually calculate priors for several dimensions. As we discuss below, managers may alternatively respond to multiple dimensions by simply ignoring most of them.30 The behavioral theory of the firm suggests a sequential treatment of goals rather than a simultaneous assessment (see Cyert and March 1963).

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D 1= +ββ ′ εG (12)

or D G G G G 1 1 1 1 2 2 1 3 3 1 n n= + + + + +− − − −β β β β. . . .ε 31

The relative impacts of the different performance gaps on the actions of a manager (i.e., the individual goal weightings contained in βʹ1) in fact reveal the preferences of the manager in terms of competing goals (or judgments in terms of the goal impor-tance). A goal or performance indicator of little importance, as a result, should not be significantly related to decisions that managers make regardless of the size of the performance gap.32

H10 Managerial priorities among agency goals can be revealed by whether or not performance gaps that operationalize these goals are correlated with various management decisions.

As an alternative to these relatively complex strategies that seek to rationalize the process, a manager might simply approach performance gaps sequentially and move on the next gap when the first one is addressed. Even a sequential process, however, is likely to be affected by managerial priorities. That is, managers would not address the gaps in a random order, and Hypothesis 10 would still apply.

Multiple goals and performance indicators also allow the researcher to assess some questions related to goal clarity and goal displacement. There are two aspects of goal clarity—mission comprehensive goal ambiguity, that is, some or all of the goals for the organization are ambiguous, and evaluative goal ambiguity, that is, goals vary in how specifically they can be evaluated (see Chun and Rainey 2005). Both can affect goal clarity. Logically the clearer the goal and the closer the goal can be approximated by a performance indicator, the more important the performance gap for that goal is likely to be in determining managerial actions. As an example, schools face the situa-tion where performance on standardized tests is a clear goal, whereas that of educat-ing democratic citizens lacks a precise set of indicators (Smith 2003). Performance gaps with clear indicators will also be much easier to understand on the part of key stakeholders, including political sovereigns who allocate resources. Hence,

H11 The clarity of the performance indicator for an organizational goal will posi-tively correlate with the performance gap’s impact on managerial decisions.

Organizations that have a series of ambiguous goals face a slightly different prob-lem. In that case, no particular performance dimension stands out, and it might not be clear to management when real performance gaps do or do not exist. In such a situation, one might expect there to be a large zone of indifference because gaps are not easily recognized and agreed upon unless they are very large. Instead, most decisions might rely heavily on past decisions or alternatively seek the safety of IR (see below). With data measured at several points in time, an analyst could actually

31 β1−i refers to the ith element of βʹ1.32 In terms of equation (12), if Gi is of little importance to the manager, β1−i should be close to 0.

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trace out the process of goal displacement as the relative impact of the various gaps changes over time.33

EXTENsIONs OF THE THEOrY

The theory does not consider hierarchy, either hierarchy within the organization or hierarchy relevant to governance structures. The focus has been on management at the top of the organization, but nothing prevents the model from being used to predict the behavior of middle managers or even first-line supervisors. In such cases, however, it seems logical that models at lower levels would need to include some assessment as to expectations by superiors, that is, the management style of the supervisor. The general prediction is that midlevel managers would make decisions based on perfor-mance, whereas treating hierarchy as a constraint that limits the range of actions that a manager might take.

Hierarchy can be operationalized by adding to the model in equation (1) a term for the superior’s preference (HD) regarding the management decision under consideration:

D G H 1 7 D= + +β β ε. (13)

This factor should be weighted by β7, which reflects the degree to which the superior cares about the consistency of decisions relative to performance. In a pure manage-ment by objectives systems, the superior would prefer performance to the type of deci-sions and β7 would be equal to 0. A superior that insisted on conformity of decisions (or adherence to rules and procedures) would produce a β7 that approached 1.0 and a β1 that approached 0. A subordinate’s past record or performance should also affect the value of the β7 parameter with lower values (i.e., greater discretion) for produc-tive employees. The β7 might also be affected by structural factors such as the degree of decentralization in the organization.34 The logic for hierarchy can be extended to organizational rules with a similar vector that specifies a vector of rules and a weight-ing factor indicating how closely rules are to be followed. Both factors might simply be viewed as the extent to which the superior permits managers to exercise discretion in the organization.

Political hierarchy raises more complexity than does organizational hierarchy sim-ply because political governance structures can be more or less effective at imposing constraints on public organizations (Hicklin and Meier 2008). A public organization might be headed by an elected official, a political appointee, or a career bureaucrat. The top manager might report to a board of some type or to an elected official or

33 The use of multiple goals and different performance gaps to reveal preferences of managers and how managers might differ from one another has a great deal of promise. For example, one might think that managers with private sector experience would respond to different performance gaps than managers without that experience (see Petrovsky et al. 2012). Similarly one might compare public, private, and nonprofit managers of similar organizations based on the gaps that trigger their respective decisions. With multiple goals, the basic discount factors and weights (βs) can vary; it is also possible that different goals might differ in the length of the decision cycle.34 Decentralization might not be exogenous; a manager who did not insist on conformity would also likely be open to structural decentralization.

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directly to a legislature. The governance literature on higher education in the United States illustrates the many possible ways that such governance structures can be com-bined (Knott and Payne 2003). How these governance structures affect the ability of political sovereigns to influence the decision-making processes of the organization head needs to be theorized.35

For an organization with one political sovereign, incorporating political hierarchy can be done in the same way that hierarchy was incorporated into the model (equation (13)). The process becomes far more complex in separation of powers systems with multiple political sovereigns or in federal systems with mul-tiple levels of political sovereigns. The decisions that organizations make relative to the preferences of political sovereigns now take place in a multidimensional space with some political sovereigns more salient than others. There is a relatively extensive formal and empirical literature usually referred to as the political con-trol literature (see McCubbins, Noll, and Weingast 1989; Wood and Waterman 1984) that documents the complexities of this process. That literature, however, spends little time focused on questions of performance per se (but see Moe 1989), but rather deals primarily with the ideological direction of policy implementa-tion.36 The theory presented here can clearly be expanded into this literature, but the exposition of the theory in this case would require an extended discussion that is beyond the scope of this article. Similarly, one could also include electoral timing as a factor in the model to reflect the conventional wisdom that bureau-crats (and politicians) are likely to take fewer risks during an election year than they normally would.

A second way that hierarchy can be incorporated into the model is to relax (as in equations (11) and (12)) the assumption of a single performance gap and to create a vector of preferences of the hierarch (HG) as to which of these performance indicators (read goals) is the most important. Relaxing this assumption modifies and expands equation (13) as follows:

D H 1 7 D 8 G= + + × +ββ ′ ′G G Hβ εβ , (14)

where HG is a vector with values indicating the relative importance of each indica-tor (normalized from 0 to 1) in the eyes of the hierarch and where β8 indicates the degree to which the manager’s decisions are influenced by the hierarch’s preferences for goal prioritization. HD could be said to reflect the hierarch’s preferences in terms of process (decisions), whereas HG represents the hierarch’s preferences regarding out-comes (goals). Equation (14) can easily be generalized to the case where there are multiple principals by simply adding additional HD and HG terms for each of the principals. Although the generalization theoretically is straightforward and simple,

35 Politicians might also “miscalculate” the prior owing to a lack of knowledge, a lack of resources, or simply because it is expedient to do so.36 An interesting question is when political sovereigns are interested in performance issues. They clearly have this interest some of the time given their concerns with the timely delivery of social security checks and other routine government services. These types of implementation issues, however, have not been addressed in the political control literature. There are also likely some interesting tradeoffs facing organizations between being responsive ideologically and being responsive in terms of performance.

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the determination of the calculus of this situation in practice will be very complex as the number of gaps and the number of hierarchs increase.37

The relative weight placed on performance gaps by a hierarch (either within an organization or from outside the organization) raises the question of goal displace-ment and the politics of performance indicators. Since Blau’s (1955) classic study, we know that managers and organizations will seek to maximize performance indicators, and this effort can result in goal displacement as goals that are more easily quanti-fied take precedence over goals that are not. The internal politics of performance indicators can also generate problems as individuals seek to maximize their own per-formance even if it adversely affects the overall performance of the organization. The external politics of performance indicators is also relevant (see above) as various stakeholders will stress some performance goals over others. The salience of differ-ent performance gaps can also change over time; the politics of performance gaps is essentially efforts by various stakeholders to set the agenda on what the organization should do. The response to performance gaps should also be influenced by what the sanctions are for poor performance and the willingness of the hierarch to use these sanctions.38 In some cases, even public organizations can be put into receivership or shut down permanently (e.g., schools under No Child Left Behind) and managers can be replaced. How managers respond to the various performance gaps should tell us something about how managers perceive the relative influence of stakeholders.

A NOTE ABOUT TIME (AND DIscOUNT rATEs)

The current model paints managerial decision making as focused on short-term deci-sions because it deals with the next time period. Some decisions might be designed to be long term; as an example, investments in technology might lower the prior for this year but increase priors in future years as the gains from technology come on line. A forward thinking manager might establish both short- and long-term priors with the more distal priors being adjusted as more results come in similarly to how corporations forecast their earnings. Managers might also have different discount rates and thus judge how to tradeoff the difference between short-term losses versus long-term gains differently.39

Time can also be considered in regard to the current gap. Just as small perfor-mance gaps might be within a zone of acceptance, a performance gap for a single year might also be considered simply the period-to-period fluctuation. A rational manager

37 Until this point, the model presented here has been linear and additive. This is striking as the models of how management affects performance are decidedly nonlinear and contingent (see O’Toole and Meier 2011). A reasonable simplifying assumption is that the determination of a performance gap is linear and additive (even though the hypotheses generated are not), but that the interplay of various performance gaps should be viewed as nonlinear. The veracity of this assumption can be judged by subsequent empirical studies.38 The willingness to use sanctions is important. The sanction for state unemployment insurance programs is withdrawal of federal funds. Because this is such a draconian penalty, it has never been used. McDermott’s (2011) recent study of state takeover laws in education documents the hesitancy of state governments to use this power and the variation in its actual use.39 Discount rates might be determined by the discount rates of political sovereigns and how patient they are in terms of results.

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might well discount a performance gap when it occurs but increase its significance if performance gaps occur two or more periods in a row. In other words, a series of improvements (or declines) in performance across more than one decision period may well affect managerial assessment of performance gaps or the expectation of future performance.

A NOTE ON FOrWArD THINKING AND PLANNING

This theory of management decision is based on retrospective evaluations, the com-parison of current performance based on priors generated from past performance of the organization or others. There are times when managers need to make decisions for which past performance might be of little assistance. Legislators might assign a new task to the agency that is significantly different from current policy, or a new technol-ogy might arise that renders the organization’s primary function irrelevant (tanks ver-sus horse cavalry). Managers might consider such problems by seeking out analogies to current policies (e.g., the FBI might have approached their new drug enforcement duties in the 1980s by comparing it to money laundering or another crime with simi-lar characteristics). The legislation might, in fact, designate new priors by setting new goals or creating evaluation standards. Forward thinking and planning should clearly play a role managing organizations. This article does not deal with this set of ques-tions, which remains to be theorized.

LINKING TO OTHEr THEOrIEs

The theory presented here is a parsimonious theory based on the assumption that managers are seeking to improve the productivity of their organization. It is one of many theories, however, and parsimonious theories are always subject to challenge for their lack of scope. Illustrating how other theoretical approaches might be incorpo-rated into the current approach to analysis, therefore, is a worthwhile pursuit.

Theories that specify other factors that motivate managers other than perfor-mance such as power, income, security, policy, and altruism (see Downs 1967) are simply other premises that could be used to build a theory of management based on performance. As long as one can assume that the performance of the organization is a necessary condition to maximize these other values (relatively easy to do), then the predictions of the theory should hold.

Four other theories that can be applied to managerial choice merit some dis-cussion—professional norms, IR, resource dependence, and population ecology. Professions are concerned with values as well as skills, and as a result, professions provide the support for making decisions in certain ways (Adolph 2013; Teodoro 2011). The basic hypothesis is that managers will make decisions to be consistent with their professional norms rather than a single-minded concern for performance. The norms hypothesis can easily be incorporated into the basic models here simply by providing a measure of a set of professional norms relative to decision choices (e.g., attitudes toward risk, information gathering, standard operating procedures). In such as situation, the hypothesis is that managerial decisions over time will converge to

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these professional norms and that this convergence will generate better predictions than a decision rule with performance at its center. The convergence hypothesis is necessary if professional norms are considered a constant that will affect all organiza-tions. Managers can vary in their adoption and endorsement of professional norms, and in this way, professional norms would become another variable in the model that predicts the managers’s decision. Professional norms might be operationalized by the portion of top management with a specific background (e.g., economics, see Eisner 1991) or membership in a specific professional organization (Teodoro 2011).

The incorporation of profession norms is done most directly by including it as a factor in equation (10) on the determinants of performance priors. Professional norms, however, might also focus directly on how decisions should be made or which performance indicators are the most important and thus which gaps should be con-sidered in equation (12). As an example of the latter, an economist looking at college education might focus on the job earnings of graduates, whereas a political theorist might prefer to focus on creating democratic citizens. If one can measure professional norms in terms of both process-oriented norms (ND) and outcome-oriented norms (NG), then professional norms can be modeled in much the same way as hierarchy was modeled in equation (14):

D N 1 9 D 1 G= + + × +ββ′ ′ εG G Nβ β 0 . (15)

IR holds that a risk-averse manager facing an uncertain environment will seek to pat-tern decisions after other managers, likely a set of successful (perhaps role model) managers (Frumkin and Galaskiewicz 2004). The manager can then defend his or her performance by contending that the decisions are the same as the best managers’ and the difference in results must be the result of factors outside the control of the manager. IR can be operationalized in a manner similar to professional norms except that rather than expecting decisions to converge to some exogenously defined set of professional values, the convergence over time is to the average decision of a set of top managers (operationalized as the mean action of all organizations or some subset of high performing organizations). If the role models for the organizations vary, then IR can be a variable rather than a constant and it can be incorporated as an additional variable in the modeling. Alternatively, IR might play a role in the relative weighting of different performance gaps as in equation (14). An example is the broken windows theory of crime, which stresses stringent enforcement of minor crimes as a way to combat serious crime (Kelling and Coles 1996).

D IR 1 11 D 12 G= + + × +ββ ′ ′ εG G IRβ β . (16)

Resource dependency theory can be applied in a direct way (Pfeffer and Salancik 2003). Resources are presently incorporated into the T vector. Measures of depend-ency might also be incorporated directly into the T vector; alternatively, the theory predicts that the T vector itself should be an important predictor of organizational decisions.

Finally, population ecology of organizations, a theory borrowed from biology, holds that organizations survive and flourish because they are in favorable environ-ments (Hannan and Freeman 1977; Kaufman 1985). Management, in this theory, has

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nothing to do with performance. In a sense, population theory is an antitheory of management and suggests that the current theory may be able to predict manage-rial actions based on performance, but that management will have no impact on any future performance of the organization.

With sufficient data, it might be possible to incorporate all of the alternative theo-ries discussed above. The complexity of the nonlinear interactions, however, places a great deal of stress on a data set in terms of multicollineary, and there are few public management data sets with the thousands of cases needed to generate reliable esti-mates. A sequential, piecemeal approach to incorporating alternative theories is likely to hold the greatest payoff.

cONcLUsION

This article presented a parsimonious theory of why public managers make the deci-sions that they make in regard to seeking information, accepting risk, structuring organizations, and other factors. It started with the assumption that managers are interested in or are rewarded for improvements in performance. The concern with performance quite logically leads to a discussion of performance gaps and how those gaps are actually determined using prior expectations about the level of performance. These prior expectations are a function of the historical performance of the organiza-tion, the performance of peer organizations, changes in task demands, and changes in the internal aspects of the organization. Performance gaps might also be adjusted as the result of shocks to the organization or the discovery of new information.

Using performance gaps as the basis for predicting managerial action, a series of hypotheses were generated about whether managers assume greater risks, seek addi-tional information, or centralize their organization. The theory was then further gen-eralized by considering multiple performance gaps and the inclusion of stakeholders and/or hierarchy in the models. Finally, the theory presented here was shown to be compatible with other theories of managerial decisions such as professional norms, IR, resource dependence, or population ecology.

The end result is a testable theory about why managers make the decisions that they make. The theory itself can be considered a “normative” theory of management as it predicts how managers should make decisions if performance is the primary moti-vator of such decisions. The utility of the theory, however, is in its use for research and the ability to predict when managers take more risks, seek more information, change organizational structures, or make countless other decisions. The theory can be tested in any situation where there is an existing performance database, preferably for several years, and a survey of managers. The testing will determine if the theory is as generaliz-able as it is presented or if the hypotheses specified are subject to contingencies related to the organization, the environment, the political context, or a wide variety of other factors.

rEFErENcEs

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Performance Gaps and Managerial Decisions