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Accounting, Organizations and Society 31 (2006) 179–210

The relation between cost-system design, managers�evaluations of the relevance and usefulness of cost data,

and financial performance: an empiricalstudy of US hospitals

Mina J. Pizzini *

School of Management, University of Texas at Dallas, J043 Box 83688, Richardson, TX 75083-0688, United States

Abstract

This study examines the association between cost-system functionality, managers� beliefs about the relevance and

usefulness of cost data, and actual financial performance using a sample of 277 US hospitals. Results indicate that man-

agers� evaluations of the relevance and usefulness of cost data are positively correlated with the extent to which systems

can provide greater cost detail, better classify costs according to behavior, and report cost information more frequently.

However, only the ability to supply cost detail is favorably associated with measures of financial performance, including

operating margin, cash flow, and administrative expense. Interestingly, cost-system design was not associated with oper-

ating expense per admission, suggesting that accounting information had not yet been successfully used to manage clin-

ical costs.

� 2004 Elsevier Ltd. All rights reserved.

Introduction

This study investigates associations between

cost-system design, managers� beliefs about the rel-evance and usefulness of cost data, and actualfinancial performance using a sample of 277 US

hospitals. Accounting literature identifies at least

0361-3682/$ - see front matter � 2004 Elsevier Ltd. All rights reserv

doi:10.1016/j.aos.2004.11.001

* Tel.: +1 972 883 6337; fax: +1 972 883 6822.

E-mail address: [email protected]

four critical attributes of cost-system design: the

level of detail provided, the ability to disaggregate

costs according to behavior, the frequency with

which information is reported, and the extent to

which variances are calculated. The first attribute,the level of detail, refers to the system�s ability to

supply data about cost objects that vary in size

from entire divisions to individual products, com-

ponents, and services. Chenhall and Morris (1986),

Feltham (1977), Kaplan and Norton (1992), and

Karmarkar, Lederer, and Zimmerman (1990)

ed.

mailto:[email protected]

1 Statistics can be found at www.cms.hhs.gov/statistics/nhe

and were compiled by Centers for Medicare and Medicaid

Services, Office of the Actuary.

180 M.J. Pizzini / Accounting, Organizations and Society 31 (2006) 179–210

incorporated level of detail in their characteriza-

tions of cost-system design. The second character-

istic of cost-system design, the ability to

disaggregate costs according to behavior, closely

relates to the first. To supply detail, the systemmust first separate and classify costs according to

behavior. Basic cost classifications explored in

the literature include fixed/variable costs, direct/

indirect costs, and controllable/non-controllable

costs (Feltham & Xie, 1994; Johnson, 1992; Kar-

markar et al., 1990; Khandwalla, 1972). The third

attribute, cost reporting frequency enables manag-

ers to expediently address problems and identifyopportunities for improvement (Hilton, 1979;

Karmarkar et al., 1990; Simons, 1987). The final

cost-system trait, variance analysis, highlights dif-

ferences between budgeted and actual outcomes

and seeks to explain such differences (Karmarkar

et al., 1990; Khandwalla, 1972; Simons, 1987).

More functional cost systems are those that can

provide greater detail, better classify costs accord-ing to behavior, report cost information more fre-

quently, and/or calculate more variances.

The conceptual model linking cost-system de-

sign to performance is typically stated in terms of

a causal chain in which more functional or refined

cost systems produce ‘‘better’’ (i.e., more relevant

and useful) data that enhance managerial decision

making, and thereby lead to improved economicperformance (e.g., Cooper & Kaplan, 1991; John-

son, 1992; Shank & Govindarajan, 1993). Despite

the intuitive appeal of this rather basic premise,

there is little systematic evidence linking cost-sys-

tem design to economic performance. Analytical

models of single-firm settings find that more de-

tailed and frequent cost data are more useful in

decision making (Feltham, 1977; Hilton, 1979);however, evidence in multi-firm settings indicates

that strategic behavior can bring about conditions

in which less informative product-cost data are

optimal (Banker & Potter, 1993; Callahan & Gab-

riel, 1999; Gal-Or, 1987; Gal-Or, 1998; Gal-Or,

1993). Research on institutional theory and infor-

mation overload further suggests that managers

may not effectively use the more detailed, disaggre-gated, and voluminous data produced by highly

functional systems (Covaleski, Dirsmith, & Mich-

elman, 1993; Schick, Gordon, & Haka, 1990).

Highly functional systems also cost more to imple-

ment and administer; consequently, the benefits of

such systems may not exceed their costs (Babad &

Balachandran, 1993; Banker & Potter, 1993; Kar-

markar et al., 1990). Finally, contingency theorysuggests that greater functionality is not a priori

better. Rather, performance improvement is a

function of the alignment between cost-system

functionality and the firm�s operating environment

(see Chenhall, 2003, for a review).

The US hospital industry is an ideal setting for

investigating the performance effects of cost-sys-

tem design. The industry provides the opportunityto sample from a large number of complex organi-

zations that operate under similar circumstances

and offer relatively standardized services. In the

United States, expenditures for hospital care ex-

ceeded $486 billion in 2002 and constituted almost

5% of the gross domestic product (GDP). 1

Healthcare literature overwhelmingly advocates

the use of highly refined cost systems to enablehospital managers to respond to growing pressure

to control costs in this rapidly changing industry

(e.g., Cooper & Suver, 1994; Chua & Degeling,

1991; Hill & Johns, 1994; Young & Pearlman,

1993). Such advocacy is consistent with manage-

ment accounting research (e.g., Chenhall & Mor-

ris, 1986; Gordon & Narayanan, 1984; Hill,

2001; Khandwalla, 1972), which suggests that asenvironmental uncertainty increases, decision

makers seek more information for planning and

control. Yet, in practice, US hospital cost systems

vary greatly in functionality, with many systems

presumably lagging the new managerial informa-

tion needs created by sweeping, industry-wide

changes (Hill & Johns, 1994; Orloff, Littell, Clune,

Klingman, & Preston, 1990; Serb, 1997).Empirical evidence on the performance implica-

tions of cost-system design is primarily limited to

small-sample studies focused upon managers� be-liefs about a single aspect of cost-system design

(activity-based costing); and here, results are mod-

est (e.g., Foster & Swenson, 1997; Ittner, Lanen, &

Larcker, 2002; Krumwiede, 1998). Using data

http://www.HCIA.com

2 Source: American Hospital Association website, www.

aha.org/aha/resource_center. Excludes federal government hos-

pitals, which constitute only a small proportion of the market

and primarily serve veterans and active members of the

military.3 Source: Centers for Medicare and Medicaid Services

website, www.cms.hhs.gov/medicare.

M.J. Pizzini / Accounting, Organizations and Society 31 (2006) 179–210 181

from 277 US hospitals, this study extends prior re-

search in several ways. First, rather than simply

identifying whether an organization has adopted

a single costing technique, comprehensive survey

data provide a relatively objective, multidimen-sional characterization of the functions a cost sys-

tem can perform. Second, the ability to link

descriptive data on cost-system design with actual

hospital-level financial data from external sources

permits some of the first direct tests of the relation

between specific cost-system attributes and actual

economic outcomes. Third, the relatively large

sample size and operational homogeneity of hospi-tals enable powerful tests of the research question.

In contrast, prior studies relied predominantly on

small-sample field studies or moderate-size sam-

ples of firms in diverse industries (e.g. Shields,

1995; Swenson, 1995). Finally, this study adds to

the literature on contingency theory by measuring

‘‘fit’’ between control system and organizational

context using both ‘‘selection’’ and ‘‘systems’’ ap-proaches (Selto, Renner, & Young, 1995, p. 679).

Results indicate that managers find cost data to

be more useful and relevant if supplied by systems

that provide greater detail, better classify costs

according to behavior, and provide cost informa-

tion on a more frequent basis. However, actual

financial performance, as characterized by operat-

ing margins, cash flow per bed, and administrativecost control, is significantly and positively associ-

ated only with those systems that provide greater

detail. There is some evidence that improved cost

classification capabilities can enhance cash flow

per bed; however, the performance effects of cost

classification cannot be separated from the effects

of providing detail. Cost-system design was not

associated with expense per admission, which re-flects clinical resource consumption, and hence, is

controlled primarily by physicians, not hospital

administrators. Performance tests that measured

cost-system functionality relative to hospitals with

similar strategies, structures, and environments

were consistent with tests that measured function-

ality relative to all hospitals in the sample. Taken

together, the results suggest that more functionalcost systems can help hospital administrators im-

prove non-clinical aspects of hospital operations,

such as cash management and administrative effi-

ciency; however, highly functional cost systems

have not been used to significantly reduce clinical

expenditures.

The remainder of this paper proceeds as follows.

The following section provides background on theUS hospital industry. Then the paper reviews the

literature on cost-system design and develops the

hypotheses within the context of the hospital indus-

try. The next section describes the research meth-

ods, followed by an analysis of the results of

statistical tests. The paper concludes with a sum-

mary of the results and limitations of the study.

Background on US hospital industry

In the US, the government is heavily involved in

the provision and regulation of healthcare; how-

ever, the US government still plays a much smaller

role in the health sector when compared with the

governments of most other countries. Privately-owned, nonprofit hospitals dominate the US hos-

pital market, accounting for approximately 60%

of all hospitals, while state and local govern-

ment-owned hospitals constitute only 22% of the

market. The remaining 18% of US hospitals are

organized as for-profit investor-owned firms. 2

Hospitals of different ownership type and for-prof-

it status often compete in the same markets. Al-most 55% of the total US healthcare bill is

funded from private sources including private

insurers (33%), individuals (15%), and other pri-

vate sources (6%). The US federal government

pays for roughly 33% of healthcare expenditures

primarily through its health insurance programs

for individuals age 65 or older (Medicare) and cer-

tain disadvantaged individuals (Medicaid). Stateand local governments cover another 13% of

healthcare expenditures. 3 Patient fees fund the

http://www.HCIA.com

http://www.HCIA.com

http://www.HCIA.com

6 Economic theories assume that individuals are rational

and that successful firms make appropriate decisions in

designing control systems. A number of alternative theories

suggest otherwise (see Luft & Shields, 2003, for a review); and,

several have used alternative theories to analyze management

control systems in healthcare (e.g., Abernethy & Chua, 1996;

Abernethy & Vagnoni, 2004; Chua & Degeling, 1991; Jones &

Dewing, 1997). However, systematic empirical evidence that US


bulk of expenditures in most nonprofit hospitals

and in all for-profit hospitals (Eldenburg, Herma-

lin, Weisbach, & Wosinska, 2004). Nonprofit pri-

vately-owned hospitals also receive significant

donations, while nonprofit government-ownedhospitals obtain substantial government subsidies.

For-profit hospitals receive few donations and, un-

like their nonprofit counterparts, their income is

taxed.

Despite differences in ownership and for-profit

status, US hospitals are strikingly similar in service

provision and organizational structure (Eldenburg

et al., 2004). All hospitals provide essentially thesame basic set of services using standard methods.

Across ownership types, hospitals are governed by

a board that is empowered to direct all hospital

functions (Phelps, 2003). The board, which is gen-

erally self-replicating (members elect their own

successors), represents the interests of various

stakeholders, including donors, community mem-

bers, and shareholders. The board provides overallstrategic direction, but normally hires nonphysi-

cian professional administrators to manage the

hospital. Senior hospital administers parallel their

counterparts in industry.

One rather unique aspect of US hospitals,

regardless of ownership type, is the distinction be-

tween hospital personnel and the medical staff of

physicians (Phelps, 2003). Hospitals employ bothadministrative professionals and nonphysician

clinical professionals (e.g., nurses and medical

technicians), but they do not generally employ

the physicians who are members of the medical

staff. Physicians gain entry to the medical staff

through a credentialing process controlled by the

existing medical staff. 4 The hospital has no expli-

cit contract with the medical staff, yet it is depen-dent upon staff physicians to attract and care for

hospital patients. Staff physicians direct patient

care within hospitals, and in doing so, control

the use of hospital employees and facilities. 5

4 Physicians apply to the hospital board for admission to the

medical and the board generally delegates responsibility for this

decision to existing medical staff (Phelps, 2003, p. 254).5 By law, most care provided to patients must be performed

by a licensed physician or under the direction of a licensed

physician (Phelps, 2003, p. 254).

For example, physicians admit patients, prescribe

all medications that hospital nurses administer,

and specify which tests hospital technicians

perform.

Given the prominence of the private sector inUS healthcare, researchers commonly use eco-

nomics to analyze and understand the US health-

care sector (for example, see Phelps, 2003,

Chapter 1). In fact, research in healthcare eco-

nomics provides strong empirical support for

the application of economic theory to the US

healthcare sector. For example, several studies

have found that physicians and hospital adminis-trators in the US respond to economic incentives

in a rational manner and there is growing evi-

dence that agency theory explains the choice of

compensation contracts in healthcare organiza-

tions (e.g., Brickley & Van Horne, 2002; DeBrock

& Arnould, 1992; Gaynor & Gertler, 1995; Gay-

nor & Pauly, 1990; Ittner, Larcker, & Pizzini,

2004; Lambert & Larcker, 1995; Lee, 1990).Empirical evidence also suggests that economic

theory underlies hospitals� strategic choices such

as location (Norton & Staiger, 1994; Wedig, Has-

san, & Sloan, 1989), investment (Krishnan, 2001),

cost allocations (Eldenburg & Kallapur, 1997),

operating strategy, and tax-status (Picone, Chou,

& Sloan, 2002). Following prior research on

healthcare institutions, this paper draws heavilyupon economic theory to develop the research

hypotheses. 6

hospitals conform to political, psychological and other non-

economic theories of behavior is quite limited. Conversely, a

significant healthcare economics literature provides strong

empirical evidence that physician behavior and managerial

decisions in US hospitals conform to economic theory (e.g.,

Brickley & Van Horne, 2002; Eldenburg et al., 2004; Gaynor &

Gertler, 1995; Gaynor & Pauly, 1990; Lambert & Larcker,

1995; Wedig et al., 1989).

7 DRG categories, which reflect the nature and intensity of

the hospital services, can essentially be thought of as the

hospital�s ‘‘products.’’ Hence, DRG-level costing enables hos-

pital managers to assess the profitability of its ‘‘products.’’8 However, a very complex method for classifying costs is

not necessarily a prerequisite for supplying useful detailed

information (Bromwich & Hong, 1999; Datar & Gupta, 1994;

Noreen, 1991). Even if cost classification is not highly accurate,

detailed cost data can still be useful (Merchant & Shields, 1993).

Hill (2001) contends that hospitals with procedure-level cost

systems will have better information for decision making than

those without such systems, even if the procedure-level system

uses a relatively unsophisticated method to assign costs to

procedures (e.g., the ratio of costs to charges).


Hypothesis development

Proposed benefits of greater functionality in hospital

cost system design

While there is little empirical evidence support-

ing associations between cost-system design and

performance, the extant literature suggests that

cost systems can be used to enhance both financial

and operational performance. Each individual ele-

ment of cost-system design potentially improves

the relevance and usefulness of cost data. More-

over, in the hospital setting, the cost system as awhole can potentially mitigate agency problems.

Proposed benefits of individual cost system design

elements

Level of detail. Management accounting literature

generally contends that an important feature of a

cost system is its ability to provide sufficient detail

and flexibility to allow costs to be analyzed for dif-ferent purposes (e.g., Karmarkar et al., 1990;

Shank & Govindarajan, 1993). Chenhall and Mor-

ris (1986) proposed that systems that can isolate

the effects of specific events on different functions

are of greater use to managers in uncertain envi-

ronments. Feltham�s (1977) analytical model sup-

ported these assertions, finding that the expected

payoff from decisions based upon more detailedinformation is generally greater than that from

decisions based on more aggregated information.

In hospitals, managers face various decisions

regarding the services (procedures, tests, diagno-

ses, etc.) provided, the customers (patients) receiv-

ing these services, the individuals (physicians,

nurses, therapists, etc.) providing these services,

and the groups (private insurance companies, gov-ernment agencies, individuals, etc.) that pay for

the services. Unless the system can identify and

aggregate costs for these cost objects (i.e., provide

sufficient detail) with reasonable accuracy, manag-

ers will not be able to make well-informed deci-

sions on related issues (Comerford & Abernethy,

1999; Covaleski et al., 1993; Evans, Hwang, &

Nagarajan, 1995). The ability to specify the costsof individual diagnostic-related groups (DRG) is

particularly relevant because Medicare and many

private insurers reimburse hospitals a predeter-

mined fixed amount based upon a patient�s DRG

(e.g., Chua & Degeling, 1991; Preston, 1992). 7

Lawrence (1990) hypothesized that actual financial

and operational performance would be higher for

hospitals that used systems that supplied greaterdetail; however, her empirical tests did not support

this hypothesis.

Classify costs according to behavior. The ability to

disaggregate costs and classify them according to

their behavior directly supports the ability to pro-

vide useful detailed cost information. For example,

to aggregate costs by procedure, direct costs mustbe traced to the procedure, and indirect fixed and

variable costs must be allocated to the procedure.

This requires the system to classify costs as di-

rect/indirect and fixed/variable. If this cost classifi-

cation is arbitrary, than the detailed cost data will

not be useful; however, if the system can meaning-

fully classify costs according to behavior, detailed

cost data should be useful. In fact, severalresearchers contend that correctly identifying cost

behavior is the first step in supplying accurate cost

information at all levels of detail (Cooper & Kap-

lan, 1991, Chapter 5; McGown, 1998; Shank &

Govindarajan, 1993, Chapter 10; Swenson,

1995). 8 Classifying costs according to behavior

is particularly important in the hospital industry,

which is characterized by large amounts of facil-ity-related support and joint costs (e.g., buildings

and sophisticated equipment shared by many

users) that cannot be directly and easily traced to

individual services. In Coombs�s (1987) field study

of two Swedish hospitals, both administrators and


physician clinic managers expressed strong interest

in systems that could better associate clinical activ-

ities with resource consumption.

Another important cost categorization is the

distinction between controllable and noncontrolla-ble costs because it aids principals in performance

evaluation (Feltham & Xie, 1994). In US hospitals,

the ability to identify functions that fall under a

manager�s control is particularly crucial because

hospital managers typically have less control over

basic business functions such as pricing, services

offered, and service delivery than their counter-

parts in other sectors. For example, if cost datashowed that emergency medicine was not profit-

able, the hospital could not simply turn people

away, raise prices, or shut down the emergency

room.

Frequency of cost reports. Cost-reporting fre-

quency enables managers to expediently address

problems and identify opportunities for improve-ment. Chenhall and Morris (1986) measured the

frequency of cost reports and contended that more

frequent reporting provides managers with feed-

back on decisions and information on recent

events that they can use to guide future courses

of action. Chenhall and Morris found that more

frequent reporting was believed to be particularly

useful to managers who operate in highly uncer-tain environments, such as hospitals. In response

to increased pressure to control costs, hospital

administrators in Coombs� (1987) field study

sought to increase the frequency with which they

received control information. Eldenburg (1994)

empirically examined whether more frequent

reporting of cost information to hospital physi-

cians could induce cost-conscious behavior. How-ever, contrary to her hypothesis, higher reporting

frequency was not significantly associated with

lower costs.

More frequent reporting may also indicate that

information is provided on a more timely basis.

For example, if cost information is reported

monthly rather than quarterly, managers can ad-

dress concerns that arise between quarters, ratherthan wait until the end of the quarter. Hilton

(1979) modeled the value of an information system

in a cost-volume-profit decision setting and found

that the more timely the information, the greater

the value of an information system. Karmarkar

et al. (1990) used timeliness to characterize cost-

system design, and they considered timeliness to

be indicative of the ‘‘elaborateness’’ of a costsystem.

Variance analysis. Proponents of variance analysis

contend that it aids in managerial decision making

by identifying corrective managerial actions (e.g.,

Johnson & Kaplan, 1987; Shank & Govindarajan,

1993). Budgetary control, which forms that basis

of variance analysis, may be appropriate for thehospital setting because uncertainty in both task

technology (Ouchi, 1979) and output (Cooper,

Hayes, & Wolf, 1981) renders administrative mon-

itoring difficult. Moreover, the prevalence of fixed-

price contracting (i.e., fixed reimbursements based

upon a DRG, patient-day, or procedure) should

make variance analysis very well suited for hospi-

tals because such contracts force hospitals to ac-cept the risk of unexpected costs and utilizations

(Abernethy & Stoelwinder, 1996; Lawrence,

1990; Preston, 1992). By specifying cost targets

and measuring performance in relation to these

targets, managers can use variance analysis to allo-

cate resources to clinical units and measure their

performance. In particular, instead of directly

monitoring physicians, hospital managers caninvestigate variances among amounts charged by

physicians to DRG-defined groups as compared

to hospital or industry standards (Covaleski

et al., 1993).

Potential benefits of a cost system as a whole:

reduction in agency conflicts

Conflicts between hospital board and hospital man-

agers. Most hospitals are nonprofit entities. The

nonprofit organizational form eliminates conflicts

between donors and residual claimants (Fama &

Jensen, 1983a); however, it does not eliminate

agency problems between donors and other inter-

nal agents, namely managers (Fama & Jensen,

1983b). Unlike for-profit entities, donors cannot

align managers� interests with their own by offeringmanagers an equity stake. Thus, donors rely on ac-

tive, independent boards to monitor nonprofit

managers and ensure that managers take actions


consistent with the interests of donors and external

stakeholders. Consistent with the growing empha-

sis on cost containment in the healthcare sector,

board members are increasingly using cost, profit,

and operational data to monitor managerial per-formance (Brickley & Van Horne, 2002; Molinari,

Hendryx, & Goodstein, 1997). More functional

systems can better associate costs with a manager�sactions and decisions, and therefore, produce mea-

sures that contain more information about a man-

ager�s effort.

Conflicts between management and healthcare pro-

viders. Physicians play a critical role in determin-

ing hospital resource use (Pauly & Redisch, 1973)

and, consequently, hospital financial performance

(Feinglass, Martin, & Sen, 1991). 9 Yet, hospital

managers typically have no explicit control over

physicians because most US physicians are not

employed by the hospitals at which they practice.

Moreover, physician and hospital incentives arenot well aligned because hospital physicians are

usually compensated based upon the volume of

services provided, while hospitals increasingly face

fixed-price contracts (Eldenburg, 1994; Pauly &

Redisch, 1973). 10 Cost data can potentially be

used instead of an explicit contract to control phy-

sician behavior (Covaleski et al., 1993). Specifi-

cally, Eldenburg (1994) and Evans et al. (1995)found that presenting physicians with data on their

own costs, along with comparative cost and oper-

ational information, provides some incentive for

physicians to modify their behavior. However, to

use cost data to mitigate physician-hospital agency

9 Physician control over admissions and in-hospital treat-

ment led Pauly and Redisch (1973) to characterize hospitals as

physician cooperatives designed to maximize physician�s clinicalefficiency and income. Chilingerian and Sherman (1990)

estimate that physicians directly influence up to 80% of all

hospital expenditures.10 Medicare and a growing number of private payers

reimburse hospitals a fixed amount based upon the diagnos-

tic-related-group (DRG) into which an illness can be catego-

rized, regardless of how much it costs the hospital to care for

the illness. Conversely, Medicare and most other payers

reimburse most hospital physicians on a fee-for-service basis,

i.e., based upon the amount of service provided.

problems, hospitals must have a cost system that

provides appropriate data.

Potential drawbacks of greater cost-system

functionality

The aforementioned benefits associated with

greater functionality implicitly assume that more

functional cost systems produce more relevant cost

data, which managers use to make performance-

enhancing decisions. Yet, research has not estab-

lished whether managers find data produced by

highly functional cost systems more relevant anduseful. Nor has extant research convincingly dem-

onstrated that more useful cost data lead to deci-

sions that positively affect a firm�s objective

function. Furthermore, even if more functional

cost systems lead to better managerial decisions,

the benefits of those decisions may not offset costs

associated with a more functional cost system.

Each of these concerns is discussed below.

Limited evidence that greater functionality leads

to ‘‘better’’ cost data

While many contend that more functional cost

systems provide ‘‘better’’ cost data (i.e., more rele-

vant and useful), this is difficult to empirically sub-

stantiate because the ‘‘true’’ costs of products and

services are unknown (Christensen & Demski,1997; Dopuch, 1993). Evidence regarding the qual-

ity of data produced by more functional systems is

limited to analytical studies of overhead alloca-

tions, and here, results are mixed (Banker & Pot-

ter, 1993; Bromwich & Hong, 1999; Christensen

& Demski, 1997; Datar & Gupta, 1994; Noreen,

1991). 11 Institutional theory suggests that hospi-

tals may adopt refined cost systems simply to con-form to societal expectations of acceptable

practice, and thereby gain external credibility and

validation (Covaleski et al., 1993). In such cases,

11 Banker and Potter (1993) and Datar and Gupta (1994)

demonstrate that greater complexity in allocation schemes

introduces classification, measurement, and other errors that

can actually reduce data accuracy. Others show that greater

functionality leads to greater accuracy only if fairly restrictive

conditions are met (Bromwich & Hong, 1999; Christensen &

Demski, 1997; Noreen, 1991).

12 In a 1992 survey on hospital cost systems, the majority of

respondents who did not use procedure-level cost systems

indicated that they did not have such systems due to resource

constraints, expense, or the concern that costs would outweigh

the benefits (Hill & Johns, 1994). Similarly, Counte and

Glandon (1988) found that 53% of respondents had not

upgraded their systems due to resource constraints, while 38%

feared the costs would not exceed the benefits.


hospital managers are unlikely to find the informa-

tion produced by more functional cost systems to

be useful.

Limitations on the usefulness of ‘‘better’’ cost data

Even if more functional systems produce ‘‘bet-

ter’’ cost data, such data may not necessarily lead

to improved firm performance. In fact, more infor-

mative cost data can actually hinder performance.

This has been demonstrated in multi-firm settings

characterized by strategic behavior (Gal-Or,

1987; Gal-Or, 1998; Gal-Or, 1993). Banker and

Potter (1993) specified demand conditions underwhich it was optimal for firms to use a relatively

simple system. Callahan and Gabriel (1999) found

that firms competing on the basis of product differ-

entiation (characterized as a Bertrand duopoly)

did not benefit from increased product-cost accu-

racy. The risk of information overload also sug-

gests that managers may be constrained in their

ability to effectively use data produced by morefunctional cost systems. Information that is too

detailed, disaggregated, and/or voluminous poten-

tially reduces decision-making effectiveness (see

Schick et al., 1990, for a review).

Finally, some researchers contend that variance

data specifically is not useful for managerial deci-

sion making. They argue that interdependence be-

tween variances may produce measurement errors,that variance analysis lacks information on the

causes of cost overruns, and that variances provide

incentives to pursue short-term cost reduction over

continuous improvement (e.g., Bastable & Bao,

1988; Cheatham & Cheatham, 1996; Cooper &

Kaplan, 1992; Mak & Roush, 1996). Others have

used political theories of organization to criticize

the application of budgeting, which underlies var-iance analysis, to the hospital sector. For example,

Abernethy and Vagnoni (2004), Covaleski and

Dirsmith (1986), and Kurunmaki (1999) suggest

that budgeting potentially serves to establish, dis-

tribute, and maintain power within hospitals,

and therefore, undermines the role that variance

analysis plays in performance-enhancing decisions.

The costs of greater functionality

The potential benefits of a more highly func-

tional cost system may not exceed the costs of that

system. Implementing a new system often entails

consulting, training, and software expenses. Com-

plex systems typically incur higher data collection,

processing, and management costs (Babad & Bal-

achandran, 1993; Banker & Potter, 1993). Surveysin the hospital industry suggest that the cost of

such systems is a major impediment to their use

(Counte & Glandon, 1988; Hill & Johns,

1994). 12 While the extant literature acknowledges

the costs of highly functional systems, it largely

ignores such costs in investigating the performance

effects of functionality. Analytical models gener-

ally do not address such costs (Banker & Potter,1993; Feltham, 1977; Hilton, 1979), and few

empirical studies use performance measures that

reflect the actual cost of administering the system

(Ittner et al., 2002; Lawrence, 1990).

In summary, there are several potential limita-

tions to cost-system functionality. However,

underlying economic theory suggests that the po-

tential benefits of more functional systems canovercome these limitations. In particular, the pro-

posed benefits of individual cost-system design ele-

ments, the potential to reduce agency problems,

and the need for more information created by

the rapidly changing hospital industry suggest

the following hypotheses:

H1a. Cost-system functionality is positively associ-

ated with managers� beliefs about the rele-

vance and usefulness of cost data in hospitals.

H1b. Cost-system functionality is positively associ-

ated with actual financial performance in

hospitals.

Contingency theory and cost-system design

The previous discussion posits that the absolute

level at which a cost-system functions is directly

13 Hill (2001) found that during the 1980s, for-profit

hospitals were less likely to adopt more functional cost systems

than their nonprofit counterparts, presumably because for-

profit hospitals could better avoid the Medicare revenue

constraints of that period. However, she speculated that during

the 1990s, managed care would place increased pressure on for-

profits to control costs.14 Hospital districts, typically found in rural areas, are

municipal entities composed of elected officials. Hospital

district commissioners have the power to levy taxes to subsidize

operations.15 Managed-care organizations (MCOs) often impose strict

cost-control measures on hospitals and negotiate lower prices

than other insurers.


correlated to a firm�s performance. However, con-

tingency theory contends that a firm�s strategy,

organizational structure, and environment dictate

its choice of control system (see Chenhall, 2003,

for a review). Any associated benefits or draw-backs are a function of the degree of alignment be-

tween the design of a firm�s cost system and the

specific set of circumstances the firm faces. Bene-

fits or drawbacks are not solely attributable to

the absolute level of cost-system functionality.

Consistent with contingency theory, hospital

cost-system design has been found to vary system-

atically with internal, organizational factors andexternal, environmental factors (e.g., Counte &

Glandon, 1988; Hill, 2001; Hill & Johns, 1994;

Lawrence, 1990). For purposes of discussion, I

group determinants of hospital cost-system design

into three categories: strategic, structural, and

environmental.

Although there is little empirical research link-

ing organizational strategy to control-system de-sign in hospitals, research in other industries

generally contends that strategy influences con-

trol-system design (see Langfield-Smith, 1997, for

a review). Using Porter�s framework (1980,

1985), strategy can be measured along two dimen-

sions: product differentiation and low-cost produc-

tion. Hospitals pursuing a differentiation strategy

are expected to focus resources on clinical care tothe detriment of administrative systems, such as

the cost system. Conversely, hospitals emphasizing

cost control will have more functional cost systems

because managers will require more information

for monitoring costs.

Structural determinants include case mix, teach-

ing affiliation, size, and whether a hospital is a

member of a multi-hospital system. Prior studieshave found positive associations between these

determinants and cost-system functionality

(Counte & Glandon, 1988; Hill & Johns, 1994;

Lawrence, 1990). Case mix measures the severity

of cases treated. Teaching hospitals, which typi-

cally treat more severe cases, are more complex

organizationally due to their research and educa-

tional responsibilities. As complexity increases,so does the need for cost information (Karmarkar

et al., 1990). Larger hospitals and system hospitals

will benefit more from functional cost systems be-

cause they can potentially spread the fixed costs of

system development over more beds (Hill, 2001).

Other important structural variables include for-

profit status and hospital district membership.

For-profit hospitals, which have the explicit goalof increasing owners� wealth, are expected to

implement more elaborate cost systems to aid in

this endeavor. 13 Hospitals that are organized by

hospital districts can potentially subsidize opera-

tions with funds obtained from local taxes. Thus,

district hospitals may have less incentive to control

costs and, consequently, less need for highly func-

tional cost systems. 14

A firm�s external environment also has been

found to influence cost-system design in the hos-

pital and other industries (Hill, 2001; Khand-

walla, 1972). Hospitals that operate in markets

with strong competition and/or significant pene-

tration from managed-care organizations (MCOs)

face greater external pressure to control costs and

therefore require more extensive and detailed costinformation (Hill, 2001; Lawrence, 1990). 15 Sim-

ilarly, hospitals that treat a large proportion of

patients covered by Medicare or managed-care

plans will face greater external pressure to con-

trol costs, because these payers often impose

the risk of cost overruns upon the hospital

through the use of fixed-price reimbursement

contracts. Such hospitals will likely need morefunctional cost systems to control costs. Taken

together, literature on contingency theory and

hospital cost-system design suggests the following

hypotheses:

Table 1

Descriptive statistics comparison of sample means to national

averages

National averages Sample means

Beds 185 272

Case mix 1.27 1.40

For profit 18.3% 7.2%

System members 39.5% 41.2%

Teaching hospitals 22.7% 36.8%

Medicare 51.6% 52.7%


H2a. Managers’ beliefs about the relevance and

usefulness of cost data are positively associ-

ated with the degree to which cost-system

functionality is aligned with the hospital’s

strategy, structure, and external environment.

H2b. Actual financial performance is positively

associated with the degree to which cost-sys-

tem functionality is aligned with the hospital’s

strategy, structure, and external environment.
Metropolitan 62.8% 67.7%
17 The sample may be skewed towards larger hospitals and

teaching hospitals because such hospitals have greater active

representation in HFMA, the industry group that helped to

distribute the survey.18 Calculating the functionality measures from multiple

items should reduce the error associated with any single-item

Research method

Data

The hypotheses are tested using survey data col-

lected in conjunction with fellow researchers at the

University of Pennsylvania�s Wharton School of

Business and the Hospital Financial ManagementAssociation (HFMA). The initial questionnaire

was based upon the extant literature and inter-

views with financial managers from five hospitals.

The survey was then reviewed by personnel from

HFMA, and modified as required. We sent the

survey to financial managers at 1703 hospitals.

Personnel at HFMA distributed 1042 surveys to

members in September 1997, and the businessschool researchers sent another 661 surveys to hos-

pitals that were not included in the HFMA mail-

ing. 16 After two months, surveys were mailed a

second time to hospitals that did not respond to

the initial mailing.

We received 385 completed surveys from Octo-

ber 1997 through January 1998. Sixteen surveys

were rendered undeliverable and returned uno-pened because the contact could not be located

or the hospital had closed. Consequently, person-

nel at 1687 of the 1703 hospitals actually received

the surveys. Of these, 385 responded, yielding a

22.8% response rate. Eleven specialty hospitals

were eliminated because their activities were not

comparable to acute-care facilities. Of the remain-

ing 374 responses, 313 surveys provided informa-

16 We generally addressed the surveys to the Chief Financial

Officer, the Controller, or the Chief Executive Officer. However,

lower-ranking individuals in the accounting or finance func-

tions often responded to the surveys.

tion sufficient to characterize cost-system

functionality. External data on financial perfor-

mance and control variables were available for

277 of the 313 hospitals. Table 1 presents descrip-

tive statistics for the 277-hospital sample and all

US hospitals. The sample is predominantly com-

posed of metropolitan hospitals that are larger

than the national average and treat relatively morecomplex cases. Compared to all US hospitals, the

sample contains more teaching hospitals (36.8%

vs. 24.5%) and fewer for-profit hospitals (7.2%

vs. 15.4%). 17

Cost-system functionality measures

The survey asked 20 questions relating to cost-system attributes. The responses were standardized

to a common scale, and factor analysis was used to

reduce the dimensionality of the questions. 18 Four

factors emerged with eigenvalues in excess of one,

with the factor solution retaining 65% of the total

variation in the data (Table 2). Following ortho-

gonal rotation, the four factors are: DETAIL,

CLASSIFY, FREQUENT, and VARIANCE.

measure. Some of the hospitals failed to respond to all survey

questions. Rather than exclude these hospitals from the

analysis, missing values were imputed prior to the factor

analysis. Following Beal and Little (1975), the missing data

were imputed using conditional means obtained from a stepwise

ordinary least squares regression.

Table 2

Factor structure correlations following orthogonal rotationa,b,c

DETAIL CLASSIFY FREQUENT VARIANCE Questionsd

Customize 0.55 . . . The cost accounting system can easily customize

reports to the specification of users

By payer 0.86 . . . To what extent does system provide data that

allow you to analyze costs by payer?

By contract 0.84 . . . To what extent does system provide data that

allow you to analyze costs by contract?

By physician 0.82 . . . To what extent does system provide data that

allow you to analyze costs by physician?

By procedure 0.74 . . . To what extent does system provide data that

allow you to analyze costs by procedure?

Per diem 0.75 . . . To what extent does system provide data that

allow you to analyze costs on a per diem basis?

By patient 0.85 . . . To what extent does system provide data that

allow you to analyze costs per patient?

Direct/indirect . 0.72 . . Does your cost system have a formalized method

of distinguishing direct and indirect costs?

Controllable . 0.77 . . Does your cost system have a formalized method

of distinguishing fixed and variable costs?

Fixed/variable . 0.55 . . Does your cost system have a formalized method

of distinguishing control/non-controllable costs?

Track F/V . 0.64 . . How sophisticated is method used to tracked fixed

and variable costs?

Use RCC . 0.63 . . To what extent is RCC used to estimate the costs

of individual services and procedures?

Frequent1 . . 0.83 . How often does the cost accounting system report

information to senior managers?


information to middle managers?


information to clinical managers?


information to nursing staff?


information to medical staff?

Efficiency variance . . . 0.68 Does your system calculate efficiency variances?

Mix variance . . . 0.58 Does your system calculate case mix variances?

Price variance . . . 0.76 Does your system calculate price variances?

Cronbach Alpha 0.93 0.83 0.88 0.53

a ‘‘.’’ Indicates correlation of less than 0.50.b Only factors with eigenvalues in excess of 1.0 were included. The lowest eigenvalue of included factors was 1.24 for the VARI-

ANCE construct.c DETAIL––the level of detail provided by the system. CLASSIFY––the system�s ability to classify costs according to behavior.

FREQUENT––the frequency with which cost information is disseminated throughout the organization. VARIANCE––the type and

number of variances calculated.d Questions were recoded, if necessary, such that higher values indicate greater functionality. For example, Use RCC was recoded

so that higher values indicate less use of RCC, and thus, greater functionality. All questions and response scales are included in the

Appendix.


DETAIL encompasses the extent to which the sys-

tem can accumulate costs at various levels of detail

(e.g., per patient, per diem, etc.). CLASSIFY mea-

sures the degree to which the system can classify

costs according to their behavior and the method-

ology used to classify costs. FREQUENT captures

the frequency with which cost-system reports are

distributed to various administrative and clinical

Table 3

Comparison of mean values of functionality constructsa for hospitals that received JCAHO accreditation and those that did not

Not JCAHO, accredited (n = 23) JCAHO, accredited (n = 270) t-Statistica,b

DETAIL –0.830 0.080 31.23***

CLASSIFY –0.498 0.046 11.93***

FREQUENT –0.206 0.030 2.03*

VARIANCE –0.262 0.041 3.51**

*p < 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided).a Functionality constructs are calculated by taking the means of standardized responses to questions that loaded greater than 0.50

on a single factor from the factor analysis (Table 2). The actual responses were standardized to a mean of 0 and standard deviation of

1. DETAIL––the level of detail provided by the system. CLASSIFY––the system�s ability to classify costs according to behavior.


number of variances calculated.b Two-sample t-statistic for test of hypothesis that values of functionality constructs for accredited hospitals are greater than those

of non-accredited hospitals. Results are similar if control variables for hospital structure and market conditions are included by using a

logistical regression.


personnel and proxies for the timeliness with

which the system can report on recent events and

provide feedback (Chenhall & Morris, 1986;

Eldenburg, 1994; Karmarkar et al., 1990). VARI-

ANCE measures the number of variances the sys-

tem calculates. 19

The questions were designed to be as objective

as possible, although we could not completelyignore subjective assessments. The goal was to

understand what kind of analysis and functions

the system could actually perform.

Each factor represents a different dimension of

cost-system functionality, which is measured using

the average standardized responses to questions

loading greater than 0.50 on a single construct

(see the Appendix for exact questions and theircoding). Cronbach�s coefficient alphas range from

0.53 to 0.93 (Table 2) and thus indicate that the re-

sponses used to calculate each functionality con-

struct are internally consistent. To further

establish that the four cost-system design con-

structs actually capture a system�s functional capa-bility, they are correlated with measures that proxy

for cost-system functionality. The first proxy for

19 The questions that load on the VARIANCE factor have

dichotomous (0, 1) responses. Technically, dichotomous

responses should not be included in a factor analysis. If

variance questions are removed, the factor solution gives three

factors with similar loadings to those reported when the

variance questions are included.

cost-system functionality is accreditation by the

Joint Commission on the Accreditation of Health-

care Organizations (JCAHO). The JCAHO is an

independent agency that evaluates the operational

efficiency of US hospitals. 20 Some of the specific

areas the JCAHO examines include organizational

improvement initiatives, leadership initiatives,

management of information, and management ofhuman resources. 21 Thus, it is expected that hos-

pitals with systems that meet accreditation stan-

dards also have more functional cost systems. As

shown in Table 3, the standardized values of all

four functionality measures are significantly greater

for hospitals that have received JCAHO accredi-

tation than those that have not. The difference is

especially significant (p < 0.001, one-sided) for theDETAIL and CLASSIFY constructs.

The remaining proxies for cost-system function-

ality are based upon individual survey questions

(see the Appendix for exact questions and coding).

One proxy is the extent to which a hospital�s costsystem differs from that used to comply with Medi-

care reporting requirements (denoted differs from

Medicare). Systems designed to comply with min-imum Medicare cost-reporting requirements pro-

20 Accreditation reports are available on the JCAHO�swebsite: www.JCAHO.org.perfrep.

21 While the JCAHO is responsible for attesting to the

quality of care, most of the measures address operational

efficiency. This has prompted many hospitals to forgo the

voluntary accreditation process (Burda, 2002).

http://www.HCIA.com

Table 4

Pearson correlations of functionality constructs with proxies for functionalitya,b

Differs from Medicare (n = 300) Accuracy (n = 304) Allocation bases (n = 304) Timely (n = 304)

DETAIL 0.42 0.42 0.54

CLASSIFY 0.54 0.35 0.61

FREQUENT 0.31

VARIANCE 0.28

a All correlations are statistically significant at the 0.001 level (two-sided).b Proxies for cost-system functionality were computed from the following questions: Differs from Medicare: ‘‘To what extent is you

organization�s cost accounting system different from the Medicare cost reporting system? (1 = same, 7 = completely different).

Accuracy: ‘‘The cost accounting system provides accurate data.’’ (1 = strongly agree, 7 = strongly disagree). Allocation bases: ‘‘In

allocating costs in revenue departments to a specific procedure, how many separate bases does your hospital use?’’ (N/A, 1, 2–3, 4–6,

>6). Timely: ‘‘Users receive information from the cost accounting function in a timely manner.’’ (1 = strongly agree, 7 = strongly

disagree). Functionality constructs are described in notes to Table 3.

23 Note that only FREQUENT is correlated with timely

because I do not expect the other functionality constructs

(DETAIL, for e.g.) to contribute to the timeliness with which

data are reported. Conversely, FREQUENT is not correlated

with the following functionality proxies, differs from Medicare,

accuracy, and allocation bases because it is not expected that


vide very little information that is relevant for

managerial decision making (Evans, Hwang, &

Nagarajan, 1997; Meeting & Harvey, 1998; Tsel-

epis, 1989; West & Balas, 1996). 22 Accordingly,

many hospitals have either augmented the Medi-

care cost-reporting system or developed a separate

system to manage costs. Thus, the more a hospi-tal�s cost system differs from the Medicare report-

ing system, the more functional it is expected to be.

As shown in Table 4, the DETAIL, CLASSIFY,

and VARIANCE constructs are all significantly

(p < 0.001, two-sided) and positively correlated

with the variable differs from Medicare. In particu-

lar, the DETAIL and CLASSIFY correlations are

0.42 and 0.54 respectively. The next proxy vari-able, accuracy, is calculated from survey partici-

pants� ratings of the accuracy of cost-system

data. Both DETAIL and CLASSIFY are posi-

tively (r = 0.42 and 0.35, respectively) and signifi-

cantly (p < 0.001, two-sided) correlated with

accuracy (Table 4). This suggests that systems that

can provide more detailed cost information are not

simply assigning costs arbitrarily to various costobjects. Furthermore, consistent with the asser-

tions of activity-based costing proponents, the

managers reported that systems that could better

classify costs according to behavior were more

accurate (Cooper & Kaplan, 1991, Chapter 5).

22 Medicare reimbursements received from the US Govern-

ment average close to 50% of a hospital�s total revenues. To

obtain these reimbursements, hospitals must file a Medicare

Cost Report.

The proxy variable, allocation bases, measures

the number of separate bases used to allocate costs

from revenue-producing departments to specific

procedures. A larger number of allocation bases

suggests greater homogeneity within individual

cost pools and a stronger association between

activities that give rise to the costs in the poolsand the activities used to allocate these costs. It

is expected that cost systems that can provide high

levels of detail and meaningfully classify costs will

use more allocation bases (Cooper & Kaplan,

1991, Chapter 5). Consistent with this premise,

allocation bases is positively and significantly

(p < 0.001, two-sided) correlated with DETAIL

(r = 0.54) and CLASSIFY (r = 0.61). Finally, thesurvey asks participants to rate the timeliness with

which the cost system provides data. This question

is reflected in the variable, timely, which is posi-

tively and significantly correlated with FRE-

QUENT (r = 0.31, p < 0.001). 23

more frequent reporting of cost data would contribute to the

extent to which a system differs from Medicare, system

accuracy, or the number of allocation bases used. Similarly,

the extent to which variances are calculated is not expected to

influence accuracy, nor is it expected to relate to the number of

allocation bases.


Performance measures

Managers� beliefs about cost system data and

actual performance help determine whether more

functional cost systems provide more relevant costdata, which managers can potentially use to make

performance-enhancing decisions.

Managers’ beliefs about the relevance and

usefulness of cost data

The relevance of cost data is measured by com-

paring managers� beliefs about the need for cost

information to the information actually suppliedby the cost system. The survey asks hospital man-

agers to rate the importance of cost information in

performing ten managerial activities, including

performance evaluation, contract negotiation,

budgeting, and pricing (see the Appendix for ques-

tions and coding). The survey also requests that

managers specify the extent to which the existent

cost system provides information that can be usedto perform these 10 managerial activities. I take an

average of the differences between managers� rat-ings of: (1) the importance of cost information in

performing an activity and (2) the extent to which

the system actually provides information for per-

forming the activity. The smaller the difference,

the closer the system is to supplying all of a man-

ager�s information needs. To calculate a measureof relevance (denoted relevant), the differences

are subtracted from the maximum difference so

that the larger the value of relevant, the better

the system meets mangers� needs. The usefulness

of cost-system information is measured with a sin-

gle question that asks respondents to rate the ex-

tent to which users rely on cost-system data to

make decisions (use).

25 Financial performance is a key factor in HCIA�s deter-

mination of the ‘‘Top 100’’ hospitals. Five of the eight measures

Actual financial performance

Hospital financial performance is assessed

with objective, publicly available information. 24

24 While the quality of care is of utmost importance, it not

used as a performance measure because escalating healthcare

costs (and not concerns with quality) prompted the redesign of

cost systems.

Although a nonprofit hospital�s objective is not

the accumulation of residual profits, financial vari-

ables are still important in evaluating manage-

ment�s success in meeting hospital objectives. 25

In his classic characterization of the nonprofit hos-pital, Newhouse (1970) reasoned that a nonprofit

hospital�s mission is to maximize the quantity

and quality of service subject to a zero-budget con-

straint and that revenues in excess of expenses are

reinvested in facilities to improve quality and ac-

cess. More recently, Prince (1998) contended that

steady operating profits are necessary to make

ongoing investments in equipment and facilitiessimply to keep pace with the rapid advancements

in medical technology. Consistent with both New-

house and Prince, Nelson, Rust, Zahorik, and

Rose (1992) found that patient beliefs about qual-

ity were positively associated with financial perfor-

mance. The importance of financial performance

in hospitals is further evidenced by the recent trend

toward the use of financial measures in evaluatinghospital executives (Early & Cleverly, 1995; Moli-

nari et al., 1997). 26 Brickley and Van Horne

(2002) found that financial performance (return

on assets) was positively associated with CEO

pay and negatively associated with CEO turnover

in nonprofit hospitals. Moreover, the turnover/

performance relation appeared stronger in non-

profit hospitals than in for-profit hospitals.Perhaps even more important than the direct

pay-for-performance link is the opportunity for

managers to materially increase their wealth by

moving to larger, higher-paying hospitals (Leone

& Van Horne, 1999). Finally, in the US, many

nonprofit hospitals actively compete with for-prof-

it hospitals. In such instances, Duggan (2002)

found that nonprofit hospitals mimic the behaviorof their for-profit counterparts.

used to determine the ‘‘Top 100’’ are financial or operational

( ‘ ‘100 Top Hospitals : Benchmarks for Success , ’ ’

www.HCIA.com).26 According to a 1997 Hay Group Survey, 59% of nonprofit

hospitals and 92% of for-profit hospitals use a performance

incentive for senior executives that includes financial measures

(Fralicx & Liccione, 1997).

http://www.HCIA.com

Table 5

Sample means and Pearson correlations for financial performance measuresa,b (number of observations)

N Mean Cash flow Administrative expense Expense per admit

Profit margin 272 4.72% 0.627 (272)*** �0.078 (272) �0.213 (272)***

Cash flow 277 46,590 �0.060 (277) �0.050 (276)

Administrative expense 277 11.90% �0.053 (276)

Expense per admit 276 4,199

a *p < 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided).b Profit margin–operating profit margin (%). Operating profit as a percentage of operating revenue. Excludes funds from charitable

contributions and other non-operating revenue. Cash flow––cash flow per bed. Administrative expense––administrative expense ratio.

Administrative expense divided by total operating expense. Expense per admit––operating expense per case-mix adjusted admission.


Data on hospital profitability and cost control

come from the SDC Platinum Hospital Industry

Database. 27 Given the importance of investment

to a hospital�s objective function, I selected two

measures of profitability that reflect funds avail-

able on an ongoing basis to reinvest and servicedebt: operating profit margin (operating margin)

and cash flow per bed (cash flow). 28 Both mea-

sures exclude funds obtained from charitable con-

tributions and other non-operating additions to

income; and therefore, they provide a clear picture

of the hospital�s underlying operations. 29 As

shown in Table 5, operating margin and cash flow

per bed are highly correlated (r = 0.63). However,both performance measures are retained because

operating margin includes a major non-cash ex-

pense, depreciation, which serves as a barometer

for medical technology. One way a hospital man-

ager can improve operating margin is to forgo

quality-improving investments in medical technol-

ogy that increase depreciation expense.

Two measures of hospital costs are included be-cause cost control is typically cited as one of the

primary reasons hospitals adopt more functional

27 SDC Platinum is a proprietary product of the Securities

Data Company. SDC obtains information for its hospital

database from HCIA, Inc.28 All three profitability measures are significantly and

positively correlated with a hospital�s index of predictive

creditworthiness. The index of predictive creditworthiness is

HCIA�s comprehensive, quantitative assessment of a hospital�sfuture financial viability.

29 Similar results (in terms of statistical significance) are

obtained if operating margin and cash flow per bed are replaced

with operating revenue per admission, net income margin, and

cash margin. HCIA, Inc. uses cash flow margin in its evaluation

of the ‘‘Top 100 Hospitals’’.

cost systems. The administrative expense ratio

(administrative expense) is the ratio of administra-

tive expenses to total operating expenses, and it

measures management�s ability to control expenses

that are not directly related to patient care. Elden-

burg et al. (2004) used the administrative expenseratio to predict CEO turnover. Case-mix adjusted

operating cost per adjusted admission (expense per

admit) is a measure of the hospital�s average total

cost for delivering care on a per unit basis, ad-

justed for severity (HCIA, 1997, p. 150). 30

Control variables

As discussed during the development of the

hypotheses relating to contingency theory, various

elements of a hospital�s structure, strategy and

environment potentially influence cost-system de-

sign. Of these variables, those that also have been

found to influence performance are included as

control variables. Structural control variables are

size, case mix, teaching status, for-profit status,and system membership. Prior research has found

positive relations between size and costs (Elden-

burg, 1994; Lawrence, 1990). Size is measured by

the number of available beds set up and staffed

(beds). Case mix (denoted case mix) is expected

to be positively associated with profitability and

cost measures, as more severe cases typically gen-

erate higher costs and higher margins (Lawrence,

30 Admissions are adjusted by multiplying the number of

acute care discharges by a factor that inflates it to include

inpatient acute care, inpatient nonacute care, and outpatient

care. Case-mix adjustments account for differences in complex-

ity, according to the Medicare case mix.


1990). Teaching hospitals (denoted teach, where

1 = teaching hospital, 0 otherwise) are expected

to have higher costs due to the added expenses

associated with teaching, research, and use of the

latest technology. For-profit hospitals (denotedfor profit, where 1 = for-profit, 0 otherwise) are

predicted to have higher profitability and lower

costs, consistent with their goal of increasing

owners� wealth. Hospitals that are members of

multi-hospital systems (denoted system, where

1 = system member, 0 otherwise) are also likely

to attain higher performance because they can

attract more capable managers, share knowledgeacross facilities, negotiate shared purchase agree-

ments with suppliers, obtain quantity discounts,

and negotiate more favorable labor contracts

(Lawrence, 1990). 31

Controls for environmental factors include

measures of competition (denoted competition),

MCO penetration (denoted MCO penetration),

the local wage level (denoted wage), and payermix. Competition is expected to be negatively

associated with all performance measures, as hos-

pitals in highly competitive environments typically

have lower operating margins and greater incen-

tive to control costs. MCO penetration is expected

to bring down local price levels. The wage index

accounts for geographic differences in the cost of

living and price levels. Payer mix is captured bythe percentage of revenues received from managed

care (denoted managed) and Medicare (denoted

Medicare). Both measures are expected to be neg-

atively associated with financial performance.

All control variables come from the SDC Plati-

num Database, except for competition, wage,MCO

penetration, and managed. Competition is calcu-

lated as one minus the sum of squared marketshares based upon beds in a county using data from

the American Hospital Association�s (AHA) an-

31 These variables also potentially control for managerial

skill (Fralicx & Liccione, 1997; Lawrence, 1990; Leone & Van

Horne, 1999). This poses a concern if more capable managers,

who enable hospitals to achieve better performance, are also

more likely to update hospital cost systems. Specifically, larger

hospitals, system hospitals, and for-profit hospitals are expected

to attract more sophisticated managers, and they are expected

to achieve better financial performance.

nual survey. The Health Care Financing Associa-

tion reports wage indices on its website.

InterStudy Publications calculates MCO penetra-

tion as the number of MCO enrollees in an area di-

vided by the area�s population. The variablemanaged is determined from a survey question

about payer mix (see the Appendix).

Correlations among measures of cost-system

functionality and control variables

Table 6 contains a correlation matrix of the cost-

systemmeasures and control variables. Because thefour cost-system design constructs relate to the

same system, they are significantly and positively

correlated. The variables DETAIL and CLAS-

SIFY are highly correlated (r = 0.68). However,

this correlation seems reasonable because the ex-

tent to which a system can classify costs (CLAS-

SIFY) affects the level of detail the system can

provide (DETAIL). 32 Several control variablesare also highly correlated with each other. Size is

significantly correlated with case mix (r = 0.69)

and the teaching indicator variable (r = 0.55) be-

cause large teaching institutions treat the most

complex cases. Competition is significantly corre-

lated with HMO penetration (r = 0.54) and the

wage index (r = 0.58). However, all control vari-

ables are retained to ensure a conservative and ro-bust test of the relation between performance and

functionality. 33

Results

The hypotheses are tested by examining rela-

tions between managers� beliefs about cost dataand actual financial performance and both the

absolute level of cost-system functionality and

the extent to which cost-system functionality is

32 Both measures were retained because factor analysis of

the relevant survey questions gave two different constructs.

Using factor scores from the orthogonal rotation, which gives

uncorrelated constructs, yields similar results in tests of the

research question.33 None of the independent variables has a variance inflation

factor in excess of 3.0 in any of the reported regressions.

Table 6

Pearson correlations of functionality constructs and control variablesa,b (N = 277)

FREQUENT CLASSIFY VARIANCE Beds Case mix For profit System Competition Wages MCO penetration Managed Medicare

DETAIL 0.34*** 0.68*** 0.39*** 0.34*** 0.39*** 0.05 0.23*** 0.29*** 0.14** 0.23*** 0.19*** �0.08

FREQUENT 0.26*** 0.31*** 0.12** 0.14** 0.09 0.16*** 0.06 0.01 0.04 0.04 �0.11*

CLASSIFY 0.34*** 0.28*** 0.34*** 0.03 0.17*** 0.33*** 0.17*** 0.25*** 0.20*** �0.12*

VARIANCE 0.21*** 0.28*** 0.09 0.18*** 0.22*** 0.09 0.14** 0.13** �0.09

Beds 0.71*** �0.09 0.11* 0.42*** 0.22*** 0.31*** 0.24*** �0.36***

Case mix �0.10 0.22*** 0.51*** 0.26*** 0.38*** 0.35*** �0.41***

For profit 0.16*** 0.04 �0.03 �0.01 0.02 �0.05

System 0.17*** �0.02 0.05 0.06 �0.07

Competition 0.52*** 0.55*** 0.45*** �0.47***

Wages 0.57*** 0.42*** �0.35***

MCO

penetration

0.46*** �0.32***

Managed �0.42***

a *p < 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided).b DETAIL––the level of detail provided by the system. CLASSIFY––the system�s ability to classify costs according to behavior. FREQUENT––the frequency with

which cost information is disseminated throughout the organization. VARIANCE––the type and number of variances calculated. Size––total number of beds set up and

staffed divided by 1000. Mix––Medicare case-mix index. For profit––an indicator variable that is 1 if the hospital is a for-profit entity, and zero otherwise. System––an

indicator variable that is 1 if the hospital is part of a multi-hospital system and zero otherwise. Teach––an indicator variable that is 1 if the hospital is a teaching hospital

and zero otherwise. Competition––calculated as 1––sum of squared market shares, based upon beds in a county. Wages––Medicare wage index for MSA or state. MCO

penetration––MCO penetration. The number of MCO enrollees in an MSA divided by the area�s population. Managed––percentage of revenue received from managed

care. Medicare––percentage of revenue received from Medicare.

M.J.Pizzin

i/Acco

untin

g,Organiza

tionsandSociety

31(2006)179–210

195

Table 7

Association between absolute level of cost-system functionality

and managers� evaluations of the relevance and usefulness of

cost dataa,b

Relevant Use

Intercept 2.074*** (7.73) �0.447*** (0.76)

DETAIL 0.141*** (3.05) 0.274*** (2.72)

CLASSIFY 0.081* (1.73) 0.039 (0.38)

FREQUENT 0.098*** (2.73) 0.112 (1.43)

VARIANCE 0.026 (0.63) 0.073 (0.82)

Control Variablesc

Beds 0.420* (1.96) 0.074 (0.16)

Case mix 0.299* (1.73) 0.143 (0.38)

System 0.167*** (2.97) 0.122 (0.99)

Medicare 0.004 (1.72) 0.002 (0.40)

7.25*** 4.03***

Adj. R2 0.228 0.125

N 277 277

a *p < 0.10 (2-sided), **p < 0.05 (2-sided), *** (2-sided), t-

statistics in ( ).b Relevant––Managers� perceptions regarding the extent to

which managers believe that cost data meets their information

needs. Use––Managers� perceptions regarding the extent to

which they rely on cost data to make decisions. See Appendix

(p. A-2) for questions used to calculate relevant and use.

DETAIL––the level of detail provided by the system. CLAS-

SIFY––the system�s ability to classify costs according to

behavior. FREQUENT––the frequency with which cost infor-

mation is disseminated throughout the organization. VARI-

ANCE––the type and number of variances calculated.c Only statistically significant control variables are included

in table. Control variables used in the regression are as follows:

Size––total number of beds set up and staffed divided by 1000.

Mix––Medicare case-mix index. For profit––an indicator vari-

able that is 1 if the hospital is a for-profit entity, and zero

otherwise. System––an indicator variable that is 1 if the hospital

is part of a multi-hospital system and zero otherwise. Teach––

an indicator variable that is 1 if the hospital is a teaching

hospital and zero otherwise. Competition––calculated as 1––

sum of squared market shares, based upon beds in a county.

Wages––Medicare wage index for MSA or state. MCO pene-

tration––MCO penetration. The number of MCO enrollees in

an MSA divided by the area�s population. Managed––percent-

age of revenue received from managed care. Medicare––per-

centage of revenue received from Medicare.


aligned with a hospital�s strategy, structure, and

environment.

The level of cost-system functionality

In tests of the level of functionality, variables

capturing managers� beliefs and actual perfor-

mance are regressed on the four cost-system attri-

butes (DETAIL, CLASSIFY, FREQUENT, and

VARIANCE) and the control variables. The re-

sults for managers� beliefs about the relevance

and usefulness of cost data are contained in Table

7. Both models are significant (p > 0.001) and theadjusted r-squareds are 0.228 and 0.125 for rele-

vant and use respectively. 34 The coefficient on DE-

TAIL is positive and highly significant (p < 0.01,

two-sided) for both measures. The coefficients for

CLASSIFY and FREQUENT are positively and

significantly associated with data relevance

(p < 0.10 and p < 0.01, two-sided, respectively),

but neither is associated with usefulness. However,recall that DETAIL and CLASSIFY are highly

correlated (r = 0.68). If DETAIL is eliminated

from either model, the coefficients on CLASSIFY

and FREQUENT increase in significance in the

relevant regression (p < 0.01, two-sided), and they

become significant in the use regression (p < 0.05,

two-sided). Because all the variables are standard-

ized, one can interpret the coefficients on theDETAIL construct, for example, as follows: a

1-standard deviation increase in DETAIL is asso-

ciated with a 0.14-standard deviation increase in

relevance and a 0.27-standard deviation increase

in usefulness of cost data. The VARIANCE con-

struct is not associated with managers� beliefs

about data relevance or usefulness. This result is

consistent with claims that traditional varianceanalysis is not a useful management tool (e.g.,

Bastable & Bao, 1988; Cheatham & Cheatham,

34 White�s (1980) test indicates no evidence of heteroskedas-

ticity. Shapiro and Wilk (1965) tests indicate that the residuals

are not distributed normally; however, histograms and normal

probability plots indicate the residuals are close to normal.

Elimination of observations with the greatest influence, as

measured by the change in a regression coefficient when an

observation is omitted from the analysis (DFBETA), had no

material impact on the results.

1996; Cooper & Kaplan, 1992; Mak & Roush,

1996), and that budgeting is not well suited to hos-

pital environments (Abernethy & Vagnoni, 2004;

Covaleski & Dirsmith, 1986; Kurunmaki, 1999).

The relevance and usefulness of cost-system

data are critical to the assertion that cost-system

design can be used to enhance financial perfor-

Table 8

Association between the absolute level of cost-system functionality and financial performance a,b

Operating profit Cash flow Administrative expensec Expense per admitc

Intercept 8.874* (1.92) 16,598 (0.66) 9.518*** (4.265) 2,070*** (3.36)

DETAIL 1.264** (1.97) 11,899*** (3.36) �0.555* (1.77) 83 (0.75)

CLASSIFY �0.257 (0.40) �135 (0.04) 0.287 (0.92) 11 (0.10)

FREQUENT �0.515 (1.06) �814 (0.30) 0.086 (0.36) �15 (�0.18)

VARIANCE �0.809 (1.46) �19,264** (2.53) 0.820*** (3.02) 48 (0.51)

Control variablesd

Beds 0.984 (0.34) �21,864 (1.36) �3.771*** (2.65) 924** (2.11)

Case mixe 4.913** (2.06) 52,869*** (4.07) �1.176 (1.02)

For profit 3.673** (2.55) �2,003 (0.25) 1.590** (2.28) 81 (0.33)

System �1.389* (1.80) �9,107** (2.14) 1.147*** (3.05) 18 (0.14)

Teach �0.827 (0.82) 2,836 (0.52) �0.408 (1.02) 516*** (3.07)

Competition �2.504 (1.56) 7,487 (0.86) 1.876** (2.42) 169 (0.62)

Wages �7.047** (2.31) �29,216* (1.74) 3.506** (2.35) 2,333*** (4.45)

MCO penetration �4.693 (1.59) �37,314** (2.27) �0.609 (0.42) �827 (1.62)

F 2.862*** 5.382*** 5.553*** 7.371***

Adj. R2 0.088 0.182 0.188 0.232

N 272 277 277 276

a *p 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided), t-statistics in ( ) below coefficient.b DETAIL––the level of detail provided by the system. CLASSIFY––the system�s ability to classify costs according to behavior.


number of variances calculated.c Smaller values are considered favorable because they indicate better cost control or greater efficiency.d Only statistically significant control variables are included in table. Control variables used in the regression are as follows: Size––

total number of beds set up and staffed divided by 1000. Mix––Medicare case-mix index. For profit––an indicator variable that is 1 if

the hospital is a for-profit entity, and zero otherwise. System––an indicator variable that is 1 if the hospital is part of a multi-hospital

system and zero otherwise. Teach––an indicator variable that is 1 if the hospital is a teaching hospital and zero otherwise. Compe-

tition––calculated as 1––sum of squared market shares, based upon beds in a county. Wages––Medicare wage index for MSA or state.

MCO penetration––MCO penetration. The number of MCO enrollees in an MSA divided by the area�s population. Managed––

percentage of revenue received from managed care. Medicare––percentage of revenue received from Medicare.e Expense per admit is adjusted for case-mix; therefore, the case-mix variable is not included as a control.


mance. Unless more highly functional cost systems

provide more relevant and useful data that lead to

‘‘better’’ decisions, they should be unrelated to dif-

ferences in financial performance. 35 Accordingly,

the tests of managers� beliefs about the cost systemprovide a basis for the expectation that cost sys-

tems that can supply greater detail, better classify

35 As an alternative to separate regressions, I constructed a

path model of the relation between cost-system functionality,

managers� beliefs about relevance and usefulness, and actual

financial performance. The results of the path analysis were

consistent with the causal model linking greater functionality

with increased data relevance and use. The total effects of the

DETAIL and CLASSIFY constructs on financial performance

were positive and significant. However, the results of the path

model were not tabulated because the approach mixes the

relatively subjective relevance and use measures with the more

objective cost-system attribute and financial measures.

costs, and distribute information more frequently

and broadly can lead to enhanced financial

performance. 36

Table 8 contains coefficients from the regression

of actual financial performance measures on the

36 In theory, if all hospitals have optimally selected their cost

systems, there should be no relation between cost-system design

and performance. While hospitals may choose optimally in the

long run, observers suggest that, in the short run, many

hospitals have not yet adjusted their cost systems to accom-

modate the increased information needs created by sweeping

environmental changes (Hill & Johns, 1994; Orloff et al., 1990;

Young & Pearlman, 1993). For example, a recent survey found

that while percent 80% of managers surveyed placed a high

value on obtaining specific cost information, only 33%

expressed confidence in the quality of their cost data (Serb,

1997).


level of cost-system functionality. 37 All four mod-

els are significant (p > 0.001) and the adjusted r-

squareds range from 0.088 for operating profit to

0.232 for expense per admit. 38 The capability to

provide more detail (DETAIL) is positively andsignificantly associated with operating margin

(p < 0.05, two-sided) and cash flow per bed

(p < 0.01, two-sided). 39 DETAIL is negatively

associated with the administrative expense ratio

(p < 0.10, two-sided), which indicates that admin-

istrative expenses are lower when a cost system

can provide greater detail. The significant associa-

tions between DETAIL and measures of bothmanagers� beliefs and financial performance sup-

port the premise that more highly functional sys-

tems provide more relevant and useful cost data,

which lead to better financial performance.

Neither CLASSIFY nor FREQUENT is signif-

icantly associated with performance in Table 8.

However, if the highly correlated DETAIL con-

struct is removed from this regression, CLASSIFYis positively and significantly associated with cash

flow per bed (p < 0.05, two-sided). These results

are consistent with prior findings regarding man-

agers� beliefs in that the ability to better classify

data becomes highly significant only when DE-

TAIL is removed from the regression. Although

the factor analysis suggested that the ability to

provide greater detail and the ability to classifycosts according to behavior are separate con-

structs, it is difficult to isolate their relations with

managers� beliefs about performance and actual

cash flow. The variable, FREQUENT, is not asso-

37 I also investigated two-way and three-way interactions

among the cost-system design constructs by including multipli-

cative terms. However, the high degree of multicolinearity

between the multiplicative terms and the individual constructs

precluded a meaningful interpretation of the coefficients.38 White (1980) test indicates no evidence of heteroskedas-

ticity. Shapiro and Wilk (1965) tests suggest that only the

residuals of the cash flow regression are distributed normally.

However, histograms and normal probability plots indicate the

residuals are close to normal. Elimination of observations with

the greatest influence, as measured by the change in a regression

coefficient when an observation is omitted from the analysis

(DFBETA), had no material impact on the results.39 DETAIL is also positively and significantly associated

with revenue per bed, investment per bed, and the number of

admissions per bed (p < 0.10, two-tailed), an efficiency measure.

ciated with actual financial performance even

though managers find information that is reported

more frequently to be more relevant and useful.

Interestingly, hospitals that engage extensively in

variance analysis exhibit significantly lower cashflow per bed (p < 0.05, two-sided) and relatively

higher administrative costs (p < 0.01, two-sided).

As discussed earlier, a potential explanation is that

greater use of variance analysis may lead to inap-

propriate decisions. 40

Finally, operating expense per admission, which

is driven by clinical resource consumption, is not

significantly associated with any of the functional-ity measures. This result is consistent with prior re-

search suggesting that initial efforts to improve

hospital performance typically focus on admin-

istrative processes and not clinical processes

(Shortell, Levin, O�Brien, & Hughes, 1995).

Furthermore, as discussed previously, hospital

managers have little direct control over clinical

expenditures because physicians control clinicaldecisions. Only if managers present physicians

with appropriate cost data can managers poten-

tially influence physician behavior (Eldenburg,

1994; Evans et al., 1995). Even then, the use of

accounting information in directing physician

behavior raises significant concerns (e.g., Aber-

nethy & Stoelwinder (1996); Abernethy & Vag-

noni, 2004; Chua & Degeling, 1991; Evans,Hwang, & Nagarajan, 2003; Jones & Dewing,

1997; Preston, 1992).

In summary, there is some evidence that sys-

tems that are better at supplying detail and classi-

fying costs provide more relevant and useful cost

data, which lead to better financial performance.

However, a major concern with cross-sectional

tests is that they do not address causality. Specifi-cally, better performing hospitals may have the

means to invest in cost systems development; thus,

strong financial performance could give rise to

more functional cost systems. Still, if it is the case

40 To confirm the validity of the VARIANCE construct, I

substituted each of the individual questions used to calculate

the VARIANCE construct in place of VARIANCE in the

performance regressions The only notable difference was that

those hospitals using price variances had significantly higher

expenses per admission.


that hospitals invest in cost systems simply because

they have excess funds, it is not clear that manag-

ers would find information supplied by more func-

tional systems to be more relevant or more useful.

Only when managers believed that a specific sys-tem capability (i.e., supplied greater detail, better

classified costs) provided more relevant and useful

information was financial performance better.

Causality is further examined in the extensions.

Alternatively, performance and cost-system func-

tionality may be simultaneously determined.

Simultaneity is considered in the next section. 41

Alignment with operating environment

The levels tests of the previous section assumed

that the appropriate benchmark for determining if

a system is ‘‘highly’’ functional is the average level

of cost-system functionality for all hospitals in the

sample. However, contingency theory contends the

appropriate benchmark is the expected level offunctionality, given the hospital�s organizational

context. Accordingly, I investigate associations be-

tween financial performance and the residuals

from an empirical model estimating the bench-

mark level of cost-system functionality as a func-

tion of hospital structure, strategy, and

environment (Hypotheses 2a and 2b). The estima-

tion of the benchmark model itself is an operation-alization of the selection approach to measuring fit

between control system design and organizational

context (Selto et al., 1995, p. 669). The selection

approach assumes that context drives organiza-

tional design, and therefore, defines fit in terms

of associations between context and control sys-

tem design. The residuals from the benchmark

model are a variant of the systems approach tomeasuring fit (Selto et al., 1995, p. 670). The resid-

uals measure variations in overall systematic fit as

41 Hausman�s specification error test can potentially identify

whether the independent variables are exogenous. However,

this test requires an instrumental variables estimate of the

regression coefficient on the design construct, and there are no

obvious choices for instruments. Moreover, a simultaneous

equations approach renders a system of five equations with five

endogenous variables. and thus would require four instrumen-

tal variables.

determined by the hospital�s organizational

context. 42

Following Chenhall and Langfield-Smith

(1998), strategy is characterized along two dimen-

sions, product differentiation and cost control.Product differentiation, denoted lead, is measured

by taking the average of three survey questions that

ask respondents to rate the importance of estab-

lishing clinical leadership, technological leadership,

and leadership in business processes. The extent to

which hospitals follow a low-cost strategy, denoted

cost, is quantified with two survey questions that

ask respondents to rate the importance of control-ling and eliminating unprofitable services. 43

To model cost-system functionality, I regress

each functionality construct (DETAIL, CLAS-

SIFY, FREQUENT, VARIANCE) on measures

of a hospital�s strategy, structure, and environ-

ment. Hospital structure is characterized by hospi-

tal size (size), case mix (case mix), teaching status

(teaching), system ownership (system), and hospi-tal district membership (district). The variable, dis-

trict, is set equal to 1 if a hospital is a member of a

hospital district association, and it is 0 otherwise.

The remaining variables were defined in the section

on control variables. Environmental factors are

also captured by variables defined in the section

on control variables: the level of competition (com-

petition), the degree of MCO penetration (MCO

penetration), and the percentage of managed (man-

aged) and Medicare (Medicare) patients in a hospi-

tal�s patient mix. As discussed in the section on

contingency theory, all structural and market vari-

ables are expected to be positively associated with

functionality, except district, which is predicted to

be negatively associated with functionality.

Results of the benchmark regressions are con-tained in Table 9. The model has the greatest

explanatory power for DETAIL (adjusted

42 Others have used the top performing organizations as the

benchmark in determining systematic fit (Selto et al., 1995).

This approach was not used here because, as discussed in

footnote 29, it is unlikely that hospital cost system design is in a

state of equilibrium.43 Each set of questions loads on a different factor. Cron-

bach�s alphas are 0.56 and 0.52 for lead and cost, respectively.

Each variable is the average of the standardized responses to

each set of questions.

Table 9

Regression of functionality constructs on hypothesized determinants of functional capabilitya,b,c

DETAIL CLASSIFY FREQUENT VARIANCE

INTERCEPT �1.857*** (4.15) �1.597 (3.75) �0.228 (0.48) �1.485*** (3.59)

Cost + 0.115* (1.86) (1.40) 0.243*** (3.69) 0.178*** (3.12)

Lead � 0.080 (1.47) 0.040 (0.77) 0.050 (0.85) �0.032 (0.63)

Teach � 0.180 (1.45) 0.079 (0.67) 0.028 (0.22) 0.156 (1.36)

Size +(1.33) 0.487 0.023 (0.07) 0.104 (0.27) �0.079 (0.23)

Case mix + 0.591** (1.99) 0.651** (2.31) 0.336 (1.06) 0.689** (2.52)

For profit + 0.188 (1.05) 0.165 (0.97) 0.298 (1.56) 0.329*** (1.99)

System + 0.204** (2.13) 0.142 (1.56) 0.224*** (2.20) 0.162* (1.84)

Competition + 0.194 (1.01) 0.284 (1.56) �0.241 (1.18) 0.111 (0.63)

MCO penetration + 0.226 (0.65) 0.396 (1.20) 0.109 (0.29) 0.106 (0.33)

Managed + 0.005 (1.08) 0.002 (0.57) �0.001 (0.32) 0.002 (0.46)

Medicare + 0.010** (2.49) 0.006 (1.64) �0.005 (1.12) 0.005 (1.35)

District � �0.540*** (2.65) �0.601*** (3.11) 0.163 (0.75) 0.064 (0.34)

F 7.95*** 6.35*** 2.53*** 3.94***

Adj. R2 0.232 0.188 0.063 0.113

N 277 277 277 277

a *p < 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided), t-statistics in ( ).b DETAIL––the level of detail provided by the system. CLASSIFY––the system�s ability to classify costs according to behavior.


number of variances calculated.c Cost––the extent to which the hospital follows a cost-focused strategy. Lead––the extent to which the hospital follows a leadership

strategy. Size––total number of beds set up and staffed divided by 1000. Mix––Medicare case-mix index. For profit––an indicator

variable that is 1 if the hospital is a for-profit entity, and zero otherwise. System––an indicator variable that is 1 if the hospital is part of

a multi-hospital system and zero otherwise. Teach––an indicator variable that is 1 if the hospital is a teaching hospital and zero

otherwise. Competition––calculated as 1––sum of squared market shares, based upon beds in a county. Wages––Medicare wage index

for MSA or state. MCO penetration––MCO penetration. The number of MCO enrollees in an MSA divided by the area�s population.Managed––percentage of revenue received from managed care.Medicare––percentage of revenue received fromMedicare. District––an

indicator variable that equals 1 if the hospital is a member of a hospital district association.


R2 = 0.23) and the least explanatory power for

FREQUENT (adjusted R2 = 0.06). All statistically

significant associations have the predicted sign.

Low-cost strategic orientation, case mix, system

ownership, and district membership have the

strongest associations with cost-system functional-

ity. Residuals from the benchmark model measure

the extent to which the functionality of a hospital�scost system differs from that expected, given the

hospital�s operating environment. Positive residu-

als measure the degree to which a system is more

functional than expected; they are denoted with

the variables more DETAIL, more CLASSIFY,

more FREQUENT, and more VARIANCE. These

variables equal the residual if the residual is posi-

tive, and zero otherwise. Similarly, negative devia-tions from expected indicate that the system has

relatively little functionality, and they are denoted

with the variables less DETAIL, less CLASSIFY,

less FREQUENT, and less VARIANCE. These

variables equal the absolute value of the residual

if it is negative, and zero otherwise. For example,

a large value of less FREQUENT indicates that

the system reports cost information less frequently

than expected, given its operating environment.

The relation between performance and cost-sys-

tem alignment is investigated by regressing perfor-

mance measures on the positive and negativeresiduals and the control variables described in

prior tests. In addition to addressing hypotheses

2a and 2b, this approach has two advantages.

First, it does not constrain the coefficients on the

functionality measures to be the same regardless

of whether a system has relatively ‘‘too much’’ or

‘‘too little’’ functional capability. Second, the ap-

proach provides a partial control for endogeneityby modeling cost-system functionality as a func-

tion of a number of exogenous variables (e.g.,

competition, MCO penetration, etc.), as well as

three additional instrumental variables (cost, lead,

Table 10

Regression of managers� beliefs about relevance and usefulness

on positive and negative residuals from benchmark

regressionsa,b

Relevant Use

Intercept �2.220*** (�2.02) �1.508** (2.47)

More DETAIL 0.297** (2.01) 0.561*** (2.75)

Less DETAIL �0.744*** (�6.08) �0.043 (0.25)

More CLASSIFY 0.034 (0.22) 0.077 (0.37)

Less CLASSIFY �0.159 (1.36) �0.034 (0.21)

More FREQUENT 0.046 (0.36) 0.120 (0.67)

Less FREQUENT �0.115 (1.00) �0.090 (0.57)

More VARIANCE 0.013 (0.91) �0.096 (0.49)

Less VARIANCE 0.047 (0.35) �0.240 (1.26)

F 12.223*** 4.393***

Adj. R2 0.409 0.181

N 277 277

a **p < 0.05 (2-sided), ***p < 0.01 (2-sided), t-statistics in ( ).b Control variables are not included in table. Control vari-

ables used in the regression are as follows: Size––total number

of beds set up and staffed divided by 1000. Mix––Medicare

case-mix index. For profit––an indicator variable that is 1 if the

hospital is a for-profit entity, and zero otherwise. System––an

indicator variable that is 1 if the hospital is part of a multi-

hospital system and zero otherwise. Teach––an indicator vari-

able that is 1 if the hospital is a teaching hospital and zero

otherwise. Competition––calculated as 1––sum of squared

market shares, based upon beds in a county. Wages––Medicare

wage index for MSA or state. MCO penetration––MCO pene-

tration. The number of MCO enrollees in an MSA divided by

the area�s population. Managed––percentage of revenue

received from managed care. Medicare––percentage of revenue

received from Medicare.


and district) that are not included in the perfor-

mance regressions. 44

Table 10 contains regressions of managers� beliefsabout the relevance and usefulness of cost informa-

tion on the measures of cost-system alignment.Man-

agers find cost information to be significantly more

relevant (p < 0.05, two-sided) and useful (p < 0.01,

two-sided) when it is supplied by systems that pro-vide relatively more detail than expected (more

DETAIL). Correspondingly, managers find infor-

44 Obviously, the strategy variables are also choice variables

for the hospital. I assume that these choices precede the choice

of cost-system design, and are therefore exogenous to the choice

of cost-system attributes. Additional untabulated tests indicate

that these three variables are generally correlated with func-

tional capabilities and uncorrelated with the performance

measures, supporting their use as instruments in the function-

ality regressions and omission in the performance regressions.

mation to be significantly less relevant (p < 0.01,

two-sided) when it comes from systems that provide

relatively less detail than expected (less DETAIL).

Residuals from the CLASSIFY benchmark model

are significant only if the highly correlated residualsfrom the DETAIL benchmark model are re-

moved. 45 In this case, more CLASSIFY is posi-

tively associated with relevant (p < 0.05, two-sided)

and use (p < 0.10, two-sided), and less CLASSIFY

is negatively associated with relevant (p < 0.01,

two-sided). Residuals from the FREQUENT and

VARIANCE benchmark models are not signifi-

cantly associated with managers� beliefs about therelevance and usefulness of cost data at all.

Table 11 contains regressions of financial per-

formance on the measures of cost-system align-

ment. With respect to the DETAIL construct,

hospitals that have relatively greater functional

capability than hospitals in similar operating envi-

ronments have significantly higher operating mar-

gins (p < 0.05, two-sided) and cash flow per bed(p < 0.05, two-sided). Hospitals with cost systems

that provide less detail than expected have lower

cash flow per bed (p < 0.10, two-sided) and incur

proportionally higher administrative expenses

(p < 0.10, two-sided). As in prior tests, residuals

from the CLASSIFY benchmark model are signif-

icant only if the DETAIL residuals are removed.

In this case, less CLASSIFY is negatively associ-ated with cash flow per bed (p < 0.05, two-sided),

suggesting that hospitals that do a relatively poor

job of classifying costs have significantly lower

cash flows. The relative frequency with which costs

are reported is not associated with performance.

Finally, hospitals that conduct relatively more var-

iance analysis have significantly lower operating

margins (p < 0.05, two-sided). Overall, the resultsfrom residuals regressions are consistent with

those of the levels tests. This suggests that cost sys-

tems that have greater detail and classification

capabilities, relative to all hospitals and relative

to their peers, achieve better performance. More-

over, there is nothing to indicate that the levels

tests have been confounded by endogeneity.

45 More DETAIL and more CLASSIFY have a correlation

of 0.53 (p < 0.01, two-sided); less DETAIL and less DETAIL

have correlation of 0.51 (p < 0.01, two-sided).

Table 11

Regression of profitability and cost measures on positive and negative residuals from benchmark regressionsa,b

Operating profit Cash flow Administrative expensec Expense per admitc

INTERCEP 8.196* (1.72) 4,877 (0.19) 8.923*** (3.89) 1,645** (2.51)

More DETAIL 2.862** (2.27) 14,788** (2.11) �0.187 (0.30) 271 (1.24)

Less DETAIL 0.124 (0.12) �10,138* (1.75) 0.873* (1.70) 4 (0.02)

More CLASSIFY �1.190 (0.92) �7,714 (1.07) 0.697 (1.10) �61 (0.27)

Less CLASSIFY �0.912 (0.91) �6,231 (1.13) 0.054 (0.11) 22 (0.13)

More FREQUENT �1.152 (1.04) �516 (0.08) �0.435 (0.80) 71 (0.37)

Less FREQUENT �0.198 (0.20) 77 (0.01) �0.557 (1.16) 74 (0.44)

More VARIANCE �2.539** (2.01) �10,690 (1.57) 0.826 (1.37) 88 (0.41)

Less VARIANCE �0.739 (0.63) 3,508 (0.54) �0.859 (1.49) 68 (0.33)

F 2.510*** 4.203*** 4.420*** 5.671***

Adj. R2 0.091 0.173 0.182 0.225

N 277 277 277 277

a *p < 0.10 (2-sided), **p < 0.05 (2-sided), ***p < 0.01 (2-sided), t-statistics in ( ).b Control variables are not included in table. Control variables used in the regression are as follows: Size––total number of beds set

up and staffed divided by 1000. Mix––Medicare case-mix index. For profit––an indicator variable that is 1 if the hospital is a for-profit

entity, and zero otherwise. System––an indicator variable that is 1 if the hospital is part of a multi-hospital system and zero otherwise.

Teach––an indicator variable that is 1 if the hospital is a teaching hospital and zero otherwise. Competition––calculated as 1––sum of

squared market shares, based upon beds in a county. Wages––Medicare wage index for MSA or state. MCO penetration––MCO

penetration. The number of MCO enrollees in an MSA divided by the area�s population. Managed––percentage of revenue received

from managed care. Medicare––percentage of revenue received from Medicare.c Smaller values are considered favorable because they indicate better cost control or greater efficiency. Control measures were

included in regression, but they are not shown in the table.


Extensions––robustness of results

Because causality is a major concern in cross-

sectional tests such as these, I examine the research

question using alternative specifications that can

potentially detect evidence of causality and omit-

ted variables.

System changes

To examine causality, I estimate the likelihood

that the cost system is currently changing or has

undergone a significant change within the past

two years as a function of past performance and

the control variables. 46 This specification exam-

46 The timing of cost-system changes is determined by the

following questions: When was the last major change to your

organization�s cost accounting system completed? (Major

changes include the installation of new software, integration

of the cost accounting system with other control systems,

adoption of a new overhead allocation scheme, etc.) 36%

responded that the system was currently changing, 28%

completed last major change within the past two years, 18%

within last three-to-five years, 18% more than five years since

last major change.

ines whether above (below) average past perfor-

mance is associated with the decision to change

the cost system. A positive association between

past performance and major cost-system change

is consistent with the premise that better-perform-

ing hospitals invest in cost-system upgrades and,

consequently, have more functional systems. A

negative association may indicate that poor per-formers attempt to improve performance by

upgrading cost systems. However, I find that the

decision to change the cost system is not signifi-

cantly associated with past performance at all.

Therefore, the results do not support the premise

that strong financial performance drives invest-

ments in cost-system functionality.

Performance changes

To test for omitted variables bias, I regress

changes in performance over a one-year period

on cost-system functionality, past performance,

and the control variables. Hospitals that have

undergone major cost-system changes within the

past two years are removed from the sample in this


specification so that the functionality measures de-

scribe systems that have been in place for at least

three years. This test again controls for past per-

formance. It also helps control for omitted vari-

ables that may be related to both current levelsof cost-system functionality and financial perfor-

mance, such as managerial skill. Finally, simulta-

neity is less of a concern in this specification

because the functionality constructs describe a cost

system that has been in place for at least three

years prior to measuring the improvement in per-

formance. Thus, the system was selected prior to

many of the actions taken to affect the currentchange in performance.

Regressions using the change specification show

that the ability to provide detailed information is

significantly and positively associated with in-

creases in cash flow per bed, and negatively associ-

ated with reductions in the administrative expense

ratio. In contrast, hospitals that engaged more

extensively in variance analysis also experiencedgreater increases in administrative expenses. These

results are consistent with the findings from the

levels specification, and therefore, provide no evi-

dence that omitted variables or simultaneity con-

founded the levels tests.

Conclusion

This study examines relations among cost-sys-

tem functionality, managers� beliefs about the rel-

evance and usefulness of cost data, and actual

financial performance in US hospitals. The results

indicate that managers believe that systems that

supply greater cost detail, on average and relative

to hospitals in similar organizational contexts,provide more relevant and useful data. Hospitals

with such systems are significantly more profitable,

generate greater cash flows, and have proportion-

ately lower administrative expenses. These findings

support the premise that more functional cost sys-

tems supply managers with more relevant data,

which they use to make performance-enhancing

decisions. Interestingly, there was no relation be-tween the detail capability and expense per admis-

sion, which is largely driven by physicians� clinicaldecisions and not managers� decisions. This is con-

sistent with previous research that suggests cost

containment efforts have been directed at adminis-

trative processes and not clinical process (Shortell

et al., 1995). Ultimately, the ability to significantly

reduce healthcare expenditures lies in containmentof the direct costs of patient care, and not in sim-

ply improving administrative efficiency.

There is also some evidence that better clas-

sification of costs, in general and relative to a

homogeneous peer group, is associated with higher

managerial evaluations of data relevance and use,

as well as actual financial performance. However,

it was not possible, with this data, to isolate the ef-fects of systems that better classify costs from

those that provide greater detail. Increased report-

ing frequency was favorably associated with

managers� beliefs about data relevance and time-

lines, but it was not associated with financial

performance.

I found no relation between the extent to

which systems calculate variances and managers�evaluations of the relevance and usefulness of cost

information. Interestingly, hospitals with systems

that calculate more variances had significantly

lower profits and relatively higher administrative

expenses. These results could indicate reverse cau-

sality, as poorly performing hospitals may engage

in variance analysis to identify and correct prob-

lems. However, poor performance in the pastwas not associated with decisions to change the

cost system. Alternatively, these findings support

claims that variance analysis can be problematic,

especially in the hospital setting. Consistent with

this explanation, tests that control for prior per-

formance and the timing of cost-system changes

find increases in administrative expenses among

those hospitals that performed more varianceanalysis.

The similarity of the findings regarding the level

of functionality on average and relative to contex-

tually similar peers is consistent with claims from

the practitioner literature that hospitals have not

yet adjusted their cost systems to accommodate

the increased information needs created by sweep-

ing environmental changes (e.g., Hill & Johns,1994; Orloff et al., 1990; Serb, 1997; Young &

Pearlman, 1993). Specifically, more detail capabil-

ity relative to all hospitals and relative to similar

N Mean Median St. Dev.

Payer level

(e.g. Medicare)

324 5.1 6 2.0

Contract level 321 4.5 5 2.2

Physician level 323 4.9 6 2.0Procedure level 324 4.9 6 2.1

Per Diem level 320 4.3 5 2.2

Patient level 324 5.0 6 2.1


hospitals is positively associated with perfor-

mance, while less, in absolute and relative terms,

is negatively associated with performance. How-

ever, while healthcare management literature lar-

gely supports cost-system refinement alongseveral dimensions, this study indicates that only

the detail capability, and possibly the classification

capability, warrant investment. Moreover, even

improved detail capability has not helped manag-

ers to reduce clinical costs.

The findings are subject to a number of limita-

tions. Cross-sectional studies such as this can

establish associations, but not causality. Futurestudies using panel data would enable researchers

to investigate performance changes arising from

decisions made using cost system data. Similarly,

endogeneity is also an obvious concern with this

test design. Although alternative specifications re-

veal no evidence of reverse causality or endogene-

ity with regard to the detail and classify

constructs, these potentially confounding factorscannot be eliminated. Another factor that may

affect these results is the noisiness of the mea-

sures. A mail survey prevents an assessment of

the survey respondent�s actual knowledge of the

cost-accounting system, although the surveys

were mailed to chief financial officers. A mail sur-

vey also prevents the respondent from effectively

clarifying his or her understanding of the ques-tions. Moreover, the single-industry sample limits

the ability to generalize these results to other

industries. Finally, the hypotheses were primarily

based upon economic theories of organization,

and some argue that such theories do not ade-

quately capture the complex interplay between

various internal and external organizational ac-

tors in hospitals (e.g., Abernethy & Vagnoni,2004; Chua & Degeling, 1991; Covaleski & Dir-

smith, 1986; Jones & Dewing, 1997). Despite

these limitations, this study has important impli-

cations for research in both accounting and

healthcare financial management. The results pro-

vide the first empirical evidence of a relation be-

tween cost-system functionality and actual

financial performance and thereby help justifyclaims regarding the need for greater investment

in certain dimensions of hospital cost systems.

However, the results also suggest that cost-system

functionality has not yet been successfully intro-

duced into the management of clinical expendi-

tures. Until this takes place, the usefulness of

cost-system design in containing hospital costs is

limited.

Acknowledgements

I thank Christopher Ittner, David Larcker,

Rick Lambert, John Core, Keith Weigelt, and

workshop participants at the University of Penn-

sylvania, Emory University, Rice University, theUniversity of Washington, Stanford University,

Harvard�s Kennedy School of Government, the

University of Texas at Dallas, the University of

Virginia�s Darden School, and the 2000 AAA

Managerial Accounting Conference for their

comments. This paper has benefited greatly from

the comments of two anonymous referees. I also

thank Taylor Randall and Steve Walston, whowere instrumental in collecting the data used in

this study. The financial support of the Deloitte

and Touche Foundation is gratefully acknowl-

edged.

Appendix. Number of responses, mean response,

and coding method for survey questions

Cost-system design constructs

Detail––level of detail provided

To what extent does the cost accounting system

provide data that allow you to analyze costs at the

following levels? 1 = not at all, 7 = completely


The cost accounting function can easily custom-

ize reports to the specification of users. 1 = strongly

agree, 7 = strongly disagree, N = 320, mean = 4.2.

N Not

reported

Daily Weekly Monthly Quarterly Semi-annually Annually

Response codinga 1 7 6 5 4 3 2

Sr. Managers

(CEO, CFO)

309 40 10 12 159 40 18 30

Middle Managers

(Dept. Heads)

307 59 5 8 168 27 14 26

Clinical Managers

(Phys. Mgrs.)

307 107 5 7 115 43 11 18

Nursing Staff 307 133 0 6 107 27 10 21Medical Staff 307 136 2 1 64 64 17 20

a Method of coding responses. Higher numbers were assigned to more frequent reporting.

Classify––Classification of costs according to

behavior

Does your cost system have a formalized meth-

od of distinguishing between the following costs?

1 = not at all, 7 = completely


Direct and indirect 321 5.1 6 1.9Fixed and variable 318 4.7 5 2.1

Controllable and

non-controllable

317 3.7 4 2.0

If your hospital tracks fixed and variable costs,

how are they separated? (Check one, N = 317)

22% N/A, hospital does not track fixed

and variable costs (1)a

34% Account analysis (3)

40% Subjective/managerial experience (2)4% Statistical regression techniques (4)

a Number indicates how responses were coded; 4 = most

advanced, 1 = least advanced.

Towhat extent does the cost accounting function

use the ratio of cost to charge (RCC) to estimate the

cost of individual services and procedures? 1 = use

only RCC, 7 = do not use RCC at all: N = 318,

mean = 4.1, median = 4, St. Dev. = 2.1.

Frequent––frequency and breadth of cost-report

distribution

How often does the cost accounting system re-

port information to each of the following groups?

Variance––number and type of variances are

calculated

Which of the following variances does your orga-

nization calculate? (Check all that apply.) 48%: effi-

ciency, 53%: case mix, 58%: price, (N = 317) (coded

as 0, 1).

Perceptual performance measures

Relevant––This measure was based upon the

two sets of questions below (1 and 2). Responses

to the second set of questions (2. Actual infor-

mation supplied) were subtracted from the corre-

sponding responses to the first set of questions

(1. User Needs). The average of these differences

captures the extent to which a system meets theuser�s needs. The smaller the difference, the bet-

ter the system meets the user�s needs. To calcu-

late the variable relevant, the average difference

for each hospital was subtracted from the

maximum difference so that larger values indi-

cate that a system is better at meeting a user�sneed.

1. User Needs: Rate the importance of costaccounting information in performing each of

the following activities. You may not actually have

the information necessary to perform the activities

listed below; however, in responding, please indi-

cate whether you would consider cost information

N Mean Median St.Dev.


of value in performing these activities. 1 = not at

all important, 7 = very important

N Mean Median St.

Dev.

Evaluate individual

performance

322 4.4 5 1.6

Evaluate group

(e.g., departmental)

performance

324 5.6 6 1.3

Negotiate contracts

with payers

323 6.2 7 1.1

Budget 324 5.8 6 1.2Control costs 324 6.2 6 1.0

Price hospital services 323 5.7 6 1.3

Benchmark your

hospital�s costsagainst industry

323 5.6 6 1.2

Evaluate acquisitions

of new technology/

equipment

324 5.2 5 1.3

Evaluate strategic

alternatives

324 5.3 6 1.4

Negotiate contracts

with suppliers

(e.g. Pharm. Co.)

324 5.1 5 1.4

Budget 323 4.9 5 1.6Control costs 323 4.9 5 1.6Price hospital

services324 4.9 5 1.7

Benchmark yourhospital�scosts againstindustry

321 4.3 5 1.8

Evaluateacquisitionsof newtechnology/equipment

322 4.2 4 1.8

Evaluate strategicalternatives

321 4.1 4 1.8

Negotiate contractswith suppliers(e.g. Pharm. Co.)

321 4.0 4 1.8

2. Actual information supplied by system: Can

the cost accounting system provide your organiza-

tion with information for the following activities.

1 = not at all, 7 = completely.

N Mean Median St.Dev.

Evaluate

individual

performance

322 3.2 3 2.0

Evaluate group

(e.g., depart-mental)

performance

324 4.8 5 1.7

Negotiate

contracts

with payers

324 4.9 6 1.8

Use––Use in decision-making: Rate agreement

with the following statements: 1 = strongly agree,

7 = strongly disagree. Users rely on cost account-

ing data to make decisions. N = 324, mean = 4.5,

median = 5, St. Dev. = 1.5.

Construct validity measures––(Table 4)

Differs from Medicare: To what extent is your

organization�s cost accounting system different

from the Medicare cost-reporting system? 1 = ex-

actly the same, 7 = completely different. N =319,


Allocation bases: In allocating costs in revenue

departments to a specific procedure, how many

separate bases does your hospital use? (Circleone; circle N/A if your organization does not allo-

cate costs at the procedure level.)

No. of responses N/A 1 2–3 4–6 >6

309 114 27 60 47 61

Accurate: Rate agreement with the following

statements: 1 = strongly agree, 7 = strongly dis-

agree. The cost accounting system provides accu-

rate data. N = 321, mean = 3.5, median = 4, St.

Dev. = 1.4.


Business

process

innovation

(e.g., new

billing system)

321 4.1 4 1.6

Patient-caretechnology

(e.g., advanced

operating room)

323 4.3 4 1.7


Timely: Users receive information from the

cost accounting function in a timely manner.

1 = strongly agree, 7 = strongly disagree. N = 316,


Control variable––(Table 6)

Managed: Percentage of revenues derived from

managed care––(control variable): Indicate your

approximate payer mix by assigning the percent-

age of total inpatient revenue that is received from

the following: N = 316.

46% Medicare; 19% Managed care

13% Medicaid; 7% Self-pay/bad debt/charity

12% Indemnity; 4% Other

Strategy constructs––used to model cost-system

functionality (Table 9)

Cost––Extent to which the hospital follows a

cost-focused strategy: Rate the importance of the

following objectives in achieving your hospital�smission: 1 = not at all important, 7 = very important.

N Mean Median St.

Dev.

Control or reduce

the costs of existing

services

321 6.4 7 0.9

Identify and eliminate

unprofitable services

320 4.9 5 1.4

Lead––Extent to which hospital follows a prod-

uct differentiation strategy: Rate the importance

of the following objectives in achieving your hospi-

tal�s mission: 1 = not at all important, 7 = very

important.


Establishclinical

leadership

322 5.8 6 1.3

Is your organization a leader or follower with

respect to the time of adoption of the following:1 = leader, 7 = follower.

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