strategic groups, performance, and home industry

Strategic Groups, Performance, andStrategic Response in the NursingHome IndustryJacqueline S. Zinn, William E. Aaronson, and Michael D. Rosko

Objective. This study examines the effect of strategic group membership on nursinghome performance and strategic behavior.Data Sources and Study Setting. Data from the 1987 Medicare and MedicaidAutomated Certification Survey were combined with data from the 1987 and 1989Pennsylvania Long Term Care Facility Questionnaire. The sample consisted of 383Pennsylvania nursing homes.Study Dedgn. Cluster analysis was used to place the 383 nursing homes intostrategic groups on the basis of variables measuring scope and resource deployment.Performance was measured by indicators of the quality of nursing home care (ratesof pressure ulcers, catheterization, and restraint usage) and efficiency in servicesprovision. Changes in Medicare participation after passage of the 1988 MedicareCatastrophic Coverage Act (MCCA) measured strategic behavior. MANOVA andTukey HSD post hoc means tests determined if significant differences were associatedwith strategic group membership.Findings. Cluster analysis produced an optimal seven-group solution. Differences ingroup means were significant for the clustering, performance, and conduct variables(p < .0001). Strategic groups characterized by facilities providing a continuum ofcare services had the best patient care outcomes. The most efficient groups werecharacterized by facilities with high Medicare census. While all strategic groupsincreased Medicare census following passage of the MCCA, those dominated byfor-profits had the greatest increases.Conclusions. Our analysis demonstrates that strategic orientation influences nursinghome response to regulatory initiatives, a factor that should be recognized in policyformation directed at nursing home reform.Keywords. Nursing homes, strategic groups, patient care outcomes, efficiency, Medi-care reimbursement

The concept of strategic groups was introduced into industrial organizationtheory to account for differences in profitability in firms competing in thesame industry (Hunt 1972; Newman 1978). Because group membership is

188 HSR: Health Services Research 29:2 (June 1994)

based on strategic behavior and organizational capabilities, the concept wassoon adopted by the fields of strategy and business policy (Porter 1980).Strategic groups are defined as sets of firms that compete in an industryon the basis of similar combinations of scope and resource commitments(Cool and Schendel 1988). Because decisions made by firms within a groupmay not be imitable by firms outside the group without incurring substantialcost, mobility barriers inhibit the free movement of firms from one groupto another (Caves and Porter 1977). The presence of mobility barriersmakes the strategic group structure of most industries relatively stable andidentifiable.

Strategic group analysis provides insights into different competitors'approaches to the marketplace and the resulting implications for organi-zational and industrial performance (Harrigan 1985). Numerous empiricalstudies have analyzed the impact of strategic group membership on firmconduct and performance within and across industries (McGee and Thomas1986; Fiegenbaum and Thomas 1990). However, there has been little appli-cation in health care, and no one has attempted to identify strategic groupsin the nursing home industry. Given that strategic group structure may havepolicy as well as managerial implications, this is an unfortunate shortcoming.If strategic group orientation influences the responses of individual nursinghomes to regulatory initiatives, the response of the industry as a wholeis unlikely to be homogeneous. The outcome of public policy directed atnursing home reform may be at variance with its intent if the behavioraleffects of strategic group membership are not considered.

The objective of this study is to identify strategic group structure withinthe nursing home industry, and to determine if performance and strategicbehavior are associated with group membership. Strategic group theoryassumes that all firms in an industry face the same competitive environmentand that differences in organizational capabilities account for differencesin strategic behavior. In studies of highly regulated industries employinga national sampling frame, results may be confounded by differences inregulatory policies at the state level which differentiate the competitiveenvironment. As a result, differences in state regulatory environments, rather

Address correspondence and requests for reprints to Jacqueline S. Zinn, Ph.D., AssistantProfessor, Department of Health Administration, School of Business and Management, TempleUniversity, Philadelphia, PA 19122. William E. Aaronson, Ph.D. is Associate Professor in theDepartment of Health Administration at Temple University; and Michael D. Rosko, Ph.D.is Professor and Chair, Department of Health and Medical Services Administration, Schoolof Management, Widener University, Chester, PA. This article, submitted to Health ServicesResearch on February 24, 1993, was revised and accepted for publication onJuly 30, 1993.

Strategic Response in Nursing Home Industry

than differences in organizational strategy, may drive the identified strategicgroup structure (Heath 1988). There are indications that nursing homeslocated in different states operate under varied regulatory conditions, andadapt their behavior to meet these conditions. For example, differences incase mix, level of care, and length of stay have been attributed to differencesin state Medicaid policies (Ray et al. 1987). To control for differences in stateregulation influencing the competitive environment, this study examinesnursing home strategic group membership in a single state, the Common-wealth of Pennsylvania.

DETERMINANTS OF STRATEGICGROUP MEMBERSHIP IN THENURSING HOME INDUSTRY

Early writings identified scope and resource deployment as the key strategicdimensions defining group membership (Caves and Porter 1977; Newman1978). While applicable across diverse industries, the variables chosen tooperationalize these strategic dimensions should be industry specific, reflect-ing the bases for competition in the industry under study (Cool and Schendel1988). Variables measuring scope and resource deployment in the nursinghome industry are summarized in Table 1. The rationale for their selectionis now provided.

Scope. Scope refers to strategic decisions delineating the firm's partic-ipation in the marketplace. The selection of market segments in which tocompete and the types of services offered in these market segments are scopedecisions. Since to compete effectively nursing homes must be responsiveto differences in payer demand characteristics, source of payment is themajor basis for market segmnentation in the nursing home industry. Thelargest market segment is the private or self-pay market, accounting forapproximately 51 percent of nursing home expenditures in 1990 (Buchanan,Madel, and Persons 1991). Nursing homes compete on the basis of price andquality within this market segment (Nyman 1987). The Medicaid programis the largest public payer, accounting for approximately 41 percent oftotal nursing home expenditures in 1990 (Buchanan, Madel, and Persons1991). Because Medicaid payments are generally lower than private rates,a higher proportion of Medicaid recipients in a facility may pose a revenueconstraint requiring greater efficiency in the use of resources. Finally, whilethe Medicare program accounts for only 2 percent of total nursing homerevenue, some facilities follow a focus strategy, specializing in this marketsegment (Dor 1989).

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190 HSR Health Services Research 29:2 (June 1994)

Table 1: Definitions and Sources of Variables (N = 383)Definition Mean (s.d.)

Clwerxg VariableVariabes Measuring Scope Decisions% Medicaid* Pen°h Medicare* Pero% Independent capacityt Rat

hCase mix* FunALOSt Ave°h 85+t Pern

Variables Measuring ResourceDeployment DecisionsSize* NWPrivate pay ratet WeiOccupancy* Fac:RNs per resident* NUI

rSStaff per resident* NUI

Pfenfiw c VariablsPressure ulcer rate*

Catheter use rate*Restraint rate*

Efficiency index*

Ceuduet VariablesIncrease Medicare days perbed 1987-1989t

Increase Medicare revenueper bed 1987-1989t

cent of Medicaid recipients in facilitycent of Medicare recipients in facilityio of independent living to nursingome beds1ctional severity index,rage length of staycent of residents over age 85

nber of staffed bedsighted average self-pay priceiity occupancy ratenber of RNs per nursing homeesidentnber of nursing staff per resident

Percent of facility residents with pressureulcers

Percent of facility residents catheterizedPercent of facility residents physically

restrainedData envelope analysis technical

efficiency score

Increase in Medicare days per bedbetween 1987 and 1989

Increase in Medicare revenue per bedbetween 1987 and 1989

.52 (.27)

.03 (.08)39.5 (97.9)

2.81 (.44)474.3 (490.8)

.45 (.13)

140.5 (94.7)76.95 (18.68)

.93 (.09)

.08 (.05)

.53 (.15)

.08 (.07)

.12 (.09)

.39 (.17)

.83 (.15)

14.6 (16.4)

1496.42 (1769.84)

*Medicare and Medicaid Automated Certification Survey (MMACS).tPennsylvania Long Term Care Facility Questionnaire (PALTCFQ).

Nursing homes competing within one or more market segments mayvary in the characteristics of patients admitted 'and in the range of servicesoffered. Nursing homes with longer average lengths of stay and higherproportions of residents over the age of 85 are more likely to have residentscharacterized by long-term chronic disabilities than by short-term needs forrehabilitation or subacute care (Keeler, Kane, and Solomon 1987). Facil-ities operating continuing care retirement communities (CCRCs) providea broad range of service options from independent living to nursing home


care. The security offered by these environments has particular appeal to theaffluent segment of the market concerned about the personal and financialconsequences of admission to a nursing home (Cohen et al. 1987).

Resource Deployment. Resource deployment refers to the commitmentof resources to functional areas that are key to gaining and maintainingcompetitive advantage in targeted market segments. Because labor accountsfor approximately 70 percent of total nursing home costs, the absolute leveland composition of staffing are major indicators of resource commitment(Horen 1983).

Price and capacity decisions also reflect resource commitment. Assum-ing that nursing home prices are set at a markup from actual costs, the pricepaid by private pay patients may reasonably proxy for relative resourceuse. Studies of the influence of size on organizations have not conclusivelyestablished its effect. However, capacity decisions are a determinant ofthe costs associated with the provision of services and may reflect strate-gic intent. Industrial organization studies have found that firms followingsimilar strategies are likely to be of comparable scale (Porter 1980). Whileeconomies of scale have not been established in the nursing home industry,resource requirements for licensure and certification are frequendy tied tofacility size. Larger facilities may have greater flexibility in the distributionof resources, and lower fixed costs. On the other hand, large size mayinhibit organizational flexibility and adaptation to changing environmentalconditions (Fombrun and Zajac 1987). Industry incumbents may decide tohold excess capacity as a barrier to new market entrants (Porter 1980).In addition, nursing homes may decide to keep some beds unoccupiedin order to be responsive to varying demand characteristics across marketsegments. Thus, occupancy rates indicate the level of resources committedto excess capacity.

PERFORMANCE AND BEHAVIORAL IMPLICATIONSOF STRATEGIC GROUP MEMBERSHIPIN THE NURSING HOME INDUSTRYIn most industries, performance is measured by profitability, which is theassumed objective of industry participants. However, in the nursing homeindustry, profitability is not the sole nor even the primary indicator ofperformance. The presence of nonprofit nursing homes implies performanceobjectives other than profit maximization (Scanlon 1980). For this analysis,we chose outcome indicators of the quality of care delivered to residents,and efficiency in the production of services, as measures of nursing homeperformance. A comparison of intergroup differences in means for thesequality and efficiency indicators will be used to test the hypothesis thatperformance differences exist among strategic groups.

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The quality of nursing home care is an important concern of a diversegroup of nursing home constituencies (Institute of Medicine and NationalResearch Council 1986). In this study three indicators of the outcome ofnursing home care, recommended by the Institute of Medicine, measureperformance: the prevalence of pressure ulcers, urethral catheterization,and physical restraint usage. Because care protocols for the prevention andtreatment of pressure ulcers are well established, high prevalence may bean indicator of poor-quality care (Health Care Financing Administration[HCFA] 1991). Urethral catheterization and restraint use are nursing homepractices closely associated with adverse resident outcomes (Libow andStarer 1989). These practices are included in the Resident Assessment Proto-cols (RAPs) targeting particular aspects of nursing home care for indicationsof potential quality problems requiring additional review (HCFA 1991).

Current efforts to increase efficiency in health care reflect societalconcerns regarding the economic impact of rising health care costs. Techni-cal efficiency measures the extent to which a given combination of inputsproduces as much output as feasible in an engineering sense. Data envelopeanalysis (DEA) was used to estimate technical efficiency scores for eachnursing home in the sample (Rosko et al. 1993). DEA has been used toevaluate the efficiency of firms using similar inputs to produce multipleoutputs in a variety of industries, including health care, education, min-ing, and electricity production (Rosko 1990). The input categories werethe number of full-time and part-time clinical personnel (registered andlicensed practical nurses; nurse aides; physical, occupational, and speechtherapists; physical and occupational therapy assistants) and the numberof full- and part-time administrative personnel. Capital inputs (e.g., bedsize) were not included because they were assumed to be fixed over theplanning horizon. This is a realistic assumption in any state; for example, theCommonwealth of Pennsylvania, among other states, has a nursing homeconstruction moratorium (Gertler 1988). Five output categories (Medicare,Medicaid and self-pay SNF [skilled nursing facility] days, and Medicaidand self-pay ICF [intermediate care facility] days) were specified for themodel. Output categories were based on payer status because of the likelycorrelation with the need for different services (Shaughnessy et al. 1985).

To determine if there is an association between strategic group mem-bership and strategic behavior, we examined the competitive responses ofPennsylvania nursing homes to the incentives of a specific public policyinitiative, the Medicare Catastrophic Coverage Act (MCCA) of 1989. Asa result of this short-lived legislation, nursing homes were given severaloptions for increasing Medicare census previously unavailable to them,including the reclassification of existing nursing home patients as Medicareeligible. If the change would not result in an increase in expenses, but would

Strategic Response in Nursing Home Indusby

increase revenue, facilities had an incentive to reclassify self-pay patients orthose covered by Medicaid to Medicare status. However, some facilities,because of their previous scope and resource deployment decisions, mayhave been better positioned to respond to incentives to increase Medicareparticipation than others. Thus, the extent to which the MCCA achieved itspolicy objective of increasing access to Medicare-covered skilled nursing ser-vices may have been mediated by strategic group membership. This studywill also test the hypothesis that strategic groups significantdy differed intheir ability to increase Medicare participation in response to the incentivesof the MCCA.

METHODOLOGY

SOURCES OF DATA

The 1987 Medicare and Medicaid Automated Certification Survey(MMACS) and the 1987 and 1989 Pennsylvania Long Term Care FacilityQuestionnaire (PALTCFQ) were combined to provide data on outcomemeasures, resident characteristics, facility characteristics, utilization, andpricing. The information contained in the MMACS is collected annuallyby survey teams through the Medicare and Medicaid certification processconducted by state licensure agencies, and is required for participation inthe Medicare and Medicaid programs.

The PALTCFQis completed annually by nursing home administratorsas a mandatory requirement for relicensure, and includes data on facilityprices by payer, age distribution, and ownership. Table 1 indicates thedefinitions and sources of variables included in the analysis. Complete datafor all these variables were available for 383 Pennsylvania nursing homefacilities.

STATISTICAL ANALYSIS

Cluster analysis refers to a group of statistical techniques designed to cate-gorize observations on the basis of one or more variables (Harrigan 1985).This analysis used the Fastclus procedure, a K-means technique availablein SAS version 6.1, to cluster nursing homes into strategic groups using thevariables indicated in Table 1.

Since the variables were not measured on the same scale, and thuswere incommensurate in raw form, standardized values of the variables (Z-scores) were clustered. However, unstandardized means of the clusteringvariables are reported to facilitate interpretation. Because clustering tech-niques are sensitive to oudiers (Hambrick 1984), all clustering variables with

193


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the exception of two (percentage of Medicare recipients in total census, andfacility size) were trimmed at three standard deviations from the mean.While the percentage of Medicare recipients in the total census is negligiblein most nursing homes, some nursing homes specialize in Medicare skillednursing care, following what is essentially an "outlier" strategy. We alsofelt that trimming on the basis of facility size could eliminate facilities withunique organizational capabilities that could define a strategic group.

Unfortunately, there are no clear-cut analytical methods for deter-mining the appropriate number of clusters. A conventional decision ruleis to maximize the tightness of fit of the cluster structure by minimizingsquared error (Hambrick 1984). The cubic clustering criterion provided inthe SAS program output is an approximate measure of the cluster structureminimizing within-cluster sum-of-squared differences. The reported value ofthis measure indicated that a seven-cluster solution minimized mean-squarederror. After the clusters were formed, the distribution of facilities by controlstatus (for-profit, church-affiliated, and hospital-based) and location (degreeof urbanization) were calculated for each strategic group. Group means forthe performance and behavioral measures were also calculated.

A single-factor multiple analysis of variance (MANOVA) was usedto test the effect of strategic group membership on group means for eachset of clustering, conduct, and performance variables. Tukey's studentizedrange (HSD [honestly significant differences]) tests were used for post hoccomparison of group means on each variable (Zar 1984).

RESULTS

STRATEGIC GROUP FORMATION

The means and standard deviations for the variables-used in the analysis arereported in Table 1. The means and standard deviations for each variableby group and the univariate F-tests of the significance of differences acrossgroup means appear in Table 2. As indicated in Table 3, differences in strate-gic group means were tested by MANOVA test criteria (Wilks' Lambda,Pillai's Trace, Hotteling-Lawley Trace, and Roy's Greatest Root) and foundto be significant (p <.0001). The results of the Tukey HSD test comparisonsare also reported in Table 3. The following typology of Pennsylvania nursinghomes is based on the optimal seven-group solution identified by clusteranalysis.

Group 1: Medicare Skilled Nursing Care Focus StrategyFacilities in Group 1 have a significantly higher average Medicare census (5percent), and a significantly lower Medicaid census (26 percent) compared

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Table 3: Multivariate Tests of Significance and Results of Post HocComparisons of Means (Clustering Variables)Statistic Value F SignijcanceMutariate Tts ofSignsficauceWilks' Lambda .01746 31.99 .0001Pillai's Trace 2.6195 24.87 .0001Hotelling-Iawley Trace 7.0807 37.16 .0001Roy's Greatest Root 2.9394 94.33 .0001

Resuls ofPost Hoc Gomparisons ofMeaxs*RNs per residmnt. Group 2 > Groups 1, 3, 4, 5, 6, 7; Group 3 > Groups 4, 5Staffper resident. Group 2 > Groups 1, 4, 5; Group 3 > Groups 1, 4, 5; Group 7 > Group 4;Group 6 > Group 2

ALOS. Group 7 > Groups 1, 2, 3, 4, 5; Group 6 > Groups 1, 2, 3, 4, 5; Group 4 > Groups 1,3, 5; Group 3 > Group 1

Size. Group 7 > Groups 1, 2, 3, 4, 5, 6; Group 5 > Groups 1, 2, 3, 4, 6; Group 4 > Group 696 Independent capacity. Group 2 > Groups 1, 3, 4, 5, 6; Group 7 > Groups 1, 3, 4, 5; Group 6> Groups 1, 3, 4, 5

% Medicaid. Group 7 > Groups 1, 2, 3, 4, 5, 6; Group 5 > Groups 1, 2, 3, 6; Group 4 > Groups1, 2, 3, 6; Group 3 > Groups 1, 2, 6; Group 6 > Groups 1, 2

% Medicare. Group 1 > Groups 2, 4, 5, 6, 7; Group 3 > Groups 2, 4, 6Case mix. Group 3 > Groups 2, 4, 5, 6Occupancy. Group 6 > Groups 1, 5; Group 4 > Group 1; Group 7 > Group 1; Group 5 > Group

1; Group 3 > Group 1; Group 2 > Group 196 85+. Group 2 > Groups 1, 3, 4, 5, 7; Group 6 > Groups 1, 3, 5, 7; Groups 1, 3, 4, 7 >Group 5

Private pay rate. Group 5 > Groups 2, 3, 4, 6; Group 1 > Groups 4, 6; Group 3 > Group 6;

*Tukey's studentized range (HSD) test: alpha = .05; critical value of studentized range = 4.192.

to other groups (Table 3). The characteristics of the facilities clustered in thefirst strategic group are consistent with a focus on short-term care servicestypically required by Medicare patients after a hospital discharge. Group 1average length of stay and occupancy are the lowest of any group, suggestinga high rate of bed turnover. However, while case mix severity is relativelyhigh, both RN (registered nurses) and total staff per resident are lower thanin several of the other groups. Of this group, 79 percent are for-profit, 6percent are church affiliated, and 1 percent are hospital based. Group 1facilities tend on average to be located in urban, competitive markets.

Group 2: Differentiated Focus Strategy: Care Continuum

Total and RN staffing is significandy higher in this group, which also hasthe lowest proportion of Medicaid recipients in total resident census (Tables2 and 3). These are the smallest facilities, on average, with the greatestcommitment to independent living services. Private-pay rates are relatively

Stratgic Response in Nursing Home Industry

low compared to other groups, lending support to the contention that nurs-ing homes compete for private-pay patients on the basis of both quality andprice. Five percent of these facilities are for-profit, and 60 percent are churchaffiliated. None are hospital based. Like Group 1 facilities, these facilitiestend to be located in large, competitive urban markets.

Group 3: Generic Skilld Nursing Care StrategyWhile the facilities in this group have significantly higher Medicare censusthan other groups (4.0 percent), the high average Medicaid census (54percent) suggests a more generic skilled care orientation. Average lengthof stay and occupancy are significandy higher than in Group 1, reflectinglower rates of bed turnover in these facilities (Table 3). However, case mixis significantly more severe and total staffing higher in these facilities than intheir Group 1 counterparts. Only 59 percent of the facilities in this group arefor-profit, and 12 percent are church affiliated. This group has the highestproportion of hospital-based facilities (12 percent). These facilities tend tobe located in rural markets.

Group 4: Low-Cost Intermediate Care Strategy

The facilities in this group are characterized by a high Medicaid census, andlow overall and RN staffing. Case mix is also low compared to other groups.In 1987, Group 4 facilities offered little in the way of services to Medicarepatients and few independent living services. This, along with the size of thisgroup (n = 90), suggests that these facilities are "typical" intermediate carefacilities, possibly affiliated with proprietary nursing home chains. Seventy-six percent of these facilities are proprietary, 5 percent are church affiliated,and 2 percent hospital based. These facilities tend to be located in ruralareas with fewer nursing home care providers.

Group 5: Low-Cost Skiled and Intermediate Care StrategyWhile the facilities in this group bear a strong resemblance to those inGroup 4, there are some unique characteristics suggesting differences incase mix. Length of stay is significantly shorter, and the percentage ofresidents over the age of 85 lower, than in Group 4 facilities (Table 4).Staffing is also slightly higher, as is the Medicare census. Group 5 facilitiesdiffer from Group 4 facilities in the provision of both intermediate andskilled nursing care services. Sixty-three percent of the facilities are runfor profit, 8 percent are church affiliated, and none are hospital based.Unlike their Group 4 counterparts, these facilities tend to be located inlarger urban markets.

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198 HSR: Health Seroices Research 29:2 (June 1994)

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Group 6: Low-Cost Focus Strategy: Care ContinuumSimilar to Group 2 facilities, these facilities have a relatively high commit-ment to providing independent living services. However, this group has overtwice the percentage of Medicaid recipients, over three times the lengthof stay, and larger facilities on average. The case-mix variables suggestthat facilities in Groups 2 and 6 provide care to residents of comparablefunctional disability. The low private-pay rate suggests that these facilitiesalso compete for private-pay patients, and that rates may be subsidized tosome degree by the sponsoring organization. Sixty-three percent of thesefacilities are church affiliated, only 17 percent are for-profit, and 1 percentare hospital based. Unlike Group 2 facilities, Group 6 facilities tend to belocated in rural areas.

Group 7: Large Municipal FacilitiesThis group, with only 12 facilities, is distinguished primarily by average size(539.4 beds) and length of stay (1,121.1 days). Average Medicaid censusis significantdy higher than any other group (91 percent). None of thesefacilities is for-profit or church affiliated and only one is hospital based,indicating that these are municipally run facilities. Group 7 facilities tend tobe located in urban areas.

GROUP MEMBERSHIP, PERFORMANCE,AND STRATEGIC BEHAVIOR

As reported in Table 4, univariate F-tests for differences in means for theperformance and behavior measures were all statistically significant. AsTable 4 indicates, MANOVA test criteria rejected the hypotheses of nooverall effect of strategic group membership for the performance variablesand the behavioral variables. Results of the post hoc means tests indicatingwhich strategic group means differed significantly from one another onthe performance and behavioral variables are also reported in Table 5.These results support the hypothesis that strategic group membership isassociated with differences in performance and strategic behavior in thenursing home industry.

Facilities in Groups 1 and 3 perform similarly on the patient careoutcome measures, which may reflect fundamental similarities in strategicdirection. The relatively high rates of catheterization and pressure ulcersmay be related to higher resident acuity within these facilities, rather thanthe quality of care provided.

Groups 2 and 6 offer a similar product line, but appear to targetdifferent market segments. Group 2 offers a high-amenity continuum ofcare, while Group 6 offers a "less frills" version. Despite these differences,

199


outcomes are similar. These two have significantly lower rates of catheteri-zation and pressure ulcers compared to other groups, as well as the lowestuse of restraints.

By contrast, Groups 4 and 5 appear to embody conventional concerns

about the quality of care provided in nursing homes. These two groups

have significantly poorer overall outcomes compared to other groups. Forexample, although Groups 5 and 6 have comparable case mix, rates ofcatheterization and pressure ulcers are significantly higher in Group 5 (Table5). The small number of facilities in Group 7 (the group dominated by largemunicipal facilities) may have precluded establishing significant differenceson performance measures. However, Group 7 has a case-mix index identicalto Group 5, but somewhat better performance on outcome measures. Onthe other hand, almost half (47 percent) of the residents in Group 7 facilitiesare restrained.

With regard to efficiency, Group 1, the group with the highest average

Medicare census, also has the highest efficiency index score (.90). This is

Table 5: Results of Multivariate Tests of Significance and PostHoc Comparisons of Means (Performance and Strategic BehaviorVariables)Statistic Value F SignificanceMultivariate Tests of Signitfaxce(Performance Variables)Wilks' Lambda .68934 5.3 .0001Pillai's Trace .33237 4.9 .0001Hotelling-Lawley Trace .41981 5.6 .0001Roy's Greatest Root .33515 18.3 .0001Mudtivariate Tests of Signifwance(Behaviorial Variables)Wilks' Lambda .85531 5.1 .0001Pillai's Trace .14868 5.0 .0001Hotelling-Lawley Trace .16449 5.6 .0001Roy's Greatest Root .12797 8.0 .0001

Results ofPost Hoc Comparisons ofMeans*Catheterization rate. Group 5 > Groups 2, 6; Group 3 > Groups 2, 6; Group 3 > Groups 2, 6;Group 1 > Groups 2, 6; Group 4 > Groups 2, 6

Restraint rate. Group 3 > Group 4Pressure uker rate. Group 5 > Groups 2, 3, 4, 6; Group 1 > Groups 6, 2Efficiency index. Group 1 > Groups 2, 3, 6; Group 5 > Groups 2, 6; Group 4 > Group 6Increase Medicare days per bed 1987-1989. Group 5 > Groups 2, 6; Group 1 > Groups 2, 6;Group 4 > Group 6

Increase in Medicare revenues per bed 1987-1989. Group 1 > Groups 2, 6; Group 5 > Groups 2,6; Group 4 > Group 6

*Tukey's studentized range (HSD) test: alpha = .05; critical value of studentized range = 4.192.


consistent with other indications of efficiency in the provision of nursinghome services within this group (such as low length of stay). Facilities inGroup 5 have the second-highest average efficiency score. These facilities,which provide both skilled and intermediate care, may be able to achievescope efficiencies through the allocation of shared resources. Groups 2 and6, which had the best performance on outcome indicators, had significantlylower efficiency scores. The majority of facilities in both groups (60 and 63percent, respectively) are church affiliated. The mission of church-affiliatedorganizations may possibly create a value for slack in the objective function,allowing relatively inefficient organizations to maintain a market presence(Newhouse 1970).

While all groups, regardless of prior strategy, increased Medicare par-ticipation after the implementation of the MCCA (Table 3), some groupsincreased participation to a greater extent than others. As anticipated byprior commitment to the provision of Medicare services, the skilled nursingcare "specialist" Group 1, with the largest Medicare census prior to MCCAenactment, increased both days and revenues per bed significantly between1987 and 1989. While Groups 2 and 6 also increased in Medicare partic-ipation between 1987 and 1989, the increases were the lowest of all thegroups. By contrast, the greatest increases in Medicare days and revenuesper bed occurred in Group 5. Increases in Groups 4 and 7 were comparableto Group 1, the Medicare "specialist" group.

DISCUSSION

While the strategic groups identified in this analysis are consistent withgeneric strategic types intuitively recognizable in the industry, differencesbetween the groups exist and provide additional insight into ways in whichnursing homes compete. There appears to be a strong relationship betweentype of control and strategic group membership. For example, very fewCCRCs are run for profit in Pennsylvania, and few church-affiliated facilitiesoperate in Medicaid-dominated Groups 4 and 5. In this state, nonprofitsappear to have a strong hold on the private-pay market. The "brand name"recognition achieved through sponsorship by a religious or fraternal organi-zation may represent firm-specffic mobility barriers that for-profits find diffi-cult to surmount. Comparative studies of other state nursing home industriescould establish whether this is a unique characteristic of the Pennsylvaniamarket. In addition, there are differences in strategic group compositionbetween urban and rural nursing home markets. In rural markets, the lackof alternative providers to meet local demand may limit strategic options.

The performance of the seven strategic groups was found to differsignificantly on patient care outcome measures. Despite comparable case

201


mix, outcomes in the Medicaid-dominated, lean-staffed facilities of Groups4 and 5 were worse than in the private pay-dominated, well-staffed facilitiesof Groups 2 and 6. While CCRCs have been offered as an optimal answer tothe long-term care needs of an aging population (Bishop 1988), the price ofentry places them beyond the reach of many individuals. However, Group6 facilities, with fewer resources, are able to achieve outcomes comparableto the more affluent Group 2 facilities. If and how CCRCs achieve superioroutcomes and the relationship between outcome and efficiency are topicsmeriting further investigation.

The trade-off between efficiency and quality in nursing home care,and the relationship to ownership status, has been a frequent topic of inves-tigation (Davis 1991). Studies using DEA analysis to model nursing homeefficiency have found that for-profit facilities are more efficient after con-trolling for differences in case mix and quality (Nyman, Bricker, and Link1990; Rosko et al. 1993). Consistent with previous findings, Groups 1 and5, the most efficient groups, contain fairly high percentages of for-profitfacilities (79 and 63 percent, respectively), while the least efficient Groups 2and 6 have the lowest percentages (5 and 17 percent, respectively). Theseresults suggest that profit incentives may promote efficiency. Using a ser-vice intensity index to measure quality, Gertler (1988) found that propri-etary facilities provided 22 percent less quality than did nonprofits. In ourstudy, strategic groups with higher proportions of proprietary facilities hadsignificantly poorer outcomes than did groups with lower proportions offor-profits.

The provisions of the MCCA effectively lowered mobility barriers toparticipation in the potentially lucrative Medicare market. Facilities in allstrategic groups, regardless of previous Medicare participation, experiencedincreases in Medicare patient days and revenues per bed between 1987 and1989. Given a de facto moratorium on new nursing home bed constructionand little excess capacity in most areas of Pennsylvania, it is unlikely thatincreases in Medicare participation could be attributed to previously unmetcommunity demand. It is more likely that the increase in Medicare censusresulted from conversion of existing nursing home residents to Medicareeligibility status (Aaronson, Zinn, and Rosko 1993). Because Medicare ratestend to be higher than either Medicaid or self-pay rates, facilities able toreclassify existing residents undoubtedly experienced increased profitability.In addition, groups with the lowest staffing, highest Medicaid census, andweakest performance on quality indicators in 1987 experienced some of thegreatest increases in Medicare participation. These findings suggest that theeffect of this legislation on both access and quality bears careful examinationbefore future policy to expand Medicare participation is proposed.


Because the data used in this analysis are cross-sectional, we cautionagainst drawing causal inferences, particularly for the measures of perfor-mance. However, we believe that the results of our study support the value ofstrategic group analysis in furthering understanding of industry competitionand strategic response to environmental opportunities. An awareness of themediating influence of prior strategic posture provides insight into the likelyresponse to policy initiatives. Strategic group analysis may be particularlyuseful if, as appears likely, provider networks are retained as a cornerstone ofhealth care reform. An understanding of the group structure of all segmentsof the health care industry, and of the ways in which groups interact, mayprovide insight into the impact of policy on local health care markets.

ACKNOWLEDGMENT

We gratefully acknowledge the assistance ofJon Chilingerian and Pat Bernetin data analysis.

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