diffusion of computers in hospitals: an analysis of adopter categories

Grhuri Sv,rr,n.,. Vol. 3. pp 73 X6 C Pcrgamon Press Lid IV78 PrInted an Great Bnwn

0,47-x001 !7x!,00,-007350?.00~0

DIFFUSION OF COMPUTERS IN HOSPITALS: AN ANALYSIS OF ADOPTER CATEGORIES

VIJAY MAHAJAN

School of Management, S.U.N.Y. at Buffalo, 218 Crosby Hall, Buffalo. N.Y. 14214, U.S.A.

and

MILTON E. F. SCHOEMAN

Graduate School of Business Administration, The University of Texas at Austin, Austin, TX 78712, U.S.A.

Abstract--Very little is known, empirically or conceptually, about the diffusion of technological innovations in hospitals. Most of the empirical studies on the diffusion of innovations in hospitals have focused upon organizational change resulting from new programmatic services, rather than characterizing the early use of an idea or product. This paper discusses the diffusion of computers in hospitals. Specifically, the profiles of three distinct adopter categories are developed in terms of three factors: (a) the characteristics of the hospital; (b) the characteristics of the hospital’s environment; (c) the training and background of the hospital administrator. Discriminant analysis is used to develop these profiles.

INTRODUCTION

RECENT years have seen a burgeoning interest in the diffusion of innovations in health systems [l, 21. The following observations can be made with respect to the literature in this area: (a) the innovation process discussed involves primarily the introduction of organizational change, rather than the early use of an idea, product or program relative to other organizations; (b) most of the empirical studies focus on the innovation of programmatic services and not the diffusion of a major technological innovation, especially in hospitals [2]; (c) very little is known, conceptually or empirically, about the diffusion of technological innovations in hospitals [3]. This paper discusses the diffusion of computers in hospitals. More specifically, the emphasis is on an analysis of different adopter categories [4]. The major result is that the differences among the adopter categories can be attributed to (a) the characteristics of the hospital (b) the characteristics of the hospital environment segmented into the locational, health and economic environments and (c) the training and background of the hospital administrator. Discriminant analysis of variables related to the above three factors is used to develop the profiles of three distinct adopter categories. The State of Texas is chosen as the study site. In the West South Central Census Region, the State of Texas has the highest number of hospitals and medical schools. Some of its hospitals and medical schools are known nation-wide and world-wide for being on the forefront of medical

research [ 51.t

CONCEPTUAL FRAMEWORK

As specifically discussed in a previous article [S], a hospital may be conceptualized as an open system continuously interacting with its environment. In such a conceptual structure a hospital survives on the basis of its ability to favorably exchange its outputs (i.e. service to the community) for its requisite inputs (i.e. funds to maintain its labor, materials and physical facilities). Clearly this favorable exchange has caused hospitals to change objectives and structure to adapt to the dynamic environment [6-S].

Thus, three basic factors may be identified that affect these adaptive changes [S]: (1) the characteristics of the hospital as an organization in terms of its structure; (2) the characteristics of the explicit environment in which the hospital operates in terms

t The study does not consider federal hospitals because they have an atypical set of resources which can be applied to implementation of computer systems.

L.S 3--2,3-A 73

74 VIJAY MAHAJAN and MILTON E. F. SCHOEMAN

Table 1. Dictionary of variables

Variable

Computer activity

Variable index Definition

Y y = 1 if hospital uses computer activity = 0 otherwise

Teaching designation

Control

Bcsp&al &prg~terigics

X1 xi = 1 if hospital is a major unit of a teaching facility = 0 otherwise

.x2 xi = 1 if hospital is governmental, non-federal = 0 otherwise

-x3 x3 = I if hospital is non-governmental, not-for-profit = 0 otherwise

Service

Stay

Bed size Census

Occupancy

Facilities

If .x2 = .x3 = 0, then hospital is nongovernmental for profit

Advanced technological facilities

x,, = 1 if hospital is general medical and surgical = 0 otherwise x5 = 1 if hospital is short-term = 0 otherwise xg = Number of beds x, = Average number of patients receiving care each day during a IZ-month period: does not include new- born .xs = Ratio of average daily census to the average number of beds maintained during the l2-month period x9 = Total number of facilities (AHA indentifies 46 different facilities in a hospital. e.g. Intensive care unit. Emergency department, Volunteer services department, etc.)

.x1,, = Number of advanced technological facilities. These are: open-heart surgery; diagnostic radioisotope; therapeutic radioisotope; organ bank; electroencepha- lograohy; inpatient renal dialysis: outpatient renal dialysis: burn care x,, = Total expenses per bed in thousands of dollars for a I2 month period .yiz = 12-month salary per full-time employee in thousands of dollars. It does not include salaries paid to interns, resident. students, nurses and other trainees .x,s = Number of full-time personnels per bed. It does not include trainees. private nurses and volunteers

Total expenses/bed

Payroll/employee

Personnel/bed

Location characteristics Urban/Rural

Density

Age

Health characteristics

Economic characteristics

xi4 = 1 if county in which hospital is located is in a SMSA (standard metropolitan statistical area). There are 24 SMSA’s in Texas = 0 otherwise .xIs = county population per square mile. .xih = median age of county populace x,, = per cent of population below 14 yr of age .xis = per cent of population above 65 yr of age xi9 = median per capital income xZO = per cent of families with income below poverty level. x2, = per cent of population earning greater than $lO,ooO per annum .xZz = number of licensed hospitals in the county .xZ3 = number of medical schools in the county .xZ4 = hospital beds per 1000 population in the county .xZ5 = physicians per 1000 population in the county xl6 = per cent of administraters and managers in county population. Included are such occupations as public administrators, bank officers. buyers, inspectors. pilots, school administrators, etc. x2, = per cent of professional, technical workers in county population. This group includes such occupations as lawyers, accountants, computer specialists, engineers. mathematical specialists, physicians. nurses. teachers. and scientific technicians

Diffusion of computers in hospitals 75

Table 1 (con?inu~d)

Variable Variable index

Hospital administrators

Definition

Administrator’s background and training: f28 .rt8 = 1 if hospital administrator is a member of the

American College of Hospital Administrators = 0 otherwise

-y29 .yz9 = I if hospital administrator is a physician = 0 otherwise

x30 .x3,, = 1 if hospital administrator is a nurse = 0 otherwise

x31 ,y3, = 1 if hospital administrator is church trained = 0 otherwise

If\- Lq = .yjO = ss, = 0. then hospital administrator has training and/or college education in hospital administration

of physical location, health of the population and relevant economics; (3) the characteristics of the hospital’s administrator as the specific change agent or facilitator in terms of training and background.

In particular, adoption of computer use is viewed as an example of a change with the timing of adoption related to these factors. Thirty-one variables, as listed in Table 1. are selected to measure these factors: 13 variables dealing with hospital characteristics, 14 variables dealing with environmental characteristics, and four variables dealing with the administrator’s characteristics [3]_ The classi~cation of adopters can then be ana- lyzed relative to these variables.

DATA COLLECTION AND ANALYSIS METHODOLOGY

Approximately 30% of the non-federal hospitals in Texas as of December, 1973, were using computers. Of these 142 hospitals data on the three factors could be collected for only 114 of them. Most of these data were compiled from secondary sources [9-221. A chi-square analysis on the sample yielded that the sample represents the population of short-term, non-federal hospitals.

As stated previously, the analysis focuses on the classification of computer adopters into distinct categories based on when adoption took place. Naturally, over the time horizon being studied, the characteristics of hospitals and the environment may have changed. For example, Table 2 indicates some selected characteristics of the Texas hospitals from 1954 to 1973. There is a significant change in almost all of these variables and most especially in total expenses/bed, salary/employee and number of personnel/bed. For this reason, for the analysis of adopter categories variables x6, number of beds, x,, census, x9, total number of facilities, xIO, number of advanced technological facilities, xI1, total expenses per bed, xIZ, salary per full time employee and xts, personnel/bed, are modified to be ratios between the absolute value of the hospital characteristic at the time of initial adoption to the average value of that characteristic for all Texas hospitals at that time. All other characteristics are taken directly at the time of initial adoption.

Following an identification of adopter categories as discussed in the next section, discriminant analysis is used to investigate potential differences between categories. The results are presented in terms of the four objectives of discriminant analysis [23] : testing differences between average ‘score’ profiles of the groups; determining which variables account most for any intergroup differences; finding coefficients for the discriminant functions; evaluating the predictive capability of the discriminant functions. Total discriminatory power of the battery of variables and each factor (i.e. hospital characteristics, environmental characteristics and administrator’s characteristics) in discriminating the adopter categories is measured using an index, cG2 [24]. From Fig. 1 it is observed that this influence may result from these factors independently or from a variety of

76 VIJAY MAHAJAN and MILTON E. F. Scn0Ehi.w

Table 2. Changes in hospital characteristics over time”

Year Beds per hospital

Average census

Average occupancy

Average total

expenses per bed

(in thousands)

Average saiary

per employee

(in thousands)

Average no. of

personnel per bed

1973 127.75 94.49 74.0 20.75 6.03 1.86 1972 129.17 95.96 74.3 IS.54 5.66 1.80 1971 122.94 93.13 75.8 17.17 5.35 1.79 1970 124.05 96.30 77.6 15.13 4.99 1.72 1969 123.87 97.99 79.1 12.81 4.4 1 1.64 1968 120.71 95.06 78.8 Il.21 4.06 1.56 1967 115.90 90.63 78.2 9.69 3.75 1.47 1966 t 14.91 87.81 76.4 8.5 1 3.47 1.39 1965 116.12 88.50 76.2 7.72 3.46 1.28 1964 117.37 92.9 i 79.2 6.92 3.23 I.21 1963 I II.36 87.00 78.1 6.24 3.05 1.18 1962 110.16 84.33 76.5 5.71 2.92 1.13 1961 107.15 81.48 76.0 5.44 2.90 1.09 1960 105.83 82.85 78.3 5.06 2.81 1.06 1959 96.86 76.t7 78.6 4.94 2.73 1 .O? 1956 93.16 69.78 75.8 3.35 2.63 0.78 1954 88.70 70.5 1 79.6 3.35 2.63 0.78

‘Sources: Guide issues of Hospifals. August, 1954 thru 1973. Figures for 1954 and 1956 are approximations.

interactions; thus, several indices are calculated where [13, pp. 140-1421;

*2 ~123 = F, + F2 + F,, + F,, + f’23 + F123 + F3

^2 0 12 = F, -t F2 + F12 + F,, + F23 + F123 *2

:;3 - - f.1 + F3 -t F12 + F13 + f.23 + F,23

a23 = F2 + F3 + Ft2 + F,, + Fz3 + F,23

~5: = F1 + F,, + F13 + F123 &: = Fz + F,, + F23 + F123

6~: = F3 + F13 + F,, -I- Flz3.

ANALYSIS OF AD.OPTER CATEGORIES

For the whole population, i.e. 142 hospitals, the adopters’ time distribution is plotted in Fig. 2. This distribution is not a normal distribution (mean = 179.28 months, standard deviation = 42.15 months, skewness = 8.93, kurtosis = 11.92). A one-dimensional clustering algorithm gives three optimal adopter categories. The number of hospitals clus- tered in each group is given in Table 3 and selected characteristics of the adopter categories are presented in Table 4.

ctwoctertstxs

admlnlstrator’ characterlstlc

Fig. 1. Factor interactions.


24-

22-

4

1954 1960 1965 1970 1973

Time. year

Fig. 2. Diffusion of computers in Texas hospitals: adopters distribution.

Table 3. Optimal adopter categories

Group Years Number of y0 of Total number

hospitals of hospitals

1. Innovators 19541963 7 4.93 2. Early adopters 1964-July 1969 58 40.84 3. Late adopters Aug. 196991973 l-l 54.23

Table 4. Selected characteristics of adopter categories

Characteristic Innovators (7) Early adopters (58) Late adopters (77) y/, of Total ‘?/, of Total “< of Total

Total number of Total number of Total number of number innovators number early adopters number late adopters

Teaching designation (a) Teaching (b) Non-teaching

4 3

57.15 13 22.41 I 1.30 42.85 45 77.59 16 98.70

Service (a) General medical

& surgical (b) Others

5 2

71.43 28.57

57 88.28 1.72

75 2

97.40 2.60

Location (a) in a SMSA (b) Outside a SMSA

6 85.7 I 44 75.86 59 16.62 I 14.29 14 24.14 18 23.38

Control (a) Government (b) Voluntary (c)

2 4 1

28.57 IO 17.24 14 18.18 57.15 35 60.35 24 31.17 14.29 13 22.41 39 50.65

Bed size (a) &50 (b) 51-100 (c) 101-150 (d) 15lI200 (e) 201-250 (f) 251-300 (g) greater than 301

0.00 28.57

0.00 0.00

14.29 14.29 42.85

13.79 13.79 12.07 12.07 5. I1

IO.34 32.77

15 20 18 18

7

19.48 25.97 23.38 19.48 1.30 1.30 9.09

Hospital administrator (a) membership in ACHA 2 (b) Non-members 5 (c) Physician 4 (d) Nurse 0 (e) Church-trained 1 (B Others 2

28.57 71.43 57.15

0.00 14.29 28.57

39 19 I

45

67.34 32.76 I .72 3.45

17.24 71.59

40 31

2 IO 5

68

51.95 48.05

1.30 1.30 9.09

88.31

78 VIJAY MAHAJAN and M~LPON E. F. SCHOEMAN

Qualitative analysis

Computers were first introduced into Texas hospitals in 1954 and up to 1963 only seven hospitals had adopted their use. Sixty-six hospitals adopted between 1964 and 1969 and 69 betyeen 1970 and 1973. The first adopter category contains the first seven hospitals, 4.93% of the total number of adopters; the hospitals in this category will be referred to as ‘innovators’. The second and third adopter categories contain 58 (40.84%) and 77 hospitals (54.23% of the total number of adopters), respectively and are referred to as the ‘early adopters’ and the ‘late adopters’, respectively.

Examination of Table 4 indicates that four out of seven innovators (57.15x)) are affiliated with medical schools, five are general medical and surgical hospitals (71.43%) six are located in SMSAs (85.71%) four are voluntary, non-profit making, hospitals (57.15”/,), and five have bed capacity greater than 200 (71.43%). An interesting observa- tion is that four out of seven hospitals (57.15%) had a physician hospital administrator at the time of adoption, and five (71.43%) had hospital administrators who were not members of the American College of Hospital Administrators.

On the other hand. among the late adopters, 76 out of 77 hospitals (98.70%) are not affiliated with medical schools. Seventy-five (97.40%) are general medical and surgical hospitals, and only 18 hospitals (23.38%) are located outside SMSAs. Thirty-nine (50.65%) are proprietary hospitals and 68 (88.31”/,) have bed capacity less than 200. Seventy-five (97.407:) hospitals had a non-physician, non-nurse hospital administrator at the time of adoption and 40 (51.95%) had a hospital administrator who was a member of the American College of Hospital Administrators.

The above comparisons between the innovators and the late adopters suggest a diffusion pattern. A majority of innovators are affiliated with medical schools, have larger bed capacity, are located in SMSAs, are voluntary, non-profit making hospitals, and at the time of adoption had a physician and non-ACHA member hospital administrator. Almost all of the late adopters are not affiliated with medical schools. A majority of them are located in SMSAs, have smaller bed capacity, are proprietary, profit-making hospitals, and, at the time of adoption, had a non-physican, non-nurse, ACHA-member hospital administrator. Characteristics of early adopters are between those of the inno-

vators and late adopters.

Simple correlation

Simple correlation between the variables and the date of adoption indicates that there are only three variables (i.e. xg, total number of facilities, xI1, total expenses/bed, and x13, personnel/bed) which have simple correlation greater than 0.50 [3, p.1841. There are only four variables (i.e. x1, teaching designation, x6, number of beds, x7. census and xZ9, hospital administrator is a physician) which have simple correlation greater than 0.40 but less than 0.50. The correlations of other variables with the date of adoption are not too strong. Examination of intercorrelations among these seven variables indicates that predictor variables x1, teaching designation, x6, number of beds, x7, census and x9, total number of facilities, are highly intercorrelated [3, p. 1951. That is, the hospitals which are affiliated with medical schools tend to have large bed size, high census and a greater number of total number of facilities. Variable xI1, total expenses/bed, is highly correlated with x13, personnel/bed and xZ9, hospital administrator is a physician. This is, hospitals with high total expenses/bed tend to have a greater number of personnel/bed and have a hospital administrator who is a physician. Other intercorrelations are not very strong. These results suggest that large bed size hospitals, which are affiliated with medical schools, serve more people, (i.e. high census) and have a greater number of facilities, high expenses/bed and high personnel/bed and have a hospital administrator who is a physician are likely to adopt earlier.

Examination of simple correlations, though giving some insight about the structure of the problem, does not directly take into consideration the interactions between the

Diffusion of computers in hospitals

variables. For this reason, discriminant analysis is performed 114 hospitals [ 161.t

Group diflerences

79

next on the sample of

Are the three adopter categories significantly different? If ulr u2 and ~3 are the centroids of groups respectively, the null hypothesis is:

and the alternate hypothesis is: H1: not all US equal. The following results are obtained for Wilk’s lambda test for group means:

Wilk’s lambda = 0.139 F-ratio = 4.609 degrees of freedom = 60 and 164 probability of no group difference due to chance = 0.~.

Therefore, we reject the null hypothesis and conclude that, statistically, there are significant differences among the adopter categories.

Strength of discriminators

Which variables account most for the differences between the three groups? The variance among the three groups is explained along two discriminant axes.

Table 5 presents these results. The separation index or the discriminant criterion values, i,, and &, are 2.5023 and 1.0602. Thus, the total discriminatory power of the battery of predictor variables, h2, is 0.8578. That is, 85.78% of the total variability of the discriminant function is attributable to group differences. Or, 85.78% of the variability in the discriminant space is relevant to group differentiation. Of the total discriminatory power of the variables, 70.24% is absorbed by the first discriminant function, or 70.24% of the between-group variance is explained by the first discriminant axis, corresponding to ii, = 2.5023. Or, since /; is the ratio of among-group to within-group dispersion, the discriminant axis corresponding to a higher /T represents the dimension along which group differences are most significant. A plot of the group centroids (Fig. 3) gives a visual corroboration of this conclusion. Discriminant axis 1 mainly serves to separate Group 1 (innovators) form the other two groups. Similarly, discriminant axis 2 sets off Group 2 (early adopters) from groups 1 and 3. Thus, both discriminant functions play non-trivial roles in separating three groups, one from another.

Table 6 gives results for factor interactions. Of the total discriminatory power of the battery of variables, 12.84% is attributable to hospital characteristics, 12.36% to

Table 5. 3-Group discriminant function tests for adopter categories: Trace = 3.5625; i., = 2.5023; i2 = 1.0602; &’ = 0.8578

Axis I 2

O. of Variance explained 70.24 29.76 Chi-square 122.210 70.472 Degrees of freedom 31 29 Probability of no discrimination 0.0000 0.0001

TTatsuoka [24. p. 38) has the following advice regarding the sample for which discriminant functions are being developed: “In practice we would hardly ever use fewer variables than the number of groups being compared. . . Another rule is that total sample size should be at least two (preferably) or three times the number of variables used. . . . . In fact, to be reafistic, there are good reasons to insist that the size of the smailest group be no less than the number of variables used.” In our case, in the category of innovators there are only seven hospitals, less than the number of variables. Therefore. the results are confirmed by qualitative analysis of the data, simple correlation and multiple comparisons.


-1.0 (Late adopters) Gr.3 (0.55,-0.84)

q 0

VI

;

Q I 0 -3.5 Gr.2(0.53,-0.59)

(Early adopters)

0 Gr.1 (1.39,-0.70)

(Innovators)

I I I I 0 0.5 1.0 1.5

Axis I

Fig. 3. The three group centroids in the discriminant space for adopter categories.

Table 6. Factor interactions in adopter categories*

Factors iu, F Per cent of the explained variability

I 0.5599 O.liOl 12.84 2 0.49 I I 0.1060 12.36 3 0.5641 0.0928 10.82

(l,2) 0.7650 0.0776 9.05 (1.3) 0.7518 0.1638 19.10 (2,3) 0.7477 0.099 1 II.54

(1.2.3) 0.8578 0.2084 24.29

* I Hospital characteristics. 2 Environmental characteristics. 3 Hospital administrator’s characteristics.

Table 7. 3-Group discriminant analysis results for adopter categories

Correlation Discriminant function weights Variables axis I axis 2 F-ratio P I 2

I 0.3548 0.4269 2 0.059 I - 0.0628 3 0.0436 0.3132 4 -0.4109 - 0.0402 6 0.2138 0.3894 7 0.2364 0.3695 8 0.072 I 0.1308 9 0.4660 0.4784

10 0.2260 0.1610 II 0.6559 0.3267 I2 0.1400 0.3367 I3 0.6873 0.276 I I4 0.0473 -0.0552 I5 -0.0131 0.2044 I6 0.0267 0.2344 17 0.0128 - 0.0079 I8 -0.1357 -0.1990 I9 0.0900 0.3 I04 20 - 0.0992 - 0.3590 21 0.1195 0.2658 72 0.1192 0.1869 23 0.3 120 0. I320 24 0.3026 - 0.0383 25 0.3561 0.0957 26 -0.1077 -0.1789 27 0.2304 0.063 1 28 -0.2138 0.0585 29 0.8805 0.06 13 30 - 0.0546 0.0955 31 0.0004 0.1944

12.4900 0.0001 -0.1259 0.2520 0.7809 0.0075 3.0347 0.0506 0.0034 7.67 I8 0.001 I 0. I920 6.9069 0.0019 -0.0873 6.8715 0.0019 0.0492 0.7039 0.5013 - 0.0005

20.8396 0.0000 0.1814 2.9 I73 0.0566 0.1392

3 I .5285 0.0000 0.0749 4.3277 0.0152 - 0.0753

33.5548 0.0000 0.0585 0. I 742 0.8398 -0.1006 I.226 I 0.2970 -0.0001 1.6455 0.1957 - 0.0048 0.0083 0.9924 -0.0162 1.9252 0.1486 - 0.0023 3.2532 0.041 I 0.0001 4.3939 0.0144 0.0157 2.71 14 0.0690 0.0075 I .6064 0.2035 - 0.0033 4.7288 0.0107 0.0497 3.9317 0.02 I8 0.0257 5.8459 0.0042 0.1165 1.4192 0.2450 -0.0121 2.3119 0.1018 0.0186 I .9776 0.1411 0.0160

69.456 I 0.0000 0.9053 0.3812 0.6895 -0.1496 I.1005 0.3369 0.0100

0.0926 -0.1386 -0.0162 - 0.0286 0.2024

-0.1643 0.0006 0.7608

-0.3273 0.0425 0.0756

-0.0315 -0.1373 -0.0001

0.001 I 0.02 1 I

- 0.0043 O.OQOl

-0.0179 - 0.0027 - 0.0003

0.0004 - 0.0603

0.008 I 0.0650

- 0.0436 - 0.0369 - 0.2486

0.3352 0.0611


environment characteristics, 10.82% to hospital administrator’s characteristics, 19.10% to the interaction between hospital and hospital administrator’s characteristics, 9.05% to hospital-environment characteristics, and 24.29% to hospital-en~ronment-hospital administrator’s characteristics.

Table 7 gives the structural correlation between variables and discriminant scores as well as univariate F-ratio test results. Taking a 0.05 level of significance as the cut-off level, examination of univariate F-ratios in Table 7 indicates that x1, teaching designation, x3, hospital is general medical and surgical, x6, number of beds, x7, census, xg, total number of faciltties, xtl, total expenses/bed, xi2, safary per full-time/employee, x 1 3r personnel/bed, x1 9, median per capital income, xzo. percentage of families with income below poverty level, xz3, number of medical schools in the county, x2,+, hospital bed per 1000 population and xt9, hospital administrator is a physician, are independently, likely to contribute significantly to the overall differences among the groups. All other variables do not, independently, separate the groups at the 0.05 level of significance.

Examination of structural coefficients between predictor variables and discriminant axis 1, which separates Group 1 (innovators) from the other two groups, shows that characteristics of innovators are that they have high total expenses/bed; high personnel/ bed; and a hospital administrator who is a physician. The only characteristic that distinguishes the early adopters and late adopters is that early adopters have a greater number of facilities.

The above results show that the best discriminators in hospital characteristics on the discriminant axis 1 are xll, total expenses per bed and x13, personnel per bed. There are no good discriminators in the environmental characteristics. The best discriminator in the hospital administrator’s characteristics is medical school background (i.e. hospital administrator is a physician). On discriminant axis 2 there is only one good discriminator, x9_ total number of facilities. In other words, xX1, total expenses per bed, x13, personnel per bed and x 29r medical school background of the hospital administrator are the best discriminators to separate innovators from early and late adopters. And x9, total number of facilities is the best discriminator to separate early adopters from late adopters.

Discriminant functions and classijication

Table 7 gives the weights of the predictor variables in the discriminant functions, Table 8 gives the classification matrix using these discriminant function weights. Tests on the predictive power of the discriminant function are summarized in Table 9.

The percentage of correct classifications is 90.35% (given in Table 9). Results of t-test analysis in Table 9 show that the discriminant function gives significantly better classification than the proportional chance criterion (Q,), the maximum chance criterion (Q,), and the pure chance criterion (QJ [25].

Multiple comparisons on adopter categories

The group differences may also be studied by comparing the mean values of the variables between groups. A number of methods have been proposed to see which differences among groups appear to be real [26]. One of the proposed methods is Ltast Significant Difference (LSD) method. In this method, the difference between two means is compared against a criterion called the Least Significant Difference (LSD). The LSD

- Actual group

1 2 3

Table 8. Classification matrices for adopter caregories

Predicted group 1 2 3 Total

Innovators 5 0 2 7 Early adopters 0 42 7 49 Late adopters 0 2 56 58


Table 9. Tests on predictive power of discriminant functions for adopter categories

Classification criterion Per cent of correct

classifications

A. Random classification tests P, correct classifications

by discriminant functions Q; F%?,;~kwol chonrr

Q,. Maximum chance criterion 50.88

Q,, Pure chance criterion

90.35

44.74

33.33

B. r-test Chance criterion

I value

Level of significance

9.7941 i 0.001 8.4298 < 0.001

12.9154 < 0.001

is obtained by the multiplication of the standard error of the difference between two means and the value of t at the CI level of significance. The difference between a specific pair of means is significant at the level if it exceeds the LSD. The comparison is performed only if an F-test yields significant results [26, p. 2721.

For the three adopter categories. the results of the LSD method for the significant variables are summarized in Tables 10 and 11. An examination of Table 11 shows that there are four variables-x,, teaching designation, x9, total number of facilities, xllr total expenses per bed and x 13, personnel per bed-which discriminate across the three groups. Variables x4, hospital is general medical and surgical, xZ3, number of medical schools in the county, xZ4, hospital beds per 1000 population, xZ5, physicians per 1000 population and xZ9, hospital administrator is a physician, separate the group 1, innovators, from the other two groups. The Group 2, early adopters and the Group 3, late adopters, are distinguished by the variables xg, hospital is nongovernmental and not-for-profit, x1 2, salary per full time employee and xZO. percentage of families with income below poverty level. Finally, variables x6, number of beds, and x,, census,

Table 10. Multiple comparisons on adopter categories

Variable Number of hospitals in the sample

I 3 4 6 7 9

II 12 13 20 23 24

5.3568 25 29

Mean LSD for groups Mean Group I Group 2 Group 3

(innovators) (early adopters) (late adopters) 1-2 1-3 2-3 Nonadopters

7 49 58 250

0.5714 0.2245 0.0172 0.2407 0.2375 0.1047 0.0200 0.5714 0.59 I8 0.362 I 0.3806 0.3761 0.1655 0.3520 0.7143 0.9796 0.9828 0.1413 0.1386 0.0626 0.9080 2.8686 2.0592 1.1924 1.2165 1.2001 0.5290 0.5591 3.0729 2.0500 1.1750 1.3063 1.2946 0.5910 0.4883 0.6486 0.3988 0.2776 0.1344 0.1328 0.0626 0.1414 3.0443 1.4994 1.1979 0.4593 0.4541 0.2171 0.8255 I .0857 I .0298 0.9359 0.1534 0.1509 0.0742 0.8885 2.6443 1.3769 I.1888 0.3441 0.3414 0.1571 2.7214

10.5543 11.5094 15.3834 5.5030 5.4288 2.3932 18.5989 I .4286 0.6122 0.4828 0.6040 0.5959 0.2632 0.5 160 5.6086 4.0465 4.1710 4.1710 1.1415 1.1261 0.4966

1.8286 1.2667 1.2179 0.3594 0.3545 0.1570 0.8292 0.5714 0.0000 0.0000 0.1297 0.1283 0.0558 0.5060


Table Il. Differences among adopter categories

Groups Groups Groups Variable 1-2 l-3 2-3

I yes yes yes 3 no no yes 4 yes yes no 6 no yes yes 7 no yes yes 9 yes yes yes

II yes yes yes I2 no no yes 13 yes yes yes 20 no no yes 23 yes yes no 24 yes yes no 25 yes yes no 29 yes yes no

separate the Group 3, late adopters, from the other two groups. These results are similar to the results obtained by discriminant analysis.

The mean values of the variables for the three adopter categories and non-adopters are plotted in Fig. 4. An examination of these plots reveal that except for two variables -xr3, personnel per bed, and .xz4, hospital beds per 1000 population-mean values of the variables across adopter categories and non-adopters are monotonically increasing or decreasing. It is difficult to explain or interpret the non-linear nature of xia, personnel per bed, and x2+ hospital beds per 1000 population. Ignoring these two variables and from the preceding analyses it may be concluded that the best discriminators are: (a) x1. teaching designation, (b) xg, total number of facilities, and (c) xii, total expenses per bed.

CONCLUSIONS

The first. use of computers in Texas hospitals started in 1954. Until 1963, there were only seven hospitals which were using computers. Sixty-six hospitals adopted

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between 1964 and 1969; and 69 between 1970 and 1973. Three optimal adopter categories were obtained. The first adopter category, named innovators, contains the first seven hospitals. The second adopter category, named early adopters, contain 58 hospitals that adopted between 1964 and July, 1969. The last category, named late adopters, contains the rest of the 77 hospitals.

The three groups are found to be statistically significantly different in terms of three variables, measuring the characteristics of the hospital, the environment in which a hospital operates and the hospital administrator. The total discriminatory power of the battery of three variables is 85.78%; 19.10% of which is contributed by the interaction between hospital and hospital administrator’s characteristics, 9.05% by hospital-environment characteristics, 11.54% by environment-hospital administrator’s characteristics, and 24.29% by the interaction of all three of them. Individually, hospital characteristics contribute 12.84%, while 12.36% is contributed by environment characteristics and 10.82% by hospital administrator’s training and background.

Two significant discriminant functions are obtained. Of the total discriminatory power of the variables, 70.24% is absorbed by the first discriminant function, or 70.24% of the between-group variance is explained by the first discriminant function. The first discriminant function separates Group 1 (innovators) from the other two groups and the second discriminant function sets-off Group 2 (early adopters) from the other two groups. The best discriminators on the first axis are, in order of their importance, xz9, hospital administrator is a physician; x13, personnel/bed; and xli, total expenses/bed. On the second discriminant function the best discriminator is xg, total number of facilities.

The distinguishing characteristics of innovators are that they have high total expenses/bed; high personnel/bed; and a hospital administrator who is a physician. The only characteristic that distinguishes the early adopters from the late adopters is that early adopters have a greater number of facilities.

Multiple comparisons on adopter categories give results similar to those obtained by discriminant analysis. The best discriminators obtained are (1) teaching designation (xi), (2) total number of facilities (x9), and (3) total expenses per bed (xll).

These results suggest that adopters of computer use may be classified successfuly by examining the characteristics of the hospital, the characteristics of its operating environment, and the training and background of its administrator. In this way it is possible to anticipate not only if a hospital will adopt such an innovation, but also generally when it will relative to other hospitals.

REFERENCES

I. A. D. Kaluzny and J. B. Sprague, Innovation in health and welfare organizations: a review and critique of current theory and research, in Innovation in Health Care Organizations, A. D. Kaluzny. J. T. Gentry and J. E. Veney, Eds, University of North Carolina, Chapel Hill (1974).

2. A. D. Kaluzny, Innovation in the health system: a selective review of system characteristics and empirical research, Hlth Serv. Res. Summer (1974). V. Mahajan, Computers in hospitals: a diffusion study. pp. 14-16, Ph.D. dissertation, University of Texas at Austin (1975). E. Rogers and F. F. Shoemaker. Communication of Innocations: A Cross-Cultural Approach. pp. 1799180. Free Press, New York (1971). have hypothesized that adopter distributions follow a bell-shaped curve over time and approach normality. A number of studies in rural sociology have confirmed this hypothesis though a number of researchers have questioned its validity, especially in industrial or consumer marketing where the competition and ‘band wagon’ feeling among the firms may lead to a distribution more peaked and skewed than is normal. See, for example, R. A. Peterson, W. Rudelins and G. L. Wood, Spread of marketing innovations in a service industry, J. Business 485496 (1972). Based on the assumption of normality, Rogers and Shoemaker, op. cit., p. 182, have suggested five adopter categories: innovators, early adopters, early majority, late majority and laggards. R. A. Peterson, A note on optimal adopter category determination, J. Mktg 10 (1973), has suggested the use of a one-dimensional clustering algorithm to determine the adopter categories optimally irrespective of shape of the adopter distribution. This paper uses his algorithm to determine the adopter categories based on the time of adoption of computers (for any purpose in the hospital). V. Mahajan and M. E. F. Schoeman, The use of computers in hospitals: an analysis of adopters and nonadopters, Working Paper 75-41, Bureau of Business Research, University of Texas at Austin (1975).


6. E. H. L. Corwin, The American Hospital, Commonwealth Fund, New York (1946). 7. T. Burling, The Give and Take in Hospitals, Putnam, New York (1956). 8. W. Heydebrand, Hospital Bureaucracy, Dunellen, New York (1973). 9. Hospitals and related institutions in Texas; 1973-74, Texas Hospital Association, Austin.

10. Hospitals, J. Am. Hosp. Ass. Guide Issue, 1954-1974. 11. Utilization of automated data processing, Final Report. Texas Hospital Information Systems Society,

Austin (1974). 12. Texas Almanac, Dallas Morning News, Dallas 1960-1974. 13. Selected Demographic Characteristics From Census Data-Fourth Count, Office of the Governor, The

State of Texas, Office of Information Services, OIS-CR3 (1972). 14. Arlas of Texas, Bureau of Business Research, University of Texas at Austin (1973). 15. Distribution of Physicians in the Lrnited States, American Medical Association, Chicago (1950-1973). 16. Directory of American College of Hospital Administrators, 1960-l 973. 17. Directory of Approoed Internships and Residencies, American Medical Association, Chicago (1973). 18. Selected Demographic and Health Cure Characteristics, Texas Health Data Institute. Austin (1971). 19. Texas Business Reoiew. Bureau of Business Research, University of Texas at Austin, April, 1963. March,

1964, 1965 and 1966; January, 1967; March 1968; January, 1969; March, 1970. 20. Sales Management. Suruey of Buying Power, Sales Management, Inc. (1954-1959) 21. Census of Population 1960 and 1970, PC (1): A45 Texas, Bureau of Census, U.S. Dept. of Commerce. 22. Population Estimates and Projection: Current Population Reports. Bureau of the Census. U.S. Dept. of

Commerce. Series P-25. No. 535 (1974) and No. 517 (1974). 23. P. E. Green and D. S. Tull, Research for Marketing decisi&s. 3rd editton, Chapters 12 and 13. Prentice-

Hall, Englewood Cliffs (1975). 24. M. M. Tatsuoka. Discriminant Analysis: The Study of Group D@erenccs. Institute for Personality and

Ability Testing, Champaign (1970). 25. For the discussion of different chance criterion. see D. G. Morrison, On the interpretation of discriminant

analysis. J. Mkrng Res. 6, May, (1969); and for a discussion of classification comparisons, (e.g. the use of t-test), see R. E. Frank, W. F. Massy and D. G. Morrison, Bias in multiple discriminant analysis. J. Mktng Res. 11, August (1965).

26. G. W. Snedecor and W. Cochran. Statisfical Methods. pp. 271-275, Iowa City University Press, Ames (1967).

diffusion of computers in hospitals: an analysis of adopter categories

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