structure and organizational resource allocation

Structure and Organizational Resource AllocationAuthor(s): Michael K. MochSource: Administrative Science Quarterly, Vol. 21, No. 4 (Dec., 1976), pp. 661-674Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management,Cornell UniversityStable URL: http://www.jstor.org/stable/2391722 .

Accessed: 12/06/2014 14:21

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

.

Sage Publications, Inc. and Johnson Graduate School of Management, Cornell University are collaboratingwith JSTOR to digitize, preserve and extend access to Administrative Science Quarterly.

http://www.jstor.org

This content downloaded from 62.122.79.31 on Thu, 12 Jun 2014 14:21:07 PMAll use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=sage

http://www.jstor.org/action/showPublisher?publisherCode=cjohn

http://www.jstor.org/action/showPublisher?publisherCode=cjohn

http://www.jstor.org/stable/2391722?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp


Structure and Organiza- tional Resource Allocation

Michael K. Moch

The author would like to thank Howard Aldrich, Frank Andrews, Cortlandt Cam- mann, Robert Cooke, Jean de Kervas- doue, Gerald Gordon, Jeanne Herman, John Kimberly, William Mason, Edward Morse, David Street, and Donald Swartz for their assistance in writing this paper. The help and support of Lawrence Mohr, Everett Rogers, Kenneth Warner, and the other members of the Michigan Faculty Seminar on Innovation and Social Change also is gratefully acknowledged. The author would also like to express apprecia- tion for the very helpful comments of an anonymous ASO reviewer.

December 1976, volume 21

Literature concerning relationships among structural attributes of organizations and literature focusing on the adoption of innovations are integrated, and a model of innovation adoption is tested against data gathered in a nationwide survey of United States hospitals. The data are consistent with the hypothesis that increased organizational size leads to specialization, functional differentiation, and decentralization. The expectation that increased size, specialization, functional differentiation, and decentralization lead to innovation adoption is supported by the data. The impact of size on adoption, while substantial, is primarily indirect, operating through its effect on structural attributes. Adoption of innovations represents only one form of resource allocation. The theoretical model predicting adoption behavior may be useful for understanding relationships among size, structure, and other forms of resource allocation in organizations.*

Organizational structure specifies relationships between individuals which affect the ways in which organizational resources are allocated. For example, certain structural configurations may promote the influence of some individuals while inhibiting that of others. If the perspectives or interests of these individuals diverge, their relative influence is likely to have an impact on patterns of expenditures (Cyert and March, 1963; Downs, 1967; Pondy, 1970; Williamson, 1964). As attributes of the structure of organizations change and de- velop, we might expect patterns of resource allocation to change as well. Moreover, if a stable pattern of structural development can be discerned, it may be possible to predict the consequences of this development for future patterns of resource allocation in organizations.

Much attention has been paid to the development of organizational structure. Most of this work has focused on bivariate comparisons (Blau, 1968, 1 970b, 1973; Blau and Schoenherr, 1971; Child, 1972, 1973b; Meyer, 1968, 1972; Pugh et aL, 1968, 1969a); however, some more comprehensive models of structural development have been proposed (for example, Child, 1 973a; Inkson et al., 1968; Pugh et al., 1 969b). Child's work is exceptional for its focus on the effects of structural development on organizational outcomes-specifically, the behavior of the organizations' members. While an emphasis on such outcomes is interesting in itself, it also amplifies the theoretical and practical importance of the processes that lead to these outcomes. Childers et al. (1971), Mayhew et a/. (1972), Specht (1974) and others recently have been critical of several empirical studies of structural relationships in organizations. These authors assert that some structural relationships-most notably the relationship between size and differentiation-can be de- rived exclusively from mathematical considerations and are, therefore, theoretically trivial. Some of these relationships may be mathematically deduced. They are not, however, trivial (Schoenherr, 1974). This becomes particularly clear when their possible effects are taken into account. The theoretical importance of a causal relationship between size and differentiation would be amplified considerably if size were found to affect the ways in which organizations allocate resources

661/Administrative Science Quarterly



through its impact on differentiation. Understanding the nature of the causal relationships among size, differentiation, and other structural variables then would be critical for understanding the direct and indirect effects through which size and structure influence patterns of resource allocation. The research reported here is an attempt to document relationships between the development of organizational structure and one form of resource allocation-that directed toward acquiring new technology. Decisions to adopt or not to adopt technical innovations are often pivotal organizational decisions. Those responsible for day-to-day work activities may be interested in acquiring techniques that facilitate their work, enhance their status, or minimally disrupt the accepted ways of doing things (Rogers and Shoemaker, 1971: 145-53). Those responsible for the overall functioning and efficiency of the organization, however, are likely to be more sensitive to cost considerations. The perspectives of top management may be quite different from those of line department heads or professionals. The structure of the organization sets the parameters within which these perspectives compete for influence over decisions to adopt or not to adopt new technology. Patterns of structural change and development, therefore, may lead to predictable patterns of change in adoption behavior. Since adoption represents but one category of resource allocation decisions, understanding the relationship between structural development and adoption behavior is likely to provide insights into the effects of structural change on resource allocation patterns in general.

ORGANIZATIONAL ATTRIBUTES AND PATTERNS OF ADOPTION BEHAVIOR It has been reported that larger, more specialized functionally differentiated or decentralized hospitals are more likely to adopt innovations that are consonant with the perspectives of department managers and professionals (Moch and Marse, 1976). These individuals may be expected to influence adoption to the extent that they have specialized knowledge (the organizations are highly specialized or more functionally differentiated) or have actual resource allocation discretion (the organizations are decentralized). They also are likely to have influence to the extent that those responsible for overall op- erations incur significant costs when they attempt to exercise control. These control costs increase, probably exponentially, with organization size (Pondy, 1970; Williamson, 1967). It was also found that size and decentralization interact to affect adoption decisions. Technical innovations in the organizations studied were more frequently adopted when the organization was both large and decentralized. Decentralization provides professionals or department heads with discretion over resource allocation decisions, and greater size increases the costs top management must incur if these decisions are to be monitored and controlled. The growth of an organization is likely to facilitate adoption by virtue of the fact that larger volume makes adoption of a variety of items economically feasible. Innovations that can be applied to only a small proportion of the input material are likely to be adopted only when this proportion represents a significant amount in absolute terms. In addition to this direct

662/ASQ



1

This argument is not obviated by the fact that the size-differentiation relationship may be expected on purely mathematical grounds. Size affects the extent to which an organization may be differentiated. Be- nefits that can be realized through differentiation, however, are not accidental consequences of size. The need to realize such benefits in fact is likely to be an important consideration when decisions concerning desirable or optimal size have to be made.

2

The assumption that organizational members have differing goals or perspectives and therefore will choose to allocate resources in different ways does not neces- sarily imply that individuals make decisions in their own, as opposed to the organization's, best interests. Nor does it imply open conflict between organizational members. Organization members are exposed to different information, have different training and socialization, and often have different-and perhaps competing-subgoals (March and Simon, 1958: 150-158). It is not surprising, therefore, that individuals holding organizational positions with different subgoals, training requirements, access to information, and so forth will allocate resources in sys- tematically different ways.

Resource Allocation

effect, size also may have indirect effects on adoption. Blau (1970b; 1973), Blau and Schoenherr (1971), Child (1973b), Meyer (1 972), Pugh et al. (1 969a) and others have reported significant positive relationships between size on the one hand and differentiation or specialization on the other. Moreover, with the exception of Aldrich (1972), these authors have given size causal priority over differentiation or specialization. Generally, the assumption of the causal priority of size has been based upon the belief that size represents a contex- tual rather than an organizational characteristic. Since the context is believed to affect the structure, rather than vice versa, causal priority is given to size. A more explicit approach would be to consider size as an organizational attribute. The larger volume processed by larger organizations enables them to elaborate organizational structures to deal with different environmental or technological contingencies. Larger volume al- lows organizations to more finely differentiate tasks (functional differentiation) and personnel (specialization).1 With increased specialization and functional differentiation, top management becomes more dependent upon the knowledge of specialists and heads of functional subunits. Since resources may be expected to be allocated by specialists or department heads in ways that will facilitate their activities, this depen- dency is likely to be reflected in adoption patterns.2 Size, therefore, will have both direct and indirect effects on innovation decisions. Directly, its effect may be attributed to greater input volume. Indirectly, this effect will occur via structural attributes. Size may have another indirect effect on the patterns of innovation adoption through its impact on decentralization. Blau (1 970a) has argued that the administrative burden on top management increases as a function of size. Pondy (1970) and Williamson (1967) suggested that the control costs in- curred by top management increase with size. One way those responsible for the overall functioning of the organization can reduce these costs is to delegate control responsibilities to those closer to the sources of information upon which decisions are based. When control is relinquished, the communication costs-in time, money, and information distortion-are reduced. Top management may be reluctant to relinquish control and, as Blau maintained, may do so only when monitoring mechanisms are available. There is also the possibility that decreased communication will reduce integration and coordination. To the extent that the organization is large, however,,these losses will be compensated for by lower communication and control costs. Other things being equal, size may be expected to effect a decrease in the extent to which top management retains decision-making discretion. The discretion gained by lower-level personnel in large organizations (that is, department heads or professionals) will provide them with additional leverage to affect adoption decisions. Size, therefore, may have an indirect effect on resource allocation patterns through its effect on decentralization.

The effect of specialization and differentiation on the ways in which organizations allocate resources may be due to more than simply the presence of specialists or department heads. Top management or owners are likely to delegate resource allocation discretion to those deemed qualified to make such

663/ASQ



decisions (Blau, 1968; Hage and Aiken, 1967). Organizations with many different specialists or departments, therefore, may be expected to be more decentralized. Specialization and functional differentiation may have indirect effects through decentralization as well as direct effects on the patterns of innovation adoption in organizations. The patterns of relationships discussed above are summarized in Figure 1. The hypothesized causal relationships between organizational attributes (a-e) specify a model for the evolution of organizational structure. The figure taken in its entirety specifies a model for the impact of structural development on patterns of innovation adoption in organizations.

THE STUDY

This study is part of a larger project designed to discern organizational correlates of technical innovation in a rep- resentative sample of approximately 1,000 hospitals in the United States. Since it was not possible to survey the adoption of innovations across all medical fields, the study focused on an area central to the delivery of hospital services: respiratory disease. Respiratory illnesses often complicate other diseases, and they can be important factors in surgical opera- tions that require a general anesthetic. Many hospitals even have inhalation therapy departments; some require inhalation therapy for postoperative patients. Each hospital in the sample was sent two questionnaires, one to the chief medical officer and one to the chief administrative officer. Sixty-seven percent of the medical officers and 68 percent of the administrative officers responded to the questionnaires; and 489 (49 percent) hospitals responded to both instruments. Thirty-nine of the 489 were operated by the federal government. These hospitals are actually subunits of larger organizations (for example, Veterans Administration and armed services), and the measure of centralization constructed for the study did not include items designed to assess the extent to which resource-allocation decisions are made by administrative officers based outside the hospital. This problem is amplified by the fact that regional offices of the Veterans Administration often expend a great deal of

Specialization

a -c +

/ /_______ CentaliztionAdoption of Size - e Centralization = technological / + innovations

+ - d __+

b

Functional differentiation

Figure 1. The development of organizational structure and innovation adoption in complex organizations.

664/ASQ



3

To assess the tenability of the assumption of curvilinearity, parallel analyses were run using the untransformed measure. Using this measure failed to increase the explanatory power of the model (R2), and the log measure therefore was taken as appropriate. This measure also was highly correlated with other logged measures of size. The correlations between the annual number of patients (logged) and three other measures (also logged) were .81 for number of support personnel, .84 for number of beds, and .76 for total noncapi- tal assets.

4

A technique suggested by Upshaw (1968) was used to test the hypothesis that these coefficients were greater than .90 due to the frequency distribution of re- sponses to the component items. Using this method, the hypothesis was rejected for the measures of both specialization and differentiation.

Resource Allocation

effort evaluating medical innovations. They also allocate resources for the purchase of innovations to be used in hospitals in their region. To avoid confounding the measure of centralization, therefore, hospitals operated by the federal government were excluded from the sample. Information on several dimensions was secured from the American Hospital Association (AHA) to supplement the data base. Since these data were gathered for all hospitals in the sample (933 excluding federal hospitals), it was possible to test for response bias. Twenty-seven percent of the proprie- tary (for profit) hospitals returned both instruments, while 52 percent of the voluntary (private) and 49 percent of the local public hospitals did so (p<.001). No response bias was found, however, when response rates were compared on the basis of Standard Metropolitan Statistical Area (SMSA) classifica- tion, long-term versus short-term facilities, or the number of beds. The Independent Variables: Size and Structure The literature dealing with organizational size has many different measures of this concept. These measures, however, increasingly have been logarithmically transformed to adjust for curvilinearity in relationships between size and other variables (Blau, 1970b; Blau and Schoenherr, 1971; Child, 1973a; Hickson et a/., 1969; Pugh et a/., 1 969a). Logarithmic trans- formations may be justified on either theoretical or empirical grounds (Kimberly, 1976). In the current research, size was taken to be the log of the number of annual patient admis- sions to reflect (1) input volume and (2) the expectation that, as had been shown in the research cited above, the effect of an incremental increase in size on structural variables would decrease as the organizations became larger.3 Specialization and functional differentiation are distinctly different concepts. The former refers to the extent to which tasks are divided among many different experts. The latter refers to the extent to which different tasks are allocated to different identifiable groups. A hospital may be highly specialized without being highly differentiated and vice versa. The measure of hospital specialization was based upon the presence or absence of representatives from each of the 25 medical specialty areas. These data were secured by consult- ing the Directory of Medical Specialties (1970). The measure of functional differentiation was based upon the presence or absence of each of 14 hospital service units-an outpatient care unit, an emergency unit, an intensive care unit, and so forth. These data were available in the AHA survey. Guttman scale analysis revealed that hospitals with representatives from infrequently occurring specialty areas generally had representatives from the more frequently occurring areas as well (CR=.92). Similarly, hospitals with less frequently occurring functional units also tended to have the more frequently occurring units (CR=.91).4The number of specialties represented on the staff therefore was used as a measure of specialization, and the number of distinct hospital service units was accepted as the measure of functional differentiation.

The measure of centralization was developed to reflect the location of resource allocation discretion. The measure itself 665/ASQ



5

The indexing procedure combined re- sponses from both the chief medical and the chief administrative officers. The decisions involved hiring, supply acquisition, and equipment purchasing. The details of the construction of this measure are de- scribed elsewhere (Moch, 1973).

6

The technique developed by Upshaw (1968) to test the hypothesis that this coefficient could be attributed to the frequency distribution of the component items was applied to this measure. As with the measures of specialization and functional differentiation, the hypothesis was rejected.

was constructed using an indexing procedure which yielded a score reflecting the extent to which each of five specific resource allocation decisions were made by (1) the hospital board of directors, (2) the chief medical or administrative officers, or (3) the heads of medical departments.5 Those hospitals in which most decisions were made by the board were considered centralized; those in which most decisions were made by department heads were considered decentralized; those in which most of the decisions were made by administrative or medical officers were considered moder- ately centralized. The scores ranged from a high of 10 (centralized) to a low of 0 (decentralized). The Dependent Variable: Adoption of Technical Innovations

The adoption of new medical technology for diagnosis and treatment of respiratory disease may be viewed as a case of resource allocation compatible with the perspectives of professionals or department heads in hospitals. Department heads invariably are physicians who, together with specialists and other physicians, are engaged primarily in task-related, rather than administrative or control, activities. Their work centers on patient care, and they are likely to view fiscal issues, coordination, and control as constraints rather than as desirable ends. Members of the hospital board, however, are responsible for expenditures and may be expected to consider efficiency-and thereby coordination and control-as goals to be pursued. Technical innovations, while facilitating patient care, require expenditures and thus may be somewhat less compatible with the orientation of the hospitals' boards of directors than with the orientation of specialists or department heads. The measure of the frequency of adoption of medical innovations is based on the number of new respiratory disease technologies adopted by the hospital. Eleven new technologies were selected in consultation with experts in the field. These experts initially generated a list of more than 200 items. The final selections were made on the basis of item differences across a range of dimensions, including cost, risk, and communicability. The appropriateness of the counting procedure was supported by a Guttman scale analysis yield- ing a coefficient of reproducibility of .92.6 Information concerning the presence or absence of each innovation was gathered both from the chief medical officer and from the chief administrative officer. The correlation between the adoption measures based on these independent reports was .78. In addition, the researchers visited 16 hospitals to determine for themselves whether the items were present or absent. The correlations between these observations and the reports of the chief medical and administrative officers were .86 and .75 respectively.

THE METHOD A path analytic approach (Aldrich, 1972; Duncan, 1966; Land, 1969) was used to assess the extent to which each of the five paths in Figure 1 (a-e) contributed to closer approxima- tion of the observed correlations among the variables in the model. Maximum likelihood estimates were generated for

666/ASQ



Resource Allocation

each of the paths, and an X2 value based upon the differences between the observed and the estimated values of the correlations was computed. This measure allowed for an overall assessment of the "goodness-of-fit" of the model to the data (Joreskog and Van Thillo, 1972). Each of the hypotheses represented by the different paths, a through e, in Figure 1 was tested separately. The extent to which the elimination of each path detracted significantly from the goodness-of-fit was as- sessed by comparing the X2s of each model with that of a baseline model which included all five paths. The significance of the difference between theX2 values compared was as- sessed by computing their difference which is distributed as an X2 with degrees of freedom equal to the difference between the degrees of freedom associated with each of the models being compared (Werts et al., 1973). Using this approach, each causal path in Figure 1 was tested and either accepted or rejected. A problem arises, however, because the baseline model may be incorrect. Using the procedure out- lined above, two or more paths from correlated variables may be rejected even though one of them would significantly increase the goodness-of-fit were it to be included without the other paths. To avoid this type of error, each of the rejected paths was reentered separately into the model and its contribution to the goodness-of-fit was compared to that obtained from a baseline model that included only previously accepted paths. Several controls were introduced into the analysis. Two variables which were correlated with both independent and dependent variables-the presence of another hospital in the area and the percentage of physicians with joint appointments with a medical school-were treated as exogenous factors. The presence of physicians with outside funds for research in respiratorv disease. likely to be a func-

Table 1

Centralized Model Representing Configurations of Variables Expected to Determine Adoption Behavior in Organizations (Causal Direction-Rows to Columns)*

Variables 1 2 3 4 5 6 7 8 9

Endogenous 1. Adoption behavior - 0 0 0 0 0 0 0 0

2. Presence of physicians with outside research funds 1 - 0 0 0 0 0 0 0

3. Presence of inhalation therapy department 1 0 - 0 0 0 0 0 0

4. Centralization 1 0 0 - 0 0 0 0 0

5. Specialization 1 1 0 2 - 0 0 0 0

6. Functional differentiation 1 0 0 2 0 - 0 0 0

Exogenous 7. Size 1 0 1 2 2 2 - t t 8. Percentage of physicians with

joint appointment with medical school 1 1 1 1 1 1 t - t

9. Presence of another hospital in area 1 1 1 1 1 1 t t -

1 =coefficient estimated in all models 2=coefficient estimated or set at zero depending on the model being tested O=coefficient set at zero in all models t exogenous variables allowed to correlate

667/ASQ



tion of the number of medical specialties represented on the hospital staff, and the presence of an inhalation therapy department, likely to be a function of organization size, were treated as endogenous factors. The overall specification of the model, including controls, it presented in Table 1. In this table, causal paths not allowed are denoted by zeros; causal paths allowed for in all tests are represented by ones; and the twos represent the causal paths (a-e) which were either allowed or not allowed depending on the specific test being made.

RESULTS The top portion of Table 2 presents theX2 value obtained when each path was subtracted, the attendant degrees of freedom, the increase in theX2 value obtained relative to the baseline model, and the significance level of this difference. The bottom portion contains theX2 values, degrees of freedom, and the decrease in X2 obtained relative to the baseline model when rejected paths were reentered one at a time. This table clearly indicates that the elimination of each of the causal paths, except those from specialization and functional differentiation to centralization, provided a significantly poorer fit between the estimated and the observed correlations. The data are consistent with the hypothesis that size leads to a greater division of labor by facilitating both greater specialization and greater functional differentiation. The data also are consistent with the hypothesis that size leads to decentralization and, though less conclusively, support the introduction of a path between specialization and centralization. The hypothesis that functional differentiation leads to decentralization, however, does not appear to be consistent with the data. Given a cutoff point of p(.10, the introduction of a causal path between functional differentiation and centralization does not lead to better estimates of the observed correlations.

The model that has the fewest paths and best reproduces the observed correlations, therefore, is one in which all of the paths specified in Figure 1, except the one connecting functional differentiation and centralization, are included. This

Table 2

Summary of Tests for Goodness-of-Fit for Causal Paths Subtracted One-at-a Time

Significance Significance Level of Level of

Elimination Degrees Increase Increase in Addition Degrees Decrease Decrease in of of X 2 in X 2 Value of of X 2 in X 2 Value Causal Path Freedom Value X 2 Value (p<one-tailed) Causal Path Freedom Value X 2 Value (p< one-tailed)

Baseline (All Baseline (All Causal Paths Causal Paths among Independent Not Rejected Variables) 11 60.01 Above) 13 62.78

a. Size to c. Specialization specialization 12 243.80 183.79 .001 to centralization 12 60.27 2.11 .075

b. Size to d. Functional functional differentiation differentiation 12 181.29 121.28 .001 tocentralization 12 61.26 1.52 .100

c. Specialization to centralization 12 61.26 1.25 .300

d. Functional differentiation to centralization 12 60.67 0.66 .500

e. Size to centralization 12 68.05 8.04 .010

668/ASQ



Resource Allocation

597 .886

Presence of physicians .364 with respiratory disease

Specialization research funds

.204 .093 .656 .612

.795-.140

-.281 Centralization -.115 - *- tionof Size ___________ __________________________________________ technological

.176 innovations"

.598 .720 181

.913 .369 Functional

differentiation

8 * ~~~.244

Presence of inhalation therapy * department

The independent variables and the controls account for 62 percent of the variance. Figure 2. Path coefficients for best-fitting model of organizational adoption behavior.

7

The analytic procedure and the presentation of results do not include consideration of the interaction effect of size and centralization on the frequency of adoption mentioned earlier. The analysis focuses on assessing a model of structural development, and the interaction is unre- lated to this process. The interaction, however, could bias estimates of the main effects of the independent variables, particularly size and centralization, on frequency of adoption. This possibility did not occur. When an interaction term was included, coefficients reflecting the impact of each of the independent variables were essentially unchanged (Moch and Marse, 1976).

model, together with the estimated path coefficients, is presented in Figure 2.' To simplify the presentation, only the independent and dependent variables and the endogenous controls have been included. Coefficients between the exogenous controls and the endogenous variables are presented in Appendix 1. The model specified in Figure 2, together with the exogenous and endogenous controls, provides a good fit to the observed relationships. The observed correlations and those generated by the model are compared in Table 3. The expected and

Table 3

Expected and Observed Correlations between Independent and Depen- dent Variables

Observed (0) from Expected Difference Variables Model (Fig. 2) (E) (E-O)

Size functional differentiation .66 .66 .00 Size specialization .74 .74 .00 Size centralization -.42 -.42 -.00 Size adoption behavior .64 .64 .00 Functional differentiation specialization .69 .56 -.14 Functional differentiation centralization -.36 -.31 .05 Functional differentiation adoption behavior .66 .56 -.10 Specialization centralization -.41 -.41 -.00 Specialization adoption behavior .67 .63 -.04 Centralization adoption behavior -.43 -.41 .02

669/ASQ



observed correlations between exogenous and endogenous controls are presented in Appendix 2. With the possible exception of specialization and functional differentiation, the expected correlations conform quite closely to those observed. The coefficients in Figure 2, save for those leading from size to specialization and differentiation, are quite small. The sum of their direct and indirect effects, however, is substantial and accounts for 42 percent of the variance in the dependent variable. The direct, indirect, and spurious effects of the independent variables are summarized in Table 4. From this table it is apparent that, as specified in the model, size has a substantial effect on adoption behavior. It directly or indirectly accounts for 32 percent of the variance. This finding is consistent with the results reported by others (Aiken and Hage, 1971; Corwin, 1972; Hage and Aiken, 1967; Mytinger, 1968; Rosner, 1967, 1968). Size accounts for only 3 percent of the variance directly, however, and the rest is due to the impact of size on the other variables in the model. Mohr (1969) has suggested that size has no appreciable direct effect on adoption behavior in organizations, and the data offer some support for this view. It appears that the primary impact of size may be attributed to its effect on structural properties. Table 4

Direct, Indirect, and Spurious Effects of Independent Variables on Adop- tion Behavior

Effects Independent Variables Functional

Size Specialization differentiation Centralization

Direct (DE) .18 .20 .18 .12 Indirect (IE) .39 .05 .00 .00 Spurious (r-4DE+IE)) .07 .38 .38 .28 Variance explained by direct and indirect effects (DE+IE)2 32% 6% 3% 1%

The large amount of spurious covariance between specialization, differentiation, centralization, and the dependent variable is also noteworthy. Together, these variables account for only 10 percent of the variance in adoption. Their zero-order correlations with adoption, .74, .66, and -.42 respectively, are attributable primarily to their covariance with size, which, in turn, directly and indirectly affects adoption behavior. Several authors have reported relationships between specialization and decentralization on the one hand and adoption behavior on the other (Aiken and Hage, 1971; Corwin, 1972; Hage and Aiken, 1967; Mytinger, 1968). Although the data do not con- tradict these findings, it appears that they may have been in part attributable to organizational size. Finally, there are substantial differences between the sizes of the disturbance terms in Figure 2. The model which includes controls accounts for 64 percent of the variance in specialization and for 48 percent of the variance in differentiation. But it accounts for only 20 percent of the variance in centralization. Centralization presents many measurement problems (Pen-

670/ASQ



Resource Allocation

nings, 1976; Whisler et aL., 1968), and it is possible the remaining variance constitutes measurement error. It is also possible that variables have been omitted that would explain at least some of the remaining variance. For example, centralization may be a function either of the tasks performed (diseases treated) or of environmental contingencies (Becker and Gordon, 1966; Perrow, 1967; Thompson, 1967). The fact that size and specialization account for so little variance in centralization (16 percent) suggests that these or other pos- sibilities should be explored.

DISCUSSION

For the most part the model specified in Figure 1 is consistent with the data. The only apparent exception involves the causal relationship between functional differentiation and centralization. While the model is appropriate for data gathered from hospitals, it may be generalizable to other types of organizations as well. Hospitals are distinct in many ways; however, specialization and differentiation are limited by size in hospitals as in other organizations. Likewise, the costs of control increase with size in hospitals as they do in other organizations. The process of structural development in hospitals is therefore likely to be similar to that experienced by other types of complex organizations. The potential for divergence of interests or perspectives between specialists or department heads and top management is also present in industrial firms, government agencies, service organizations, and the like. The implications of structural development for patterns of innovation adoption, therefore, may also be generalizable to many different types of organizations. The data support the thesis that as organizations become larger, they become more specialized, differentiated, and decentralized. The effect of size on this process is pervasive. Size has direct effects on each of the three structural attributes and seems to affect decentralization indirectly through specialization. It does not appear to have an indirect effect on decentralization through differentiation. While organizations delegate resource allocation discretion to those with specialized training, they may not delegate this authority simply on the basis of the knowledge or expertise associated with specialized responsibility. The importance of this entire process is underscored by its effect on organizational adoption behavior. Larger and consequently more specialized, differentiated, and decentralized organizations are more likely to adopt technical innovations. These innovations constitute but one class of expenditures. Others might include support personnel or reinvestments. The fact that the development of organizational structure appears to influence allocations for technological innovations suggests that it may have implications for other types of expenditures as well. The process of structural development that attends increases in size enables organizations to realize benefits which accrue from the division of labor and from increasing expertise. It also introduces constraints on the patterns of resource allocation. Specialists and heads of departments are responsible for successfully attaining the intermediate goals of the organization. They may not be so concerned with the effective and efficient operation of the organization as a whole. In fact, they

671 /ASQ



may resist attempts to more efficiently integrate their efforts with those of others in order to preserve their capacity for independent action. As organizations increase in size, the interests and perspectives of specialists and department heads may diverge from the concerns of those responsible for the operation of the organization as a whole. Unable to direct a large array of experts more experienced and skilled than they, top management may assume the role of arbiter among conflicting interests. Resource allocation decisions then will be made on the basis of judgments offered by these experts. While diverging in particulars, these individuals are likely to be unified in their support for task-related, often technological, allocations and in their opposition to allocations proposed in the interests of coordination and control. To the extent that organizations are large, these common interests are likely to be reflected in actual resource allocation decisions.

As larger amounts of capital become available and markets continue to expand, organizations may be expected to continue to grow. Overall coordination and control may be sac- rificed as a necessary consequence of organizations respond- ing to these opportunities. Organizations in the future may begin to resemble pluralist political systems rather than hierarchically coordinated social systems. Research on large complex organizations, therefore, is likely to benefit from including political as well as rational factors in theory construction and testing. Michael K. Moch is an assistant professor in the Depart- ment of Business Administration, College of Commerce and Business Administration, University of Illinois at Urbana-Champaign.

REFERENCES

Aiken, Michael, and Jerald Hage 1971 "Organic organization and in-

novation." Sociology, 5: 63-82.

Aldrich, Howard 1972 "Technology and organiza-

tional structure: a reexamina- tion of findings of the Aston group." Administrative Sci- ence Quarterly, 17: 26-43.

Becker, Selwyn W., and Gerald Gordon 1966 "An entrepreneurial theory of

formal organizations." Ad- ministrative Science Quarterly, 2: 315-344.

Blau, Peter M. 1968 "The hierarchy of authority in

organizations." American Journal of Sociology, 73: 453-467.

1 970a "Decentralization in bureauc- racies." In Mayer N. Zald (ed.), Power in Organizations: 150-174. Nashville, Tenn.: Vanderbilt University Press.

1 970b "A formal theory of differentiation in organizations." American Sociological Re- view, 35: 201-218.

1973 The Organization of Academic Work. New York: Wiley- lnterscience.

Blau, Peter M., and Richard A. Schoenherr

1971 The Structure of Organiza- tions. New York: Basic Books.

Child, John 1972 "Organization structure and

strategies of control." Ad- ministrative Science Quarterly, 17: 163-177.

1 973a "Strategies of control and organizational behavior." Ad- ministrative Science Quar- terly, 18: 1-17.

1 973b "Predicting and understanding organization structure." Administrative Science Quar- terly, 18: 168-185.

Childers, Grant W., Bruce H. Mayhew, Jr., and Louis N. Gray 1971 "System size and structural

differentiation in military organizations: testing a baseline model of the division of labor." American Journal of Sociology, 76: 813-830.

Corwin, Ronald 1972 "Strategies for organizational

innovation: an empirical com- parison." American Sociologi- cal Review, 37: 441-454.

Cyert, Richard M., and James G. March 1963 A Behavioral Theory of the

Firm. Englewood Cliffs, N.J.: Prentice-Hall.

Downs, Anothony 1967 Inside Bureaucracy. Boston:

Little-Brown. Duncan, Otis Dudley 1966 "Path analysis: sociological

examples." American Journal of Sociology, 72: 1-16.

672/ASQ



Resource Allocation

Hage, Jerald, and Michael Aiken 1967 "Program change and organi-

zational properties: a compara- tive analysis." American Jour- nal of Sociology, 72: 503-519.

Hage, Jerald, and Robert Dewar 1973 "Elite values versus organiza-

tional structure in predicting innovations." Administrative Science Quarterly, 18: 279- 290.

Hickson, D.J., D. S. Pugh, and D.C. Pheysey 1969 "Operatives technology and

organization structure: an empirical reappraisal." Adminis- trative Science Quarterly, 14: 378-397.

Inkson, J. H. K., D. J. Hickson, and D.S. Pugh 1968 Administrative Reduction of

Variance in Organization and Behavior. Unpublished paper given to the British Psycholog- ical Society, Annual Confer- ence.

Joreskog, Karl G., and Mariello Van Thillo 1972 Lisrel: General Computer

program for Estimating a Linear Structural Equation Sys- tem Involving Multiple Indi- cators of Unmeasured Vari- ables, Educational Testing Service, Princeton, N.J. (mimeo).

Kimberly, John R. 1974 The Effects of Organizational

Integration into Informational Environments on Adoption of Innovation. Paper presented at the Annual Meeting of Ameri- can Sociological Association, Montreal, August 25-29.

1976 "Organizational size and the structuralist perspective: a review, critique, and proposal." Administrative Science Quar- terly, 21: 570-596.

Land, K. 1969 "Principles of path analysis."

In E. Borgatta (ed.), Sociologi- cal Methodology: 3-37. San Francisco: Jossey-Bass.

March, James G., and Herbert A. Simon 1958 Organizations. New York:

John Wiley & Sons.

Mayhew, Bruce H., Robert L. Levinger, J. Miller McPherson, and Thomas F. James 1972 "System size and structural

differentiation in formal organizations: a baseline generator for two major theoretical prop- ositions." American Sociologi- cal Review, 37: 629-633.

Meyer, Marshall W. 1968 "Automation and bureaucratic

structure." American Journal of Sociology, 74: 256-264.

1972 "Size and the structure of organizations: a causal analysis." American Sociological Review, 34: 434-440.

Mohr, Lawrence B. 1969 "Determinants of innovation in

organizations." American Polit- ical Science Review, 63: 111-1 25.

Moch, Michael K. 1973 The Structure of Organizational

Resource Allocation. Unpub- lished PhD. Dissertation, Cor- nell University.

Moch, Michael K., and Edward V. Marse 1976 Size, Centralization, and Or-

ganizational Adoption of Inno- vation. Mimeo, University of Il- linois.

Mytinger, Robert B. 1968 Innovation in Local Health Ser-

vices: A Study of the Adoption of New Programs by Local Health Departments with Par- ticular Reference to Newer Medical Care Activities. United States Government Printing Office, U.S.P.H.S. Publication No. 1664-2.

Pennings, Johannes M. 1973 "Measures of organizational

structure: a methodological note." American Journal of Sociology, 79: 686-704.

Perrow, Charles 1967 "A framework for the com-

parative analysis of organizations." American Sociological Review, 32: 194-208.

Pondy, Louis R. 1970 "Toward a theory of internal

resource-allocation." In Mayer N. Zald (ed.), Power in Organi- zations: 270-31 1. Nashville, Tenn:: Vanderbilt University Press.

Pugh, D. S., D. J. Hickson, C. R. Hinings, and C. Turner 1968 "Dimensions of organizational

structure." Administrative Sci- ence Quarterly, 13: 65-105.

1 969a "The context of organizational structures." Adminis- trative Science Quarterly, 14: 91-124.

1 969b "An empirical taxonomy of structures of work organizations." Administrative Sci- ence Quarterly, 14: 11 5-126.

Rogers, Everett M., and F. Floyd Shoemaker 1971 Communication of Innova-

tions: A Cross Cultural Ap- proach. New York: Free Press.

Rosner, Martin M. 1967 "Economic determinants of

organizational innovation." Administrative Science Quar- terly, 12: 614-625.

1968 "Administrative controls and innovation." Behavioral Sci- ence, 13: 36-43.

Schoenherr, Richard A. 1974 Size, Differentiation, and Ad-

ministrative Ratio: A Curiouser and Curiouser Debate. Paper presented at the Annual Meet- ing of the American Sociologi- cal Association, Montreal, Au- gust 25-30.

Specht, David A. 1974 "On baseline models of sys-

tem structure." American Sociological Review, 19: 143-146.

Thompson, James D. 1967 Organizations in Action. New

York: McGraw-Hill.

Upshaw, Harry S. 1968 "Attitude measurement." In

H. Blalock and A. Blalock (eds.), Methodology in Social Research: 60-111. New York: McGraw-Hill.

Werts, Charles E., Karl G. Jores- kog, and Robert L. Linn 1973 "Identification and estimation

in path analysis with unmeasured variables." American Journal of Sociology, 78: 1469-1484.

Whisler, Thomas L., H. Meyer, B. H. Baum, and P. F. Sorensen, Jr. 1967 "Centralization of organiza-

tional control: an empirical study of its meaning and measurement." In A. Tannenbaum (ed.), Control in Organizations: 283-305. New York: McGraw-Hill.

Williamson, Oliver E. 1964 The Economics of Discretion-

ary Behavior: Managerial Ob- jectives in a Theory of the Firm. New York: Prentice-Hall.

1967 "Hierarchical control and op- timum firm size." Journal of Political Economy, 75: 123- 138.

673/ASQ



APPENDIX I

Correlations or Paths between Exogenous Controls and Independent and Endogenous Control Variables

Exogenous Controls Independent Variables Endogenous Controls

Presence of physicians with Presence respiratory of disease inhalation research therapy

Size Specialization Differentiation Centralization funds department Adoption r p p p P P P

Percentage of physicians with joint appointments withmedicalschool .134 .190 .153 -.065 .178 .035 -.084

(r= .292) Presence of another hospital in area .260 .214 .140 -.084 .015 .089 .133

APPENDIX 2

Expected and Observed Correlations between Exogenous and Endogen- ous Controls

1 2 3 4 Expected

1. Percentage of physicians with joint appointment with a medical school .29 .31 .11

2. Presence of another hospital in the area .29 .23 .20

3. Presence of physicians with respiratory disease research funds .31 .23 .14

4. Presence of an inhalation therapy department .11 .20 .18

Observed

674/ASQ



structure and organizational resource allocation

Documents