¹ Hyneman, Charles S., Bureaucracy in a Democracy (New York: Harper and Brothers, 1950), p. 26 Google Scholar.

² For a much more recent treatment of bureaucratic innovation and public responsiveness, see Simon, Herbert A., “The Changing Theory and Changing Practice of Public Administration,” in Pool, Ithiel de Sola (ed.), Contemporary Political Science: Toward Empirical Theory (New York: McGraw-Hill Book Co., 1987), pp. 86–120 Google Scholar. “The ‘power’ to innovate,” says Simon (p. 106), “… is probably the principal power of the bureaucracy in the realm of policy and value.”

³ Emerson, Haven, Local Health Units for the Nation: A Report (New York: The Commonwealth Fund, 1945), p. 2 Google Scholar.

⁴ See Thompson, Victor A., “Bureaucracy and Innovation,” Administrative Science Quarterly, 10 (06, 1965), p. 2 CrossRefGoogle Scholar; Rogers, Everett M., Diffusion of Innovation (New York: Free Press of Glencoe, 1962), p. 308 Google Scholar; Wilson, James Q., “Innovation in Organization: Notes Toward A Theory,” in Thompson, James D. (ed.), Approaches to Organizational Design (Pittsburgh: Univ. of Pittsburgh Press, 1966), p. 196 Google Scholar; Barnett, Homer G., Innovation (New York: McGraw-Hill Book Co., 1953), p. 7 Google Scholar.

⁵ The same general distinction has been made by others. Cf., Rogers, op. cit., pp. 195–196, and Simon, op. cit., p 107.

⁶ See Steiner, Gary A. (ed.), The Creative Organization (Chicago: The University of Chicago Press, 1965), pp. 16–18 Google Scholar.

⁷ Harold Guetzkow, “The Creative Person in Organizations,” in Steiner, op. cit., pp. 35–45; Burns, Tom and Stalker, G. M., The Management of Innovation (London: Tavistock Publications, 1961), pp. 85–86, 89, 121–122 Google Scholar; Thompson, op. cit., p. 12.

⁸ Mansfield, Edwin, “The Speed of Response of Firms to New Techniques,” Quarterly Journal of Economics, (05, 1963), 293–304 Google Scholar.

⁹ Mytinger, Robert E., Innovations in Public Health (unpublished doctoral dissertation, Univ. of California at Los Angles, 1965), p. 212 Google Scholar.

¹⁰ Hage, Jerald and Aiken, Michael, “Program Change and Organizational Properties: A Comparative Analysis,” American Journal of Sociology, 72 (03, 1967), pp. 516–517 CrossRefGoogle ScholarPubMed.

¹¹ Eisenstadt, Samuel N., The Political Systems of Empires (New York: The Free Press of Glencoe, 1963), pp. 27, 33–112 Google Scholar.

¹² Rogers, op. cit., pp. 40, 285–292.

¹³ Hage and Aiken, op. cit., pp. 503–519.

¹⁴ Zald, Mayer N. and Denton, Patricia, “From Evangelism to General Service: The Transformation of the YMCA,” Administrative Science Quarterly, VIII, No. 2 (09, 1963), p. 234 Google Scholar.

¹⁵ Burns and Stalker, op. cit., p. 96.

¹⁶ Rogers, op. cit., pp. 285–292.

¹⁷ Blau, Peter M., The Dynamics of Bureaucracy (2d ed. rev.; Chicago: Univ. of Chicago Press, 1963), p. 246 Google Scholar; Rogers, loc. cit.; Fliegel, Frederick C., “A Multiple Correlation Analysis of Factors Associated with Adoption of Farm Practices,” Rural Sociology, 21 (09-12, 1956), pp. 288–289, 291 Google Scholar; Rogers, Everett M., “A Conceptual Variable Analysis of Technological Change,” Rural Sociology, 23 (06, 1958), pp. 139–140, 143–145 Google Scholar; Eisenstadt, loc. cit.

¹⁸ Merton, Robert K., Social Theory and Social Structure (Glencoe: The Free Press, 1949 and 1957), pp. 387–420 Google Scholar; Gouldner, Alvin W., “Cosmopolitans and Locals: Toward an Analysis of Latent Social Roles,” Administrative Science Quarterly, II (12, 1957), pp. 281–306 CrossRefGoogle Scholar, and (March, 1958), pp. 444–480.

¹⁹ Mytinger, op. cit., p. 195; Rogers, Diffusion of Innovation, op. cit., pp. 285–292.

²⁰ Blau, op. cit., p. 246.

²¹ Rogers, loc. cit.

²² See Leavitt, Harold J., “Applied Organizational Change in Industry: Structural, Technical, and Human Approaches,” Handbook of Organizations, ed. March, James B. (Chicago: Rand McNally & Company, 1965), pp. 1144–1170 Google Scholar, for a valuable summary and critique.

²³ Rogers, Diffusion of Innovation, op, cit., pp. 88–93.

²⁴ See the introductory paragraphs, above, for examples of traditional and non-traditional programs. The primary sources for determining the precise composition of the two lists were “An Official Declaration of Attitude of the American Public Health Association on Desirable Standard Minimum Functions and Suitable Organization of Health Activities,” American Journal of Public Health, 30 (09, 1940), 1099–1106 CrossRefGoogle Scholar; and Mustard, Harry S., Government in Public Health (New York: The Commonwealth Fund, 1945), pp. 128, 140–182 Google Scholar.

²⁵ Cf., Mytinger's discussion of “barriers” to innovation. Mytinger, Robert E., “Barriers to Adoption of New Programs as Perceived by Local Health Officers,” Public Health Reports, 82 (02, 1967), 108–114 CrossRefGoogle ScholarPubMed.

²⁶ A significance level does not have the usual meaning here, since the group studied is a population rather than a probability sample. However, the test does provide some additional information about the strength of the reported relationship. It tells us the probability that a relationship this strong would appear if the group were divided into categories at random rather than according to actual scores on the independent variable. In light of these considerations, I have elected not to sprinkle significance levels throughout the report but to provide some bench marks that may be used as a guide by the interested reader: For N = 93, using a one-tailed test and the .05 level, the correlation r = .18 is significant; the comparable coefficients for other sample sizes in which we will be interested are r = .21 (for N = 69), r = .24 (for N = 49), and r = .30 for (N = 33).

²⁷ Since the standard deviation of the ideology scores is greater than that of the activism scores (10.9 to 7.8), ideology contributes slightly more than activism to the summed index.

²⁸ Separately, the correlation between activism and innovation, controlling for community size, is partial r = .32; for ideology and innovation, controlling for size, partial r = .29.

²⁹ It will be of interest to note here how much innovation actually took place. Progressive programming ranged in these agencies from zero to 25.5 man-years, with a mean of 3.5 and a standard deviation of 4.9. Adoption, which will be considered in a moment, ranged from 2 programs to 27, with a mean of 11.7 and a standard deviation of 6.1.

³⁰ r = −.17, a non-significant correlation.

³¹ Nor would it have been feasible to consider only post-1960 adoptions: such a procedure would have penalized the early adopters, many of whose innovations were made prior to 1960.

³² For example, one health officer had adopted 1 non-traditional program per year during 1960–1964 and had been in the job for 10 years. One might, therefore, reasonably estimate that 10 of the department's non-traditional programs are associated with his incumbency. Since this department had a total of 14 non-traditional programs it was not included in the subgroup. If its total had been 10 or less, it would have been included. If the health officer had been there for 15 years, the department would have been included regardless of total number of adoptions, for almost none of these programs had been introduced in local health departments before 1950.

³³ The means for the whole group and for the subgroup, respectively, are: number of programs adopted, 11.7 and 11.2; population of jurisdiction, 110,000 and 109,000; 1959 expenditures, $188,000 and $165,000; activism-ideology, 119.9 and 120.1.

³⁴ See Haefner, Don P., Kegeles, S. Stephen, Kirscht, John P., and Rosenstock, Irwin M., “Preventive Actions Concerning Dental Disease, Tuberculosis, and Cancer,” Public Health Reports, 82 (05, 1967), 451–459 CrossRefGoogle Scholar.

³⁵ Wilson, James Q. and Banfield, Edward C., “Public-Regardingness as a Value Premise in Voting Behavior,” this Review 58 (12, 1964), 876–887 Google Scholar.

³⁶ The percent who completed two or more years of college may well have been a better measure for our purpose. Unfortunately, such information is not available from the normal central sources, such as the County and City Data Book published by the U.S. Bureau of the Census. It should definitely not be concluded on the basis of these correlations that aggregate education level has little or nothing to do with resistance to change in communities.

³⁷ Extent of public health training was measured on a ten-point scale. The data were obtained through a self-administered questionnaire left with the supervisors at the time of the health-officer interview. It would have been far better to have obtained this information on all professional employees in the department, but turnover is such that very few in each department had been there from 1960 through 1964.

³⁸ The correlation between community size and health department expenditures is r = .88; between expenditures and adoptions, r =.65; between size and adoption, r = .63.

³⁹ See Blalock, Hubert M. Jr., Causal Inference in Nonexperimental Research (Chapel Hill: University of North Carolina Press, 1961), pp. 83–91 Google Scholar.

⁴⁰ Simon, Herbert A., “Comment: Firm Size and Rate of Growth,” Journal of Political Economy 72 (02, 1964), p. 81 CrossRefGoogle Scholar. See also the articles by Mansfield and by Simon and Bonini cited there.

⁴¹ Cyert, Richard M. and March, James G., A Behavioral Theory of the Firm (Englewood Cliffs, New Jersey: Prentice-Hall, 1963), pp. 278–279 Google Scholar.

⁴² Adams, Walter and Dirlam, Joel B., “Big Steel, Invention, and Innovation,” Quarterly Journal of Economics, 80 (05, 1966), 167–189 CrossRefGoogle Scholar.

⁴³ Mansfield, op. cit. See also his, “Technical Change and the Rate of Imitation,” Econometrica, 29 (1961), 741–766 CrossRefGoogle Scholar.

⁴⁴ For an excellent methodology for evaluation of public programs, see Deniston, O. L., Rosenstock, I. M. and Getting, V. A., “Evaluation of Program Effectiveness,” Public Health Reports, 83 (04, 1968), 323–335 CrossRefGoogle ScholarPubMed.

⁴⁵ Blalock, Causal Inferences in Nonexperimental Research, op. cit., pp. 91–92. For other suggested treatments of multiplicative relationships, see Blalock, Hubert M. Jr., “Theory Building and the Statistical Concept of Interaction,” American Sociological Review, 30 (06, 1965), 374–380 CrossRefGoogle Scholar; Coleman, James S., Introduction to Mathematical Sociology (New York: The Free Press of Glencoe, 1964), pp. 224–235 Google Scholar; Russett, Bruce M., Alker, Hayward R. Jr., Deutsch, Karl W., and Lasswell, Harold D., World Handbook of Political and Social Indicators (New Haven: Yale University Press, 1964), pp. 322–340 Google Scholar; Alker, Hayward R. Jr., Mathematics and Politics (New York: The Macmillan Company, 1965), pp. 108–111 Google Scholar; Alker, Hayward R. Jr., “The Long Road to International Relations Theory: Problems of Statistical Nonadditivity,” World Politics, 18, (07, 1966), 639–644 CrossRefGoogle Scholar.

⁴⁶ The resources term in the equation becomes, in this case, an obstacles-resources term. It was calculated by adding together the expenditures and percent white collar scores, after first weighting each with the appropriate partial slope from the regression of adoption on activism-ideology, expenditures, and percent white collar.

⁴⁷ Blalock, “Theory Building and the Statistical Concept of Interaction,” op. cit., p. 375.

⁴⁸ Atkinson, John W., “Motivational Determinants of Risk-Taking Behavior,” Psychological Review, 64 (1957), 359–372 CrossRefGoogle ScholarPubMed. Atkinson actually uses two aspects of total inclination to act—basic psychological drive, such as the need for achievement, and incentive value attached to the object of the behavior in question. These are multiplied together and it is their product that is multiplied by the expectancy factor to obtain a prediction of behavior. The present study does not distinguish the basic drive from the incentive component of overall motivation; both are probably involved to a certain extent in motivation as measured here. Atkinson's article has most recently been reprinted (and also updated) in Atkinson, John W. and Feather, Norman T. (eds.), A Theory of Achievement Motivation (New York: John Wiley, 1966), ChapterGoogle Scholar I. See also Chapter II, “Notes Concerning the Generality of the Theory of Achievement Motivation,” by Atkinson and Chapter 20, “Review and Appraisal,” by Atkinson and Feather.

⁴⁹ Palmore, Erdman B. and Hammond, Phillip E., “Interacting Factors in Juvenile Delinquency,” American Sociological Review, 29 (12, 1964), 848–854 CrossRefGoogle Scholar.

⁵⁰ Blalock, “Theory Building and the Statistical Concept of Interaction,” op. cit., p. 379.

⁵¹ Merton, op cit., pp. 131–194. See especially, p. 140.

⁵² Harary, Frank, “Merton Revisited: A New Classification for Deviant Behavior,” American Sociological Review, 31 (10, 1966), 693–697 CrossRefGoogle ScholarPubMed.