Scientific Performance as Related to Time Spent on Technical Work, Teaching, or Administration

Scientific Performance as Related to Time Spent on Technical Work, Teaching, orAdministrationAuthor(s): Frank M. AndrewsSource: Administrative Science Quarterly, Vol. 9, No. 2 (Sep., 1964), pp. 182-193Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management,Cornell UniversityStable URL: http://www.jstor.org/stable/2391233 .

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Frank M. Andrews

Scientific Performance as Related

to Time Spent on Technical

Work, Teaching, or

Administration

The relationship between performance and allocation of time to technical work, teaching, or administration was examined in this study involving 552 university, government, and industrial scientists and engineers for whom questionnaire and performance data were available.

Respondents who spent full time on their technical work performed less well than did those who spent part time. Among nonsupervisory PhD's in both research and development labs, and among "assistant scientists" in PhD-dominated labs, about three-quarters time spent on technical work was optimal. Among nonsupervisory engineers in development labs, half time or less was optimal.

Among those devoting part time to technical activities, nonsupervisory PhD's in research labs performed better if they did more administration than teaching. Among engineers, some time in teaching was advantageous.'

Frank M. Andrews is study director at the Survey Research Center, University of Michigan.

'This analysis was supported by a grant from the U.S. Army Research Office (Durham). It is part of a larger project on motivations and working relations of scientists and engineers under the direction of Donald C. Pelz of the Institute for Social Research, University of Michigan. The Carnegie Corporation, the National Science Foundation, and various industries provided financial support



SCIENTIFIC PERFORMANCE 183

MANY scientists and engineers spend full time on their technical work, that is, on research or development. Other scientists devote a portion of their time to teaching or administration. If research were similar to operating a machine, one would expect that the scientific contributions of those who spend full time on their technical work would surpass the contributions of those who spend only part time. But in many ways research is different.

In analyzing data from a nationwide study of 4,000 physiologists, Meltzer found that those who spent about three-quarters time on technical activities had the highest rate of publication and that full-time researchers published less. The same trend occurred within each professional rank.2

Could this trend be replicated? How is a scientist's or engineer's performance affected if he allocates part time to activities other than his technical job? If he has a choice, should he administer or teach? This article reports some findings on these questions.

METHOD

Subjects and Analysis Groups Between 1958 and 1960, 552 scientists and engineers provided

responses to a wide variety of questionnaire items inquiring about their work, laboratory organization, and amount of scientific output.

These respondents were employed in 11 different research sites: five industrial laboratories, five government labs, and one large university (seven academic departments).

These heterogeneous respondents were divided into the following five analysis groups:3

1. PhD's in research-oriented laboratories (these labs were all

for collection of data and preliminary analysis. I am indebted to Pelz for his comments and suggestions on a draft of this article.

2This information comes from unpublished data; for other reports on this study see Leo Meltzer and James Salter, Organizational Structure and Performance and Job Satisfaction of Scientists, American Sociological Review, 27 (1962), 351-362; Ralph W. Gerard, Mirror to Physiology: A Self Survey of Physiological Science (American Physiological Society, Washington, D.C., 1958).

3For a description of motivations and attitudes in the five analysis groups, see Donald C. Pelz and Frank M. Andrews, Organizational Atmosphere, Motivation, and Research Contribution, American Behavioral Scientist, 6 (December 1962), 43-47.



184 ADMINISTRATIVE SCIENCE QUARTERLY

in university or government; none of the industrial labs was research-oriented).4

2. PhD's in development-oriented laboratories (either industry or government).

3. Non-PhD research scientists who worked in research-oriented labs (all in government) which had few (under 20 per cent) PhD's.

4. Non-PhD engineers who worked in development-oriented labs which had few (under 20 per cent) PhD's. (Over half held degrees in engineering specialties.)

5. Non-PhD assistant scientists who worked in labs (either research or development) which had many (42 to 52 per cent) PhD's. (Since all respondents were professionals, this group did not con- tain technicians or other subprofessional assistants.)

Independent Variables: Allocation of Time to Technical Work, Teaching, or Administration

The independent variables were derived from responses to the following question: "Of your total work time, about what proportion do you normally spend on the following types of activities?" Respondents indicated the percentage of their time spent on (1) teaching and training, (2) technical work other than teaching, and (3) administrative or other nontechnical work.

Technical work was defined to include the respondent's own research or development activity (including technical reading and writing), supervising the technical work of others, collaborating and discussing with colleagues, and consultation. Administrative work included: internal administration (expediting services, and so on); communicating with superiors, outside groups, or clients; or other nontechnical work.

Respondents who said they spent 81 to 100 per cent of their time on research or development activities were said to spend full time on their technical work. The remainder were divided into various groups depending on the proportion of time spent.

Using these definitions, approximately half of the non-PhD scientists and about three-quarters of the assistant scientists spent

'The characterization of a department as research- or development-oriented was based on an index of six items concerned with whether executives were believed to stress products or knowledge.




full time on their technical work. In each of the other groups, however, substantially less than half said they spent full time. Among the research PhD's, only one-fifth worked full time at research. The numbers of cases are presented in Table 1 below.

Only among the research PhD's (many of whom were located in academic departments) did teaching account for a substantial part of respondents' nontechnical work time. In the other four groups, teaching (if it occurred at all) was secondary to administration.

Dependent Variables: Measures of Scientific Performance Measures of scientific performance were derived from two

sources. The first was the scientist's own reports of his five-year output of patents, papers, and unpublished reports.5 The second was judgments of each respondent's performance by peers and supervisors who felt knowledgeable about the man's work. Judg- ments were made with respect to the man's scientific or technical contribution and (separately) his general usefulness to the organization.

The self-reported outputs were transformed to log scores to produce more nearly normal distributions, and the evaluations were converted to percentile scores (within site).6 All scores were then adjusted (separately for each of the five relatively homogeneous analysis groups of the sample) to hold constant four background effects which it seemed advisable to partial out: education, length of work experience (time since degree and time in division), type of research setting, and either time between BS and PhD or age at BS. These four factors, taken together, accounted for 5 to 38 per cent (median = 15 per cent) of the variances in the unadjusted scores, the exact value varying from group to group and measure to measure.

WFor the engineers, patents have been used as the most relevant form of output; for the other four groups, papers were used.

A comparison of self-reported output and company records for one advanced research division of 28 members showed fairly good agreement; for patents, rho = .91 and for papers, rho = .82. Self-reported output of unpublished reports did not correlate with company records, but did correlate fairly well (rho = .58) with colleagues' judgments of scientific contribution.

"This conversion was based on a mathematical procedure proposed by L. R. Ford, Jr., in American Mathematics Monthly, 64 (1957), 28-33.




RESULTS

Relationship between Performance and Proportion of Time Spent on Technical Work

In Table 1 mean performance scores (after adjustments) are shown for each of the five analysis groups according to the proportion of total work time respondents spent on technical activities.7 These data are plotted in Figure 1. Results were clear: respondents who spent full time (81 to 100 per cent) on their technical work performed less well than those who spent only part time.

These results were consistent across all three measures of performance in all groups except the non-PhD scientists. Even for this group, the same trend was observed for evaluated performance if people spending 61 per cent or more time were compared with those spending less. In seven of the fifteen cells, the differences among means were statistically significant (at or beyond the .05 level); in no cell was the observed trend signifi- cantly reversed.

For PhD's and assistant scientists, scientific contribution was highest for those who spent three-quarters time (61 to 80 per cent) on their technical work. These results were consistent with Meltzer's findings on physiologists. For engineers and non-PhD research scientists, the maximum occurred at half time (41 to 60 per cent) or less.

Analyses to Check Stability of Findings To be sure that the results shown in Table 1 and Figure 1 were

not due to some spurious relationship, it seemed wise to run three other analyses (data not shown).

Performance unadjusted for experience. What would have happened had the performance measures not been adjusted to

7In this table, Table 2, and Figure 1 means were based on a weighted sample intended to be representative of the entire professional staff of the laboratories studied. Numbers of cases and significance levels, however, were based on unweighted data; they represent the characteristics of the 552 cases actually used in this analysis. (Use of the sampling weights in computing means actually made little difference.) The significance levels were based on F tests using unweighted means, by a one- factor analysis of variance. Significance levels are approximate and should be interpreted with caution. Readers will remember that large and important differences may be statistically nonsignificant if means were based on few respondents.




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Figure 1. Mean performance (adjusted) for five groups of scientists, as related to time spent on technical work.

See Table I for footnotes and numbers of cases.

hold constant the length of experience and the time between BS and PhD) or the age at BS? When the analysis of Table I was repeated using performance measures from which these factors had




not been partialled out, the results were similar to those in Table 1, but somewhat sharper.

This was not surprising, since the less experienced people (who showed lower performance) were more likely to spend full time on their technical work than scientists with longer experience. Thus, the low scores of the full-time people were partly due to a disproportionately large number of young people in the group. But differences in performance due to length of experience were partialled out of the scores shown in Table 1; so this factor did not account for the results observed there.

Supervisory status. Could the results of Table 1 have been due merely to differences between supervisors and nonsupervisors? This seemed possible since supervisors got higher performance scores than nonsupervisors and also were less likely to spend full time on their technical work. As a check, the analysis of Table 1 was repeated for the nonsupervisors and supervisors separately.

Among the four nonsupervisory groups for which there were sufficient cases to determine a trend (Group 3, non-PhD research scientists, was too small to consider), the findings of Table 1 were not markedly altered.

Among the supervisors there were sufficient cases in Groups 1, 2, and 4-the PhD's in research and development and the engineers. The supervisory PhD's in development showed the Table 1 trend. For supervisory engineers, the proportion of time spent on technical work was virtually independent of performance. For supervisory PhD's in research, peak performance occurred for those spending half time or less on technical work (a change from the peak at three-quarters shown in Table 1).

Thus, among all of the nonsupervisory groups examined, and among supervisory-level PhD's in research and development, differences in supervisory status did not account for the lower performance of the full-time people.

Total time. A third possible explanation for the findings of Table 1 was examined.

Among the nonsupervisors it was found (data not shown) that those who spent only part time on technical activities actually spent more total hours on all work-related matters in a typical month than those who spent full time. Apparently, among non-




supervisors time spent on training or administration was not fully balanced by reductions in time spent on technical activities. (Supervisors tended to spend more than average total time regard- less of the proportion of time devoted to technical work.) Could the higher performance of the part-time nonsupervisors have been due to their having spent more total hours on their work?

When additional controls for total time on technical work and supervisory status were entered in the analysis of Table 1, the trends shown there were not greatly changed. It was concluded that differences in total time did not account for the differences in performance.

Effects of Allocating Nontechnical Time to Teaching or to Administration

The three check analyses just described provided some confi- dence that the higher performance of the part-time people (at least among the nonsupervisors) was not merely an artifact due to differences in length of experience, supervisory status, or total time spent on work-related matters. But if performance was enhanced by spending some time away from the bench, why? What occurred during the scientist's nontechnical activities which might have stimulated him?

There was no direct way to answer this question, but one thing which could be done was to ask whether performance varied according to the kind of nontechnical activity. Was there any difference, for example, between scientists who spent their nontechnical time on teaching or on administration?

Data were available to answer this question for three of the five analysis groups (most non-PhD scientists and assistant scientists spent full time on their technical work). Table 2 shows some of the results.

Among the PhD's in research, many of whom were in academic departments, those who spent their nontechnical work time primarily on teaching could be compared with those who gave equal or greater emphasis to administration. For the nonsupervisors, there was a clear-and surprising-tendency for those emphasizing administration during their nontechnical work time to perform at higher levels than those emphasizing teaching (statistically significant in two of the three cells). Among the super-




Table 2. Mean performance (adjusted) of two groups of scientists who spent nontechnical time on teaching or on administration,

by supervisory status.*

Judged performance Papers or

Allocation of Mean Mean patents work time N scientific over-all -

contribution usefulness N Mean

Nonsupervisors PhD's in research

Primarily teaching 15 45.7 37.3 25 2.83 Primarily administrationt 8 64.6 59.5 11 3.50

Stat. sig. of diffs. ns .05 .01 Engineers

Some teachingt 27 55.8 55.8 28 2.05 All administrationt 33 47.6 47.0 32 1.86

Stat. sig. of diffs. ns ns ns

Supervisors ? PhD's in research

Primarily teaching 7 65.6 58.4 9 3.32 Primarily administrationt 17 67.7 76.4 20 3.19

Stat. sig. of diffs. ns ns ns Engineers

Some teaching: 24 70.3 76.8 25 2.34 All administrationt 52 57.7 70.7 52 2.54

Stat. sig. of diffs. ns ns ns

*Same footnotes apply as in Table 1.

tTime on administration either equal to or greater than time on teaching. t"Some" teaching was 6 per cent or more; "all administration" meant not more

than 5 per cent of time teaching. ?In university departments, full professors were defined as supervisors.

visors this trend appeared for the measure of over-all usefulness, but it was reversed for the output of papers (neither trend statistically significant).

Several post hoc explanations for this surprising finding came to mind. Possibly administration really provided more stimulation for the nonsupervisors' research than did teaching. Perhaps ad-




ministration, as done by these nonsupervisory PhD's in research, had more direct relevance for their on-going technical activities than did teaching. A third possibility was simply that the more able nonsupervisors elected administration. A fourth possibility was that those who taught were achieving results not measured by the performance measures used-perhaps they were producing scientists rather than research. A choice among these alternatives could not be made from the available data. These possibilities, however, pose leads for further investigation.

Among the engineers few people did very much teaching. The comparison for this group was made between those who did "some" teaching (6 per cent or more of their total time) and those who devoted all their nontechnical time to administration. Table 2 shows that among both supervisory and nonsupervisory engineers there was a slight tendency for those who did some teaching to perform at a higher level than those who did administration (the trend appeared in five of the six cells, nowhere statistically significant).

Data for PhD's in development labs are not shown in Table 2 but the same comparison was made for this group as for the engineers. Only slight (and inconsistent) performance differences appeared between those who spent nontechnical work time on ''some teaching" and those who did "all administration." Neither teaching nor administration was superior for this group.

SUMMARY AND CONCLUSIONS

For five separate and quite different groups of scientists, those who spent full time on their technical work had lower performance than those who spent part time in administration or teaching. These findings, based on a wide range of scientists and performance measures, were in close agreement with findings by Meltzer for the publication rate of physiologists.

Among PhD's in research or development, and among assistant scientists, maximum performance was obtained from those who spent three-quarters time on their technical work. Among engineers and non-PhD research scientists, however, half or less time on technical work was optimal.

These trends continued to be present among the nonsuper-




visors even after several factors which might have accounted for them were controlled. Among the factors considered were length of working experience, supervisory status, and total time spent on all work-related activities.

If these findings continue to be supported by other research, their implications for the administration of research and development activities will be considerable. They suggest that scientists are not necessarily most effective if they spend full time on their technical activities.

Although the reason for the higher performance of those spending part time on their technical work could not be determined from the data available, a number of possibilities might be sug- gested. One is that nontechnical tasks were pushed upon high- performing scientists with the expectation that they would be the most effective administrators or teachers. Another is that high- performing scientists accepted nontechnical tasks as a means of getting others to work on their projects or ideas.

A third, and quite different, possibility is that the presence of diversity in scientists' work situations enhanced their performance. Pelz and Andrews have reported that scientists with several areas of specialization, and those performing several research and development functions, tended to perform at higher levels than those with just a single skill or function.8 The findings reported here can also be viewed as supporting the diversity effect. Spend- ing part time on activities other than one's technical work is one form of diversity in the work situation, and it was associated with above-average performance. Perhaps scientists confronted with diversity were exposed to more new ideas, problems, or people useful to their work than were those in homogeneous situations.

8Donald C. Pelz and Frank M. Andrews, Diversity in R & D Tasks, International Science and Technology, 31 (1964), 28-36.



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Scientific Performance as Related to Time Spent on Technical Work, Teaching, or Administration