En.wikipedia.org wiki hawthorne_effect

The Hawthorne effect (also referred to as the observer effect[1]) is a type of reactivity in which individuals
modify an aspect of their behavior in response to their awareness of being observed.[2][3] The original research at
the Hawthorne Works in Cicero, Illinois, on lighting changes and work structure changes such as working hours
and break times was originally interpreted by Elton Mayo and others to mean that paying attention to overall
worker needs would improve productivity. Later interpretations such as that done by Landsberger suggested
that the novelty of being research subjects and the increased attention from such could lead to temporary
increases in workers' productivity. This interpretation was dubbed "the Hawthorne effect".
History
Relay assembly experiments
Bank wiring room experiments
Interpretation and criticism
Trial effect
Secondary observer effect
See also
References
External links
The term was coined in 1958 by Henry A. Landsberger,[4] when analyzing earlier experiments from 1924–32 at
the Hawthorne Works (a Western Electric factory outside Chicago). The Hawthorne Works had commissioned a
study to see if their workers would become more productive in higher or lower levels of light. The workers'
productivity seemed to improve when changes were made, and slumped when the study ended. It was suggested
Hawthorne effect
Aerial view of the Hawthorne Works, ca. 1925
Contents
History
Page 1 of 7Hawthorne effect - Wikipedia
6/23/2016https://en.wikipedia.org/wiki/Hawthorne_effect

that the productivity gain occurred as a result of the motivational effect on the workers of the interest being
shown in them.
This effect was observed for minute increases in illumination. In these lighting studies, light intensity was
altered to examine its effect on worker productivity. Most industrial/occupational psychology and
organizational behavior textbooks refer to the illumination studies.[5] Only occasionally are the rest of the
studies mentioned.[6]
Although illumination research of workplace lighting formed the basis of the Hawthorne effect, other changes
such as maintaining clean work stations, clearing floors of obstacles, and even relocating workstations resulted
in increased productivity for short periods. Thus the term is used to identify any type of short-lived increase in
productivity.[4][7][8]
In one of the studies, researchers chose two women as test subjects and asked them to choose four other
workers to join the test group. Together the women worked in a separate room over the course of five years
(1927–1932) assembling telephone relays.
Output was measured mechanically by counting how many finished relays each worker dropped down a chute.
This measuring began in secret two weeks before moving the women to an experiment room and continued
throughout the study. In the experiment room they had a supervisor who discussed changes with their
productivity. Some of the variables were:
 Giving two 5-minute breaks (after a discussion with them on the best length of time), and then changing to
two 10-minute breaks (not their preference). Productivity increased, but when they received six 5-minute
rests, they disliked it and reduced output.
 Providing food during the breaks.
 Shortening the day by 30 minutes (output went up); shortening it more (output per hour went up, but overall
output decreased); returning to the first condition (where output peaked).
Changing a variable usually increased productivity, even if the variable was just a change back to the original
condition. However it is said that this is the natural process of the human being adapting to the environment,
without knowing the objective of the experiment occurring. Researchers concluded that the workers worked
harder because they thought that they were being monitored individually.
Researchers hypothesized that choosing one's own coworkers, working as a group, being treated as special (as
evidenced by working in a separate room), and having a sympathetic supervisor were the real reasons for the
productivity increase. One interpretation, mainly due to Elton Mayo,[9] was that "the six individuals became a
team and the team gave itself wholeheartedly and spontaneously to cooperation in the experiment." (There was
a second relay assembly test room study whose results were not as significant as the first experiment.)
The purpose of the next study was to find out how payment incentives would affect productivity. The surprising
result was that productivity actually decreased. Workers apparently had become suspicious that their
Relay assembly experiments
Bank wiring room experiments

productivity may have been boosted to justify firing some of the workers later on.[10] The study was conducted
by Elton Mayo and W. Lloyd Warner between 1931 and 1932 on a group of fourteen men who put together
telephone switching equipment. The researchers found that although the workers were paid according to
individual productivity, productivity decreased because the men were afraid that the company would lower the
base rate. Detailed observation of the men revealed the existence of informal groups or "cliques" within the
formal groups. These cliques developed informal rules of behavior as well as mechanisms to enforce them. The
cliques served to control group members and to manage bosses; when bosses asked questions, clique members
gave the same responses, even if they were untrue. These results show that workers were more responsive to the
social force of their peer groups than to the control and incentives of management.
Richard Nisbett has described the Hawthorne effect as "a glorified anecdote", saying that "once you have got the
anecdote, you can throw away the data."[11] Other researchers have attempted to explain the effects with various
interpretations.
Adair warns of gross factual inaccuracy in most secondary publications on Hawthorne effect and that many
studies failed to find it.[12] He argues that it should be viewed as a variant of Orne's (1973) experimental demand
effect. So for Adair, the issue is that an experimental effect depends on the participants' interpretation of the
situation; this is why manipulation checks are important in social sciences experiments. So he thinks it is not
awareness per se, nor special attention per se, but participants' interpretation that must be investigated in order
to discover if/how the experimental conditions interact with the participants' goals. This can affect whether
participants believe something, if they act on it or do not see it as in their interest, etc.
Possible explanations for the Hawthorne effect include the impact of feedback and motivation towards the
experimenter. Receiving feedback on their performance may improve their skills when an experiment provides
this feedback for the first time.[13] Research on the demand effect also suggests that people may be motivated to
please the experimenter, at least if it does not conflict with any other motive.[14] They may also be suspicious of
the purpose of the experimenter.[13] Therefore, Hawthorne effect may only occur when there is usable feedback
or a change in motivation.
Parsons defines the Hawthorne effect as "the confounding that occurs if experimenters fail to realize how the
consequences of subjects' performance affect what subjects do" [i.e. learning effects, both permanent skill
improvement and feedback-enabled adjustments to suit current goals]. His key argument is that in the studies
where workers dropped their finished goods down chutes, the participants had access to the counters of their
work rate.[13]
Mayo contended that the effect was due to the workers reacting to the sympathy and interest of the observers.
He does say that this experiment is about testing overall effect, not testing factors separately. He also discusses
it not really as an experimenter effect but as a management effect: how management can make workers perform
differently because they feel differently. A lot to do with feeling free, not feeling supervised but more in control
as a group. The experimental manipulations were important in convincing the workers to feel this way: that
conditions were really different. The experiment was repeated with similar effects on mica-splitting workers.[9]
Interpretation and criticism

Clark and Sugrue in a review of educational research say that uncontrolled novelty effects cause on average 30%
of a standard deviation (SD) rise (i.e. 50%–63% score rise), which decays to small level after 8 weeks. In more
detail: 50% of a SD for up to 4 weeks; 30% of SD for 5–8 weeks; and 20% of SD for > 8 weeks, (which is < 1% of
the variance).[15]:333
Harry Braverman points out that the Hawthorne tests were based on industrial psychology and were
investigating whether workers' performance could be predicted by pre-hire testing. The Hawthorne study
showed "that the performance of workers had little relation to ability and in fact often bore an inverse relation
to test scores...".[16] Braverman argues that the studies really showed that the workplace was not "a system of
bureaucratic formal organisation on the Weberian model, nor a system of informal group relations, as in the
interpretation of Mayo and his followers but rather a system of power, of class antagonisms". This discovery was
a blow to those hoping to apply the behavioral sciences to manipulate workers in the interest of management.
The economists Steven Levitt and John A. List long pursued without success a search for the base data of the
original illumination experiments, before finding it in a microfilm at the University of Wisconsin in Milwaukee
in 2011.[17] Re-analysing it, they found that the variance in productivity could be fully accounted for by the fact
that the lighting changes were made on Sundays and therefore followed by Mondays when workers' productivity
was refreshed by a day off.[18] This finding supported the analysis of an article by S R G Jones in 1992 examining
the relay experiments.[19][20] Despite the absence of evidence for the Hawthorne Effect in the original study, List
has said that he remains confident that the effect is genuine.[21]
It is also possible that the illumination experiments can be explained by a longitudinal learning effect. Parsons
has declined to analyse the illumination experiments, on the grounds that they have not been properly
published and so he cannot get at details, whereas he had extensive personal communication with
Roethlisberger and Dickson.[13]
Evaluation of the Hawthorne effect continues in the present day.[22][23][24]
Various medical scientists have studied possible trial effect (clinical trial effect) in clinical trials.[25][26][27]
Some postulate that, beyond just attention and observation, there may be other factors involved, such as slightly
better care; slightly better compliance/adherence; and selection bias. The latter may have several mechanisms:
(1) Physicians may tend to recruit patients who seem to have better adherence potential and lesser likelihood of
future loss to follow-up. (2) The inclusion/exclusion criteria of trials often exclude at least some comorbidities;
although this is often necessary to prevent confounding, it also means that trials may tend to work with
healthier patient subpopulations.
Despite the observer effect as popularized in the Hawthorne experiments being perhaps falsely identified (see
above discussion), the popularity and plausibility of the observer effect in theory has led researchers to
postulate that this effect could take place at a second level. Thus it has been proposed that there is a secondary
observer effect where researchers working with secondary data such as survey data or various indicators may
Trial effect
Secondary observer effect

impact the results of their scientific research. Rather than having an effect on the subjects (as with the primary
observer effect), the researchers likely have their own idiosyncrasies that influence how they handle the data
and even what data they obtain from secondary sources. For one, the researchers may choose seemingly
innocuous steps in their statistical analyses that end up causing significantly different results using the same
data; e.g., weighting strategies, factor analytic techniques, or choice of estimation. In addition, researchers may
use different software packages that have different default settings that lead to small but significant
fluctuations. Finally, these data that researchers use may not be identical, even though it seems so. For example,
the OECD collects and distributes various socio-economic data; however, these data change over time such that
a researcher who downloads the Australian GDP data for the year 2000 may have slightly different values than
a researcher who downloads the same Australian GDP 2000 data a few years later. The idea of the secondary
observer effect was floated by Nate Breznau in a thus far relatively obscure paper.[28] Although little attention
has been paid to this phenomenon, the scientific implications are very large.[29] Evidence of this effect may be
seen in recent studies that assign a particular problem to a number of researchers or research teams who then
work independently using the same data to try and find a solution. This is a process called crowdsourcing data
analysis and was used in a groundbreaking study by Silberzahn, Rafael, Eric Uhlmann, Dan Martin and Brian
Nosek et al. (2015) about red cards and player race in football (i.e., soccer).[30][31]
 Demand characteristics
 John Henry effect
 Mass surveillance
 Novelty effect
 Panopticon
 Placebo effect
 Pygmalion effect
 Quantum Zeno effect
 Reflexivity (social theory)
 Self-determination theory
 Social facilitation
 Stereotype threat
 Subject-expectancy effect
 Watching-Eye Effect
 Goodhart's law
1. Monahan, Torin; Fisher, Jill A. (June 1, 2010). "Benefits of 'Observer Effects': Lessons from the Field". U.S.
National Library of Medicine, National Institutes of Health.
2. McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P; Warner; Iliffe; Van Haselen; Griffin;
Fisher (2007). "The Hawthorne Effect: a randomised, controlled trial". BMC Med Res Methodol. 7: 30.
doi:10.1186/1471-2288-7-30. PMC 1936999  . PMID 17608932.
See also
References

3. Fox NS, Brennan JS, Chasen ST; Brennan; Chasen (2008). "Clinical estimation of fetal weight and the
Hawthorne effect". Eur. J. Obstet. Gynecol. Reprod. Biol. 141 (2): 111–4. doi:10.1016/j.ejogrb.2008.07.023.
PMID 18771841.
4. Henry A. Landsberger, Hawthorne Revisited, Ithaca, 1958.
5. The Industrial Organization Psychologist, Volume 41, What We Teach Students About the Hawthorne
Studies, Santa Clara University 2004
6. What We Teach Students About the Hawthorne Studies: A Review of Content Within a Sample of
Introductory I-O and OB Textbooks
7. Elton Mayo, Hawthorne and the Western Electric Company, The Social Problems of an Industrial
Civilisation, Routledge, 1949.
8. Bowey, Dr. Angela M. "MOTIVATION AT WORK: a key issue in remuneration". Archived from the original
on 1 July 2007. Retrieved 22 November 2011.
9. Mayo, Elton (1945) Social Problems of an Industrial Civilization. Boston: Division of Research, Graduate
School of Business Administration, Harvard University, p. 72
10. Henslin, James M. (2008). Sociology: a down to earth approach (9th ed.). Pearson Education. p. 140.
ISBN 978-0-205-57023-2.
11. Kolata, G. (December 6, 1998). "Scientific Myths That Are Too Good to Die". New York Times.
12. Adair, J.G. (1984). "The Hawthorne Effect: A reconsideration of the methodological artifact" (PDF). Journal
of Applied Psychology. 69 (2): 334–345. doi:10.1037/0021-9010.69.2.334.
13. Parsons, H. M. (1974). "What happened at Hawthorne?: New evidence suggests the Hawthorne effect
resulted from operant reinforcement contingencies". Science. 183 (4128): 922–932.
doi:10.1126/science.183.4128.922. PMID 17756742.
14. Steele-Johnson, D.; Beauregard, Russell S.; Hoover, Paul B.; Schmidt, Aaron M. (2000). "Goal orientation
and task demand effects on motivation, affect, and performance". The Journal of Applied Psychology. 85
(5): 724–738. doi:10.1037/0021-9010.85.5.724. PMID 11055145.
15. Clark, Richard E.; Sugrue, Brenda M. (1991). "30. Research on instructional media, 1978-1988". In
G.J.Anglin. Instructional technology: past, present, and future. Englewood, Colorado: Libraries Unlimited.
pp. 327–343.
16. Braverman, Harry (1974). Labor and Monopoly Capitalism. New York: Monthly Review Press. pp. 144–145.
ISBN 0853453403.
17. BBC Radio 4 programme More Or Less, "The Hawthorne Effect", broadcast 12 October 2013, presented by
Tim Harford with contributions by John List
18. Levitt, Steven D. & List, John A. (2011). "Was There Really a Hawthorne Effect at the Hawthorne Plant? An
Analysis of the Original Illumination Experiments". American Economic Journal: Applied Economics. 3 (1):
224–238. doi:10.1257/app.3.1.224.
19. "Light work". The Economist. June 6, 2009: 80.
20. Jones, Stephen R. G. (1992). "Was there a Hawthorne effect?". American Journal of Sociology. 98 (3):
451–468. doi:10.1086/230046. JSTOR 2781455.
21. Podcast, More or Less 12 October 2013, from 6m 15 sec in
22. Kohli E, Ptak J, Smith R, Taylor E, Talbot EA, Kirkland KB; Ptak; Smith; Taylor; Talbot; Kirkland (2009).
"Variability in the Hawthorne effect with regard to hand hygiene performance in high- and low-performing
inpatient care units". Infect Control Hosp Epidemiol. 30 (3): 222–5. doi:10.1086/595692. PMID 19199530.
23. Cocco G (2009). "Erectile dysfunction after therapy with metoprolol: the hawthorne effect". Cardiology. 112

(3): 174–7. doi:10.1159/000147951. PMID 18654082.
24. Leonard KL (2008). "Is patient satisfaction sensitive to changes in the quality of care? An exploitation of the
Hawthorne effect". J Health Econ. 27 (2): 444–59. doi:10.1016/j.jhealeco.2007.07.004. PMID 18192043.
25. Menezes P, Miller WC, Wohl DA, Adimora AA, Leone PA, Eron JJ; Miller; Wohl; Adimora; Leone; Miller;
Eron (2011), "Does HAART efficacy translate to effectiveness? Evidence for a trial effect", PLoS ONE, 6
(7): e21824, doi:10.1371/journal.pone.0021824.
26. Braunholtz DA, Edwards SJ, Lilford RJ.; Edwards; Lilford (2001), "Are randomized clinical trials good for us
(in the short term)? Evidence for a "trial effect" ", J Clin Epidemiol, 54 (3): 217–224, doi:10.1016/s0895-
4356(00)00305-x, PMID 11223318.
27. McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P; Warner; Iliffe; Van Haselen; Griffin;
Fisher (2007), "The Hawthorne Effect: a randomised, controlled trial", BMC Medical Research
Methodology, 7: 30, doi:10.1186/1471-2288-7-30, PMC 1936999  , PMID 17608932.
28. Breznau, Nate (2016-05-03). "Secondary observer effects: idiosyncratic errors in small-N secondary data
analysis". International Journal of Social Research Methodology. 19 (3): 301–318.
doi:10.1080/13645579.2014.1001221. ISSN 1364-5579.
29. Shi, Yuan; Sorenson, Olav; Waguespack, David (2017-01-30). "Temporal Issues in Replication: The
Stability of Centrality-Based Advantage". Sociological Science. 4: 107–122. doi:10.15195/v4.a5.
ISSN 2330-6696.
30. "OSF | Crowdsourcing Analytics - Final Manuscript.pdf". osf.io. Retrieved 2016-12-07.
31. "Crowdsourcing Data to Improve Macro-Comparative Research". Policy and Politics Journal. 2015-03-26.
Retrieved 2016-12-07.
 Evan Davis on the Hawthorne Effect, OpenLearn from The Open University
 The Hawthorne, Pygmalion, placebo and other expectancy effects: some notes, by Stephen W. Draper,
Department of Psychology, University of Glasgow.
 BBC Radio 4: Mind Changers: The Hawthorne Effect
 Harvard Business School and the Hawthorne Experiments (1924–1933), Harvard Business School.
 How lack of attention decreases productivity?, by Marcin Kazaryn, Mateusz Klupczyński, Jagoda
Piotrowska, Lab1 insights.
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