Article Critique Paper Psychological Purpose

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Purpose of The Article Critique Paper

1). Psychological Purpose

This paper serves several purposes, the first of which is helping you gain insight into research papers in psychology. As this may be your first time reading and writing papers in psychology, one goal of Paper I is to give you insight into what goes into such papers. This article critique paper will help you learn about the various sections of an empirical research report by reading at least one peer-reviewed articles (articles that have a Title Page, Abstract*, Literature Review, Methods Section, Results Section, and References Page—I have already selected some articles for you to critique, so make sure you only critique one in the folder provided on Blackboard). This paper will also give you some insights into how the results sections are written in APA formatted research articles. Pay close attention to those sections, as throughout this course you’ll be writing up some results of your own!

In this relatively short paper, you will read one of five articles posted on blackboard and summarize what the authors did and what they found. The first part of the paper should focus on summarizing the design the authors used for their project. That is, you will identify the independent and dependent variables, talk about how the authors carried out their study, and then summarize the results (you don’t need to fully understand the statistics in the results, but try to get a sense of what the authors did in their analyses). In the second part of the paper, you will critique the article for its methodological strengths and weaknesses. Finally, in part three, you will provide your references for the Article Critique Paper in APA format.

2). APA Formatting Purpose

The second purpose of the Article Critique paper is to teach you proper American Psychological Association (APA) formatting. In the instructions below, I tell you how to format your paper using APA style. There are a lot of very specific requirements in APA papers, so pay attention to the instructions below as well as Chapter 14 in your textbook! I highly recommend using the Paper I Checklist before submitting your paper, as it will help walk you through the picky nuances of APA formatting.

3). Writing Purpose

Finally, this paper is intended to help you grow as a writer. Few psychology classes give you the chance to write papers and receive feedback on your work. This class will! We will give you feedback on this paper in terms of content, spelling, and grammar.


Article Critique Paper (50 points possible)

Each student is required to write an article critique paper based on one of the research articles present on Blackboard (only those articles listed on Blackboard can be critiqued – if you critique a different article, it will not be graded). The article critique paper will account for 50 points. In addition to deepening your understanding of conceptual issues discussed in lectures, this article critique assignment is designed to improve critical thinking and writing skills. Please follow the instructions and guidelines below. If you are unclear about any of this information, please ask.

What is an article critique paper?

An article critique is a written communication that conveys your understanding of a research article and how it relates to the conceptual issues of interest to this course. There are five elements emphasized in this critique: The title page (in APA formatting), summary of the article, critique of the article, brief (one paragraph) summary of the article, and appropriate referencing for the article. I suggest also looking at the example papers, which will give you a nice visual image of APA style that you can mimic in your own paper.

This article critique paper will include 5 things:

  • Title page: 1 page (4 points)
  • Summary of the Article: 1 ½ page minimum, 3 pages maximum - 14 points)
  • Critique of the study: 1 ½ pages minimum - 3 pages maximum - 16 points)
  • Brief summary of the article: One or paragraphs (8 points)
  • References – 1 page (4 points)
  • Grammar and Writing Quality (4 points)
    • Few psychology courses are as writing intensive as Research Methods (especially Research Methods Two next semester!). As such, I want to make sure that you develop writing skills early. This is something that needs special attention, so make sure to proofread your papers carefully.
    • Avoid run-on sentences, sentence fragments, spelling errors, and grammar errors. Writing quality will become more important in future papers, but this is where you should start to hone your writing skills.
    • We will give you feedback on your papers, but I recommend seeking some help from the FIU writing center to make sure your paper is clear, precise, and covers all needed material. I also recommend asking a few of your group members to read over your paper and make suggestions. You can do the same for them!
  • Use APA style to present the appropriate information:
    • A Running head must be included and formatted APA style
      • The phrase “Running head” is at the top of the title page followed by a short title of your creation (no more than 50 characters) that is in ALL CAPS. This running head is left-justified (flush left on the page). Note that the “h” in head is all lower case! Look at the first page of these instructions, and you will see how to set up your Running head.
      • There must be a page number on the title page that is right justified. It is included in the header
    • Your paper title appears on the title page. This is usually 12 words or less, and the first letter of each word is capitalized. It should be descriptive of the paper (For this paper, you should use the title of the article you are critiquing. The paper title can be the same title as in the Running head or it can differ – your choice)
    • Your name will appear on the title page
    • Your institution will appear on the title page as well
    • For all papers, make sure to double-space EVERYTHING and use Times New Roman font. This includes everything from the title page through the references.
    • This is standard APA format. ALL of your future papers will include a similar title page

An article critique should briefly summarize, in your own words, the article research question and how it was addressed in the article. Below are some things to include in your summary.

  • The CAPS portion of your running head should also appear on the first page of your paper, but it will NOT include the phrase “Running head” this time, only the same title as the running head from the first paper in ALL CAPS. Again, see the example paper. There is a powerpoint presentation on using Microsoft Word that can help you figure out how to have a different header on the title page (where “Running head” is present) and other pages in the paper (where “Running head” is NOT present). You can also find how-to information like this using youtube!
  • The same title used on the title page should be at the top of the page on the first actual line of the paper, centered.
  • For this paper, add the word “Summary” below the title, and have it flush left. Then write your summary of the article below that
  • The summary itself will include the following: (Note – if the article involved more than one experiment, you can either choose to focus on one of the studies specifically or summarize the general design for all of the studies)
  • This portion of the article critique assignment focuses on your own thoughts about the content of the article (i.e. your own ideas in your own words). For this section, please use the word “Critique” below the last sentence in your summary, and have the word “Critique” flush left.
  • This section is a bit harder, but there are a number of ways to demonstrate critical thinking in your writing. Address at least four of the following elements. You can address more than four, but four is the minimum.
  • 1). In your opinion, how valid and reliable is the study? Why? (make sure to define what reliable and valid mean, and apply these definitions to the study you are critiquing. Merely mentioning that it is valid and reliable is not enough – you have to apply those terms to the article)
  • 2). Did the study authors correctly interpret their findings, or are there any alternative interpretations you can think of?
  • 3). Did the authors of the study employ appropriate ethical safeguards?
  • 4). Briefly describe a follow-up study you might design that builds on the findings of the study you read how the research presented in the article relates to research, articles or material covered in other sections of the course
  • 5). Describe whether you feel the results presented in the article are weaker or stronger than the authors claim (and why); or discuss alternative interpretations of the results (i.e. something not mentioned by the authors) and/or what research might provide a test between the proposed and alternate interpretations
  • 6). Mention additional implications of the findings not mentioned in the article (either theoretical or practical/applied)
  • 7). Identify specific problems in the theory, discussion or empirical research presented in the article and how these problems could be corrected. If the problems you discuss are methodological in nature, then they must be issues that are substantial enough to affect the interpretations of the findings or arguments presented in the article. Furthermore, for methodological problems, you must justify not only why something is problematic but also how it could be resolved and why your proposed solution would be preferable.
  • 8). Describe how/why the method used in the article is either better or worse for addressing a particular issue than other methods
  • Write the words “Brief Summary”, and then begin the brief summary below this
  • In ONE or TWO paragraphs maximum, summarize the article again, but this time I want it to be very short. In other words, take all of the information that you talked about in the summary portion of this assignment and write it again, but this time in only a few sentences.
  • The reason for this section is that I want to make sure you can understand the whole study but that you can also write about it in a shorter paragraph that still emphasizes the main points of the article. Pretend that you are writing your own literature review for a research study, and you need to get the gist of an article that you read that helps support your own research across to your reader. Make sure to cite the original study (the article you are critiquing).
  • Provide the reference for this article in proper APA format (see the book Chapter 14 for appropriate referencing guidelines or the Chapter 14 powerpoint).
  • If you cited other sources during either your critique or summary, reference them as well (though you do not need to cite other sources in this assignment – this is merely optional IF you happen to bring in other sources). Formatting counts here, so make sure to italicize where appropriate and watch which words you are capitalizing!

The key point is that your experimental paper should describe a “position” that you have taken with respect to the content of the article. Please note that you do not need to refer to any other sources other than the article on which you have chosen to write your paper. However, you are welcome to refer to additional sources if you choose.

Other guidelines for the article critique papers

  • 1). Pay attention to the page length requirements – 1 page for the title page, 1.5 pages to 3 pages for the summary, 1.5 pages to 3 pages for the critique, one or two paragraphs for the brief summary, and 1 page for the references page. If you are under the minimum, we will deduct points. If you go over the maximum, we are a little more flexible (you can go over by half page or so), but we want you to try to keep it to the maximum page.
  • 2). Page size is 8 1/2 X 11” with all 4 margins set one inch on all sides. You must use 12-point Times New Roman font (Note: these instructions are in 12 point Times New Roman font).
  • 3). As a general rule, ALL paragraphs and sentences are double spaced in APA papers. This includes the spacing in your Paper I: Article Critique Paper. It even includes the references, so make sure to double space EVERYTHING
  • 4). When summarizing the article in your own words, you need not continually cite the article throughout the rest of your critique. Nonetheless, you should follow proper referencing procedures, which means that:
    • If you are inserting a direct quote from any source, it must be enclosed in quotations and followed by a parenthetical reference to the source. “Let’s say I am directly quoting this current sentence and the next. I would then cite it with the author name, date of publication, and the page number for the direct quote” (Winter, 2013, p . 4).
      1. Note: We will deduct points if you quote more than once per page, so keep quotes to a minimum. Paraphrase instead, but make sure you still give the original author credit for the material by citing him or using the author’s name (“In this article, Smith noted that …” or “In this article, the authors noted that…”)
    • If you choose to reference any source other than your chosen article, it must be listed in a reference list.
  • 5). PLEASE use a spell checker to avoid unnecessary errors. Proofread everything you write. I actually recommend reading some sentences aloud to see if they flow well, or getting family or friends to read your work. Writing quality will become more important in future papers, so you should start working on that now!

  • If you have any questions about the articles, your ideas, or your writing, please ask. Although we won’t be able to review entire drafts of papers before they are handed in, we are very willing to discuss problems, concerns or issues that you might have.


I already attached the files to critique about, so please check them up before the writing the paper. Also, no plagarism allowed please.

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Journal of Personality and Social Psychology 1976, Vol. 33, No. 5, 541-546 Personal Space Invasions in the Lavatory: Suggestive Evidence for Arousal R. Dennis Middlemist Oklahoma State University Eric S. Knowles Ohio State University Charles F. Matter University of Wisconsin—Green Bay The hypothesis that personal space invasions produce arousal was investigated in a field experiment. A men's lavatory provided a setting where norms for privacy were salient, where personal space invasions could occur in the case of men urinating, where the opportunity for compensatory responses to invasion were minimal, and where proximity-induced arousal could be measured. Research on micturation indicates that social stressors inhibit relaxation of the external urethral sphincter, which would delay the onset of micturation, and that they increase intravesical pressure, which would shorten the duration of micturation once begun, Sixty lavatory users were randomly assigned to one of three levels of interpersonal distance and their micturation times were recorded. In a three-urinal lavatory, a confederate stood immediately adjacent to a subject, one urinal removed, or was absent. Paralleling the results of a correlational pilot study, close interpersonal distances increased the delay of onset and decreased the persistence of micturation. These findings provide objective evidence that personal space invasions produce physiological changes associated with arousal. In the study of person-environment relations, the concept of personal space has been postulated as a variable that, in part, determines how people respond to their social and physical environments. Sommer (1969) denned personal space as the "area with invisible boundaries surrounding a person's body into which intruders may not come" (p. 26). Investigations of personal space phenomena suggest that individuals seek to maintain psychologically comfortable interpersonal distances. If an invasion of personal space takes place, individuals will move away from others and reestablish the personal space boundaries (Felipe & Sommer, 1966; Sommer, 1969) or engage in compensatory behaviors that minimize the closeness (Patterson, The authors thank Daniel Kasten for serving as the confederate and Anthony G. Greenwald for providing comments on an earlier draft. Portions of this research were presented at the Midwestern Psychological Association Convention, Chicago, May 1975. Requests for reprints should be addressed ot Eric S. Knowles, College of Community Sciences, University of Wisconsin-Green Bay, Green Bay, Wisconsin 54302. Mullens, & Romano, 1971; Cowan, Note 1). Other findings suggest that individuals will avoid invading the personal space of others (Barefoot, Hoople, & McClay, 1972; Sommer & Becker, 1969) or will engage in submissive gestures or verbalized apologies to minimize the impact of invasion (Efran & Cheyne, 1974; Felipe & Sommer, 1966; Knowles, 1973). Although these behavioral responses related to personal space invasions have been documented and described, there has been little systematic investigation of the reasons why these responses occur. In a recent review, Evans and Howard (1973) concluded that "we do not as yet thoroughly understand all the variables which are relevant to [personal space] behavior, and we are even further away from being able to explain why and how personal space operates for human beings" (p. 341). The most common explanatory position is that emotional arousal is an important variable intervening between personal space and the behavioral responses to personal space invasion. Evans and Howard (1973) and Sommer (1969) are among those who have suggested that invasions of personal 541 542 R. D. MIDDLEMIST, E. S. KNOWLES, AND C. F. MATTER space are interpersonally stressful, increasing arousal and discomfort, and that it is this arousal that produces the behavioral responses. These behavioral responses occur because they reduce the arousal caused by the personal space invasion. Although there is a clear relationship between personal space invasions and the behavioral responses to invasions, there is little unambiguous evidence that arousal plays any role, much less a mediating role, in this relationship. Findings from animal species other than man that chronic crowding is related to adrenal hypertrophy (Christian & Davis, 1966; Deevey, 1966) suggest prolonged arousal, but do not imply that similar processes operate in humans. Various self-report data suggest that human subjects report discomfort and negative feelings as a result of personal space invasions (Efran & Cheyne, 1974; Porter, Argyle, & Salter, 1970) or crowded conditions (Dabbs, 1971), but these reports may have been produced by factors other than arousal. Several authors have attempted to obtain more direct indications of arousal. Efran and Cheyne (1974) attempted to measure changes in cardiovascular activity as a result of invasions, and Dabbs (1971) attempted to obtain measures of palmar sweating under conditions of crowding. In both cases the results were inconclusive. McBride, King, and James (196S) measured subjects' galvanic skin responses when they were approached at various distances from various angles by male and female experimenters. They found greater decreases in skin resistance with closer approaches, with frontal rather than side approaches, and with opposite-sex experimenters. Although this study is often cited as providing the most direct indication that personal space invasions produce arousal, it alsu is not conclusive, at least by itself. The subjects were instrumented, participating in an experiment, and aware of the dimension being manipulated, all of which may have made their behavior and responses different from disguised or naturally occurring invasions (Knowles & Johnsen, 1974). As an alternative to the laboratory, a men's lavatory provides a setting where personal space violations can occur in a natural yet sufficiently standardized way. Although Kira (1970) has pointed out that use of the bathroom evokes concerns for privacy among members of the middle class, public facilities do not allow complete privacy, particularly in the case of men urinating. Urinals are open and placed side by side so that, under crowded conditions, men stand shoulder to shoulder, coactively engaging in private elimination. Unlike other settings, including the laboratory, these personal space intrusions in the lavatory are minimally confounded by compensatory responses—moving away, changing body orientation, using hands and arms as an interpersonal buffer, reducing eye contact—that a subject makes to an invasion. If compensatory behaviors occur to reduce the arousal caused by invasions, then it would be impossible to measure the degree of arousal accurately if subjects were free to engage in these compensatory behaviors. In addition, research on micturation suggests that it is a process sensitive to arousal (Scott, Quesada, & Cardus, 1964; Straub, Ripley, & Wolf, 1950; Tanaeho, 1971). At the onset of micturation, the detrusor muscles of the bladder contract, increasing intravesical pressure and forcing urine out of the bladder. At the same time, the two sphincters of the urethra relax, particularly the external sphincter, allowing urine to flow. Social stressors appear to affect both these mechanisms of micturation. Straub et al. (19SO) showed that a stressful interview produced a marked and sustained increase in intravesical pressure. Scott et al. (1964) reported that fright and embarrassment inhibited relaxation of the external sphincter of the urethra. The relationships between social arousal and micturation suggest that, if an individual intent on micturating were subjected to a stressor, the onset of micturation would be delayed because of a reduction in the degree of relaxation of the external sphincter, while the duration of urine flow, once begun, would be foreshortened because of increased intravesicle pressure. If personal space invasions produce arousal, then subjects standing closest to others at lavatory urinals would show increases in the delay of onset of micturation and decreases in the persistence of mictura- PERSONAL SPACE AND AROUSAL tion. Because of the novelty of these hypotheses, a pilot study was first undertaken to investigate whether any relationship between interpersonal distance and micturation times could be observed. Pilot Study A field observation conducted at a men's lavatory at a western U.S. university provided evidence for a correlation between interpersonal distance and micturation times. Men entering a restroom to urinate were allowed to choose a urinal under prevailing ecological conditions. Data were recorded for 48 subjects, users of the men's lavatory. A user was included as a subject if the degree of interpersonal distance between him and the next nearest user remained constant throughout the duration of his urination. The restroom contained two banks of five urinals, which were bowl-type receptacles jutting out of the wall and containing about 3 inches (8 cm) of standing water, which the user flushed. An observer was stationed at the sink facilities and appeared to be grooming himself. When a potential subject entered the room and walked to a urinal, the observer recorded the selected urinal and the placement of the next nearest user. He also noted (with a chronographic wristwatch) and recorded the micturation delay (the time between when a subject unzipped his fly and when urination began) and the micturation persistence (the time between the onset and completion of urination). The onset and cessation of micturation were signaled by the sound of the stream of urine striking the water in the urinal. Of the 48 subjects recorded, none selected a urinal immediately adjacent to another user, 23 were separated by one urinal from the next nearest user, 16 were separated by two urinals, and 9 were separated by three or more urinals. The fact that no subjects were observed choosing an adjacent urinal may reflect active avoidance of the most proximate interpersonal distance. Even with this restricted range of interpersonal distance, significant correlations were found for both measures. Micturation delay showed a negative 543 correlation with the three levels of interpersonal distance, r(46) = -.315,p < .OS.1 Subjects standing one urinal away had a mean delay of 7.9 seconds, subjects two spaces away had a delay of 5.9 seconds, and subjects three or more spaces away had a delay of 5.7 seconds. Micturation persistence showed a positive relationship with the three levels of interpersonal distance, r(46) = +.562, p < .001. The mean persistence was 19.0 seconds with one space, 24.4 seconds with two spaces, and 32.0 seconds with three or more spaces. This pilot study, while lacking controls on subject self-selection and open to various interpretations, did suggest that the hypotheses warranted more controlled investigation. The correlations found were in the direction predicted by the hypotheses. Moreover, the pilot study suggested that the micturation measures could be used as the dependent variables in an experimental study. Thus, the following experiment was conducted to test the hypothesis that decreases in interpersonal distance lead to arousal as evidenced by increases in micturation delay and decreases in micturation persistence. METHOD Overview In a field experiment conducted in a men's lavatory at a midwestern U.S. university, subjects were randomly assigned to one of three levels of interpersonal distance. Men who entered a threeurinal lavatory to urinate were forced to use the leftmost urinal. A confederate was placed immediately adjacent to the subject, one urinal removed, or was absent from the lavatory. An observer stationed in a toilet stall timed the delay and persistence of micturation. Subjects Data were gathered on 60 users of the men's lavatory. A user was included as a subject if no other user (besides the confederate) was present during his urination. If someone else was present or entered during urination, the user was not counted. Conditions were randomly assigned and prepared before the subject entered the lavatory. Subjects were not informed that they had participated in an experiment. 1 Two-tailed probabilities are used throughout. R. D. MIDDLEMIST, E. S. KNOWLES, AND C. F. MATTER 544 30" -•PERSISTENCE 25- 20- subject's face. The observer started two stop watches when a subject stepped up to the urinal, stopped one when urination began, and stopped the other when urination was terminated. These times allowed calculation of the two dependent variables: delay of onset and persistence of micturation. RESULTS 15- • DELAY OF ONSET CLOSE DISTANCE MODERATE DISTANCE CONTROL FIGURE 1. Micturation times. Procedure The observed lavatory was just off a main hallway, adjacent to a large classroom. The observed use rate averaged about one person every 6 minutes. The restroom contained two toilet stalls and three urinals. The urinals were 18 inches (46 cm) wide with 18 inches of tile between adjacent urinals and extended up from the floor about 4 feet (1.2 m). The urinals were automatically flushed at 10-minute intervals. The subjects were forced to use the leftmost urinal under one of three levels of interpersonal distance. In the close distance condition, a confederate appearing to urinate was stationed at the middle urinal, and a "Don't use, washing urinal" sign accompanied by a bucket of water and a sponge was placed on the rightmost urinal. This arrangement left a distance of approximately 16 to 18 inches (40 to 46 cm) between the shoulders of the subject and confederate. In the moderate distance condition, the confederate stood at the rightmost urinal and the bucket and sign were placed in the middle urinal. This arrangement left a distance of 52 to 54 inches (132 to 137 cm) between the subject and the confederate. In a control condition, the confederate was not present in the lavatory and both the middle and right urinals had signs on them with the water bucket in between. An observer was stationed in the toilet stall immediately adjacent to the subjects' urinal. During pilot tests of these procedures it became clear that auditory cues could not be used to signal the initiation and cessation of micturation. The urinals were so silent that even the confederate standing adjacent to the subject could not hear the urine striking the urinal.2 Instead, visual cues were used. The observer used a periscopic prism imbedded in a stack of books lying on the floor of the toilet stall. An 11-inch (28-cm) space between the floor and the wall of the toilet stall provided a view, through the periscope, of the user's lower torso and made possible direct visual sightings of the stream of urine. The observer, however, was unable to see a The hypotheses that decreases in interpersonal distance would lead to increases in the delay of micturation and decreases in the persistence of micturation were tested in a multivariate analysis of variance of the effects of conditions on the two micturation measures. Each measure was heteroscedastic, but square root transformations of the data made the cell variances comparable, and the analysis was performed on these transformed scores. The multivariate analysis indicated a significant difference among distance conditions, F(4,112) = 10.38, p < .001. A priori multivariate comparisons among conditions showed that the close distance produced responses significantly different from the moderate distance, F(2, 56) = 10.04, p < .001, and that the confederate-present conditions produced responses significantly different from the confederate-absent condition, F ( 2 , 5 6 ) — 14.53, p < .001. Figure 1, which presents the mean seconds for micturation delay and persistence in each condition, shows that the effects were in the predicted direction. A test of the univariate effects of distance on micturation delay revealed significant differences among conditions, F(2, 57) = 12.44, p < .001. Micturation delay increased from a mean of 4.9 seconds in the control condition to 6.2 seconds in the moderate distance condition to 8.4 seconds in the close distance condition. The a priori tests indicated that the close condition led to significantly longer delays than the moderate condition, F(l, 57) = 9.01, p < .004, and that the confederatepresent conditions led to significantly longer delays than the confederate-absent condition, 7^(1,57) = 15.86, p< .001. 2 Although the silence of the urinals necessitated a change from the pilot study in the mode of observation, it had the advantage of making the confederate credible. During tests of the experimental procedures, none of the test subjects had any suspicions about the confederate's activity. PERSONAL SPACE AND AROUSAL 545 Micturation persistence also showed signifi- iors. Subsequent research is needed to investicant differences among conditions, 7 ? (2,S7) gate the second half of the arousal model. = 4.41, p < .017. The pattern of means, from The results of this experiment reproduced 24.8 seconds in the control condition to 23.4 and complemented the results of the pilot seconds in the moderate distance to 17.4 sec- study. Both micturation delay and persistence onds in the close distance condition, shows were shown to be related to interpersonal the predicted decrease in the persistence of distance, and similar patterns of means were micturation. The close distance produced observed. Although neither set of data is preshorter persistence times than the moder- cise enough to allow assessment of the form ate distance, F(l, 57) = 4.49, p < .038, and of the relationship between distance and the confederate-present conditions produced micturation times, it appears that the closest shorter persistence times than the confederate- distance had much more of an effect than the absent condition, F(l, 57) = 4.33, p < .042. next closest distance. This pattern is reminisThe analysis of the effects of interpersonal cent of the nonlinear, exponential relationdistance on micturation times supported both ships observed for much greater distances hypotheses. Closer distances led to increases (Bratfisch, 1969; Ekman & Bratfisch, 1965; in micturation delay and decrease in mictura- Lundberg, Bratfisch, & Ekman, 1972). The tion persistence. Both of these effects, which present data are not incompatible with across conditions produced a negative correla- Ekman's suggestion that emotional involvetion between cell means, appeared in spite of ment decreases as an inverse power function the fact that the two measures tended to be of distance. positively correlated. The within-cell correlaFinally, the present study suggests that the tion between micturation delay and persist- dependent measures may have some utility as ence was +.349, which reflected compa- unobtrusive measures of social arousal in labrable correlations within each condition (rs oratory as well as field settings. In a labora- +.308, +.304, and +.542 for the control, tory, the effects of intravesical pressure could moderate, and close distance conditions). be more sensitively estimated by using the volume of urine expelled as a covariate to the persistence measure. Presumably, differences DISCUSSION in the amount of urine expelled contributed Variations in interpersonal distance in a a great deal of variance to the persistence lavatory appear to be related in systematic measures in the present study. Yet, both ways to variations in micturation times. The micturation delay and persistence were sensipattern of results supports the hypothesis tive to situational differences. Although the that arousal increases with decreases in inter- parameters of these measures have not been personal distance. The arousal model of per- extensively studied, the present study implies sonal space invasions proposes that close that they have some construct validity as interpersonal distances are interpersonally indicators of arousal. stressful, increasing arousal and discomfort, and that it is this arousal that produces beREFERENCE NOTE havioral responses to invasions. The purpose 1. Cowan, R. A. Invisible walls. University of Caliof this field study was to investigate the first fornia. 16-mm black and white sound film, 1968. half of the model, that arousal results from (Available from University Extension Media Center, University of California, Berkeley, Berkeley, interpersonal closeness, and the findings supCalifornia 94720.) port this part of the arousal model. What has not been shown by this study or earlier REFERENCES studies is whether the arousal leads to or causes the behavioral responses to personal Barefoot, J. C., Hoople, H., & McClay, D. Avoidance of an act which would violate personal space. space violations, as hypothesized in the secPsychonomic Science, 1972, 28, 205-206. ond half of the model. The arousal indicated Bratfisch, O. A further study of the relation between in this study may be a concomitant of invasubjective distance and emotional involvement. Acta Psychologka, 1969, 29, 244-2SS. sion that has no effect on immediacy behav- 546 R. D. MIDDLEMIST, E. S. KNOWLES, AND C. 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SEE LAST PAGE Journal of Applied Psychology 2014, Vol. 99, No. 3, 504 –513 © 2014 American Psychological Association 0021-9010/14/$12.00 DOI: 10.1037/a0035559 RESEARCH REPORT This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Rainmakers: Why Bad Weather Means Good Productivity Jooa Julia Lee and Francesca Gino Bradley R. Staats Harvard University University of North Carolina at Chapel Hill People believe that weather conditions influence their everyday work life, but to date, little is known about how weather affects individual productivity. Contrary to conventional wisdom, we predict and find that bad weather increases individual productivity and that it does so by eliminating potential cognitive distractions resulting from good weather. When the weather is bad, individuals appear to focus more on their work than on alternate outdoor activities. We investigate the proposed relationship between worse weather and higher productivity through 4 studies: (a) field data on employees’ productivity from a bank in Japan, (b) 2 studies from an online labor market in the United States, and (c) a laboratory experiment. Our findings suggest that worker productivity is higher on bad-, rather than good-, weather days and that cognitive distractions associated with good weather may explain the relationship. We discuss the theoretical and practical implications of our research. Keywords: weather, productivity, opportunity cost, distractions Supplemental materials: http://dx.doi.org/10.1037/a0035559.supp We theorize that thoughts related to salient outdoor options come to mind more easily on good weather days than on bad weather days. Consistent with our theorizing, Simonsohn (2010) found that cloud cover during visits to a college known for its academic rigor by prospective students predicted whether they enrolled in the visited school. Prospective students who visited on a cloudier day were more likely to enroll than were those who visited on a sunnier day. Cloudy weather reduced the opportunity costs of outdoor activities such as sports or hiking and thus increased the attractiveness of academic activities. To gain insight into how people intuitively think about this relationship, we asked 198 adults (Mage ⫽ 38 years, SD ⫽ 14.19; 42% male) to predict the impact of weather on individuals’ work productivity. Among our respondents, about 82% stated that good weather conditions would increase productivity, and about 83% responded that bad weather conditions would decrease productivity. These survey results indicate that people indeed believe that weather will impact their productivity and that bad weather conditions in particular will be detrimental to it. This conventional wisdom may be based on the view that bad weather induces a negative mood and therefore impairs executive functions (Keller et al., 2005). In contrast to this view, we propose that bad weather actually increases productivity through an alternative psychological route. We theorize that the positive effects of bad weather on worker productivity stem from the likelihood that people may be cognitively distracted by the attractive outdoor options available to them on good weather days. Consequently, workers will be less distracted and more focused on bad weather days, when such outdoor options do not exist, and therefore will perform their tasks more effectively. In this article, we seek to understand the impact of weather on worker productivity. Although researchers have investigated the effect of weather on everyday phenomena, such as stock market returns (Hirshleifer & Shumway, 2003; Saunders, 1993), tipping (Rind, 1996), consumer spending (Murray, Di Muro, Finn, & Popkowski Leszczyc, 2010), aggression in sports (Larrick, Timmerman, Carton, & Abrevaya, 2011), and willingness to help (Cunningham, 1979), few studies have directly investigated the effect of weather on work productivity. Moreover, to date, no studies have examined psychological mechanisms through which weather affects individual worker productivity, the focus of our current investigation. This article was published Online First January 13, 2014. Jooa Julia Lee, Harvard Kennedy School, Harvard University; Francesca Gino, Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University; Bradley R. Staats, Operations, Kenan-Flagler Business School, University of North Carolina at Chapel Hill. This research was supported by Harvard Business School, the University of North Carolina at Chapel Hill’s Center for International Business Education and Research, and the University Research Council at the University of North Carolina at Chapel Hill. We thank Max Bazerman and Karim Kassam for their insightful comments on earlier drafts of this article. We are also grateful to Kanyinsola Aibana, Will Boning, Soohyun Lee, Nicole Ludmir, and Yian Xu for their assistance in collecting and scoring the data. We gratefully acknowledge the support of management at our field site, and the support and facilities of the Harvard Decision Science Laboratory and the Harvard Business School Computer Laboratory for Experimental Research (CLER). Correspondence concerning this article should be addressed to Jooa Julia Lee, Harvard University, Harvard Kennedy School, 124 Mt. Auburn Street, Suite 122, Cambridge, MA 02138. E-mail: jooajulialee@fas.harvard.edu 504 BAD WEATHER INCREASES PRODUCTIVITY This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Psychological Mechanisms of the “Weather Effect” on Productivity When working on a given task, people generally tend to think, at least to some extent, about personal priorities unrelated to that task (Giambra, 1995; Killingsworth & Gilbert, 2010). Taskunrelated thoughts are similar to other goal-related processes in that they can be engaged in without explicit awareness, though they are not directed toward the given task (Smallwood & Schooler, 2006). Thus, when the mind wanders, attention shifts away from the given task and may lead to failures in task performance (Manly, Robertson, Galloway, & Hawkins, 1999; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). Prior work notes that general cognitive interference can have costly effects on worker productivity (for a review, see Jett & George, 2003). Workers who experience cognitive interference are distracted, showing an inability to focus on a task (Fisher, 1998) and a greater likelihood of committing errors while completing the task (Flynn et al., 1999). Thinking about salient and attractive outdoor options is a form of task-unrelated thinking that serves as a cognitive distraction that shifts workers’ attention away from the task at hand. Accordingly, we expect it will be harder for workers to maintain their taskrelated thoughts on good weather days than on bad weather days. As a result, we also predict that workers will be less productive on good weather days than on bad weather days. More specifically, we argue that on a bad weather day, individuals will have a higher ability to focus on a given work task not because of the negative mood induced by the weather but because fewer distracting thoughts related to outdoor options will be readily available in their minds. Consequently, they will be able to better concentrate on their tasks and work more productively on bad weather days. In our research, we consider tasks where productivity requires high levels of attention and focus, which allow workers to complete their work faster. Thus, we expect fewer cognitive distractions to be associated with higher levels of work productivity. Taken together, these arguments lead to the following hypotheses: Hypothesis 1. Good weather conditions, such as lack of rain, will decrease worker productivity on tasks that require sustained attention and focus, compared to bad weather conditions. Hypothesis 2. Good weather conditions will increase the salience and attractiveness of outdoor options, compared to bad weather conditions. Hypothesis 3. The relationship between good weather conditions and worker productivity will be mediated by greater cognitive distractions (i.e., salience of one’s outdoor options). To test our predictions, we used empirical data on worker productivity, measured by individual performance on tasks conducted in a Japanese bank (Study 1), an online marketplace (i.e., Amazon Mechanical Turk, Studies 2 and 3), and the laboratory (Study 4). We focused on precipitation as the key measure of bad weather given the previous finding that precipitation is the most critical barrier to outdoor physical activities (Chan, Ryan, & Tudor-Locke, 2006; Togo, Watanabe, Shephard, & Aoyagi, 2005). 505 Study 1: Field Evidence From a Japanese Bank Method In Study 1, we examined the proposed link between weather conditions and productivity by matching data on employee productivity from a mid-size bank in Japan with daily weather data.1 In particular, we assessed worker productivity using archival data from a Japanese bank’s home-loan mortgage-processing line. For the sake of brevity, we discuss the overall structure of the operations here; more detailed information can be found in Staats and Gino (2012). Our data includes information on the line from the rollout date, June 1, 2007 until December 30, 2009, a 2.5-year time period. We examined all transactions completed by the permanent workforce, 111 workers who completed 598,393 transactions. Workers at the bank conducted the 17 data-entry tasks required to move from a paper loan application to a loan decision. Included were tasks such as entering a customer’s personal data (e.g., name, address, phone number) and entering information from a real estate appraisal. Workers completed one task at a time (i.e., one of 17 steps for one loan); when a task was completed, the system assigned the worker a new task. The building in which the work took place had windows through which workers could observe the weather. Workers were paid a flat fee for their work; there was no piece-rate incentive to encourage faster completion of work. In addition to the information on worker productivity, we also assembled data on weather conditions in Tokyo, the city where the individuals worked. The National Climactic Data Center of the U.S. Department of Commerce collects meteorological data from stations around the world. Information for a location, such as Tokyo, was calculated as a daily average and includes summaries for temperature, precipitation amount, and visibility. Measures Completion time. To calculate completion time, we took the natural log of the number of minutes a worker spent to complete the task (␮ ⫽ 0.39, ␴ ⫽ 1.15). As we detail below, we conducted our analyses using a log-linear learning curve model. Weather conditions. Since our main variable of interest is precipitation, we included a variable equal to the amount of precipitation each day in inches, down to the hundredth of an inch (␮ ⫽ 0.18, ␴ ⫽ 0.53). To control for effects from other weatherrelated factors, we also included temperature (␮ ⫽ 62.1, ␴ ⫽ 14.6) and visibility (␮ ⫽ 10.3, ␴ ⫽ 5.1). With respect to the former, it may be that productivity is higher with either low or high temperatures. Therefore, we entered both a linear and quadratic term for temperature (in degrees Fahrenheit). Finally, because worse visibility could be related to lower productivity, we included the average daily visibility in miles (to the tenth of a mile). Control variables. We controlled for variables that have been shown to affect worker productivity. These included: same-day, cumulative volume (count of the prior number of transactions handled by a worker on that day); all prior days’ cumulative volume (count of transactions from prior days); load (percentage 1 The data reported in Study 1 have been collected as part of a larger data collection. Findings from the data have been reported in separate articles: Staats and Gino (2012) and Derler, Moore, and Staats (2013). 506 LEE, GINO, AND STAATS of individuals completing work during the hour that the focal task occurred; see Kc & Terwiesch, 2009); overwork (a comparison of current load to the average, see Kc & Terwiesch, 2009); defect; day-of-week, month, year, stage (an indicator for each of the 17 different steps); and individual indicators. errors and correcting them). To account for the potential influence of weather-driven moods, in addition to new productivity measures, we collected data on whether workers felt positive or negative affect while completing the task. Method This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Results and Discussion We used a log-linear learning curve model because individuals’ performance improves over time with experience. Using this approach, we conducted our analyses at the transaction level. Therefore, in our models, we controlled for the effects of the worker, task, and time, and then examined the effect of weather on worker productivity. For our primary model, we used a fixed effects linear regression model with standard errors clustered by individual. Column 1 in Table 1 shows our main model, which we used to test Hypothesis 1. Examining rain, we found that the coefficient is negative and significant (coefficient ⫽ ⫺0.01363). In terms of the effect size, we found that a one-inch increase in rain is related to a 1.3% decrease in worker completion time for each transaction. Given that there are approximately 100 workers in the operation, a 1.3% productivity loss is approximately equivalent to losing one worker for the organization on a given day. Based on the average yearly salary of the associate-level employees at this bank and the average frequency of precipitation, this loss could cost approximately $18,750 for this particular operation a year. When accumulated over time for the entire bank of nearly 5,000 employees, a 1.3% productivity loss could be interpreted as a significant loss in revenue for the bank: at least $937,500 a year. Further, in a city the size of Tokyo (approximately 9 million people) our identified effect could translate into hundreds of millions of dollars in annual lost productivity. Next, it is important to properly account for the standard errors in our model as we have many observations nested within a small number of individual workers. Therefore, in Column 2, we clustered the standard errors by day, not by worker. In Column 3, we used Prais-Winsten regression with panel-corrected standard errors adjusted for heteroskedasticity and panel-wide, first-order autocorrelation. Then, in Column 4, we used the fixed effects regression model from Columns 1–3 but used block-bootstrapped standard errors. In each model, the coefficient on rain is negative and statistically significant. Finally, in Columns 5 and 6 we added additional controls with first individual fixed effects interacted with monthly fixed effects and then individual fixed effects interacted with stage fixed effects. In conclusion, using a within-subject design, this study showed that greater rain is related to better worker productivity. Study 2: Online Study of Weather and Productivity Although Study 1 offers valuable information on employees’ actual work productivity, only the time taken to complete a task was used as an outcome variable, as error rates were low (less than 3%) and showed little variation across employees. In Study 2, we sought a conceptual replication of the effect of weather on completion time while also using a task that would permit us to measure error rates. We could thus investigate productivity not only in terms of quantity (speed at which workers completed their given task) but also in terms of quality (accuracy of detecting Participants and procedure. We recruited U.S. residents to participate in an online survey in early March, when weather conditions vary significantly depending on where workers are located. Three hundred twenty-nine online workers (Mage ⫽ 36.52 years, SD ⫽ 12.79; 48% male) participated in a 30-min study and received a flat fee of $1. We first gave all workers a threeparagraph essay that included 26 spelling errors; we asked them to find as many errors as they could and correct the errors they found.2 Once all the workers had completed the task, they completed a questionnaire that included measures of state emotions to control for potential effects of affect. Finally, we asked workers to complete a demographics questionnaire that also included questions about the day’s weather and their zip code. Measures. Productivity. We computed the time (in seconds) workers spent on the task of correcting spelling errors (i.e., speed). Given that each worker spent a different amount of time on the task, we calculated speed by dividing the number of typos detected by the total time taken in seconds. We then log-transformed the variable to reduce skewness. In addition, we computed how many spelling errors were correctly identified and fixed as a measure of accuracy. State emotions. We used the 20-item form of the Positive and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988). Participants indicated how much they felt each emotion “right now” using a 7-point scale. We calculated two summary variables for each participant: positive (␣ ⫽ .90) and negative affect (␣ ⫽ .91). Weather questionnaire. Workers were asked to report their zip code, which enabled us to find the daily weather data of the specific area on a specific day (http://www.wunderground.com). To ensure that workers’ perceived weather matched actual weather conditions, we also asked them to think about the weather conditions of the day, relative to their city’s average weather conditions, using a 5-point scale (1 ⫽ one of the best to 5 ⫽ one of the worst). Results and Discussion We first tested whether actual weather matched workers’ perceptions of the day’s weather. Indeed, subjective perceptions of bad weather were associated with lower temperature (r ⫽ ⫺.24, p ⬍ .001), higher humidity (r ⫽ .21, p ⬍ 0.001), more precipitation (r ⫽ .23, p ⬍ 0.001), more wind (r ⫽ .31, p ⬍ 0.001), and lower visibility (r ⫽ ⫺.26, p ⬍ 0.001). Table 2 reports summary statistics. Table 3 summarizes a series of regression analyses. Consistent with Hypothesis 1, more rain was associated with higher productivity, measured in terms of both speed and accuracy (Model 1). This relationship holds even after controlling for key demographic variables and state emotions 2 More detailed instructions and materials are available online as supplemental materials (Appendix A). B 0.006068 ⫺0.01363ⴱ 0.004340 0.006964ⴱ 3.710e⫺05 ⫺6.425e⫺05ⴱ 7.311e⫺04 9.799e⫺04 SE B 0.006869 ⫺0.01284ⴱⴱⴱ 0.003341 0.006789ⴱⴱⴱ 2.680e⫺05 ⫺6.323e⫺05ⴱⴱⴱ 6.991e⫺04 8.483e⫺04ⴱ SE SE ⴱ B B B 0.006055 0.004438 3.756e⫺05 7.102e⫺04 SE 6 Individual ⫻ Stage fixed effects 0.004827 ⫺0.01336ⴱ 0.003473 0.006863 2.946e⫺05 ⫺6.449e⫺05 5.755e⫺04 7.808e⫺04 SE 5 Individual ⫻ Month fixed effects 0.005686 ⫺0.01167ⴱ 0.004364 0.004519 3.819e⫺05 ⫺4.588e⫺05 7.040e⫺04 8.176e⫺04 SE 4 Block bootstrap 0.002788 ⫺0.01363ⴱ 0.001773 0.006964 1.382e⫺05 ⫺6.425e⫺05 3.443e⫺04 9.799e⫺04 3 Prais-Winsten — — 598,393 0.4591 — — 598,393 0.3563 — 598,393 0.3374 — — 598,393 0.3563 — 6.198e⫺11 1.524e⫺09ⴱ 0.01030 ⫺0.4181ⴱⴱⴱ 0.009857 0.2603ⴱⴱⴱ 0.006690 0.2206ⴱⴱⴱ 0.08566 ⫺0.3350 — 598,393 0.08806 Yes 7.360e⫺10 1.380e⫺09 0.05965 ⫺0.3283ⴱⴱⴱ 0.05339 0.1898ⴱⴱⴱ 0.03900 0.2398ⴱⴱⴱ 0.2394 0.1733 Yes 598,393 0.04908 — 5.905e⫺10 0.04788 0.04108 0.03500 0.2154 1.205e⫺09 1.323e⫺09ⴱ 0.05141 ⫺0.3651ⴱⴱⴱ 0.04601 0.2166ⴱⴱⴱ 0.03609 0.2487ⴱⴱⴱ 0.2160 1.0083ⴱⴱⴱ 1.461e⫺10 1.508e⫺09ⴱⴱⴱ 0.02195 ⫺0.4014ⴱⴱⴱ 0.02583 0.2468ⴱⴱⴱ 0.01661 0.3108ⴱⴱⴱ 0.1693 ⫺2.4212ⴱⴱⴱ 1.524e⫺09ⴱ ⫺0.4181ⴱⴱⴱ 0.2603ⴱⴱⴱ 0.2206ⴱⴱⴱ ⫺0.3350 6.132e⫺10 1.524e⫺09ⴱⴱⴱ 0.05738 ⫺0.4181ⴱⴱⴱ 0.04925 0.2603ⴱⴱⴱ 0.03507 0.2206ⴱⴱⴱ 0.2192 ⫺2.1010ⴱⴱⴱ 2.380e⫺05 ⫺3.477e⫺05ⴱ 1.661e⫺05 ⫺4.507e⫺05ⴱ 1.801e⫺05 ⫺4.507e⫺05ⴱⴱⴱ 3.674e⫺06 ⫺4.581e⫺05ⴱⴱⴱ 1.672e⫺06 ⫺4.507e⫺05ⴱ 1.823e⫺05 ⫺1.809e⫺05 ⫺1.696e⫺04ⴱ 6.511e⫺05 ⫺1.696e⫺04ⴱⴱⴱ 2.259e⫺05 ⫺1.040e⫺04ⴱⴱⴱ 9.373e⫺06 ⫺1.696e⫺04ⴱⴱ 6.031e⫺05 ⫺1.274e⫺04ⴱⴱⴱ 2.909e⫺05 ⫺1.661e⫺04ⴱⴱ 5.830e⫺05 ⫺0.01363ⴱ 0.006964 ⫺6.425e⫺05 9.799e⫺04 B 2 Cluster by day Model Note. n ⫽ 598,393. All models include indicators for the individual, stage, month, year, and day of week. p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001. Rain (inches) Temperature (degrees) Temperature2 Visibility (miles) Same-day, cumulative volume All prior days’ cumulative volume All prior days’ cumulative volume2 Load Overwork Defect Constant Individual ⫻ Month fixed effect Individual ⫻ Stage fixed effect Observations 2 R Variable 1 Main model Table 1 Summary Regression Results on Completion Time for Study 1 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. BAD WEATHER INCREASES PRODUCTIVITY 507 LEE, GINO, AND STAATS 508 Table 2 Summary Statistics for Study 2 Variable This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Speed Accuracy Precipitation Perceived bad weather Positive affect Negative affect Female Age Education Income M ␴ 1 2 3 4 5 6 2.84 17.87 0.28 3.01 4.00 1.53 1.52 37.23 4.19 3.84 0.64 4.82 0.93 0.82 1.14 0.83 0.50 12.88 1.49 2.68 .82 .12 .01 ⫺.04 ⫺.03 .02 ⫺.08 .13 .13 .11 .01 ⫺.04 ⫺.05 .05 .05 .18 .13 .24 .02 ⫺.02 ⫺.10 .00 .05 ⫺.01 ⫺.09 .01 .06 .06 .02 ⫺.06 ⫺.06 .08 .10 .06 .01 ⫺.09 ⴚ.15 ⫺.05 .03 7 8 .13 .05 .11 .04 ⫺.02 9 .25 Note. Bold denotes significance of less than 5%. (Model 2). These findings suggest that bad weather is associated with both indicators of productivity, increased speed, and accuracy. Study 3: Online Study of Weather and Salience of Outdoor Options We conducted a third study to test Hypothesis 2, which suggests that good weather conditions raise the attractiveness of outdoor options compared to bad weather conditions. Method Participants and procedure. We recruited 77 online workers (Mage ⫽ 33.02 years, SD ⫽ 11.99; 53% male) on MTurk to participate in a 5-min study for a flat fee of $0.20. We randomly assigned participants to one of two weather conditions (good vs. bad). Participants were primed on the weather; half of them read, “Please imagine that it is a beautiful, sunny day outside for the next 10 seconds,” and the rest read, “Please imagine that it is raining outside for the next 10 seconds.” We then asked all workers to write down as many non-work-related activities as possible that they would like to engage in (up to 10). Workers were also asked to rate the attractiveness of these activities using a 5-point scale (from 1 ⫽ the least attractive to 5 ⫽ the most attractive). Among all activities listed, we counted the number of outdoor and indoor activities separately. Results and Discussion Workers who were told to imagine good weather conditions listed significantly more outdoor activities they would like to engage in (M ⫽ 4.47, SD ⫽ 2.91) than did workers who imagined bad weather conditions (M ⫽ 1.31, SD ⫽ 2.10), t(75) ⫽ ⫺5.48, p ⬍ .001, although the total number of non-work-related activities (which include both indoor and outdoor activities) did not differ across weather conditions, t(75) ⫽ 1.48, p ⫽ .14. Similarly, attractiveness ratings for these outdoor activities were higher for those who imagined good weather (M ⫽ 3.77, SD ⫽ 0.14), compared to bad weather (M ⫽ 1.38, SD ⫽ 0.29), t(75) ⫽ –7.32, p ⬍ .001. This finding suggests that outdoor activities were indeed more salient and attractive when workers perceived weather to be good than bad. Study 4: Laboratory Study of Outdoor Options and Productivity In Study 4, we carefully chose the days on which we conducted our study sessions to take advantage of natural variation, then we experimentally manipulated subjects’ exposure to outdoor options. Through moderation, we seek to provide evidence in support of our mediation hypothesis that the salience of attractive outdoor options is directly linked to cognitive distractions. To test for the mediating role of outdoor options and cognitive distractions through a moderation approach (Spencer, Zanna, & Fong, 2008), we chose weather conditions and manipulated the mediating factor (in our case, exposure to outdoor options).3 Using a 2 ⫻ 2 design, we expect to find an interaction between weather conditions and exposure to outdoor options in predicting work productivity (consistent with Hypothesis 3). Further, we predict that productivity will be lower on good weather days compared to bad weather days, regardless of the outdoor-options manipulation, as these options are already salient and attractive without our prompt. Thus, we expect to see our predicted effect (better performance on bad weather days) in the condition in which we do not introduce outdoor options as distractions. Method For our first manipulation, we varied whether the task was undertaken on days with poor weather (rainy) or good weather (sunny). For our second manipulation, the participants either were primed by exposure to a variety of outdoor options prior to the task or were not primed by exposure to outdoor options. We used this second manipulation to vary the level of cognitive distraction created by thinking about outdoor activities one may engage in, a manipulation based on prior research (e.g., Simonsohn, 2010). During the entire experiment, the laboratory’s lighting and temperature levels were fixed at the same level, and participants were 3 We selected this method of manipulating the availability of outdoor options instead of relying on self-reports, which are less reliable and more likely to be biased (i.e., asking participants how distracted they felt or how frequently they thought about outdoor options). This approach is considered a stronger test of the mediation hypothesis than measuring the mediating factor through the use of self-reported measures (Rucker, Preacher, Tormala, & Petty, 2011; Spencer et al., 2005). BAD WEATHER INCREASES PRODUCTIVITY 509 Table 3 Summary Regression Results in Study 2 Speed Model 1 B This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Variable Precipitation Female Age Education Income Positive affect Negative affect Constant Observations R2 Root MSE ⴱ p ⬍ .05. ⴱⴱ SE ⴱⴱ 0.07 0.02 2.81ⴱⴱⴱ 321 0.01 0.62 0.04 p ⬍ .01. ⴱⴱⴱ Accuracy Model 2 B Model 1 SE ⴱⴱ 0.07 0.04 ⫺0.00 0.05ⴱ 0.02 ⫺0.02 ⫺0.03 2.72ⴱⴱⴱ 321 0.05 0.62 0.02 0.07 0.00 0.02 0.01 0.04 0.04 0.24 B Model 2 SE ⴱⴱ 0.54 0.15 17.62ⴱⴱⴱ 321 0.01 4.79 0.28 B SE ⴱⴱ 0.52 0.30 0.01 0.47ⴱ 0.18 ⫺0.23 ⫺0.39 15.45ⴱⴱⴱ 321 0.06 4.71 0.15 0.54 0.02 0.01 0.09 0.28 0.36 1.72 p ⬍ .001. able to see the outside weather through the lab’s window. There was no significant difference in show-up rates between bad versus good weather days. Participants and procedure. We recruited 136 students (Mage ⫽ 21.82 years, SD ⫽ 3.51; 48.89% male) through the study pool at the Harvard Decision Science Laboratory. Students signed up online in advance to participate in an hour-long study and were paid a $10 participation fee. They were also told that, depending on the completion time of their data entry, they could receive an additional $10 bonus. Participants in the exposure-to-outdoor-options condition viewed photos of outdoor activities taking place in good weather conditions and were asked to evaluate the attractiveness of each activity. Participants were then asked to pick their favorite depicted activity or the activity in which they engaged most frequently and to discuss as vividly as possible what they would do in the depicted scene. By contrast, participants in the control group were asked to describe their typical daily routine. Next, all participants completed the data-entry task, which involved entering five sets of questionnaire responses written in Italian from printed copies into a spreadsheet.4 All participants finished entering five surveys and received the additional $10. After all participants completed their data-entry task, they answered a questionnaire that included state emotions, subjective weather perceptions, and demographic questions. Measures Productivity. We assessed speed and accuracy as measures of productivity. For speed, we first calculated the number of words entered, then divided this number by the amount of time spent completing the task, given that each survey data consisted of a different number of words. We assessed accuracy by counting the number of correct words entered for each person. State emotions. Similar to Study 2, we controlled for the potential influence of affect by measuring both positive (␣ ⫽ .93) and negative affect (␣ ⫽ .89) using PANAS. Subjective weather perceptions. As a manipulation check for our weather manipulation, we asked participants whether they thought the weather on the day of their participation was “good” or “bad.” Results and Discussion We excluded 10 participants who failed to follow our instructions, as their completion time was not recorded correctly. Table 4 reports the descriptive statistics and correlations among the key variables used in our analyses. Manipulation check. Almost 90% of the participants who participated on a good weather day (60 out of 67) felt that the weather was good; almost 93% of participants who participated on a rainy day (64 out of 69) felt that the weather was bad, ␹2(1, N ⫽ 136) ⫽ 92.29, p ⬍ .001. These weather variables were not significantly correlated with our manipulation of exposure to outdoor options, which we randomized. Main analyses. Hypothesis 3 predicted that bad weather conditions increase productivity by decreasing thoughts about outdoor options, which should reduce cognitive distractions. Given the design of Study 4, this hypothesis would be supported by a significant interaction between weather conditions and exposure to outdoor options in predicting productivity. To test this hypothesis, we conducted a series of regression analyses (Table 5). As shown in Model 1, exposure to outdoor options decreased data-entry speed and accuracy. We did not find a statistically significant effect of bad weather on productivity (for speed, ␤ ⫽ 1.60, p ⫽ .10; for accuracy, ␤ ⫽ 13.33, p ⫽ .10). As predicted, the effect of weather on speed was qualified by a significant interaction between exposure to outdoor options and weather conditions, while the interaction effect on accuracy did not reach significance criteria. We conducted similar analyses while controlling for demographics and state emotions (Model 2). After holding these variables constant, the interaction effect on speed remained robust, and the interaction effect on accuracy became statistically significant. A simple slope analysis supports Hypothesis 3 (see Figure 1). When no outdoor options were made salient to participants, bad weather significantly increased data-entry speed (␤ ⫽ 3.04, p ⫽ .04). When participants were exposed to outdoor options, however, 4 Further details of the instructions and materials used in this study are available online as supplemental materials (Appendix B). LEE, GINO, AND STAATS 510 Table 4 Summary Statistics for Study 4 Variable This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Speed Accuracy Good weather indicator Outside option indicator Age Female Income Education Positive affect Negative affect Note. M ␴ 1 2 3 4 5 6 7 8 9 30.03 190.49 0.48 0.52 21.94 1.52 4.90 3.42 35.93 19.59 3.55 31.81 0.50 0.50 3.57 0.50 3.40 1.03 12.16 9.67 .90 ⫺.02 ⫺.14 ⴚ.20 .18 .13 ⫺.11 .03 .00 ⫺.01 ⴚ.20 ⫺.16 .10 .09 ⫺.04 .07 ⫺.01 ⫺.04 .05 .01 .00 .09 .09 .04 ⫺.06 ⫺.07 .06 .00 ⫺.07 .13 ⫺.07 ⴚ.19 .71 .11 ⫺.15 .06 ⫺.07 ⫺.09 .10 ⴚ.26 .06 ⫺.05 ⫺.00 .01 ⫺.13 Bold denotes significance of less than 5%. weather conditions no longer predicted speed significantly (␤ ⫽ 0.19, p ⫽ .76). Similarly, when there were no outdoor options, bad weather significantly increased data-entry accuracy (␤ ⫽ 24.90, p ⫽ .05), a relationship that no longer held for those distracted by outdoor options (␤ ⫽ 1.87, p ⫽ .74). To summarize, we found that having attractive outdoor options decreased productivity through increased cognitive distractions. In line with previous work (Bailey & Konstan, 2006; Speier, Valacich, & Vessey, 1999), we demonstrate that making outdoor options salient in people’s minds alone could impair their ability to concentrate. Good weather conditions were harmful for productivity, an effect that seemed to disappear when outdoor options were made salient. This interaction effect between weather conditions and exposure to outdoor options suggests that people can be relatively more productive at work on rainy days, unless they are actively distracted. On sunny days, participants are likely to already be distracted, as outdoor options are salient in their minds. Together, consistent with Hypothesis 3, these findings show that cognitive distractions created by the salience of outdoor options may serve as a mechanism through which bad weather conditions increase productivity. General Discussion and Conclusion Our main goal in this article was to provide an alternative psychological route of limited attention through which bad weather conditions influence productivity, even when we hold affective influences constant. Our evidence from both the field and the lab was consistent with the predictions of our theoretical model. Although numerous previous studies used weather to induce either positive or negative moods (Cunningham, 1979; Goldstein, 1972; Keller et al., 2005; Parrott & Sabini, 1990; Schwarz & Clore, 1983) to study the effect of moods, our result does not support this weather-mood hypothesis. Using a meta-analysis, Shockley, Ispas, Rossi, and Levine (2012) found that positive affect is associated with enhanced overall job performance. In Studies 2 and 4, however, weather conditions did not induce positive nor negative affect, and affect did not predict productivity. Yet it is not our goal to suggest that the weather-mood hypothesis is unwarranted or that affect plays no role in cognition. Although these influences were not realized in our study, they may still be in place, even if to a lesser extent than previous research posited. Table 5 Summary Regression Results in Study 4 Speed Accuracy Model 1 B Variable Exposure to outdoor options Good weather indicator Interaction (Outdoor Options ⫻ Weather) Age Female Income Education Positive affect Negative affect Constant Observations R2 Adjusted R2 Root MSE ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001. Model 2 SE ⴱ ⫺2.20 ⫺1.49 2.51ⴱ 0.87 0.91 1.27 31.28ⴱⴱⴱ 123 0.05 0.03 3.50 0.64 B Model 1 SE ⴱ ⫺2.26 ⫺1.58 2.60ⴱ ⫺0.27 1.14 0.14 0.46 0.02 ⫺0.01 32.70ⴱⴱⴱ 122 0.14 0.07 3.43 0.87 0.92 1.27 0.13 0.63 0.63 0.44 0.03 0.03 2.69 B Model 2 SE ⴱⴱ ⫺22.82 ⫺12.02 20.61 203.27ⴱⴱⴱ 125 0.07 0.04 31.08 7.68 8.09 11.16 5.67 B SE ⴱⴱ ⫺23.90 ⫺13.61 21.88ⴱ ⫺2.62ⴱ 5.42 1.10 6.50 0.26 ⫺0.14 219.07 124 0.13 0.07 30.82 7.75 8.26 11.34 1.14 5.65 0.87 3.97 0.24 0.03 24.22 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. BAD WEATHER INCREASES PRODUCTIVITY 511 could measure other aspects of job performance. For example, weather-induced positive moods may improve workers’ productivity on tasks that require creativity, as well as affective interpersonal skills such as empathy and emotional intelligence. Research also shows that bad weather conditions may lead people to prefer spending time at work because attractive outdoor options are not available to them (e.g., Connolly, 2008; Zivin & Neidell, 2010). Although our studies did not allow for testing this possibility, future studies should investigate the potential role of differing incentives. If workers have incentives to finish their work early on sunny days, rather than having fixed work hours per day, their motivation to leave early might offset productivity loss due to cognitive distractions. In addition, there might be individual differences in people’s responses to weather conditions (see Klimstra et al., 2011, for “weather reactivity”) and their preference for outdoor activities. Such dispositions may contribute to the variance in how outside weather conditions are perceived and may also explain the lack of significant correlations between weather and moods. Future studies should further examine the role of such individual differences in modulating the role of outside weather in influencing worker productivity. Theoretical and Practical Implications Figure 1. Exposure to outdoor options moderates the relationship between weather conditions and productivity. One potential moderator that could address these seemingly contradictory results is workers’ exposure to outside weather, either by spending time and working outside or by looking outside through windows. In fact, Keller et al. (2005) found that the amount of time spent outdoors moderated the effects of weather on mood and cognition. Both of our studies were conducted in a climate-controlled environment where individuals were asked to complete a series of tasks requiring attention and focus, such as a workplace (Study 1), an online labor market (Studies 2–3), and the laboratory (Study 4). Thus, this may explain why outside weather conditions played a lesser role in influencing workers’ affective state but created a more significant variation in the level of cognitive distraction. In such contexts, weather may primarily act on people’s cognition rather than on their affective states, as weather influences their level of distraction when they think about attractive outdoor options, as we have shown. Future research examining the role of weather across these different contexts (i.e., workers who typically work outside the office, or workers who work in an office without windows) would further our understanding of the relationship between weather, affect, and cognition. It should also be noted that our measure of job performance was limited to the data entry task, which requires attention, and thus more likely to be affected by cognitive distractions, rather than affective influences. Positive affect tends to encourage less constrained, less effortful, and more creative problem solving (Schwarz & Clore, 1983). In fact, positive moods induced by good weather conditions may broaden workers’ cognition, thus increasing the flexibility of their thoughts (Keller et al., 2005). Consequently, future research should include different types of tasks that Our research extends previous work on the influence of weather conditions on behavior. Prior work has focused on the effects of weather on behavior through people’s affective reactions to weather conditions (e.g., Larrick et al., 2011). Our work demonstrates that weather conditions also influence individuals’ cognition. By reducing the potential for cognitive distractions, bad weather was actually better than good weather at sustaining individuals’ attention and focus, and, as a result, increasing their productivity. Our results also deepen understanding of the factors that contribute to work productivity. Prior research has focused primarily on factors that are directly under one’s control or the control of the organization (e.g., Staats & Gino, 2012). We document the influence of weather conditions, incidental factors that affect work productivity. Distractions that arise at work have been studied under the assumption that they can be avoided. In fact, engaging in distractions, such as Internet surfing, may have positive effects on productivity (due to increased stimulation, Jett & George, 2003). Similarly, perceived autonomy over lunch breaks reduced fatigue at the end of the day (Trougakos, Hideg, Cheng, & Beal, in press). Thus, a concerted effort to take advantage of good weather for break purposes could offset potential negative effects on productivity. Future studies may explore the consequences of different types of distractions at work, including how to structure break programs to restore the workers’ cognitive resources. Weather is one of the many factors that may lead workers to engage in non-work-related thoughts. Bad weather eliminates only one type of distracting thoughts; other factors may influence worker productivity to a larger degree (i.e., explicit incentives and implicit goal-oriented motives). Despite our small effect size in Study 1, our findings shed light on how seemingly irrelevant, uncontrollable factors may influence workers’ productivity and also learning over time. In fact, operational im- LEE, GINO, AND STAATS This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 512 provement efforts often focus on issues that have effect sizes less than 1%. Companies realize that even small efficiency improvements can translate to cost advantages. This finding calls for further investigation of the factors that can increase task-unrelated thoughts that may adversely affect productivity. Research could also examine how expectations of certain conditions (e.g., rain when sunshine was expected) might moderate the effect of task-unrelated thoughts. Our research also has practical implications. Although weather conditions are exogenous and uncontrollable, to tap into the effects of bad weather on productivity, organizations could assign more clerical work of the type that does not require sustained attention but does allow for more flexible thinking on rainy days than sunny days. Since we found that cognitive distractions led to higher error rates, individuals may wish to avoid working on a task in which errors would be costly when they have task-unrelated priorities. In addition, organizations may give productivity feedback to each employee and allow flexible working hours that could maximize productivity. 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Received May 22, 2013 Revision received November 25, 2013 Accepted December 2, 2013 䡲 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Correction to Lee et al. (2014) In the article “Rainmakers: Why Bad Weather Means Good Productivity” by Jooa Julia Lee, Francesca Gino, and Bradley R. Staats (Journal of Applied Psychology, Advance online publication. January 13, 2014. doi: 10.1037/a0035559), there is an error in the last paragraph. The sentence “Although weather conditions are exogenous and uncontrollable, to tap into the effects of bad weather on productivity, organizations could assign more clerical work of the type that does not require sustained attention but does allow for more flexible thinking on rainy days than sunny days” should have read . . . “to tap into the effects of bad weather on productivity, organizations could assign more clerical work of the type that requires sustained attention on rainy days, and more creative work that allows for more flexible thinking on sunny days.” DOI: 10.1037/a0036192 ORIGINAL RESEARCH ARTICLE published: 02 December 2013 doi: 10.3389/fnhum.2013.00824 The impact of physical exercise on convergent and divergent thinking Lorenza S.Colzato 1 *, Ayca Szapora 1 , Justine N. Pannekoek 2 ,3 and Bernhard Hommel 1 1 Cognitive Psychology Unit, Institute for Psychological Research and Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa 3 Leiden University Medical Centre and Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands 2 Edited by: Carsten De Dreu, University of Amsterdam, Netherlands Reviewed by: Marieke Roskes, Ben Gurion University of the Negev, Israel Simone Ritter, Radboud University Nijmegen, Netherlands *Correspondence: Lorenza S. Colzato, Cognitive Psychology Unit, Institute for Psychological Research and Leiden Institute for Brain and Cognition, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, Netherlands e-mail: colzato@fsw.leidenuniv.nl Anecdotal literature suggests that creative people sometimes use bodily movement to help overcome mental blocks and lack of inspiration. Several studies have shown that physical exercise may sometimes enhance creative thinking, but the evidence is still inconclusive. In this study we investigated whether creativity in convergent- and divergentthinking tasks is affected by acute moderate and intense physical exercise in athletes (n = 48) and non-athletes (n = 48). Exercise interfered with divergent thinking in both groups. The impact on convergent thinking, the task that presumably required more cognitive control, depended on the training level: while in non-athletes performance was significantly impaired by exercise, athletes showed a benefit that approached significance. The findings suggest that acute exercise may affect both, divergent and convergent thinking. In particular, it seems to affect control-hungry tasks through exercise-induced “ego-depletion,” which however is less pronounced in individuals with higher levels of physical fitness, presumably because of the automatization of movement control, fitness-related neuroenergetic benefits, or both. Keywords: physical exercise, creativity, convergent thinking, divergent thinking, fitness INTRODUCTION Anecdotal literature suggests that creative people sometimes use bodily movement to help overcome mental blocks and to get deeper into a problem. Indeed, the philosopher Henry David Thoreau stated: “the moment my legs begin to move my thoughts begin to flow – as if I had given vent to the stream at the lower end and consequently new fountains flowed into it at the upper” (Thoreau, 1851). Several studies have indeed shown that physical exercise in healthy adults may sometimes enhance creative thinking – even though the size of this effect can vary substantially (Gondola and Tuckman, 1985; Gondola, 1986, 1987; Steinberg et al., 1997; Blanchette et al., 2005). Gondola and Tuckman (1985) investigated the effects of long-term physical exercise on creativity performance, showing small but significant improvements in Alternate Uses (spontaneous flexibility) and Remote Consequences (originality) tasks, but not for an Obvious Consequences (different ideas) task. Gondola (1986) used the same creativity tasks to compare the effect of long-term and acute physical exercise and found improvements for both conditions and all three creativity measures. Gondola (1987) tested another form of acute aerobic activity (dance) and reported comparable enhancing effects. Steinberg et al. (1997) found only small improvements in a group of fit participants, and only in one of the three measures of the Torrance test of creative thinking. Blanchette et al. (2005) used the same test and found enhancing effects of exercise over a 2 h period. It is possible that in some or all of these previous studies physical exercise provided the opportunity for mind-wandering or incubation in trained (and, thus, less challenged) people. Indeed, Baird et al. (2012) have reported that engaging in simple external tasks that allow the mind to wander may facilitate creative problem solving. Frontiers in Human Neuroscience The methodological diversity across the available studies with regard to sample characteristics and creativity assessment (mainly targeting aspects of divergent thinking) is considerable, which renders it questionable whether they were actually assessing the same constructs and processes. Moreover, there is still no mechanistic model explaining how creative processes operate and how physical exercise might affect these operations. To address this issue, we tried to avoid addressing creativity as a whole but focused on particular components of creative performance – components that are more transparent at the process level and thus easier to investigate. More concretely, we investigated the impact (during and after) of acute moderate and intense physical exercise on creativity tasks tapping into convergent and divergent thinking. Guilford (1950, 1967) has considered these two as the main ingredients of most creative activities, even though other processes are also likely to contribute (Wallas, 1926). Divergent thinking is taken to represent a style of thinking that allows many new ideas being generated, in a context where more than one solution is correct. The probably best example is a brainstorming session, which has the aim of generating as many ideas on a particular issue as possible. Guilford’s (1967) alternate uses task (AUT) to assess the productivity of divergent thinking follows the same scenario: participants are presented with a particular object, such as a pen, and they are to generate as many possible uses of this object as possible. Convergent thinking, in turn, is considered a process of generating one possible solution to a particular problem. It emphasizes speed and relies on high accuracy and logic. Mednick’s (1962) remote associates task (RAT) that aims to assess convergent thinking fits with this profile: participants are presented www.frontiersin.org December 2013 | Volume 7 | Article 824 | 1 “fnhum-07-00824” — 2013/11/27 — 21:26 — page 1 — #1 Colzato et al. Physical exercise impacts creativity with three unrelated words, such as “time,” “hair,” and “stretch,” and are to identify the common associate (“long”). Interestingly for our purposes, performance on the AUT and the RAT were found to be uncorrelated (Akbari Chermahini and Hommel, 2010) and differently affected by the same experimental manipulations (Hommel et al., submitted), which supports Guilford’s (1967) suggestion that convergent and divergent thinking represent different, separable components of human creativity. Such a scenario would fit with considerations of De Dreu et al. (2008), who proposed the Dual Pathway to Creativity model suggesting that creative performance arises from the interaction between cognitive flexibility and cognitive persistence – two dissociable cognitive control functions (Goschke, 2000; De Dreu et al., 2012). Consistent with this, divergent thinking was less pronounced in avoidance-motivated than in approach-motivated individuals, suggesting that the former need to compensate for their inflexible processing style by effortful and controlled processing (Roskes et al., 2012). Along the same lines, Colzato et al. (2012) have argued that convergent thinking requires strong top-down control because it represents the tightly constrained search of very few or just one item. In contrast, divergent thinking should rely on weak top-down control, given that it implies a broad, loosely defined search space so to activate many items that satisfy the often relatively soft criteria (Hommel, 2012). Hence, convergent and divergent thinking are likely to differ in their reliance on executive control for the processing of information. If so, acute exercise should affect these two processes differently. According to the ego-depletion hypothesis (Baumeister et al., 1998), the cognitive resources required for cognitive-control operations are tightly limited and thus deplete quickly during and after control-demanding tasks. Following a similar, though more motivational rationale, Inzlicht and Schmeichel (2012) have developed a process model to explain self-control failure. According to that model, “exerting self-control at Time 1 reduces success at self-control at Time 2 by initiating shifts in motivation and attention that conspire to reduce self-control and increase immediate gratification” (p. 460). According to this reasoning, poorer self-control at Time 2 is attributed to reduced motivation to exert control and to reduced attention to cues that signal a need for control, as well as more impulsive behavior and more attention to reward cues. Given that exercising must use up some amount of control resources, more controldemanding tasks (like convergent thinking) should suffer more from exercise than less control-demanding tasks (like divergent thinking). However, how resource-hungry exercise should not only depend on the kind of exercise (e.g., the complexity of the coordination required) but also on the skill level of the exercising individual. The same exercise that exhausts the resources of the less sportive student may have little impact on the highly practiced athlete. In athletes, many movement routines are overlearned and automatized, which can lead to dramatic reductions of conscious monitoring and control demands (Beilock and Carr, 2001; Schneider and Chein, 2003). Moreover, long-term fitness training leads to an increase of oxygenation and glucose in the frontal brain regions, which has been found to produce rather selective benefits for executive-control processes Frontiers in Human Neuroscience (Colcombe and Kramer, 2003). This means that athletes may not exhibit the same effects as non-athletes. While the latter should show exercise-induced costs in more control-demanding tasks (like convergent thinking), the former might either not show such costs or perhaps even show exercise-induced benefits. To investigate these possibilities, we tested the impact of acute physical exercise on convergent and divergent thinking in athletes and non-athletes. We also took into account possible moderating factors, such as the intensity of the exercise (which was moderate or high, in different sessions) and the temporal overlap between exercise and creativity task (with the latter being performed during or after the exercise). METHODS PARTICIPANTS Ninety-six healthy, native Dutch speakers (48 females and 48 males), of which 48 were athletes (mean age = 20.6 years; mean body mass index, BMI = 22.3) and 48 non-athletes (mean age = 20.7 years; mean BMI = 22.2), participated for an energy bar and a sports drink or one study credit. Participants were considered athletes if they exercised at least three times a week during the recent 2 years and non-athletes if they did not exercise on a regular basis (less than 1 time per week). All participants had normal systolic and diastolic blood pressure at rest (mean systolic blood pressure, SBP = 122 and diastolic blood pressure, DPB = 74), and reported no current or history of medication or drug use. Informed consent was obtained from all participants after the nature of the study was explained to them. The protocol was approved by the local ethical committee (Leiden University, Institute for Psychological Research). REMOTE ASSOCIATION TASK (CONVERGENT THINKING) In this task, participants are presented with three unrelated words (such as “time,” “hair,” and “stretch”) and asked to find a common associate (“long”). Our Dutch version comprised of 30 previously validated items (Akbari Chermahini et al., 2012). In each of the three sessions, participants completed 10 different items. ALTERNATE USES TASK (DIVERGENT THINKING) In this task, participants were asked to list as many possible uses for six common household items (“pen,” “towel,” “bottle”). In the three sessions, participants completed 1 of these items. The results can be scored in several ways with flexibility, the number of different categories used, being the theoretically most transparent and the empirically most consistent and reliable score (Akbari Chermahini and Hommel, 2010). In the case of the item “pen,” “writing an essay,” and “writing a letter” would fall into the same category, but “drumming on the table” would fall into a different category. In this study we considered four scores: Flexibility: The number of different categories used. Originality: Each response is compared to the total amount of responses from all of the subjects. Responses that were given by only 5% of the group count as unusual (1 point) and responses given by only 1% of them count as unique (2 points). Fluency: The total of all responses. Elaboration: The amount of detail (e.g., “a door stop” counts 0, whereas “a door stop to prevent a door slamming shut in a strong wind” counts www.frontiersin.org December 2013 | Volume 7 | Article 824 | 2 “fnhum-07-00824” — 2013/11/27 — 21:26 — page 2 — #2 Colzato et al. Physical exercise impacts creativity 2 (1 point for explanation of door slamming and another for further detail about the wind). moment in which participants carried out the creativity tasks (during vs. after exercise) as between-group factor. A significance level of p < 0.05 was adopted for all tests. EXERCISE CONDITIONS During the rest condition, participants sat on a cycle ergometer (Kettler Cycle) without cycling. During the moderate cycling condition, participants cycled at a normal pace (level 8) without exhausting themselves. During the intense cycling condition, the resistance level on the bicycle was adjusted to high (level 16), and the participants cycled at a maximum level of effort. RESULTS PARTICIPANTS No significant group differences were obtained for age, t(94) = 0.05, p = 0.95, and BMI, t(94) = 0.34, p = 0.73, but there was a significant difference for sport units per week, t(94) = 21.68, p = 0.00001: athletes exercised more often per week (3.4) than non-athletes did (0.5). PHYSIOLOGICAL AND MOOD MEASUREMENTS Heart rate (HR) and systolic and diastolic blood pressure (SBP and DPB) were measured from the non-dominant arm with an OSZ 3 Automatic Digital Electronic Wrist Blood Pressure Monitor (Speidel and Keller). BMI was measured by Omron BF511 medical device. Mood was rated on a 9 × 9 Pleasure × Arousal grid (Russell et al., 1989) with values ranging from –4 to 4. PROCEDURE AND DESIGN A between-group (athletes vs. non-athletes) randomized cross-over design with counterbalancing of the order of the exercise conditions (rest vs. moderate vs. intense) was used (Latinsquare design). All participants were tested individually. Half of the participants in each group (n = 24) executed the creativity tasks during cycling, the other half (n = 24) thereafter. Upon arrival, participants were asked to rate their mood and HR, SBP, DPB, and BMI were collected (baseline measurement). Next, the participant was introduced to the assigned exercise condition. When the rest condition was preceded by the moderate or intense exercise condition, the participant started the next exercise condition only after a couple of minutes (never more than 5) when HR returned to the baseline measurement level. After each condition, HR, SBP, DPB, and mood were measured again. The creativity tasks (AUT and RAT) were performed either during or after the physical exercise, depending on the condition subjects had been randomly assigned to, see Figure 1. Participants had 3 min to execute the RAT (10 items per test condition) and 3 min for the AUT (1 item per test condition). Participants were confronted with a printed version of the creativity tasks on a clipboard positioned on the cycle ergometer in front of them so that they could fill in their responses comfortably while cycling. After the experimental session was ended, participants were rewarded for their participation in the study. STATISTICAL ANALYSIS Independent t-tests were performed to test differences between the two groups. Mood, HR, BPS, and BPD, and five creativity measures (from the two tasks) were extracted for each participant: flexibility, originality, fluency, and elaboration scores from the AUT, the number of correct items from the RAT. All four AUT measures were scored by two independent raters [Cronbach’s alpha = 1.00 (fluency); 0.85 (flexibility); 0.71 (originality); 0.74 (elaboration)]. All measures were analyzed separately by means of repeatedmeasures ANOVAs with Session (rest vs. normal vs. intense) as within-subjects factor and group (athletes vs. non-athletes) and Frontiers in Human Neuroscience PHYSIOLOGICAL AND MOOD MEASUREMENTS We found a main effect of session on HR, F(2,184) = 768.01, p < 0.00001, MSE = 109.063, η2p = 0.89, SBP, F(2,184) = 165.76, p < 0.00001, MSE = 163.793, η2p = 0.64, and DBP, F(2,184) = 29.18, p < 0.001, MSE = 104.509, η2p = 0.24. Participants showed increased HR, SBP, and DBP in the moderate (95, 130, 76) and intense (133, 150, 85) exercise condition as compared to the rest condition (75, 116, 74). No other significant interaction involving group was found, p > 0.14. Replicating earlier findings (Steptoe and Bolton, 1988), arousal, F(2,184) = 768.01, p < 0.00001, MSE = 109.063, η2p = 0.89, but not mood, F(2,184) = 43.71, p < 0.0001, MSE = 1.077, η2p = 0.32, was elevated after intense exercise (1.9, 1.1) as compared to normal exercise (1.1, 1.3) and rest (0.6, 1.2), respectively. As in the case of physiological measurements, no other significant interaction involving group was found, F < 1. CREATIVITY TASKS In general, performance in the AUT and RAT was good and comparable to performance in other studies without exercise manipulations (e.g., Akbari Chermahini and Hommel, 2010); see Table 1. Convergent thinking: As expected, we found a significant interaction between group and session on RAT scores, F(2,184) = 5.16, p < 0.01, MSE = 2.838, η2p = 0.05. Post-hoc multiple comparisons tests revealed that, even if not quite significant, athletes tended to perform better in convergent thinking in the moderate (4.1) and intense (4.2) exercise conditions than in the rest condition (3.5), p = 0.072, 0.095. This effect was reversed in non-athletes, where intense exercise (3.6) impaired convergent thinking compared to moderate exercise (4.4), p = 0.002 and rest (4.6), p = 0.029. The interaction was not modified by testing moment, as the insignificant three-way interaction indicated, F(2,184) = 1.01, p = 0.364, MSE = 2.838, η2p = 0.01. Divergent thinking: From the four scores of the AUT, only flexibility yielded a significant main effect of session, F(2,184) = 3.69, p < 0.05, MSE = 3.169, η2p = 0.03; post-hoc tests revealed that participants showed greater flexibility in the rest condition (7.4) than with intense (6.7) exercise, p = 0.011, while the difference between rest and moderate exercise (7.0) only approached significance, p = 0.150. Numerically similar, but statistically insignificant trends were obtained for originality, F(2,184) = 0.42, p = 0.66, MSE = 0.320, η2p = 0.05, fluency, F(2,184) = 2.47, p = 0.09, MSE = 5.420, η2p = 0.03, and elaboration, F(2,184) = 2.19, www.frontiersin.org December 2013 | Volume 7 | Article 824 | 3 “fnhum-07-00824” — 2013/11/27 — 21:26 — page 3 — #3 Colzato et al. Physical exercise impacts creativity FIGURE 1 | Sequence of events for the participants who performed the creativity tasks during exercise (A) or after exercise (B). p = 0.11, MSE = 0.247, η2p = 0.02. In contrast to the RAT findings, the flexibility effect was not modulated by group, F < 1, and the same was true for originality, F(2,184) = 1.20, p = 0.302, MSE = 0.320, η2p = 0.01, fluency, F < 1, and elaboration, F(2,184) = 1.07, p = 0.346, MSE = 2.838, η2p = 0.01. There was also no indication of any three-way interaction, p’s > 0.21. Frontiers in Human Neuroscience DISCUSSION In this study we investigated whether creativity in convergentand divergent-thinking tasks is affected by acute physical exercise. The results provide some preliminary evidence for a link between exercise and creativity, but they also suggest that the nature and the consequences of this www.frontiersin.org December 2013 | Volume 7 | Article 824 | 4 “fnhum-07-00824” — 2013/11/27 — 21:26 — page 4 — #4 Colzato et al. Physical exercise impacts creativity Table 1 | Means for the number of correct items from the remote associates task (RAT), for flexibility, originality, fluency, and elaboration scores from the alternate uses task (AUT), and perceived mood ratings as a function of group (athletes vs. non-athletes), session (rest vs. normal vs. intense) and moment in which participants carried out the creativity tasks (during vs. after exercise). Group Athletes Moment During After Non-athletes During After Session RAT AUT- AUT- AUT- AUT- flexibility originality fluency elaboration HR BPS BPD Mood Arousal 77.0 113.5 74.4 1.5 0.7 Rest 3.6 7.3 0.50 11.0 0.83 Normal 3.9 6.7 0.79 11.0 0.67 94.4 127.9 74.8 1.9 1.3 Intense 4.0 6.2 0.75 10.5 0.62 126.1 148.6 83.1 1.8 2.0 Rest 3.5 6.9 0.83 11.1 0.96 71.9 116.9 71.5 1.2 0.2 Normal 4.3 6.7 0.79 10.8 0.87 91.0 134.8 74.6 1.1 1.1 Intense 4.3 6.8 0.70 10.8 0.96 134.8 151.5 83.1 0.8 1.7 Rest 4.7 7.2 0.46 10.4 0.92 75.5 117.5 77.2 1.2 0.6 Normal 4.8 6.4 0.50 9.2 0.79 93.2 130.6 76.4 0.9 1.1 Intense 3.4 6.7 0.37 8.6 0.62 131.6 150.8 88.1 0.9 2.0 Rest 4.5 7.9 0.54 10.6 1.04 76.0 117.2 74.3 0.9 0.8 Normal 4.0 7.9 0.46 11.0 1.00 102.8 127.3 79.2 1.5 0.8 Intense 3.9 7.1 0.42 10.2 0.96 140.7 148.0 85.5 0.9 1.8 link depend on the particular task and the fitness of the individual. First, non-athletes did not benefit from acute exercise; in fact, exercise caused their performance to drop in both creativity tasks. The fact that this drop was not modified by the moment of testing suggests that it was not due to dual-tasking or related online demands. Rather, in this group acute exercise seems to lead to ego-depletion, hence, exhaust limited cognitive-control resources (Baumeister et al., 1998) that are then no longer available for the control of processes involved in convergent and divergent thinking....
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Article Critique- Bad Weather
Summary
Thesis Statement: Weather has an impact on workers’ productivity and further the research
shows that there is an existing impact of weather on productivity. In fact, good weather affected
the concentration of workers.
Critique of the study
The study by Lee, Gino, and Staats (2014) is valid because it has used scientifically proven
methods for instance in the first study titled, Field Evidence From a Japanese Bank, they were
participants, there was the control variable and in the end a linear curve was used to display the
results.
Brief Summary
The researchers have shown that it is affirmative that warm sunny weather leads to lower
productivity as a result of individuals having constant thoughts of outdoor activities.


Running head: BAD WEATHER MEANS PRODUCTIVITY

Bad weather means productivity
Summary
Name
Date

1

BAD WEATHER MEANS PRODUCTIVITY

2

Summary
Relating the effects of weather to factors such as stock market has been common in a
number of studies. In this study, however, Lee, Gino, and Staats (2014) examine how the
weather impacts on workers’ productivity and further unveils how this study can help companies
in engaging their employees.

The researchers engaged four different experiments that all

insinuated that good weather conditions distract workers due to increased outdoor activities
while the bad weather did the exact opposite and hence workers were likely to be distracted
during good weather.
The researcher began theorizing that thoughts that are connected to outdoor options easily
come into people’s mind during good weather as opposed to days with bad weather. Based on
the theory of Simonsohn (2010), that; cloud cover at the time of visits to a...


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