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Linking Lighting Appraisals to Work Behaviors

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Among those concerned with practical matters of office design, demonstrations that the work environment affects employees’ well-being and work behaviors are thought to be important to support client decision making. Veitch, Newsham, Boyce, and Jones developed a conceptual model in which lighting appraisal and visual capabilities predicted aesthetic judgments, mood, and performance. This article extends that model to include measures of work engagement, using experimental data originally reported by Newsham, Veitch, Arsenault, and Duval. Structural equation modeling showed strong fit to a model in which lighting appraisals indirectly influenced work engagement through aesthetic judgments and mood. This evidence that providing a satisfactory work environment can contribute to employee effectiveness merits further study by environmental and organizational psychologists.
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http://dx.doi.org/10.1177/0013916511420560
Environment and Behavior, 45, 2, pp. 198-214, 2011-09-16
Linking lighting appraisals to work behaviors
Veitch, Jennifer A.; Stokkermans, Mariska G. M.; Newsham, Guy R.
© 2011 Her Majesty the Queen in Right of Canada.
This is an author version of the article: Veitch, J. A., Stokkermans, M. G. M., & Newsham, G. R. (2013). Linking lighting appraisals to
work behaviors. Environment and Behavior, 45, 198-214. DOI: 10.1177/0013916511420560.
Linking Lighting Appraisals to Work Behaviors
Jennifer A. Veitch, Mariska G. M. Stokkermans and Guy R. Newsham
NRC Construction, Ottawa, ON, Canada
Abstract
Among those concerned with practical matters of office design, demonstrations that the work
environment affects employees’ well-being and work behaviors are thought to be important to support
client decision-making. Veitch, Newsham, Boyce and Jones (2008)developed a conceptual model in
which lighting appraisal and visual capabilities predicted aesthetic judgments, mood, and performance.
This paper extends that model to include measures of work engagement, using experimental data
originally reported by Newsham, Veitch, Arsenault and Duval (2003, 2004b). Structural equation modeling
showed strong fit to a model in which lighting appraisals indirectly influenced work engagement through
aesthetic judgments and mood. This evidence that providing a satisfactory work environment can
contribute to employee effectiveness merits further study by environmental and organizational
psychologists.
Introduction
Although not familiar to most psychologists, more than a century of lighting and applied vision
science research has established clear relationships between the lit environment and visual performance
(Boyce, 2003; Rea & Ouellette, 1991)and visual comfort (Commission Internationale de l’Eclairage (CIE),
1995; Committee on Recommendations for Quality and Quantity of Illumination (RQQ), 1966)that are
reflected in present-day lighting recommendations (Commission Internationale de l'Eclairage (CIE), 2001;
Illuminating Engineering Society of North America (IESNA), 2004). As a result, most offices in the
industrialized world enjoy conditions that are adequate to see visual tasks and that do not cause extreme
discomfort to their occupants. However, questions remain as to the possibility that lighting conditions
might be further improved beyond this minimum level, to the point at which they could become positive
contributors to employee performance and well-being (Boyce, 2004). Those concerned with providing
office lighting and furnishings have long sought such evidence to support clients’ design decisions.
Positive affect theory (Isen & Baron, 1991)suggests that working conditions do inflience
employees’ well-being and work behaviors. Positive affect induced by fragrance favorably influenced
social behavior and task performance in two experiments (Baron, 1990; Baron & Thomley, 1994). Baron,
Rea, and Daniels (1992)found modest evidence that variations in fluorescent lamp type and light level
could induce positive affect and influence task performance and prosocial behavior. The latter study
suggested that positive affect might be a mediating mechanism for lighting effects on work behaviors, but
did not provide complete or clear guidance as to the lighting conditions that might be the most powerful
triggers for positive affect.
Other investigations point to light distribution and the availability of individual (personal) control as
desirable characteristics for office lighting. Lighting systems that use both direct and indirect lighting are
preferred over direct-only systems (Boyce et al., 2006; Houser, Tiller, Bernecker, & Mistrick, 2002; Veitch
& Newsham, 2000b). Surveys consistently report a preference for individual control over the work
environment among office workers (Leaman & Bordass, 2001). Boyce, Veitch, Newsham, Jones, and
Heerwagen (2006)found that individually-controllable lighting conditions were rated as more comfortable
by a larger percentage of people than conventional fixed conditions. The availability of individual control
appeared to confer resistance to fatigue, as individuals who had lighting control during the workday did
not show declines in vigilance or persistence over the day, whereas those without control did (Boyce, et
al., 2006).
The beneficial effects of individual environmental control might not be through commonly
understood personal control mechanisms as they relate to stressful situations (Averill, 1973). In the case
of control over the physical environment, positive affect might be the mechanism. Veitch and Newsham
(2000a)reported no simple effects of control over lighting on task performance, mood, or satisfaction, but
in a re-analysis of data from participants without control they found that those whose working conditions
more closely approximated their personal preference showed improved mood and higher ratings of
Lighting Appraisal and Work Page 2
lighting satisfaction and overall environmental satisfaction (Newsham & Veitch, 2001). The data showed
that for any given fixed light level (between 100 lx and 800 lx, the range possible in this experiment), at
most 50% of the sample had a light level preference within 100 lx of that value, meaning that the only
practical means to deliver one’s preferred light level to individuals would be to provide individual control.
Veitch, Newsham, and Boyce (2008)re-analyzed thedata reported by Boyce et al. (2006)to test
a conceptual model built from a series of mediated linked regressions. The conceptual model had two
paths, consisting of sequences of variables that had been measured in temporal sequence over the
working day. The appraisal path was the most strongly supported, with explained variance on the order of
50% in three independent replications. This path led from the appraisal of lighting quality towards
judgments of the attractiveness of the space. This in turn led to an improvement in pleasure,and pleasure
in turn predicted end-of-day physical and visual comfort and environmental satisfaction. There also was a
direct connection between the space attractiveness rating and the end-of-day measures. A second path,
called the vision path,led from visual capabilities directly to typing task performance. Other hypothesized
mediated links through competence or motivation towards task performance turned out to be not
significant (Veitch, et al., 2008).
Newsham et al. (2003; 2004a)collected data concurrently with the data collection of the
experiments by Boyce et al. (2006). There were many similarities in the questionnaires and tasks in the
two projects, although different experimental designs. This paper reports a re-analysis of the Newsham et
al. data, examining a
conceptual model based on
the one reported by Veitch
et al. (2008).
The initial model
(Model 1, Figure 1) tested
here has an appraisal and a
vision path,as in Veitch,et
al. (2008) (although the
exact composition is slightly
different), and an affect
path. The appraisal path
leads from lighting
conditions that either do or
do not meet the individual’s
preferences; if they do,
room appearance will be
more favourably judged and
this in turn will influence pleasure and workplace satisfaction. This path is the one reported by Veitch et
al. (2008). The vision path is based on a century of lighting research;we predicted that visual capabilities
would have a direct effect on simple task performance (Eklund, Boyce, & Simpson, 2000; Rea &
Ouellette, 1991). The affect path represents our prediction, which we based on the positive affect model,
that pleasure would positively predict both simple task performance and work engagement.
In addition to refinements of the model concerning performance measures, this analysis differs
from the original Veitch et al. (2008)model in using structural equation modeling (SEM) instead of linked
mediated regressions. The advantage of SEM over multiple regression analysis is the provision of both
estimates of single pathways and indices of the fit of the entire model (Byrne, 2006; Kline, 1997).
Method
We give a summary of the methodsused for the original data collection. Further details about the
experimental space, setting, participants, procedure and variables can be found in Newsham et al. (2003,
2004a).
Setting and Lighting Conditions
The experiment took place in a dedicated office research laboratory in a government research
institute in Ottawa, Canada. The experimental space was a windowless room 7m x 4.9 m (20’ x 16’) and
2.75 m (9’) high, with a standard suspended ceiling T-bar system. Within this space were two
workstations of approximately 2.7 m x 2.9 m (8’ 9” x 9’ 6”), and a corridor area (Newsham, et al., 2004a).
Figure 1
. Hypothesized model. Thin lines represent the appraisal path, thick lines the
perceptual path, and dotted the vision path.
Lighting Appraisal and Work Page 3
The experiment was a mixed between-within subjects design. Participants experienced one of
four different lighting designs (see Figure 2), set at one of four initial illuminance levels. Lighting design 1
represented a typical office lighting installation of ceiling-recessed parabolic fixtures, and served as a
base case. Design 2 featured a “partition washer” fixture. Further, design 3 featured an angle-arm desk
light, and design 4 was designed to resemble a new, low-energy lighting design through a workstation-
specific dimmable direct/indirect fixture. The initial illuminance conditions of the first three designs were
200, 400, 600 and 800 lx. The fourth design deviated from these values because it was not able to
generate that much light at the desktop, having instead four equally-spaced conditions of 150, 200, 250
and 300 lux (Newsham, et al., 2003). Lighting design and starting illuminance were between-subjects
variables; individual control was a within-subjects variable, with all participants being given control for the
last part of the day, after the midafternoon coffee break.
Figure 2.
Photograph of experimental space
showing the four lighting designs. Design 1 used recessed parabolic louvered 1 ft
x 4 ft two-lamp luminaires in the ceiling. Design 2 used the same overhead luminaires as Design 1 and added a strip of 1-lamp
“partition washer” luminaires to increase the luminance of the panel directly behind the computer monitor. Design 3 used the
same overhead luminaires as Design 1 and added a compact fluorescent angle-arm task light on the side desk. Design 4 lit the
cubicle with one workstation-specific direct-indirect suspended luminaire with three lamps, one providing indirect light by
reflection from the ceiling and two lamps providing direct illumination to the work area; Design 4 also featured the same task
light as Design 3.
Participants
All 118 participants were recruited from a local temporary-employment agency, and had a
minimum age of 18 and maximum of 69, with an average of 28 years. Sixty of them were male, and 58
female. Recruitment and consent procedures were followed, and the participants were paid for their time
Lighting Appraisal and Work Page 4
at the standard rate for a clerical worker. The investigation was reviewed by the institute’s Research
Ethics Board.
Experimental Procedure
The day was divided into four parts, with the first being occupied by the consent procedure and
training. After the training, participants repeated most tests and questionnaires three times. The first two
parts were exactly the same, except for the first part being in the morning (between coffee break and
lunch), and the second part during the early afternoon (immediately after lunch). During the third part of
the day (after the afternoon coffee break), all participants were able to control the lighting, resulting in
widely varying light levels in the last part of the day. The effect of control having been thoroughly
examined by Newsham et al. (2004a), we here examined only data from the second period (immediately
following lunch), being a time without control but after the participants had had time to experience and to
assess the lighting.
Newsham et al. (2003)extensively described the procedure and measurements. Here, we
describe only the aspects involved in the model test.
Lighting appraisal.We used the lighting quality scale developed by Veitch and Newsham (2000a).
This is an average of four items measured on Likert scales, in which higher scores indicate that the
lighting has been appraised as better.
Visual capabilities. These were measured using contrast sensitivity measured in correct
responses per second. This was done by presenting a target grating (a small square area of horizontal or
vertical stripes that varied in width and contrast as well as orientation) on the computer screen. The
participant had to indicate, as fast as he could, whether he saw a target or not,by pressing a ‘Yes’ or ‘No’
key on the keyboard. Both accuracy and speed of detection were measured.
Room appearance. Participants assessed the attractiveness of the room on semantic differentials
presented on the computer with a sliding scale scored from 0 to 100. For the present analysis we used
attractiveness as described in Veitch,et al. (2008), which is the average of 9 items: attractive-
unattractive; somber-cheerful; interesting-monotonous; colorful-colorless; subdued-stimulating; non-
uniform-uniform; pleasant-unpleasant; beautiful-ugly; and like-dislike.
Simple performance. This was assessed by a typing task, measuring speed and accuracy.
Participants had to retype 300 word passages from printed originals. The printed versions were given in
three different print sizes: 8, 12 and 16. Software measured the speed and number of errors made per
print size.
Pleasure. This measure of mood state is the average the scores of 6 semantic differential pairs
on 9-point scales (Russell & Mehrabian, 1977).
Work engagement. Three measures indicated work engagement:complex cognitive appraisal,
motivation, and work structure.
Complex cognitive appraisal was assessed using participants’ ratings of their interest in reading
an article based on a summary of it. Participants had to indicate their interest on the computer with a 0-
100 slider.
Motivation was a behavioral measure, the willingness to persist at an impossible psychomotor
task. This measure is analogous to the paper-based task developed by Feather (1962). The psychomotor
task simulated a conveyor belt, across which symbols were travelling from the left to the right of the
computer screen, through a box called the removal zone. Participants were instructed to remove target
symbols as quickly as possible after they entered the removal zone, by pressing the spacebar. A
spacebar press after a target entered the removal zone was recorded as a ‘hit’. The speed that the
symbols travelled gradually increased, until it was no longer possible to respond other than randomly.
Participants’ persistence was measured by the speed they gave up trying.
Work structure concerned participants’ use of breaks between tasks. Changes in motivation were
thought to be related to break durations, in this case between trials of the conveyor belt task and the
typing task.
Workplace satisfaction. Two measures indicated overall workplace satisfaction: Environmental
satisfaction, using the four-item scale developed by Sundstrom, Town, Rice, Osborn & Brill (1994), was
one measure. The other measure was participants’ opinionsconcerning the effects that the physical
environment had on their self-assessed productivity (Wilson & Hedge, 1987).
Lighting Appraisal and Work Page 5
Results
Results for the effects of lighting design were reported in (Newsham, et al., 2003)and control
effects in (Newsham, et al., 2003, 2004b). The analysis reported here is collapsed across all lighting and
illuminance conditions.
Descriptive Statistics
Before starting the analyses, we excluded five cases that were univariate outliers on one or more
variable (standardized score > 3 or <-3) and two cases with missing data. No multivariate outliers were
detected. Further, we tested all variables for normality with skewness values between -3 and +3 and
kurtosis between -8 and +8 (Kline, 1997). All variables met these criteria. Table 1 shows the descriptive
statistics of all variables used in the analyses and the scale on which they were measured.For composite
scales, Cronbach’s alpha values are given and the number of items on which they were based.Table 2
shows bivariate correlations for all variables, where asterisks mark significant correlations.
Table 1: Descriptive Statistics
Variable MSD Skewness Kurtosis Cronbach’s α # items Scale
Lighting appraisal 2.72 .81 -.77 .20 .88 40 - 4
Room appearance 37.95 13.73 -.06 .14 .89 90 -100
Pleasure 4.36 1.27 .33 -.30 .88 60 - 8
Env. satisfaction 2.63 .80 -.73 .06 .85 40 - 4
Self assessed productivity .59 1.84 -.42 -.34 - 1 -4 -+4
Visual capabilities 1.36 .39 -.43 -.18 -
Simple task performance 2.76 .91 .43 .90 -
Work structure 1.56 .39 .07 .29 -
Complex cognitive appraisal 48.93 15.56 -.76 .29 -
Motivation 6.31 1.68 .19 -1.02 -
Note. N=111.
Table 2: Bivariate Correlations
Variable 123456789
1. Pleasure -
2. Lighting appraisal .11 -
3. Environmental Satisfaction .21* .50* -
4. Work structure .03 .07 .06 -
5. Self-assessed productivity .19* .45* .59* .06 -
6. Visual capabilities -.10 -.04 .00 -.11 .02 -
7. Motivation .23* .06 .12 -.16 .21* .21* -
8. Simple task performance .01 -.04 .05 .01 .09 .41* .19* -
9. Room appearance .39* .42* .47* .12 .48* -.06 .08 -.07 -
10. Complex cognitive appraisal .29* -.09 -.02 .01 .20* .12 .24* .24* .20*
Note. * p<.05.
Structural Equation Modeling
SEM is a collection of statistical techniques that can examine relationships between one or more
independent variables and one or more dependent variables (Tabachnick & Fidell, 2001). Newsham et al.
(Newsham, et al., 2003)had found few effects of lighting design on task performance or satisfaction in the
second part of the day; therefore we did not include lighting design as a specific independent variable in
the model.
We followed procedures outlined by Kline (1997), Byrne (2006), and Tabachnick and Fidell
(2001), using EQS 6.1 for Windows (Bentler & Wu, 2003). We looked at the statistical significance of
individual parameters and several indicators of goodness of fit for the model in general. These are: Chi-
square; Chi-square/degrees of freedom; Goodness of Fit Index (GFI); Adjusted Goodness of Fit Index
(AGFI); Comparative Fit Index (CFI); Bentler-Bonett Normed Fit Index (NFI); Bentler-Bonett Nonnormed
Fit Index (NNFI); Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of
Approximation (RMSEA). Further, the proportion of standardized residuals between -.1 and .1 were
examined. In considering whether to modify the model, we considered the Lagrange Multiplier (LM) test
results, always in the context of the theoretical basis of the proposed change. The LM test indicates
whether model fit would be improved by estimating additional parameters (Tabachnick and Fidell, 2001).
Lighting Appraisal and Work Page 6
Model 1
As explained above, Model 1 was partly derived from the linked mechanisms model of Veitch et
al. (2008). After running this model in EQS,several parameter estimates were not significant,and also
the general fit of the model was not significant. Table 3 summarizes the goodness of fit indicators of the
models. Most of the indices for Model 1 do not indicate a good fit, as seen by comparing the test results
to the column of target values, which are derived from established texts (Byrne, 2006; Kline, 1997;
Tabachnick & Fidell, 2001). Increased pleasure did not lead to an improvement in workplace satisfaction
and simple task performance. However, there was asignificant path leading from lighting preferences to
pleasure, and to workplace satisfaction through room appraisal. Also, the paths pleasure-work
engagement and visual capabilities-simple task performance were significant.
Modified Models
We modified the model in response to these results. First, paths with insignificant parameter
estimates (β-values) were deleted. These were: pleasure workplace satisfaction; pleasure simple task
performance; and work structure as an indicator of work engagement. This led to Model 2, which is
summarized in Table 3.
Table 3: Fit Indices
Goodness of fit Target values Model 1 Model 2 Model 3 Model 4
N111 111 111 111
Χ260.97 54.98 35.45 17.50
Χ
/
df < 3 1.85 2.04 1.36 1.46
GFI > .90 .90 .90 .93 .96
AGFI > .90 .84 .84 .89 .90
CFI > .90 .84 .85 .95 .97
NFI > .90 .73 .75 .84 .90
NNFI > .90 .79 .79 .93 .94
SRMR < .10 .09 .10 .08 .06
RMSEA < .10 .09 .10 .06 .07
% St. Res (-1 to +1) > 90 76.37 76.77 82.22 89.28
Note. Χ2= chi-squared; Χ2/df = chi-squared divided by degrees of freedom; GFI = goodness-of-fit index; AGFI = adjusted goodness-
of-fit index; CFI = Comparative Fit Index; NFI = Bentler-Bonett Normed Fit Index; NNFI = Bentler-Bonett Nonnormed Fit Index;
SRMR = Standardized Root Mean Square Residual; RMSEA = Root Mean Square Error of Approximation; % St. Res (-1 to +1) =
percentage of standardized residuals between -1 and +1.
Model 2’s results showed an improvement in fit, but did not meet most of the target test values.
We next considered the results of the Lagrange Multiplier (LM) test for Model 2. For Model 2, the result
suggested the addition of a direct path between lighting appraisal and workplace satisfaction; this was
logical and also conceptually consistent with other research (Veitch, Charles, Farley, & Newsham, 2007).
Other possible additions suggested by LM test for Model 2 had no theoretical justification, so we did not
include them.Adding the extra path gave a better fit of the data than Model 2 (Model 3, shown in Table
3). For Model 3, only two indicators (AGFI and NFI) did not meet the criteria.
Although there is abundant evidence that visual capabilities should influence simple task
performance (Boyce, 2003), the lack of connection between that path to other parts of the model was
unsatisfactory; in this data set the lighting appraisal path appeared to be completely separate from the
vision path. We ran a fourth variation without these two variables. Model 4, the final model, is pictured in
Figure 3, and the standardized solution is shown in Table 4. As shown in Table 3, almost all fit indices
improved for Model 4 in comparison with the other models. We consider its overall fit to be very good.
Table 4: Standardized Solution and Explained Variance for Model 4
Dependent variable R
V2. Pleasure = .39 * Room appearance + .92 * E2 0.15
V7. Environmental Satisfaction = .78 * Wor kplace satisfaction + .62 * E7 0.61
V8. Self assessed productivity = .75 * Wor kplace satisfaction + .66 * E8 0.57
V12. Motivation = .44 * Work engagement + .90 * E12 0.19
V14. Room appearance = .42* Lighting appraisal + .91 * E14 0.18
V16. Complex cognitive appraisal = .55 * Wor k engagement + .83 * E16 0.31
F1. Workplace satisfaction = .43 * Room appearance + .44 * Lighting appraisal + .68 * D1 0.54
F2. Work engagement = .52 * Pleasure + .86 * D2 0.27
Note. V# = variable number to identify measured variables. E# = error associated with the respective variable number. F#
= factor number to identify latent factors. D# = disturbance associated with the respective factor number.
Lighting Appraisal and Work Page 7
Discussion
The most important conclusion from this investigation is that lighting appraisals predict workplace
satisfaction and work engagement. People who appraise their lighting as good will also appraise the room
as more attractive, be in a more pleasant mood, be more satisfied with the work environment, and more
engaged in their work. Therefore, it is important to take into account what office workers think about their
lighting, as this will lead to an interdependent chain of variables that influence work behaviors. The final
model had excellent model fit and all paths were statistically significant.The environmental satisfaction
variables were very well predicted, with explained variance comparable to that previously reported
(Veitch, et al., 2008).
The final model, Model 4, had some important differences from the original model. Pleasure did
not influence workplace satisfaction, as it had previously (Veitch, et al., 2008). In the present model,
lighting appraisals had a direct effect on workplace satisfaction. Veitch et al. (2008)did not report having
tested this direct effect, so we cannot say whether or not it might have existed in the earlier data. Other
analyses of similar concepts have found that lighting satisfaction predicts overall environmental
satisfaction (Veitch, et al., 2007).
Although in the present analysis, visual capabilities had the predicted effect on simple task
performance (models 1-3), the best-fitting model (Model 4) did not include these variables. This is not a
repudiation of well-known visual phenomena; rather it reflects the separation between the purely visual
aspects of work performance and the role of affective responding to the work environment. As Boyce
(2003)has written, lighting conditions can influence employees through vision and through motivation (or,
as he says, “the message”). The absence of interconnections is explained by the fact that all the lighting
conditions provided adequate levels of illumination to see the visual tasks, none had a direct glare source,
and the simple task performed (typing) was a very well-learned one for these participants.
Veitch et al. (2008)reported an unexpected negative relationship between pleasure and
motivation: People in a more pleasant mood were more likely to give up on the impossible task than to
persist. The present study found, as had been predicted by positive affect theory, that pleasure positively
predicted work engagement and, in turn, motivation. This is a stronger finding than the weak results
reported by Veitch et al. (2008)in one of three samples.
Some of the differences in the models might be explained by differences between the two
projects. The most important of these is the wider variation in lighting conditions to which participants
responded (16 combinations of lighting design and illuminance here, versus 4 and 2 in the data sets
analyzed by Veitch et al. (2008)). This could be expected to result in greater variability in the data and
therefore less risk of restricted range (Kline, 1997). Moreover, the analysis technique used here permitted
simultaneous tests of the paths and returned indices of overall fit, which was not provided by the earlier
work. These are strengths of the present work.
Conversely, this investigation lacked one element that provided strength in the previous model:
Veitch et al. (2008)had a temporal sequence to their data. That is, there was a logical sequence of time
Figure 3
. Model 4 (final), showing
standardized regression weights for all paths. Paths with asterisks were free to vary. All paths
were statistically significant (p<.05). E# = error associated with the respective variable number. D# = disturbance associated with
the respective factor number.
Lighting Appraisal and Work Page 8
in the links between all variables. For example, for the appraisal path, appraisal was measured first early
in the day, then preferences and mood, and eventually health and well-being at the end of the workday.
Although the linked mediated regressions did not provide a causal test, the temporal sequence lends
more causal weight to the original model than is possible here. All the variables in the present model were
measured in the same time block. The model we present here establishes associations, not causal
relationships.
The model does provide strong support for the idea than lighting appraisals affect mood, making
workplace lighting a means to activate the positive affect mechanism. The importance of this to
workplaces has several dimensions, from influence on social behavior (Baron, 1990; Baron & Thomley,
1994)to cognitive performance (Baron, 1990; Baron & Thomley, 1994; Miner & Glomb, 2010). We have
reported a connection between lighting-induced positive affect and work engagement, suggesting that
providing lighting conditions that are appraised as being good will promote desirable work behaviors.
In addition, lighting appraisals predicted workplace satisfaction both directly and through room
appearance. The field counterpart to the workplace satisfaction measure used here has been called
overall environmental satisfaction, and is a predictor of job satisfaction (Veitch, et al., 2007). Job
satisfaction, in turn, is a predictor of organizational commitment and intent to turnover (Carlopio, 1996).
Newsham, et al. (2009)examined an integrativ e model with various work related variables, linking
(among others) lighting conditions to environmental satisfaction then job satisfaction and in turn employee
well-being. Interestingly, they did not find a direct link of environmental satisfaction and job satisfaction,
but it was mediated through satisfaction with management and employment another example of the
importance of the message delivered by the work environment. Harter, Schmidt, and Hayes (2002)
demonstrated the organizational value of improved job satisfaction: improved customer satisfaction and
business unit performance, and reduced turnover.
Lighting is of course but one element of the work environment, but it is an important one in that it
serves sev eral purposes: it illuminates all manner of tasks, it provides for safe movement through space,
and it enables the aesthetic appreciation of the space. Lighting is also the largest single source of
electricity consumption in North American offices, consuming 38% of all site electricity and 20% of all site
energy when surveyed in 2003 (United States Energy Information Administration, 2009), making it a
target for energy-efficiency strategies. Findings such as these underscore the importance of seeking to
maintain or enhance lighting appraisals at a high level, or risk unintended organizational consequences of
indiscriminate attempts to save energy at the expense of other lighting outcomes.
Acknowledgements
This is a re-analysis of data from an experiment sponsored by the National Research Council
Canada (NRC), the Panel on Energy Research & Development, and Public Works & Government
Services Canada (PWGSC) (NRC Project Number B3208). The authors thank Chantal Arsenault, Cara
(Duval) Donnelly, Roger Marchand and Jana Svec (NRC) and Ivan Pasini and Karen Pero (PWGSC) for
their contributions to the original investigation, and Morad Atif and Trevor Nightingale (NRC), for their
support both then and now.
During the preparation of this paper, the second author was supported by Eindhoven University of
Technology as an MSc student in Human-Technology Interaction on a visiting scholar arrangement with
NRC.
Correspondence should be addressed to Jennifer Veitch, NRC Construction, 1200 Montreal
Road, Building M-24, Ottawa, Ontario, K1A 0R6, Canada. E-mail: jennifer.veitch@nrc-cnrc.gc.ca.
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Author Biographical Notes
Jennifer A. Veitch, Ph.D., is an environmental psychologist and a Senior Research Officer at the National Research
Council of Canada, where she leads research into the effects of environmental conditions, particularly lighting, on cognitive
perfor mance, affect, and health. Correspondence should be addressed to Jennifer Veitch, NRC Institute for Research in
Construction, 1200 Montreal Road, Building M-24, Ottawa, Ontario, K1A 0R6, Canada. E-mail: jennifer.veitch@nrc-cnrc.gc.ca.
Mariska G. M. Stokkermans is an MSc student in Human-Technology Interaction at Eindhoven University of Technology
and holds a BSc from Utrecht University in Science and Innovation Management. Her interest in light effects on human well-being
led her to Canada as a visiting scholar at the National Research Council of Canada in 2010.
Guy R. Newsham, Ph.D., is a Senior Research Officer and Lighting Sub-program Manager at the National Research
Council of Canada. His research interests include building design and operating solutions that save energy, promote occupant
satisfaction, and contribute to organizational productivity.
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