Factors Affecting Safety Performance of Construction ... · Factors Affecting Safety Performance of Construction Workers: Safety Climate, Interpersonal Conflicts at Work, and Resilience
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Factors Affecting Safety Performance of Construction Workers: Safety Climate, Interpersonal Conflicts at Work,
and Resilience
by
Yuting Chen
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Graduate Department of Civil Engineering University of Toronto
1. Validate the role of safety climate in affecting safety performance in the Canadian
construction industry
2. Determine the impact of individual resilience on safety performance in the construction
industry
3. Examine the impact of interpersonal conflicts at work on safety performance of construction
workers
4. Determine whether organizational resilience is related to safety performance of construction
workers
The starting point of this research is validating the role of safety climate in affecting safety
performance. However, safety climate explained less than 30% of the variance of safety
outcomes, thus, three additional factors, namely ICW, OR, and IR, were identified and
investigated.
Although ICW was used previously as one dimension of safety climate (McCabe et al. 2008), it
is technically a risk factor affecting job performance (Bruk-Lee and Spector 2006), not a safety
climate factor. Few studies have examined the role of ICW in the safety performance of
construction workers.
OR has been proposed as a new approach for the next generation of safety improvement
(Hollnagel 2015). Its efforts focus on enhancing the organization’s ability to respond, monitor,
anticipate, and learn (Nemeth et al. 2008, Hollnagel 2009). Current resilience studies on safety
have mainly focused on two themes: defining resilience and quantifying resilience. Resilience
measures in most of the existing literature include management commitment, reporting culture,
learning culture, anticipation, awareness, and flexibility (Hollnagel 2015; Woods and Hollnagel
4
2006). Compared with qualitative studies focused on defining resilience measures, relatively few
quantitative studies have been done to quantify OR, providing a gap that needs to be explored.
To the knowledge of the author, only four papers focused on quantitative analysis of resilience
in the industrial sectors. They used three methods: principal component analysis and numerical
taxonomy (Shirali et al. 2013, 2016); fuzzy cognitive mapping (Azadeh et al. 2014a); and data
envelope analysis (Azadeh et al. 2014b). Further, no study has investigated interactions of the
resilience indicators and how they affect individual safety performance, e.g. how top
management affects reporting and learning, and ultimately accidents.
It is believed that IR may facilitate safety focused behaviors (Eid et al. 2012). However, no
empirical studies have examined the impact of IR on safety performance.
Table 1-1 shows the focus of this research and the research time frame for each factor. Safety
climate and interpersonal conflict at work were examined from 2014 to 2016. Organizational
resilience and individual resilience were examined in 2015 and 2016.
Table 1-1. Focus of this research
Previous research (McCabe et al. 2008)
This research
Years 2004-2006 2013-14 2015 2016
No. of surveys
911 444 406 431
Major findings
23-28% variance of safety outcomes explained by safety climate
Focus
Safety climate and interpersonal conflicts at work
Organizational and individual resilience
1.2. Thesis overview
This thesis consists of three chapters, which resulted in three papers for academic journals. The
organization of these three papers is shown in Figure 1-3. The circled numbers 2, 3, and 4
represent Chapter 2, Chapter 3, and Chapter 4.
5
Safety climate
Dimensions?Conflicts at work is not a
safety climate dimension,
but it is a job stressor
Safety outcomes:Physical injuries
Unsafe events
Job stress
Organizational
resilience
④
③
Individual
resilience ②
Management commitment to
safety
Supervisor safety perception
Coworker safety perception...
What about?
②
Figure 1-3. Organization of the research questions
The major research instrument employed is a self-administered survey that is adapted from
previous research (McCabe et al. 2008). As summarized in Table 1-2, from 2013 to 2016, 1281
surveys were collected in Ontario, Canada, among which 62 surveys were collected from 2013,
382 from 2014, 406 from 2015, and 431 from 2016. The surveys collected from 2013 to 2015
were used for a co-authored paper (McCabe et al. 2016). The remaining 837 surveys from 2015
and 2016 were used for this thesis.
The surveys were modified in 2015 and 2016 using questions designed to test IR and OR.
Appendix A shows three versions of the surveys: version 1 is the original survey from (McCabe
et al. 2008); version 2 tests IR and OR; version 3 tests OR. For the original survey, 13 factors
were used:
Conscientiousness
Fatalism
Management commitment to safety
Safety program perception
Supervisor safety perception
Supervisor Leadership
Co-worker safety perception
Safety consciousness (knowledge)
Role overload
Work pressure
Job safety perception
Interpersonal conflict at work
Job involvement
6
For the version 2 survey, questions of conscientiousness and leadership were removed.
Questions of new five factors were introduced, including IR, reporting, learning, awareness and
anticipation. Four more questions for management commitment to safety were added, and three
questions of job involvement were removed. Based on the factor analysis results from 2015, no
clear structure of OR factors was found. Therefore, in 2016 May, OR questions were re-
designed based on questions from the literature.
Chapter 2 and Chapter 3 used version 2 surveys, which included the 837 surveys from 2015
and 2016. Chapter 4 used version 3 surveys, which included the 431 surveys from 2016.
Chapter 2 used six factors: management commitment to safety, supervisor safety perception,
co-worker safety perception, safety consciousness (knowledge), role overload, and work
pressure. Chapter 3 used two factors, IR and ICW, the second of which was split into conflicts
with coworkers (ICWC) and conflicts with supervisors (ICWS). Chapter 4 used management
commitment to safety, supervisor safety perception, co-worker safety perception, reporting,
learning, anticipation, and awareness. Factors including fatalism, safety program perception, job
safety perception, and job involvement were used in the quasi-longitudinal study (McCabe et al.
2016).
Table 1-2. Survey details
Factors Factors in survey versions
2013-14 version 1
2015 version 2
2016 version 3
Used in chapter #
Number of copies 444 406 431
Conscientiousness Removed
Leadership Removed
Fatalism
Safety program perception
Job safety perception
Job involvement modified
Management commitment to safety
modified 2,4
Supervisor safety perception modified 2,4
Co-worker safety perception 2,4
Safety consciousness (knowledge)
2
Role overload 2
Work pressure 2
Interpersonal conflict at work 3
Individual resilience introduced 2,3
Reporting introduced modified 4
7
Factors Factors in survey versions
2013-14 version 1
2015 version 2
2016 version 3
Used in chapter #
Learning introduced modified 4
Anticipation introduced modified 4
Awareness introduced modified 4
Chapter 2
Chapter 3
Chapter 4
1.2.1. Data collection Considerable efforts were made to collect the surveys to cover most of the province. Table 1-3
shows our data collection team members in each summer and the surveyed areas. Thirteen
undergraduate students and eight graduate students including Ph.D. and MSc students were
involved in the data collection. During the data collection process, support from other
universities was provided, including Lakehead University, Queens University, University of
Ottawa, and University of Windsor. Approximately half of the surveys were collected from the
Toronto area, 10 percent from Milton, 8 percent from Ottawa, and the remaining areas
contributed 30 percent. For the summer 2013-2014, the surveyed areas were mainly in GTA
and Thunder Bay. For 2015, the surveyed areas were extended to Guelph, Kingston, Kitchener-
Cambridge-Waterloo, Ottawa, Windsor. In 2016, the GTA was surveyed to test the OR
questions.
Table 1-3. Number of surveys by year and location
2013-2014 2015 2016 Total
Data collection team* 2 G; 3 U/G 5 G ; 3 U/G 1 G ; 7 U/G 8G ; 13 U/G
Aurora 5 5
Brampton 4 9 13
Cambridge 15 15 30
Guelph 14 14
Kingston 45 45
London 17 17
Markham 33 28 61
Milton 115 115
Mississauga 16 33 49
North York 15 5 20
Ottawa 105 105
Richmond Hill 6 6
Scarborough 10 19 29
Thunder Bay 12 37 49
Toronto 260 77 310 647
8
2013-2014 2015 2016 Total
Vaughan 7 15 22
Waterloo 10 21 11 42
Windsor 12 12
Total 444 402 435 1281
* U/G: undergraduate student; G: graduate student
Before data were collected, a minimum sample size was determined by the following equation
(Brase and Brase 2016):
2
2
0.025
2n
MOE
z
Where n is the estimated sample size;
0.025z is the critical value when confidence level is 95%;
is the estimated population standard deviation;
MOE is margin of error.
According to (Bartlett et al. 2001), can be estimated using data from previous studies of the
same or a similar population. Thus, the maximum standard deviation of data (only for questions
using 1-5 Likert scale) from 2004 (McCabe et al. 2008) was used, i.e. 1.36 of the question “I
cannot avoid taking risks in my work”. For ordinal data, 3% margin of error is acceptable, which
would result in the researcher being confident that the true mean of a five point scale is within
±.15 (.03 times five points on the scale) of the mean calculated from the research sample. The
minimum n was determined to be 316. Therefore, 1281 is large enough for Ontario safety
research.
1.2.2. Data analysis
All three chapters used a similar analysis approach, as shown in Figure 1-4. The statistical
analyses were performed using IBM SPSS Statistics and Amos (Windows version 23). First,
data cleaning was done. For chapter 2 and chapter 3, approximately 50 cases were removed
because a high proportion of data were missing (>10%). For chapter 4, 28 cases were removed.
After data cleaning, confirmatory factor analysis was used to test whether the factors used for
each paper were conceptually distinct. Internal-consistency reliability tests were then conducted
to show how well the individual scale statements reflected a common, underlying construct.
Finally, structural equation modeling (SEM) techniques were used to build the models in each
9
paper. The round corner rectangles in Figure 1-4 represent the product and the right angle
rectangles represent the analysis actions. In the following paragraphs, a more detailed
introduction of each method used is given.
Raw
data
Data cleaningMeasurement
scalesMissing value > 10%
Recode injuries
Reverse questions
Models
Confirmatory factor
analysis Internal-consistency reliability test
Structural equation
modeling
Figure 1-4. Data analysis process
Confirmatory factor analysis (CFA) is a method to examine whether previously identified
factor structures work in new data. The analysis accounts for the relationships (i.e. correlations,
covariation, and variation) among the items (i.e. the observed variables in the survey)
(Harrington 2009). It is based on a common factor model, where each observed variable is a
linear function of one or more common factors (i.e. the underlying latent variables) and one
unique factor (i.e. the error). It partitions item variance into two components: (1) common
variance, which is accounted for by underlying latent factors, and (2) unique variance, which is a
combination of indicator-specific reliable variance and random error. For instance, work
pressure was previously measured by two question items (McCabe et al. 2008). For each item,
its variance was split into two parts: common variance accounted for by the latent variable “work
pressure”, and unique variance mainly explained by the error. In my work, work pressure was
confirmed using CFA.
Internal-consistency describes the extent to which all the items in a test measure the same
concept or construct and hence it is connected to the inter-relatedness of the items within the
test (Tavakol and Dennick 2011). Alpha is used to measure the internal-consistency of a factor,
and it is expressed as a number between 0 and 1. If the items of a factor are correlated to each
other, the value of alpha is increased. However, a high coefficient alpha does not always mean
a high degree of internal consistency. This is because alpha is also affected by the length of the
test. If the test length is too short, the value of alpha is reduced. Thus, to increase alpha, more
related items testing the same construct should be added to the test. It is also important to note
that alpha is a property of the scores on a test from a specific sample. Therefore, alpha
estimates should be measured each time the test is administered.
Structural equation modeling (SEM) is a combination of factor analysis and regression or
path analysis (Hox and Bechger 1998). There are two major reasons why SEM were chosen.
First, it is a good approach to testing a potential causal relationship between latent constructs.
10
Second, in contrast to ordinary regression analysis, SEM considers several equations
simultaneously. The same variable may represent an independent variable in one equation and
a dependent variable in another equation. Basic assumptions of SEM are the univariate
normality and multivariate normality of all the variables. Robust maximum likelihood estimation
technique was used to handle the multivariate non-normality (Brown 2015; Byrne 2001a). In
Amos, the robust estimation was achieved by a bootstrapping procedure (10000 bootstrap
samples and 95% confidence intervals). The key idea underlying bootstrapping is that it creates
multiple subsamples from an original data set and the bootstrapping sampling distribution is
rendered free from normality assumptions (Byrne 2001b). In my work, bootstrap samples were
randomly selected 10,000 times and the sample size for each selection is the same as the
number of valid cases after data cleaning. For example, in chapter 4, there were 403 cases after
data cleaning. Thus, for each random selection, 403 random observations were selected from
the original data. Some data points may be selected more than once, while others may be not at
al. For each newly generated data, parameters (e.g. path coefficients) and their associated
standard errors were calculated. Finally, the average of parameter estimates across the
bootstrap samples were calculated.
SEM assesses both the measurement model and the structural model (Gefen et al. 2000). In
the measurement model, loadings of observed items on their expected latent variables
(constructs) are obtained. The structural model describes the causation among a set of
dependent and independent constructs. It is worth noting that the structural model only implies
the causation when the data are longitudinal data. In SEM, latent constructs are represented by
ellipses, and observed variables are represented by rectangles. The item loadings of observed
items are the correlation coefficients (r) and the causation relationships are defined by
standardized regression coefficients (β). Figure 1-5 gives an example of SEM. In the model,
interpersonal conflicts at work (ICWC) is a latent construct defined by three observed questions
in the survey. This part is the measurement model. Item loadings of CC1, CC2, and CC3 on
ICWC are represented by the correlation coefficients r1, r2, and r3. After obtaining ICWC, the
structural model defines the causation relationship between ICWC and unsafe events, where
unsafe events (dependent variable) are predicted by ICWC (independent variable).
11
CC1
ICWCCC2
CC3
Unsafe eventsβ
r1
r2
r3
Figure 1-5. Example of SEM
Mediation is a hypothesized causal chain in which one variable affects a second variable that,
in turn, affects a third variable (Kenny 2016). Mediation analysis is a regression-based
approach, which is conducted by several steps. For example, in Figure 1-6 (a part of the model
in chapter 3), ICWs fully mediates the impact of IR on ICWc. This is because if ICWc is only
predicted by IR (blue dashed line), then the standardized regression coefficient is -0.31; if ICWs
is predicted by IR, then the standardized regression coefficient is -0.29; however, if ICWc is
correlated with IR and ICWs simutenously, then only ICWs is a significnat predictor while IR not.
In addition, if the IR regression coeffecint in step (3) (Figure 1-7) is less than that in step (1) but
still significant, then ICWs partially mediates the impact of IR on ICWc.
Figure 1-6. Example of mediation analysis
Regarding model fit indices of SEM, there is no consensus about which fit indices to use.
Hooper et al. (2008) suggested reporting a variety of indices because different aspects of model
12
fit are reflected. The fit indices used for SEM included an overall fit statistic 2, the relative 2
(i.e. 2 / degrees of freedom), root mean square error of approximate (RMSEA), standardized
root mean square residual (SRMR), comparative fit index (CFI), and the parsimonious normed
fit Index (PNFI).
Although 2 value is very sensitive to sample size, it should be reported along with its degree of
freedom and associated p value (Kline 2005). The relative 2 (i.e. 2 / degrees of freedom)
(Wheaton et al. 1977) can address the sample size limitation, and thus it was used. A
suggested range for this statistic is between 2 (Tabachnick and Fidell 2007) and 5 (Wheaton et
al. 1977). RMSEA is regarded as one of the most informative fit indices (Byrne 2001b;
Diamantopoulos and Siguaw 2000). In a well-fitting model, its value range is suggested to be
from 0 to 0.08 (Browne and Cudeck 1992; Hooper et al. 2008). The maximum acceptable upper
bound of SRMR is 0.08 (Hu and Bentler 1999). CFI values greater than 0.95 have been
suggested (Hooper et al. 2008), but CFI values greater than 0.90 are deemed acceptable.
Higher values of PNFI are better, but there is no agreement about how high PNFI should be.
When comparing two models, differences of 0.06 to 0.09 indicate substantial differences (Ho
2006; Williams and Holahan 1994). The requirement of these indices is summarized in Table
1-4.
Table 1-4. SEM model fit indices
Lower acceptable
bound
Upper acceptable
bound
2
relative 2 0 2~5
RMSEA 0 0.08
SRMR 0 0.08
CFI 0.90 1.0
PNFI differences 0.06 0.09
1.3. Chapter Summaries
This section provides an executive summary of each of the 3 chapters. The hypotheses, the
built SEM models, and the major findings of each model are described.
13
1.3.1. Chapter 2 “Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: a case study of the Ontario construction industry”
Chapter 2 investigated the impact of individual resilience and safety climate on safety
performance of the Ontario construction workers. Six most cited factors in the literature on
construction safety climate including management commitment to safety, supervisor safety
perception, co-worker safety perception, work pressure, role overload, and safety knowledge
were used to define safety climate. Four hypotheses were tested, among which three were
supported as shown by the check marks. A SEM model was built, as shown in Figure 1-7.
H1: safety climate is negatively related to physical safety outcomes
H2: IR is negatively related to physical safety outcomes
H3: IR is negatively related to job stress
H4: Safety outcomes are positively related to job stress.
Safety climate
Management
commitment
to safety
Supervisor
safety
perception
Coworker
safety
perception
Work
pressure
Role overload
Safety
knowledge
Individual
resilience
0.8
50.77
0.51
-0.78
-0.40
0.61
Physical injuries
Unsafe events
Job stress
0..60
-0.11
-0.17
-0.24
0.58
0.39
0.15
Figure 1-7. Safety climate, individual resilience, and safety outcomes
The major findings of this paper are that safety climate affects not only physical safety outcomes
but also job stress, and individual resilience affects job stress of construction workers, especially
post-trauma psychological health. Given these findings, construction organizations need to not
14
only monitor employees’ safety performance but also their psychological well-being. Promoting
a positive safety climate together with developing training programs focusing on improving
employees’ psychological health, especially post-trauma psychological health, can improve
organizations’ safety performance. This chapter has been published in Journal of Safety
Research.
1.3.2. Chapter 3 “The relationship between individual resilience,
interpersonal conflicts at work, safety outcomes of construction workers”
Interpersonal conflicts at work (ICW) mainly has two forms on a construction site: conflicts with
supervisors (ICWS) and conflicts with coworkers (ICWC). Chapter 3 examined the occurrence of
ICWS and ICWC on construction sites, and investigated the relationship among ICWS, ICWC and
physical safety outcomes together with job stress. In addition, possible antecedents of ICWS
and ICWC including workhours, mobility, and individual resilience were examined. Six major
hypotheses were tested, and four of them were supported as shown by the check marks. A
SEM model was built, as shown in Figure 1-8.
H1: ICW is positively associated with physical safety outcomes
H1(a): ICWS is positively associated with physical injuries
H1(b): ICWS is positively associated with unsafe events
reporting (RP), learning (LN), anticipation (AN), and awareness (AW). Eight hypotheses were
proposed and supported, as shown in Table 1-5. Some of the hypotheses were directly
supported as shown by the check marks, and the remaining ones were indirectly supported as
shown by the arrow marks. A SEM model describing all the hypothesized relationships was
shown in Figure 1-9.
16
Table 1-5. Hypotheses and testing results
Hypothesis number
Investigated factors
relation Potential correlated factors
(a) (b)
H1 MC positive LN RP
H2 SS positive LN RP
H3 LN positive AN AW
H4 RP positive AN AW
H5 SS positive AN AW
H6 CS positive AN AW
H7 AN negative Unsafe events -
H8 AW negative Unsafe events -
: direct impact supported : indirect impact supported The major findings of this paper are that safety improvement needs effort from all organizational
levels including management, supervisors, and front line workers. Management commitment to
safety is the key to promoting a good site-level safety culture via the impact on supervisors.
Safety awareness is the final variable that affects not only physical injuries, unsafe events, but
also job stress. In addition to supervisor safety perception, co-worker safety perception was a
critical factor affecting employee’s awareness. Given these findings, construction organizations
can improve employees’ safety awareness by promoting a good reporting and learning culture,
and enhancing the safety perceptions of workers’ supervisors and coworkers. This chapter has
been submitted to Safety Science.
17
Management
commitment to safety
Reporting
Learning
Supervisor safety
perception
Coworker safety
perception
Awareness
Anticipation
Physical
injuries
Unsafe events
Job stress
ns
0.29
0.71 0.68
0.2
40.5
8
0.17
0.41
0.57
0.1
5
0.4
3
-0.16
ns
0.63
0.17
-0.26
Figure 1-9. Resilience model
18
19
20
Chapter 2 Impact of individual resilience and safety climate on safety
performance and psychological stress of construction workers: a case study of the Ontario construction industry
Yuting Chen, Brenda McCabe and Douglas Hyatt
Abstract
Introduction
The construction industry has reached a plateau in terms of safety performance. Safety climate
is regarded as a leading indicator of safety performance; however, relatively little safety climate
research has been done in the Canadian construction industry. Given that safety climate may
be geographically sensitive, it is necessary to examine how the construct of safety climate is
defined and used to improve safety performance in different countries and regions. On the other
hand, more and more attention has been paid to job related stress in the construction industry.
Previous research proposed that individual resilience may be associated with a better safety
performance and may help employees manage stress. Unfortunately, few empirical research
studies have examined this hypothesis. This paper aims to examine the role of safety climate
and individual resilience in safety performance and job stress in the Canadian construction
industry.
Method
The research was based on 837 surveys collected in Ontario between June, 2015 and June,
2016. Structural equation modeling (SEM) techniques were used to explore the impact of
individual resilience and safety climate on physical safety outcomes and on psychological stress
among construction workers.
Results
The results show that safety climate not only affected construction workers’ safety performance
but also indirectly affects their psychological stress. In addition, it was found that individual
21
resilience has a direct negative impact on psychological stress but had no impacts on safety
outcomes.
Conclusions
These findings highlight the roles of both organizational and individual factors in individual
safety performance and in psychological well-being.
Practical applications
Given these findings, construction organizations need to not only monitor employees’ safety
performance, but also to assess their employees’ psychological well-being. Promoting a positive
safety climate together with developing training programs focusing on improving employees’
psychological health – especially post-trauma psychological health - can improve the safety
performance of an organization.
2.1. Introduction
The construction industry plays an important role in Ontario’s economic growth and
employment. Since 2003, the Ontario government invested nearly $3 billion in the residential
sector, which created 60,000 jobs (Ontario 2014). However, safety remains one of the biggest
challenges in construction (Becerik-Gerber and Siddiqui 2014). Over the 10 year period from
2004 to 2013, the construction sector accounted for 26.6% of all workplace traumatic fatalities in
Ontario, the highest percentage of any industry (WSIB 2013). Meanwhile, the fatality rate in the
Ontario construction has shown little improvement since the 1990s, as shown in Figure 2-1.
22
1970 1980 1990 2000 2010
0
10
20
30
40
Construction Safety Act
1973tra
umat
ic fa
talit
y ra
te p
er 1
00,0
00 w
orke
rs
Year
Safety culture was brought to attention
after Chernobyl disaster
1986
Figure 2-1. Traumatic Fatality Rate in Ontario Construction (1965-2013)1,2,3
1: (IHSA 2008)
2: (AWCBC 2013)
3: (Statistics Canada 2015a)
Between 1965 and 1995, there was a steady decrease in the fatality rate. The decrease is due
in part to the enforcement of an increasingly more comprehensive construction safety act that
brought about greater safety awareness. After 1995, however, the industry continued to
experience approximately 5 fatalities per 100,000 construction workers per year. The plateau
phenomenon in safety performance can be observed in other jurisdictions as well, such as New
Zealand (Guo et al. 2016) and Australia (Lingard et al. 2010). Similarly, the rate of safety
improvement in other countries, such as the U.S.A., has been slowing (BLS 2014; Mendeloff
and Staetsky 2014; NIOSH 2001).
In addition to the physical safety outcomes, herein safety outcomes refer to unsafe outcomes
(e.g. eye injuries and pinch), job related stress in the construction industry is also attracting
more and more attention. The relatively dangerous work environment, intense job demand,
group work style, and interpersonal relationships, etc., increase construction workers’ risk for
adverse psychological outcomes (Goldenhar et al. 2003). Stress affects both employees’
performance and their health if they are unable to manage it (Cattell et al. 2016). In a review of
46 papers published between 1989 and 2013 about work related stress in the construction
industry (Leung et al. 2015), the geographical distribution of the studies indicated which areas
23
around the world are leading this emerging field. Half of the work on work related stress was
from Hong Kong (50%), with the remaining research distributed between Europe (22%),
Australia (15%), Africa (11%), and USA (2%). More research on job stress in the North
American construction industry may identify factors that are associated with psychological
stress of workers, and thus may uncover ways to escape the safety plateau.
Safety culture has been shown to improve safety performance. Safety culture is a set of beliefs,
norms, attitudes, roles, and social and technical practices focused on minimizing the exposure
of employees to dangerous conditions (Pidgeon 1991; Turner et al. 1989). It is an abstract
phenomenon and therefore challenging to measure. One indicator of safety culture is safety
climate, which refers to the shared perception of people toward safety in their work environment
(Zohar 1980). Measuring safety climate gives insight into safety culture in its current state (Cox
and Cheyne 2000; Glendon and Stanton 2000). In addition, individual resilience is associated
with higher coping abilities (Wanberg and Banas 2000); thus, it is believed that individual
resilience is associated with lower job stress and better safety performance. The remainder of
Section 1 discusses the dimensions of construction safety climate and the definition of individual
resilience, and proposes four hypotheses.
2.1.1. Safety climate dimensions
Safety climate has been widely recognized as a leading indicator for measuring safety
performance versus lagging indicators such as lost time injury and accident rates (Flin et al.
2000). Although there is no agreement on the dimensions of safety climate, management
commitment to safety is a widely acknowledged organizational level safety climate factor across
industries. For example, perceived management attitudes toward safety was originally proposed
as a leading safety climate factor based on surveys from 20 industrial organizations (Zohar
1980). More recent work used four factors to measure safety climate: management commitment
to safety, return to work policies, post-injury administration, and safety training (Huang et al.
2006). In addition to management commitment to safety in the construction industry research
(Cigularov et al. 2013; Dedobbeleer and Béland 1991; Gillen et al. 2002; Guo et al. 2016; Hon
et al. 2014; Tholén et al. 2013), a set of dimensions have been proposed, mainly including work
pressure focusing on the balance between production and safety (Cigularov et al. 2013;
Glendon and Litherland 2001; Guo et al. 2016), support from supervisors and/or coworkers
(Cigularov et al. 2013; Guo et al. 2016; Kines et al. 2010), and, safety equipment or knowledge
needed to have control over safety (Cigularov et al. 2013; Gillen et al. 2002; Glendon and
Litherland 2001; Guo et al. 2016). It is worth noting that statements of a scale with the same
24
name may be different and the same statement may be put into different factors. For instance,
safety communications may fall under the scale of management commitment to occupational
health and safety (OHS) and employee involvement (Hon et al. 2014), while others may use a
separate scale to measure safety communication (Tholén et al. 2013).
2.1.2. Safety climate and safety outcomes
Safety climate is regarded a leading indicator of safety outcomes and positive evidence has
been identified in the construction industry. For example, it has been found that safety climate
was negatively related to near misses and injuries in the Hong Kong construction industry (Fang
et al. 2006; Hon et al. 2014) and positively related to safety behavior in the Queensland
construction sites (Mohamed 2002). Safety climate was also found to be inversely related to
underreporting of workplace injuries and illness in a northwestern US construction site (Probst
et al. 2008). Moreover, some research found that safety climate may be affected by the country
of a culture (Ali 2006), because a manager’s decision on safety management may be influenced
by his/her cultural norms. From this point of view, safety climate may be geographically
different. Given that relatively little evidence of the safety climate in the Canadian construction
industry was reported in the past decade, there is clear value in assessing the safety climate in
the Canadian construction and exploring its relationship with safety outcomes. This leads to
hypothesis 1:
H1: safety climate is negatively related to safety outcomes
2.1.3. Individual resilience, safety outcomes, and psychological stress
Individual resilience (IR) is “the capacity of individuals to cope successfully in the face of
significant change, adversity, or risk. This capacity changes over time and is enhanced by
protective factors in the individual and environment” (Stewart et al. 1997). It is regarded as one
type of positive psychological capacity for performance improvement (Luthans 2002; Youssef
and Luthans 2007). To extend an individual's physical and psychological resources, IR may
help individuals deal with stressors that are inherent in the work environment but cannot be
changed, e.g., work pressure (Cooper and Cartwright 1997), thus it may improve employees’
performance by reducing counter-productive behaviors and help manage their work related
stress (Avey et al. 2011). Several studies found evidence to support its positive role. For
example, IR was found to be directly related to job satisfaction, work happiness, and
organizational commitment (Youssef and Luthans 2007). It was also found to be indirectly
associated with less work irritation, and weaker intentions to quit given that IR is associated with
25
higher change acceptance (Wanberg and Banas 2000). IR was also reported to be negatively
related to depressive symptoms of frontline correctional officers (Liu et al. 2013). It is further
believed that positive psychological resource capacities may facilitate safety focused behaviors
(Eid et al. 2012). However, the authors were unable to find any empirical studies that have
examined if IR is associated with better safety performance and lower job stress in the
construction industry. This leads to two more hypotheses:
H2: IR is negatively related to safety outcomes
H3: IR is negatively related to psychological stress
2.1.4. Injuries and psychological stress
Serious injuries, exposure to actual or threatened death, and other traumatic experiences may
result in post-traumatic stress disorder (PTSD) (Ontario Centre for Suicide Prevention 2015). A
study of 41 male construction workers in China found that workers exposed to a fatal accident
had significantly higher symptoms of depression, such as insomnia and decreased interest in
work and other activities (Hu et al. 2000). In turn, individuals under high psychological stress
tend to have more incidents; psychological stress has been found to predict accidents rates (Siu
et al. 2004) or safety behaviors (Leung et al. 2016) in the Hong Kong construction industry. This
is a vicious spiral. Finding ways to help employees manage job related stress is important. It is
reasonable to expect that injuries and job stress are positively correlated. This leads to another
hypothesis:
H4: Safety outcomes are positively associated with job stress.
2.2. Methods
2.2.1. Data and procedure
2.2.1.1. Survey instrument
To test the four hypotheses developed, this research used a self-administered questionnaire
adapted from the previous research (McCabe et al. 2008). Minor modifications to the survey
questions were done, such as adding individual resilience questions. The self-administered
questionnaires comprised three sections: demographics, attitude statements, and incident
reporting. The demographics section included questions, such as age and working time with the
current employer. In the attitudinal section, respondents indicated the degree to which they
agree with the statements using a Likert scale between 1 (strongly disagree) and 5 (strongly
26
agree). In the incident reporting part, the respondents were asked how frequently they
experienced incidents on the job in the 3 months previous to the survey. There are three
categories of incidents: physical injuries, unsafe events, and job stress. Physical injuries and
unsafe events are regarded as physical safety outcomes. Job stress describe job related stress.
Physical injuries, such as respiratory injuries, may be associated with certain jobs in the
construction industry. Unsafe events comprise events that respondents may have experienced
without necessarily resulting in an injury, such as “slip/trip/fall on same level”. One example of
job stress is “lost sleep due to work-related worries”.
2.2.1.2. Data collection
A multi-site data collection strategy was employed. In total, 837 surveys were collected from 112
construction sites between July 2015 and July 2016. For each site, at least two research
assistants were on site to distribute surveys to workers. They provided immediate help to
workers if they had a question, which improved the reliability and completeness of the data. No
follow up was undertaken as the questionnaires were strictly anonymous. The number of
surveys collected from each site ranged from 1 to 42, with an average around 8 workers. Each
survey required approximately 4 person-hours of research time, including finding sites,
communicating with corporate employees and site superintendents, transportation to site, and
data collection. This is consistent with findings from 2014 (Chen et al. 2015).
2.2.1.3. Demographics of the respondents
The respondents were from the high-rise residential, low-rise residential, heavy civil,
institutional, and commercial sectors. Among the respondents, 69.3% were from construction
sites in the Greater Toronto Area (GTA) with the remainder from areas outside the GTA but
within the Province of Ontario area, extending from Ottawa to Thunder Bay. Table 2-1 shows
demographic information of the respondents. The mean age of the respondents was 37 years
(SD=12) and 98% were male; 69% of workers were journeymen or apprentices. The
respondents had been employed by their current employers for just over 6 years on average,
but half of them had worked with their employers less than 4 years. Respondents reported
relatively high mobility between projects. The weekly working hours of the respondents were
approximately 44 hours, and 37.8% worked more than 44 hours, which is considered overtime
(Ontario Ministry of Labour 2015). The respondents also reported a very high safety training
percentage (97.7%) and 38.1% reported that they had the experience of being a safety
committee member. Finally, approximately 61% of the respondents were union members.
27
Table 2-1. Demographics of respondents
Demographic factors Response
range Mean or percent
Median
Gender Male / Female 98% male -
Age 16 to 67 37.11 36.00
Years in construction 0.01 to 46 14.30 11.00
Years with the current employer 0.01 to 45 6.30 3.70
Number of construction employers in previous 3 yrs 1 to 100 2.33 1.00
Number of projects worked in previous 3 yrs 1 to 300 9.85 5.00
Average hours worked per week in previous month 9 to 100 44.24 42.00
Did respondent have any job-related safety training Yes or No 97.7% yes -
Was respondent ever a safety committee member Yes or No 38.1% yes -
Was respondent a member of a union Yes or No 60.7% yes -
Job position
Supervisor 31.3% -
Journeyman 50.5% -
Apprentice 18.2% -
Our data were also compared to Statistics Canada Ontario construction workforce data on
gender, age, and employee distribution by company size from 2011 to 2015, as shown in Table
2-2. Age distribution is reasonably similar, while our data had a lower percentage of female
workers and a lower percentage of workers from micro-sized companies. One possible reason
for fewer female respondents is that our data is site focused while the government data may
include administration employees in site offices. It is very challenging to capture the employees
of micro-sized companies as they are typically less motivated to participate in any activities that
distract from their work, including research.
Table 2-2. Data representativeness
Category Our Sample Verification data1,2
2011-2015
Gender distribution
Male 98.0% 88.9%
Female 2.0% 11.1%
Age distribution
15-24 years 14.7% 11.9%
25-54 years 75.8% 71.6%
55 years & over 9.4% 16.5%
Employee distribution by employer size
Micro (1-4 employees) 5.1% 16.6%
Small (5-99 employees) 55.7% 57.4%
28
Medium (100-499 employees) 25.7% 13.8%
Large (500+ employees) 13.5% 12.3% 1:(Statistics Canada 2015b)
2: (Statistics Canada 2015c)
2.2.1.4. Incidents
Incident reporting responses were discrete choices of ‘never’, ‘once’, ‘two to three times’, ‘four to
five times’, and ‘more than 5 times. For each of the incident questions, these were transcribed
as 0, 1, 2, 4, and 5 respectively. As such, incident counts reported herein are conservative.
Then, for each of the three incident categories, namely, physical injuries, job stress, and unsafe
events, the incident counts were summed for each respondent.
Table 2-3 shows the frequency of safety outcomes. In total, 80.6% and 66.7% of the
respondents reported at least one occurrence of physical injuries and unsafe events in the
previous 3 months. This number is not surprising, because the aggregated value of physical
injuries and unsafe events included incidents like cuts that are not severe but have very high
occurrences. Cut or puncture, headache/dizziness, strains or sprains, and persistent fatigue are
the most frequently experienced physical injuries and approximately 50% of the participants
experienced at least one of these four symptoms in the previous 3 months. In terms of unsafe
events, 42% of the respondents experienced overexertion, and approximately 34% experienced
slip/trip/fall on the same level, pinch, and exposure to chemicals at least once in the previous 3
months. With regard to the more severe incidents, such as dislocated or fractured bone and fall
from height, it is very surprising that 30 to 40 respondents experienced these incidents recently.
Table 2-3. Frequency of safety outcomes
Report at least one
occurrence in previous 3 months (%)
Physical injuries 80.6
cut/puncture 53.4
headache/dizziness 52.8
strains/sprains 50.8
persistent fatigue 47.7
skin rash/burn 24.7
eye injury 11.8
respiratory injuries 10.7
temporary loss of hearing 8.9
29
electrical shock 6.7
dislocated/fractured bone 4.3
hernia 4.0
Unsafe events 66.7
overexertion while handling/lifting/carrying 41.9
slip/trip/fall on same level 34.5
pinch 34.3
exposure to chemicals 33.6
struck against something stationary 8.8
struck by falling/flying objects 8.4
fall from height 5.5
contact with moving machinery 3.1
struck by moving vehicle 2.9
trapped by something collapsing/caving/overturning
2.3
Table 2-4 shows the frequency of job stress. In total, more than a half of the respondents
reported at least one occurrence of job stress. Approximately 29% to 37% of the respondents
reported that they were unable to enjoy daily activities, unable to concentrate on work tasks, felt
constantly under strain, and lost sleep because of the work related worries. Relatively fewer
incidents of feeling incapable of making decisions and losing confidence were reported (16%
and 15%, respectively).
Table 2-4. Frequency of job stress
Report at least one
occurrence in previous 3 months (%)
Job stress 55.2
lost sleep due to work-related worries 36.7
felt constantly under strain 30.1
unable to concentrate on work tasks 28.8
unable to enjoy day-to-day activities 28.6
felt incapable of making decisions 16.1
losing confidence in self 15.0
2.2.2. Measures
2.2.2.1. Individual resilience
Six statements were used to measure IR. Three statements were adapted from a self-efficacy
scale (Schwarzer and Jerusalem 1995); an example statement is “I am confident that I could
deal efficiently with unexpected events”. The remaining three statements (Connor and Davidson
30
2003) focus on a person’s tolerance of negative impacts, and positive acceptance of change. An
example statement from this category is “I am able to adapt to changes”. The coefficient alpha
of the scale is 0.84.
2.2.2.2. Safety climate
Management commitment to safety examines the priority that management puts on safety,
especially when it conflicts with production. Six statements were used. Three were adapted from
the previous research (Hayes et al. 1998). An example is “our management provides enough
safety training programs.” Two statements were adapted from (Zohar and Luria 2005); an
example is “our management is strict about safety when we are behind schedule.” The final
statement is “after an accident, our management focuses on how to solve problems and
improve safety rather than pinning blame on specific individuals” (Carthey et al. 2001). The
coefficient alpha of the scale is 0.87.
Supervisor safety perception is the workers’ perception about whether their supervisors commit
to safety. Six statements were used (Hayes et al. 1998). An example statement is: “my
supervisor behaves in a way that displays a commitment to a safe workplace”. The coefficient
alpha of the scale is 0.86.
Coworker safety perception is one’s perceptions about whether their co-workers have good
safety behaviors. Four statements were used (Hayes et al. 1998). One example is: “my
coworker ignores safety rules”. The coefficient alpha of the scale is 0.72.
Role overload examines whether a worker feels that there is more work than can be
accomplished in the time frame available in one’s job. Two statements were adapted to
measure role overload (Barling et al. 2002). One example statement is: “I am so busy on the job
that I cannot take normal breaks.” The coefficient alpha of the scale is 0.62.
Work pressure is one’s perceptions of whether there is excessive pressure to complete work
faster, thereby reducing the amount of time available to plan and carry out work. Two
statements were adapted from (Glendon and Litherland 2001) to measure it. One example
statement is: “there are enough workers to carry out the required work.” These two statements
were reversed to have a consistent direction with the meaning of the factor. The coefficient
alpha of the scale is 0.65.
Safety knowledge is about whether workers know what to do confronted with unexpected
events. Five statements were extracted from the safety consciousness factor (Barling et al.
31
2002). One example statement is “I know what to do if an emergency occurred on my shift.” The
coefficient alpha of the scale is 0.79.
The suggested alpha values range from 0.7 to 0.90 (Tavakol and Dennick 2011). Although the
alpha values of work pressure and role overload are less than 0.7, lower alpha values can be
accepted (Loewenthal 2001).
2.2.3. Data analysis
2.2.3.1. Data screening
Before performing any analysis, some data management was undertaken. Fifty four cases were
removed because of a high proportion of data missing (>10%). Thus, 783 cases were used for
the analysis (672 surveys complete and 111 with on average 5% missing information). Four
statement responses were reversed to have the same perception direction as other statements
in the same scale. For example, “My coworkers ignore safety rules” was reversed to be
directionally consistent with “My coworkers encourage others to be safe”. Finally, missing values
of one variable were replaced with the means of that variable across all subjects.
Regarding the univariate normality of all the variables, none of the observed variables were
significantly skewed or highly kurtotic. The absolute values of the skewness of the variables
were less than or equal to 2 and the kurtosis was less than or equal to 7 (Kim 2013). However,
the original data had multivariate non-normality and outlier issues, hence, variable
transformations using log10 function were attempted based on their distributions (Tabachnick
and Fidell 2007). For example, one variable “I always wear the protective equipment or clothing
required on my job” was transformed using log10 function because it had substantial negative
skewness. Although there was a slight improvement after variable transformations, multivariate
non-normality and outliers still existed. One hundred cases with extreme values were reported
via Mahalanobis distance detection. Thus, data transformations were not considered for the
following analysis. After examination, it is believed that the outliers are the natural variation of
the data. Thus, the cases with extreme values were kept.
2.2.3.2. Analysis procedure
The statistical analyses were performed using IBM SPSS Statistics and Amos (Windows version
23). The first step was to determine whether the proposed six dimensions of safety climate were
conceptually distinct. Considering that the measures used in the present study were adapted
from the research completed ten years ago (McCabe et al. 2008), a set of confirmatory factor
32
analyses were used to assess the adequacy of the previously mentioned scales. Robust
maximum likelihood estimation technique was used to handle the multivariate non-normality
(Brown 2015; Byrne 2001a). In Amos, the robust estimation was achieved by a bootstrapping
procedure (10000 bootstrap samples and 95% confidence intervals). The key idea underlying
bootstrapping is that it creates multiple subsamples from an original data set and the
bootstrapping sampling distribution is rendered free from normality assumptions (Byrne 2001b).
Internal-consistency reliability tests were also conducted to show how well the individual scale
statements reflected a common, underlying construct. Then descriptive statistics and
correlations of the studied variables were analyzed. Finally, structural equation modeling
techniques were used to examine IR and the six hypothesized safety climate factors in relation
to safety outcomes and job stress.
2.2.3.3. Model fit indices
There is no consensus about which indices to use. Hooper et al. (2008) have suggested
reporting different types of indices because different indices reflect different aspects of model fit.
The fit indices used for structural equation modeling included an overall fit statistic 2, the
relative 2 (i.e. 2 / degrees of freedom), root mean square error of approximate (RMSEA),
standardized root mean square residual (SRMR), comparative fit index (CFI), and the
parsimonious normed fit Index (PNFI).
Although 2 value is very sensitive to sample size, it should be reported along with its degree of
freedom and associated p value (Kline 2005). The relative 2 (i.e. 2 / degrees of freedom)
(Wheaton et al. 1977) can address the sample size limitation, and thus it was used. A
suggested range for this statistic is between 2 (Tabachnick and Fidell 2007) and 5 (Wheaton et
al. 1977). RMSEA is regarded as one of the most informative fit indices (Byrne 2001b;
Diamantopoulos and Siguaw 2000). In a well-fitting model, its value range is suggested to be
from 0 to 0.08 (Browne and Cudeck 1992; Hooper et al. 2008). The maximum acceptable upper
bound of SRMR is 0.08 (Hu and Bentler 1999). CFI values greater than 0.95 have been
suggested (Hooper et al. 2008), but CFI values greater than 0.90 are deemed acceptable.
Higher values of PNFI are better, but there is no agreement about how high PNFI should be.
When comparing two models, differences of 0.06 to 0.09 indicate substantial differences (Ho
2006; Williams and Holahan 1994).
33
2.3. Results
2.3.1. Measurement model
A hypothesized six factor model was examined, composed of management commitment to
safety, supervisor safety perception, coworker safety perception, work pressure, role overload,
and safety knowledge. Five selected alternative competing models were also assessed (Table
2-5). These alternative models included a one-factor model, two-factor model, three-factor
model, four-factor model, and five-factor model.
All of the alternative competing models are nested in the proposed six-factor model, so the
hypothesized six-factor model was compared to each of the competing models based on the
Chi-square difference (2 diff) associated with the models. The 2 difference also follows a 2
distribution. For instance, the 2 value of the hypnotized six-factor model is 778.37 with a degree
of freedom is 252 and the 2 value of the alternative model 1 is 2119.35 with a degree of
freedom is 267. The 2 difference between these two models is 1340.98 with a degree of
freedom of 15, which is significant. This suggests that the six-factor model is superior to model
1. The results in Table 2-5 suggest that the hypothesized six-factor model performs better than
all the alternative models. The findings also show that the six scales are conceptually different.
Following these steps, individual resilience in relation to the six proposed factors of safety
climate were further examined. In the final measurement model (a total of seven factors, MC,
SS, CS, WP, RO, SK, and IR), 2 (405) =1124.68, P<0.01. The fit indices have the following
values: 2/ d.f.=2.78, RMSEA=0.048, SRMR=0.06, CFI=0.93, PNFI=0.78. Overall, the fit indices
suggest the final measurement model fits the data well.
Table 2-6 shows the factor loadings and squared multiple correlation (SMC) of each scale
statement. Table B-1 in Appendix B shows the detailed scale questions. All the estimates in
Table 2-6 are significant (p<0.001). The factor loadings are the correlation coefficients, ranging
from 0.42 to 0.82. In Amos, SMC of a statement variable is the proportion of its variance that is
accounted for by its predictors (Arbuckle 2012), which is actually R2. For example, SMC of
statement MC1 is 0.47, i.e. 47% variance of MC1 was explained by the factor “management
commitment to safety”. Lower and upper bound of SMC estimates were also given based on
10000 bootstrap samples with 95% confidence intervals. On the whole, SMCs ranged from 0.18
to 0.68. Accordingly, the adequacy of the measurement model was supported.
34
2.3.2. Inter-correlations among the variables
Table 2-7 displays descriptive statistics and the inter-correlations between the studied variables.
In general, management commitment to safety, supervisor safety perception, coworker safety
perception, safety knowledge, and individual resilience had significantly negative correlations
with physical injuries, unsafe events, and job stress. Work pressure and role overload were
positively related to physical injuries, unsafe events, and job stress. In addition, management
commitment to safety, supervisor safety perception, coworker safety perception, safety
knowledge, and individual resilience positively correlated with each other. Work pressure was
positively related to role overload. Physical injuries, unsafe events, and psychological stress
also positively correlated with each other. Finally, management commitment to safety and
supervisor safety perception had the strongest negative correlations with physical injuries and
unsafe events; and coworker safety perception had the strongest negative correlation with job
stress. Work pressure had the strongest positive correlations with physical injuries, unsafe
events, and psychological stress.
35
Table 2-5. Comparisons of the hypothesized six-factor model of safety climate with selected alternative models
7. Work pressure 2 0.57 0.52 0.65 0.48 -0.34 -0.51
8. Role overload 2 0.70 0.54 0.62 -0.22 -0.32
9. Safety knowledge 5 3.19 0.42 0.79 0.53
10. Individual resilience
6 3.14 0.37 0.84
All the correlations are significant (p<0.01), two tailed; numbers underlined in the diagonal of the matrix are the Cronbach’s alpha of
the scales; physical injuries, unsafe events, and job stress are observed variables, so Cronbach’s alpha is not applicable.
38
2.3.3. Structural model
To examine the impact of safety climate and individual resilience on safety outcomes and job
stress, a structural model was built (model 1 in Figure 2-2). The latent construct of safety
climate was indicated by six dimensions: management commitment to safety, supervisor safety
perception, coworker safety perception, work pressure, role overload, and safety knowledge.
The overall model fit of model 1was assessed by 2 (509) =1459.80, p<0.01. Because 2 tends
to be affected by sample size, it is advisable to use other fit indices. In our model, 2/ d.f.=2.87,
RMSEA=0.049, SRMR=0.07, CFI=0.91, PNFI=0.79. These fit measures all indicate that the
hypothesized model fits the data well. Further, all structural coefficients were significant
(p<0.01).
Safety climate
Management
commitment
to safety
Supervisor
safety
perception
Coworker
safety
perception
Work
pressure
Role overload
Safety
knowledge
Individual
resilience
0.8
5/0
.850.77/0.77
0.51/0.51
-0.78/-0.78
-0.40/-0.40
0.61
/0.6
1
Physical injuries
unsafe events
Job stress
symptoms
0..60/0.61
-0.11/-0.11
-0.17/-0.18
-0.24/-0.32
0.58/0.57
0.39/0.39n.s.
n.s.
0.15/0.15
Figure 2-2. Structural equation model. Model 1: without non-significant coefficients from individual resilience to physical injuries and unsafe events. Model 2 shown by dashed line and by italic numbers: with non-significant coefficients from individual resilience to physical injuries and unsafe events
The model was also compared to a model with the non-significant paths from individual
resilience to physical injuries and unsafe events, i.e. Model 2 in Figure 2-2. The fit indices of
model 2 together with those of model 1 are listed in Table 2-8. The 2 difference of model 1 and
39
model 2 is 4.34 with 2 degrees of freedom, which was not significant. It suggested that the
parsimonious model (i.e. model 1) is the better choice. It is also worth mentioning that other
models, such as a model with direct path from safety climate to job stress, were also compared.
All the findings showed that model 1 is the best-fit model.
awareness, management commitment, reporting, and anticipation.
5.2. Conference paper conclusions
The author also finished two conference papers that are not shown in the thesis:
Chen, Y., McCabe, B., and Hyatt, D. (2016). “Safety and Age: A Longitudinal Study
of Ontario Construction Workers.” Construction Research Congress 2016, American
Society of Civil Engineers, San Juan, Puerto Rico.
Chen, Y., Alderman, E., McCabe, B., and Hyatt, D. (2015). “Data Collection
Framework for Construction Safety.” ICSC15 – The Canadian Society for Civil
Engineering’s 5th International/11th Construction Specialty Conference, Vancouver,
Canada.
The major conclusions of the two conference papers were:
Safety tends to be a sensitive topic associated with liability.
It is challenging to access construction sites and collect surveys. Support of top
management and site management are the key to getting access to construction
sites.
Recruitment time per survey is approximately 4 hours, which is surprisingly high.
For high impact incidents, compared with data collected ten years ago (McCabe et
al. 2008), overexertion, and two struck-by incidents (struck against something fixed
or stationary, and struck by flying/falling object(s)) had a significant decrease, while,
no significant changes of strains or sprains, slipping tripping and fractured bone were
found.
Although overexertion decreased, it still has large frequency. There were 37% of the
workers reporting at least 1 incident in the previous 3 months before the survey time
based on data collected from 2013 to 2014, which is still a huge percentage.
87
5.3. Contributions
This thesis resulted in several original contributions:
This study designed and tested questions of individual resilience.
This study is the first empirical study that investigated the impact of individual
resilience on safety outcomes.
This study is the first study testing the antecedents of interpersonal conflicts at work
and the resulting safety outcomes in the construction industry.
This study designed and tested organizational resilience questions in the context of
construction industry.
This is the first study testing the mechanism about how the resilience factors interact
with each other and eventually affect safety outcomes.
This study is the first study using SEM to quantify organizational resilience.
5.4. Recommendations
Given these findings, the following recommendations were provided. First, construction
organizations need to not only monitor employees’ safety performance but also their
psychological well-being. Promoting a positive safety climate together with developing
training programs focusing on improving employees’ psychological health, especially
post-trauma psychological health, can improve safety performance of organizations.
Second, safety professionals may consider adding coping skill training programs to
improve the individual resilience of their workforce and reduce conflict-related safety
outcomes. Finally, construction organizations can improve employees’ safety awareness
by promoting a good team-level safety culture, and by building a good reporting and
learning culture.
5.5. Future work
Based on the results of this research, the following recommendations for future work are
outlined. First, given that all the three SEM models in the research were built based on
the survey data, future work needs to incorporate safety experts’ opinions to interpret
and justify the models in practice. Second, future work can focus on benchmarking
safety climate and safety performance at the site level using data envelopment analysis
(DEA). DEA is a powerful benchmarking technique. It identifies the best practice units
after comparing all service units considering all resources used and services provided
88
(Sherman and Zhu 2006). Thus, it is possible to improve inefficient units. For 134
participated construction sites in the research, the best practice site with regards to
safety performance and safety climate can be identified using DEA and safety
performance of inefficient sites can be improved accordingly. Third, from a probability
perspective, belief network (BN) model can be built in future to identify the key factors
leading to safety outcomes. BN is a directed acyclic graph (DAG) which encodes the
causal relationships between particular variables, represented in the DAG as nodes
(Cheng and Greiner 2001) . BN can learn the structure of the model automatically based
on the data, incorporate the prior knowledge of the experts, and give occurrence
possibilities. Thus, it is a good technique for research in future.
89
References Ali, T. H. (2006). “Influence of National Culture on Construction Safety Climate in
Pakistan.” Griffith University.
Andrew Baum. (1990). “Stress, Intrusive Imagery, and Chronic Distress.” Health Psychology, 9(6), 653–675.
Arbuckle, J. L. (2012). “IBM® SPSS® AmosTM 21 User’s Guide.” IBM Corporation, <ftp://public.dhe.ibm.com/software/analytics/spss/documentation/amos/21.0/en/Manuals/IBM_SPSS_Amos_Users_Guide.pdf> (May 1, 2016).
Avey, J. B., Reichard, R. J., Luthans, F., and Mhatre, K. H. (2011). “Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance.” Human Resource Development Quarterly, 22(2), 127–152.
Ayoko, O. B., Callan, V. J., and Härtel, C. E. J. (2003). “Workplace conflict, bullying, and counterproductive behaviors.” The International Journal of Organizational Analysis, 11(4), 283–301.
Azadeh, A., Salehi, V., Arvan, M., and Dolatkhah, M. (2014a). “Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: A petrochemical plant.” Safety Science, Elsevier Ltd, 68, 99–107.
Azadeh, A., Salehi, V., Ashjari, B., and Saberi, M. (2014b). “Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant.” Process Safety and Environmental Protection, 92(3), 231–241.
Azen, R., and Budescu, D. V. (2006). “Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis.” Journal of Educational and Behavioral Statistics, SAGE Publications, 31(2), 157–180.
Baldoni, J. (2009). “Help Your Team Build Resilience.” Harvard Business Review.
Barling, J., Loughlin, C., and Kelloway, E. K. (2002). “Development and test of a model linking safety-specific transformational leadership and occupational safety.” Journal of Applied Psychology, 87(3), 488–496.
Bartlett, J. E., Kotrlik, J. W., and Higgins, C. C. (2001). “Organizational Research: Determining Appropriate Sample Size in Survey Research.” Information Technology, Learning, and Performance Journal, 19(1), 43–50.
Becerik-Gerber, B., and Siddiqui, M. (2014). “Civil Engineering Grand Challenges: Opportunities for Data Sensing, Information Analysis, and Knowledge Discovery.” Journal of Computing in Civil Engineering, 28(4), 1–13.
Bergstrom, J., van Winsen, R., and Henriqson, E. (2015). “On the rationale of resilience in the domain of safety: A literature review.” Reliability Engineering and System Safety, Elsevier, 141, 131–141.
Bosher, L. (2011). “Disaster risk reduction and ‘built‐in’ resilience: towards overarching principles for construction practice.” Disasters, 35(1), 1–18.
Bosher, L., Dainty, A., Carrillo, P., and Glass, J. (2007). “Built‐in resilience to disasters: a
90
pre‐emptive approach.” Engineering, Construction and Architectural Management, Emerald Group Publishing Limited, 14(5), 434–446.
Brase, C. H., and Brase, C. P. (2016). Understandable Statistics: Concepts and Methods. Cengage Learning, Boston, MA.
Brockman, J. L. (2014). “Interpersonal Conflict in Construction: Cost, Cause, and Consequence.” Journal of Construction Engineering and Management, American Society of Civil Engineers, 140(2), 4013050.
Broomell, S., Lorenz, F., and Helwig, N. E. (2010). “Dominance function in Matlab.” <http://www.andrew.cmu.edu/user/sbb59/code.html> (Oct. 1, 2016).
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research. The Guilford Press, New York.
Browne, M. W., and Cudeck, R. (1992). “Alternative Ways of Assesing Model Fit.” Sociological Methods & Research, (K. A. Bollen and J. S. Long, eds.), SAGE Publications, 21(2), 230–258.
Bruk-Lee, V., and Spector, P. E. (2006). “The Social Stressors-Counterproductive Work Behaviors Link: Are Conflicts With Supervisors and Coworkers the Same?” Journal of Occupational Health Psychology, 11(2), 145–156.
Bruyelle, J.-L., O’Neill, C., El-Koursi, E.-M., Hamelin, F., Sartori, N., and Khoudour, L. (2014). “Improving the resilience of metro vehicle and passengers for an effective emergency response to terrorist attacks.” Safety Science, 62, 37–45.
Budescu, D. V., and V., D. (1993). “Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression.” Psychological Bulletin, American Psychological Association, 114(3), 542–551.
Bureau of Labor Statistics (BLS). (2014). “Number, percent, and rate of fatal occupational injuries by selected worker characteristics, industry, and occupation, 1996-2014.” <http://www.bls.gov/iif/> (Oct. 10, 2016).
Byrne, B. M. (2001a). “Structural Equation Modeling With AMOS, EQS, and LISREL: Comparative Approaches to Testing for the Factorial Validity of a Measuring Instrument.” International Journal of Testing, 1(1), 55–86.
Byrne, B. M. (2001b). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associations, Inc., Mahwah, NJ.
Carmeli, A., Friedman, Y., and Tishler, A. (2013). “Cultivating a resilient top management team: The importance of relational connections and strategic decision comprehensiveness.” Safety Science, 51(1), 148–159.
Carthey, J., de Leval, M. R., and Reason, J. T. (2001). “Institutional resilience in healthcare systems.” Quality in health care : QHC, 10(1), 29–32.
Cattell, K., Bowen, P., and Edwards, P. (2016). “Stress among South African construction professionals: a job demand-control-support survey.” Construction Management and Economics, Routledge, 34(10), 700–723.
Chen, Y., Alderman, E., and McCabe, B. (2015). “Data Collection Framework for
91
Construction Safety.” ICSC15 – The Canadian Society for Civil Engineering’s 5th International/11th Construction Specialty Conference, Vancouver, Canada.
Cheng, J., and Greiner, R. (2001). “Learning Bayesian Belief Network Classifiers: Algorithms and System.” Conference of the Canadian Society for Computational Studies of Intelligence, 141–151.
Cigularov, K. P., Lancaster, P. G., Chen, P. Y., Gittleman, J., and Haile, E. (2013). “Measurement equivalence of a safety climate measure among Hispanic and White Non-Hispanic construction workers.” Safety Science, 54, 58–68.
Clarke, S. (2010). “An integrative model of safety climate: Linking psychological climate and work attitudes to individual safety outcomes using meta-analysis.” Journal of Occupational and Organizational Psychology, Blackwell Publishing Ltd, 83(3), 553–578.
Connor, K. M., and Davidson, J. R. T. (2003). “Development of a new Resilience scale: The Connor-Davidson Resilience scale (CD-RISC).” Depression and Anxiety, 18(2), 76–82.
Cooper, C. L., and Cartwright, S. (1997). “An intervention strategy for workplace stress.” Journal of Psychosomatic Research, 43(1), 7–16.
Costella, M. F., Saurin, T. A., and de Macedo Guimaraes, L. B. (2009). “A method for assessing health and safety management systems from the resilience engineering perspective.” Safety Science, 47(8), 1056–1067.
Cox, S. J., and Cheyne, A. J. T. (2000). “Assessing safety culture in offshore environments.” Safety Science, 34(1–3), 111–129.
Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., and Webb, J. (2008). “A place-based model for understanding community resilience to natural disasters.” Global Environmental Change, 18(4), 598–606.
Dedobbeleer, N., and Béland, F. (1991). “A safety climate measure for construction sites.” Journal of Safety Research, 22(2), 97–103.
Demerouti, E., Bakker, A. B., Nachreiner, F., and Schaufeli, W. B. (2001). “The Job Demands–Resources Model of Burnout.” Journal of Applied Psychology, 86(3), 499–512.
Diamantopoulos, A., and Siguaw, J. A. (2000). Introducing LISREL. SAGE Publications, London.
Dong, X. (2005). “Long workhours, work scheduling and work-related injuries among construction workers in the United States.” Scandinavian Journal of Work, Environment & Health, 31(5), 329–335.
Eid, J., Mearns, K., Larsson, G., Laberg, J. C., and Johnsen, B. H. (2012). “Leadership, psychological capital and safety research: Conceptual issues and future research questions.” Safety Science, 50(1), 55–61.
Endsley, M. (1988). “Design and evaluation for situational awareness enhancement.” Proceedings of the Human Factors Society 32nd Annual Meeting, Santa Monica.
Fang, D., Chen, Y., and Wong, L. (2006). “Safety Climate in Construction Industry: A
92
Case Study in Hong Kong.” Journal of Construction Engineering and Management, 132(6), 573–584.
Ferreira, P. N. P. (2011). “Resilience in the planning of rail engineering work.” University of Nottingham.
Flin, R., Mearns, K., O’Connor, P., and Bryden, R. (2000). “Measuring safety climate: Identifying the common features.” Safety Science, 34(1–3), 177–192.
Gefen, D., Straub, D. W., and Boudreau, M.-C. (2000). “Structural equation modeling and regression: guidelines for research practice.” Communications of AIS, 4, 1–77.
Gillen, M., Baltz, D., Gassel, M., Kirsch, L., and Vaccaro, D. (2002). “Perceived safety climate, job demands, and coworker support among union and nonunion injured construction workers.” Journal of Safety Research, 33(1), 33–51.
Glendon, A. I., and Litherland, D. K. (2001). “Safety climate factors, group differences and safety behaviour in road construction.” Safety Science, 39(3), 157–188.
Glendon, A. I., and Stanton, N. A. (2000). “Perspectives on safety culture.” Safety Science, 34(1–3), 193–214.
Goldenhar, L. M., Williams, L. J., and Swanson, N. G. (2003). “Modelling relationships between job stressors and injury and near-miss outcomes for construction labourers.” Work & Stress, Taylor & Francis Group, 17(3), 218–240.
Guo, B., Yiu, T., and González, V. (2016). “Predicting safety behavior in the construction industry: Development and test of an integrative model.” Safety Science, 84, 1–11.
Hair, J. F., Anderson, R., Tahthan, R., and Black, W. (1995). Multivariate data analysis with readings. Macmillan Pub., New York.
Han, S., Lee, S., and Pena-Mora, F. (2010a). “Framework for a resilience safety management system: a simulation and visualization approach.” Proceedings of the International Conference on Computing in Civil and Building Engineering, Nottingham, UK.
Han, S., Lee, S., and Peña-Mora, F. (2010b). “System Dynamics Modeling of a Safety Culture Based on Resilience Engineering.” Construction Research Congress 2010, American Society of Civil Engineers, Reston, VA, 389–397.
Härmä, M. (2006). “Workhours in relation to work stress, recovery and health.” candinavian Journal of Work, Environment & Health, 32(6), 502–514.
Harrington, D. (2009). Confirmatory Factor Analysis. Oxford Scholarship Online.
Hauge, L. J., Skogstad, A., and Einarsen, S. (2009). “Individual and situational predictors of workplace bullying: Why do perpetrators engage in the bullying of others?” Work & Stress, Taylor & Francis Group, 23(4), 349–358.
Hayes, B. E., Perander, J., Smecko, T., and Trask, J. (1998). “Measuring Perceptions of Workplace Safety: Development and Validation of the Work Safety Scale.” Journal of Safety Research, 29(3), 145–161.
Hemingway, M. A., and Smith, C. S. (1999). “Organizational climate and occupational stressors as predictors of withdrawal behaviours and injuries in nurses.” Journal of Occupational and Organizational Psychology, Blackwell Publishing Ltd, 72(3), 285–
93
299.
Hirota, M., Holmgren, M., Van Nes, E. H., and Scheffer, M. (2011). “Global Resilience of Tropical Forest and Savanna to Critical Transitions.” Science, 334(6053), 232–235.
Ho, R. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. CRC Press, New York.
Hofmann, D. A., and Morgeson, F. P. (1999). “Safety-related behavior as a social exchange: The role of perceived organizational support and leader–member exchange.” Journal of Applied Psychology, 84(2), 286–296.
Hollnagel, E. (2015). Safety-I and Safety-II, the past and future of safety management. Ashgate.
Hon, C. K. H., Chan, A. P. C., and Yam, M. C. H. (2014). “Relationships between safety climate and safety performance of building repair, maintenance, minor alteration, and addition (RMAA) works.” Safety Science, 65, 10–19.
Hooper, D., Couglan, J., and Mullen, M. R. (2008). “Structural equation modelling: guidelines for determining model fit.” Electronic Journal of Business Research Methods, 6(1), 53–60.
Hox, J. J., and Bechger, T. M. (1998). “An introduction to structural equation modeling.” Family Science Review, 11, 354–373.
Hu, B. S., Liang, Y. X., Hu, X. Y., Long, Y. F., and Ge, L. N. (2000). “Posttraumatic Stress Disorder in Co-workers following Exposure to a Fatal Construction Accident in China.” International Journal of Occupational and Environmental Health, Taylor & Francis, 6(3), 203–207.
Hu, L., and Bentler, P. M. (1999). “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.” Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Huang, Y.-H., Chen, P. Y., Krauss, A. D., and Rogers, D. A. (2004). “Quality of the Execution of Corporate Safety Policies and Employee Safety Outcomes: Assessing the Moderating Role of Supervisor Safety Support and the Mediating Role of Employee Safety Control.” Journal of Business and Psychology, Kluwer Academic Publishers-Plenum Publishers, 18(4), 483–506.
Huang, Y.-H., Ho, M., Smith, G. S., and Chen, P. Y. (2006). “Safety climate and self-reported injury: Assessing the mediating role of employee safety control.” Accident Analysis & Prevention, 38(3), 425–433.
Jaselskis, E. J., Anderson, S. D., and Russell, J. S. (1996). “Strategies for Achieving Excellence in Construction Safety Performance.” Journal of Construction Engineering and Management, 122(1), 61–70.
Johnsen, S. O., and Veen, M. (2013). “Risk assessment and resilience of critical communication infrastructure in railways.” Cognition, Technology and Work, 15(1), 95–107.
Kaber, D. B., and Endsley, M. R. (1998). “Team situation awareness for process control safety and performance.” Process Safety Progress, American Institute of Chemical Engineers, 17(1), 43–48.
94
Kenny, D. A. (2016). “Mediation.” <http://davidakenny.net/cm/mediate.htm>.
Kim, H.-Y. (2013). “Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis.” Restorative dentistry & endodontics, Korean Academy of Conservative Dentistry, 38(1), 52–4.
Kines, P., Andersen, L. P. S., Spangenberg, S., Mikkelsen, K. L., Dyreborg, J., and Zohar, D. (2010). “Improving construction site safety through leader-based verbal safety communication.” Journal of Safety Research, 41(5), 399–406.
Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. The Guilford Press, New York.
Lazarus, R. S. (1966). “Psychological stress and the coping process.” McGraw-Hill, New York.
Leung, M.-Y., Liang, Q., and Olomolaiye, P. (2016). “Impact of Job Stressors and Stress on the Safety Behavior and Accidents of Construction Workers.” Journal of Management in Engineering, American Society of Civil Engineers, 32(1), 1943–5479.
Leung, M., Chan, I. Y. S., and Cooper, C. L. (2015). Stress management in the construction industry. John Wiley & Sons, Ltd., Chichester, West Sussex, UK.
Li, F., Jiang, L., Yao, X., and Li, Y. (2013). “Job demands, job resources and safety outcomes: The roles of emotional exhaustion and safety compliance.” Accident Analysis & Prevention, 51, 243–251.
Lingard, H. C., Cooke, T., and Blismas, N. (2010). “Safety climate in conditions of
construction subcontracting: a multi‐level analysis.” Construction Management and Economics, Routledge , 28(8), 813–825.
Liu, L., Hu, S., Wang, L., Sui, G., and Ma, L. (2013). “Positive resources for combating depressive symptoms among Chinese male correctional officers: perceived organizational support and psychological capital.” BMC Psychiatry, BioMed Central, 13(1), 89.
Loewenthal, K. M. (2001). An Introduction to Psychological Tests and Scales. Psychology Press, Hove, UK.
Luthans, F. (2002). “The need for and meaning of positive organizational behavior.” Journal of Organizational Behavior, John Wiley & Sons, Ltd., 23(6), 695–706.
MacCallum, R. C., Widaman, K. F., Zhang, S., and Hong, S. (1999). “Sample size in factor analysis.” Psychological Methods, American Psychological Association, 4(1), 84–99.
McCabe, B., Loughlin, C., Munteanu, R., Tucker, S., and Lam, A. (2008). “Individual safety and health outcomes in the construction industry.” Canadian Journal of Civil Engineering, 35(12), 1455–1467.
McCabe, B. Y., Alderman, E., Chen, Y., Hyatt, D. E., and Shahi, A. (2016). “Safety Performance in the Construction Industry: Quasi-Longitudinal Study.” Journal of Construction Engineering and Management, 4016113.
Meier, L. L., Semmer, N. K., and Gross, S. (2014). “The effect of conflict at work on well-
95
being: Depressive symptoms as a vulnerability factor.” Work & Stress, 28(1), 31–48.
Mendeloff, J., and Staetsky, L. (2014). “Occupational fatality risks in the United States and the United Kingdom.” American journal of industrial medicine, 57(1), 4–14.
Mitropoulos, P., Cupido, G., and Namboodiri, M. (2009). “Cognitive Approach to Construction Safety: Task Demand-Capability Model.” Journal of Construction Engineering and Management, 135(9), 881–889.
Mitropoulos, P. T., and Cupido, G. (2009). “The role of production and teamwork practices in construction safety: A cognitive model and an empirical case study.” Journal of Safety Research, 40(4), 265–275.
Mohamed, S. (2002). “Safety Climate in Construction Site Environments.” Journal of Construction Engineering and Management, 128(5), 375–384.
Mullen, J. E., and Kelloway, E. K. (2009). “Safety leadership: A longitudinal study of the effects of transformational leadership on safety outcomes.” Journal of Occupational and Organizational Psychology, Blackwell Publishing Ltd, 82(2), 253–272.
Nahrgang, J. D., Morgeson, F. P., and Hofmann, D. A. (2011). “Safety at work: A meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.” Journal of Applied Psychology, 96(1), 71–94.
National Institute for Occupational Safety and Health (NIOSH). (2001). “Fatal Injuries to Civilian Workers in the United States, 1980-1995.” <http://www.cdc.gov/niosh/docs/2001-129/pdfs/2001-129.pdf> (Oct. 10, 2016).
Neuman, J. H., and Baron, R. A. (1998). “Workplace Violence and Workplace Aggression: Evidence Concerning Specific Forms, Potential Causes, and Preferred Targets.” Journal of Management, SAGE Publications, 24(3), 391–419.
Nixon, A. E., Mazzola, J. J., Bauer, J., Krueger, J. R., and Spector, P. E. (2011). “Can work make you sick? A meta-analysis of the relationships between job stressors and physical injuries.” Work & Stress, Taylor & Francis Group, 25(1), 1–22.
Ontario. (2014). “Progress Report : jobs and economy.” <https://www.ontario.ca/government/progress-report-2014-jobs-and-economy> (Dec. 12, 2015).
Ontario Centre for Suicide Prevention. (2015). “First Responders & Trauma Intervention and Suicide Prevention.” <https://www.suicideinfo.ca/wp-content/uploads/2015/05/First-Responders-Toolkit-WEB.pdf> (Oct. 10, 2016).
Ontario Infrastructure Health & Safety Association (IHSA). (2008). “Fatalities, injuries, and disease.” <http://ihsa.ca/pdfs/research_docs/Injury_Statistics_2008.pdf>.
Ontario Workplace Safety and Insurance Board (WSIB). (2013). “By the Numbers : 2012 WSIB Statistical Report Table of Contents.” <http://www.wsibstatistics.ca/wp-content/uploads/2015/05/WSIB_BTN_SCHED1.pdf> (Mar. 20, 2003).
96
Ostrom, L., Wilhelmsen, C., and Kaplan, B. (1993). “Assessing safety culture.” Nuclear Safety, 34(2), 163–172.
Patterson, E. S., Woods, D. D., Cook, R. I., and Render, M. L. (2007). “Collaborative cross-checking to enhance resilience.” Cognition, Technology and Work, 9(3), 155–162.
Penney, L. M., and Spector, P. E. (2005). “Job stress, incivility, and counterproductive work behavior (CWB): the moderating role of negative affectivity.” Journal of Organizational Behavior, John Wiley & Sons, Ltd., 26(7), 777–796.
Pidgeon, N. F. (1991). “Safety Culture and Risk Management in Organizations.” Journal of Cross-Cultural Psychology, 22(1), 129–140.
Probst, T. M., and Brubaker, T. L. (2001). “The Effects of Job Insecurity on Employee Safety Outcomes: Cross-Sectional and Longitudinal Explorations.” Journal of Occupational Health Psychology, 6(2).
Probst, T. M., Brubaker, T. L., and Barsotti, A. (2008). “Organizational injury rate underreporting: The moderating effect of organizational safety climate.” Journal of Applied Psychology, American Psychological Association, 93(5), 1147–1154.
De Raeve, L., Jansen, N. W. H., van den Brandt, P. A., Vasse, R., and Kant, I. J. (2009). “Interpersonal conflicts at work as a predictor of self-reported health outcomes and occupational mobility.” Occupational Environmental Medicine, 66, 16–22.
De Raeve, L., Jansen, N. W. H., van den Brandt, P. A., Vasse, R. M., and Kant, I. J. (2008). “Risk factors for interpersonal conflicts at work.” Scandinavian Journal of Work, Environment & Health, 34(2), 96–106.
Ross, A. J., Anderson, J. E., Kodate, N., Thompson, K., Cox, A., and Malik, R. (2014). “Inpatient diabetes care: complexity, resilience and quality of care.” Cognition, Technology & Work, 16(1), 91–102.
Sackett, P. R. (2002). “The Structure of Counterproductive Work Behaviors: Dimensionality and Relationships with Facets of Job Performance.” International Journal of Selection and Assessment, Blackwell Publishers Ltd, 10(1&2), 5–11.
Salin, D. (2003). “Ways of Explaining Workplace Bullying: A Review of Enabling, Motivating and Precipitating Structures and Processes in the Work Environment.” Human Relations, SAGE Publications, 56(10), 1213–1232.
Saurin, T. A., Formoso, C. T., and Cambraia, F. B. (2008). “An analysis of construction safety best practices from a cognitive systems engineering perspective.” Safety Science, 46(8), 1169–1183.
Sawacha, E., Naoum, S., and Fong, D. (1999). “Factors affecting safety performance on construction sites.” International Journal of Project Management, 17(5), 309–315.
Schaufeli, W. B., and Taris, T. W. (2014). “A Critical Review of the Job Demands-Resources Model: Implications for Improving Work and Health.” Bridging
Occupational, Organizational and Public Health: A Transdisciplinary Approach, G.
F. Bauer and O. Hämmig, eds., Springer Netherlands, Dordrecht, 43–68.
Schwarzer, R., and Jerusalem, M. (1995). “Generalized Self-Efficacy Scale.” Measures in health psychology: A user’s portfolio, J. Weinman, S. Wright, and M. Johnston,
97
eds., NFER-NELSON, Windsor, UK, 35–37.
Sherman, H. D., and Zhu, J. (2006). Service Productivity Management: Improving Service Performance using Data Envelope Analysis (DEA). Springer, New York.
Shirali, G. A., Mohammadfam, I., and Ebrahimipour, V. (2013). “A new method for quantitative assessment of resilience engineering by PCA and NT approach: A case study in a process industry.” Reliability Engineering and System Safety, 119, 88–94.
Shirali, G., Mohammadfam, I., Motamedzade, M., Ebrahimipour, V., and Moghimbeigi, A. (2012). “Assessing resilience engineering based on safety culture and managerial factors.” Process Safety Progress, 31(1), 17–18.
Shirali, G., Shekari, M., and Angali, K. (2016). “Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant.” Journal of Loss Prevention in the Process, 40, 277–284.
Siu, O., Phillips, D. R., and Leung, T. (2004). “Safety climate and safety performance among construction workers in Hong Kong. The role of psychological strains as mediators.” Accident; analysis and prevention, 36(3), 359–66.
Sneddon, A., Mearns, K., and Flin, R. (2006). “Situation awareness and safety in offshore drill crews.” Cognition, Technology & Work, Springer-Verlag, 8(4), 255–267.
Sneddon, A., Mearns, K., and Flin, R. (2013). “Stress, fatigue, situation awareness and safety in offshore drilling crews.” Safety Science, 56, 80–88.
Spector, P. E., and Fox, S. (2005). “The Stressor-Emotion Model of Counterproductive Work Behavior.” Counterproductive work behavior: Investigations of actors and targets., S. Fox and P. E. Spector, eds., American Psychological Association, Washington, DC, 151–174.
Spector, P. E., Fox, S., Penney, L. M., Bruursema, K., Goh, A., and Kessler, S. (2006). “The dimensionality of counterproductivity: Are all counterproductive behaviors created equal?” Journal of Vocational Behavior, 68(3), 446–460.
Spector, P. E., and Jex, S. M. (1998). “Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inventory, and Physical injuries Inventory.” Journal of occupational health psychology, 3(4), 356–67.
Statistics Canada. (2015a). “Labour force survey estimates (LFS), by North American Industry Classification System (NAICS), sex and age group.” Cansim, <http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=2820008&tabMode=dataTable&srchLan=-1&p1=-1&p2=9> (Jun. 14, 2015).
Statistics Canada. (2015b). “Table 282-0008, 2011-2015, Labour force survey estimates (LFS), by North American Industry Classification System (NAICS), sex and age group annual (persons x 1,000).” <http://www5.statcan.gc.ca/> (Jun. 14, 2015).
Statistics Canada. (2015c). “Table 281-0042, 2011-2015, Survey of Employment, Payrolls and Hours (SEPH), employment for all employees, by enterprise size and North American Industry Classification System (NAICS), annual (persons).”
98
<http://www5.statcan.gc.ca/> (Jun. 14, 2016).
Stewart, M., Reid, G., and Mangham, C. (1997). “Fostering children’s resilience.” Journal of Pediatric Nursing, W.B. Saunders, 12(1), 21–31.
Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics. Pearson Education, Inc., Boston, MA.
Tavakol, M., and Dennick, R. (2011). “Making sense of Cronbach’s alpha.” International Journal of Medical Education, IJME, 2, 53–55.
The Association of Workers’ Compensation Boards of Canada (AWCBC). (2013). Number of Fatalities, by Industry and Jurisdiction, 2011-2013.
Tholén, S. L., Pousette, A., and Törner, M. (2013). “Causal relations between psychosocial conditions, safety climate and safety behaviour – A multi-level investigation.” Safety Science, 55, 62–69.
Turner, B., Pidgeon, N., Blockley, D., and Toft, B. (1989). “Safety culture: its importance in future risk management.” Second World Bank Workshop on Safety Control and Risk Management, Karlstad, Sweden.
Turner, N., Chmiel, N., Hershcovis, M. S., and Walls, M. (2010). “Life on the line: Job demands, perceived co-worker support for safety, and hazardous work events.” Journal of occupational health psychology, 15(4), 482–493.
Wanberg, C. R., and Banas, J. T. (2000). “Predictors and outcomes of openness to changes in a reorganizing workplace.” Journal of Applied Psychology, American Psychological Association, 85(1), 132–142.
Wheaton, B., Muthén, B., Alwin, D. F., and Summers, G. F. (1977). “Assessing Reliability and Stability in Panel Models.” Sociological Methodology, 8, 84–136.
Wiegmann, D., Zhang, H., and Thaden, T. Von. (2004). “Safety culture: An integrative review.” The International Journal of Aviation Psychology, 14(2), 117–134.
Williams, L. J., and Holahan, P. J. (1994). “Parsimony-based fit indices for multiple‐indicator models: Do they work?” Structural Equation Modeling: A Multidisciplinary Journal, Taylor & Francis Group, 1(2), 161–189.
Woods, D. D., and Hollnagel, E. (2006). “Prologue: Resilience engineering concepts.” Resilience Engineering: Concepts and Precepts, E. Hollnagel and D. D. Woods, eds., Ashgate, Aldershot, UK, 1–6.
Woods, D. D., and Wreathall, J. (2003). “Managing Risk Proactively : The Emergence of Resilience Engineering.” Psychology, (November).
Yip, B., and Rowlinson, S. (2009). “Job Burnout among Construction Engineers Working within Consulting and Contracting Organizations.” Journal of Management in Engineering, American Society of Civil Engineers, 25(3), 122–130.
Youssef, C. M., and Luthans, F. (2007). “Positive Organizational Behavior in the Workplace: The Impact of Hope, Optimism, and Resilience.” Journal of Management, SAGE Publications, 33(5), 774–800.
Zapf, D. (1999). “Organisational, work group related and personal causes of mobbing/bullying at work.” International Journal of Manpower, 20(1/2), 70–85.
99
Zohar, D. (1980). “Safety climate in industrial organizations: theoretical and applied implications.” The Journal of Applied Psychology, 65(1), 96–102.
Zohar, D. (2000). “A group-level model of safety climate: testing the effect of group climate on microaccidents in manufacturing jobs.” The Journal of applied psychology, 85(4), 587–596.
Zohar, D., and Luria, G. (2005). “A multilevel model of safety climate: cross-level relationships between organization and group-level climates.” The Journal of applied psychology, 90(4), 616–628.
100
Appendix A Survey-worker version1
1: original survey from (McCabe et al. 2008)
2: version 2 modified in 2015 May
3: version 3 modified in 2016 May
SAFETY SURVEY
Worker Survey
We would like to ask you questions about your job, safety, and interpersonal relations at work. This questionnaire is anonymous and there is no way to identify you personally. Therefore, please be completely honest and respond as you really feel and think. Thank you for your participation.
GENERAL INFORMATION:
1. Gender: (circle) Male Female (1, 2, 3) 2. Age: __________ (1, 2, 3) 3. What is your trade? ______________________________________(1, 2, 3) 4. How long have you worked in construction? __________YEARS (1, 2, 3) 5. How long have you worked for this employer? __________ YEARS (1, 2, 3) 6. How many construction employers have you worked for in the last 3 years?
________(1, 2, 3) 7. How many projects have you worked on in the last 3 years? ___________(1, 2, 3) 8. What is the average number of hours worked per week in the last month?
_________(1, 2, 3) 9. Have you received any job-related safety training? YES NO (1, 2) 10. Have you ever served on a safety committee? YES NO (1, 2) 11. Are you a member of a union? YES NO (1, 2, 3) 12. What is your job position? (1, 2, 3)
1 Only worker version survey is attached here. The major differences between supervisor and worker version survey are questions 44-57, and questions 71-73, where for supervisor survey, “I” was used, and for worker survey, “my supervisor” was used. For example, for question 44, in a supervisor survey, it becomes “I encourage workers to express their ideas and opinions about safety at work”.
101
Supervisors fill out a different survey
Journeyman or equivalent– those people responsible for the physical labour on the site including operation of equipment, maintenance, trades and other non supervisory workers. Apprentice or equivalent – a new entrant to the industry who is receiving training on the job under the supervision of a master craft worker or member of a construction trade. Apprentices receive wages while training on the job.
12b) What is the size of your employer? (1, 2, 3)
1-4 employees
5-99 employees
100-499 employees
500 or more employees
12c) Did you complete the survey 10 years ago? YES NO (1, 2)
102
I would describe myself as... Extr
em
ely
Wro
ng
Wro
ng
Neither
Corr
ect
Extr
em
ely
Corr
ect
1
2
3
13. Careful 1 2 3 4 5
14. Efficient 1 2 3 4 5
15. Systematic 1 2 3 4 5
16. Sloppy 1 2 3 4 5
17. Disorganized 1 2 3 4 5
18. Prompt 1 2 3 4 5
19. Thorough 1 2 3 4 5
20. Not dependable 1 2 3 4 5
21. Inconsistent 1 2 3 4 5
22. Conscientious 1 2 3 4 5
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
23. It is easy for me to stay focused and accomplish my goals
1 2 3 4 5
24. I am confident that I could deal efficiently with unexpected events
1 2 3 4 5
25. I can remain calm when facing difficulties because I can rely on my coping abilities
1 2 3 4 5
26. When I am confronted with a problem, I can usually find several solutions
1 2 3 4 5
27. I can cope with stress 1 2 3 4 5
28. I can focus and think clearly when I am under pressure
1 2 3 4 5
29. I am able to adapt to changes
1 2 3 4 5
30. I tend to bounce back after illness or hardship
1 2 3 4 5
103
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
31. I have mastered the required tasks of my job
1 2 3 4 5
32. I have not fully developed the appropriate skills and abilities to successfully perform my job
1 2 3 4 5
33. I have received necessary training to do my job properly and safely
1 2 3 4 5
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
34. If I worry about safety all the time I would not get my job done
1 2 3 4 5
35. I cannot avoid taking risks in my job 1 2 3 4 5
36. Accidents will happen no matter what I do
1 2 3 4 5
37. I can’t do anything to improve safety in my workplace
1 2 3 4 5
38. I always wear the protective equipment or clothing required on my job
1 2 3 4 5
39. I do not use equipment that I feel is unsafe
1 2 3 4 5
40. If I find some safety issues in my job, I will not continue the work until the problem is fixed
1 2 3 4 5
41. I inform management of any potential hazards I notice on the job
1 2 3 4 5
42. I know what procedures to follow if a worker is injured on my shift
1 2 3 4 5
43. I would know what to do if an emergency occurred on my shift
1 2 3 4 5
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
44. My supervisor encourages workers to express their ideas and opinions about safety at work
1 2 3 4 5
104
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
45. People are willing to report safety issues in my workplace
1 2 3 4 5
46. Workers are informed about lessons learned from past accidents in my workplace
1 2 3 4 5
47. My supervisor delays in responding to safety questions or requests for assistance
1 2 3 4 5
48. My supervisor spends time showing workers the safest way to do things at work
1 2 3 4 5
49. My supervisor avoids making decisions that affect safety on the job
1 2 3 4 5
50. My supervisor suggests new ways of doing jobs more safely
1 2 3 4 5
51. My supervisor expresses satisfaction when a worker performs his/her job safely
1 2 3 4 5
52. My supervisor talks about my values and beliefs in the importance of safety
1 2 3 4 5
53. My supervisor makes sure that workers receive appropriate rewards for achieving safety targets on the job
1 2 3 4 5
54. My supervisor behaves in a way that displays a commitment to a safe workplace
1 2 3 4 5
55. My supervisor provides continuous encouragement to workers to do their jobs safely
1 2 3 4 5
56. My supervisor listens to workers concerns about safety on the job
1 2 3 4 5
57. My supervisor shows determination to maintain a safe work environment
1 2 3 4 5
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
58. I am so busy on the job that I can't get to take normal breaks
1 2 3 4 5
59. There is too much work to do in my job for it all to be done well
1 2 3 4 5
60. There are enough workers to carry out the required work
1 2 3 4 5
61. There is sufficient “thinking time” to enable me to plan and carry out the required work
1 2 3 4 5
62. Our jobs are dangerous 1 2 3 4 5
63. In our jobs you could get hurt easily 1 2 3 4 5
64. I am clear about what my responsibilities are for safety in my job
1 2 3 4 5
105
65. I am aware of major worries and concerns about safety in my workplace
1 2 3 4 5
66. I can identify when my decisions or behaviors are pushing the boundaries of safe performance
1 2 3 4 5
67. My coworkers ignore safety rules 1 2 3 4 5
68. My coworkers encourage others to be safe 1 2 3 4 5
69. My coworkers take chances with safety 1 2 3 4 5
70. My coworkers keep work areas clean 1 2 3 4 5
71. My supervisor keeps workers informed of safety rules
1 2 3 4 5
72. My supervisor involves workers in setting safety goals
1 2 3 4 5
73. My supervisor acts on safety suggestions 1 2 3 4 5
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
74. Our management provides enough safety training programs
79. Our management provides safe working conditions
1 2 3 4 5
80. Our management keeps workers informed of hazards
1 2 3 4 5
81. Does your company have a formal safety program (policies)?
Yes No Don’t know 1 2 3 4 5
82. Our management is strict about working
safely when work falls behind schedule
1 2 3 4 5
83. Our management gives safety personnel the power they need to do their job
1 2 3 4 5
84. Our management can adjust strategies when faced with unexpected events
1 2 3 4 5
85. After some unsafe events, our management focuses on how to solve problems and improve safety, rather than seeking to pin blame on specific individuals
1 2 3 4 5
86. Our safety program is worthwhile 1 2 3 4 5
87. Our safety program helps prevent accidents
1 2 3 4 5
88. Our safety program is unclear 1 2 3 4 5
106
Str
on
gly
dis
agre
e
Dis
agre
e
Uncert
ain
Agre
e
Str
on
gly
agre
e
1
2
3
89. Safety related issues are considered at high level meetings on a regular basis, not just after some unsafe events
1 2 3 4 5
90. I need permission from my management if I want to stop work in an emergency
1 2 3 4 5
Never
Rare
ly
Som
etim
es
Quite o
fte
n
Very
oft
en
1
2
3
91. How often do you get into arguments with your coworkers?
1 2 3 4 5
92. How often are your coworkers rude to you at work?
1 2 3 4 5
93. How often do your coworkers do nasty things to you at work?
1 2 3 4 5
94. How often do you get into arguments with your subordinates at work?
1 2 3 4 5
95. How often are your subordinates rude to you at work?
1 2 3 4 5
96. How often do your subordinates do nasty things to you at work?
1 2 3 4 5
97. How often do you assist others to make sure they perform their work safely
1 2 3 4 5
98. How often do you speak up and encourage others to get involved in safety issues
1 2 3 4 5
99. How often do you try to change the way the job is done to make it safer?
1 2 3 4 5
100. How often do you take action to stop safety violations in order to protect the well-being of coworkers?
1 2 3 4 5
Str
on
gly
dis
agre
e
Modera
tely
dis
agre
e
Slig
htly
dis
agre
e
Slig
htly
agre
e
Modera
tely
agre
e
Str
on
gly
agre
e
1
2
3
101. The most important things that happen to me involve my present job
1 2 3 4 5 6
102. Most of my interests are centred around my job
1 2 3 4 5 6
107
103. To me, my job is a very large part of who I am
1 2 3 4 5 6
104. I am very much personally involved with my job
1 2 3 4 5 6
105. My job is a very important part of my life
1 2 3 4 5 6
106. Workers are told about changes in working procedures and their effects on safety in a timely manner
1 2 3 4 5 1
107. Workers are told when changes are made to the working environment
1 2 3 4 5 1
108. I can detect failures or errors in my job before they create problems
1 2 3 4 5 1
109. I assess the potential safety impacts for each of my decisions or behaviors
1 2 3 4 5 1
110. I speak or act without thinking
1 2 3 4 5 1
Never
Rare
ly
Som
etim
es
Quite o
fte
n
Very
oft
en
1
2
3
111. People are encouraged to report incidents in my workplace
1 2 3 4 5
112. People hesitate to report minor injuries and incidents in my workplace
1 2 3 4 5
113. People who raise a safety concern fear of retribution
1 2 3 4 5
114. People who raise safety concerns are seen as trouble makers
1 2 3 4 5
115. Workers are informed about lessons learned from past accidents
1 2 3 4 5
116. Safety is discussed at meetings, not just after an accident
1 2 3 4 5
117. Timely feedback is provided when a safety hazard is reported
1 2 3 4 5
118. Workers’ ideas and opinions on safety are solicited and used
1 2 3 4 5
119. People are clear about their responsibilities for safety in my workplace
1 2 3 4 5
120. People are aware of the safety hazards in their work area
1 2 3 4 5
121. People are careful to minimize and avoid safety hazards in my workplace
1 2 3 4 5
108
Never
Rare
ly
Som
etim
es
Quite o
fte
n
Very
oft
en
1
2
3
122. People ignore safety in my workplace 1 2 3 4 5
109
In the last 3 months, how frequently have you experienced these on the job?
MC4 Our management is strict about working safely when work falls behind schedule
MC5 Our management gives safety personnel the power they need to do their job
MC6 After an unsafety event, our management focuses on how to solve problems and improve safety, rather than seeking to pin blame on specific individuals
Supervisor safety perception
SS1 My supervisor spends time showing me the safest way to do things at work
SS2 My supervisor expresses satisfaction when I perform my job safely
SS3 My supervisor talks about values and beliefs in the importance of safety
SS4 My supervisor makes sure that we receive appropriate rewards for achieving safety targets on the job
SS5 My supervisor behaves in a way that displays a commitment to a safe workplace
SS6 My supervisor keeps workers informed of safety rules
Coworker safety perception
CS1 My coworkers ignore safety rules (R)
CS2 My coworkers encourage others to be safe
CS3 My coworkers take chances with safety (R)
CS4 My coworkers keep work area clean
Work pressure
WP1 There are enough workers to carry out the required work (R)
WP2 There is sufficient “thinking time” to enable workers to plan and carry out the required work (R)
Role overload
RO1 I am so busy on the job that I can't take normal breaks.
RO2 There is too much work to do in my job for it all to be done well
Safety knowledge
SK1 I always wear the protective equipment or clothing required on my job
SK2 I do not use equipment that I feel is unsafe
SK3 I inform management of any potential hazards I notice on the job
SK4 I know what procedures to follow if a worker is injured on my shift
SK5 I would know what to do if an emergency occurred on my shift
Individual resilience
IR1 It is so easy for me to stay focused and accomplish my goals
IR2 I am confident that I could deal efficiently with unexpected events
IR3 I remain calm when facing difficulties because I can rely on my coping abilities
IR4 When confronted with a problem, I can usually find several solutions
IR5 I can cope with stress
IR6 I can focus and think clearly when I am under pressure
R: reverse
112
Appendix C Table C-1. Measurement model: square multiple correlations (SMCs) and factor loadings