factors affecting safety performance of construction ... · factors affecting safety performance of...

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

© Copyright by Yuting Chen 2017

ii

Factors Affecting Safety Performance of Construction Workers:

Safety Climate, Interpersonal Conflicts at Work, and Resilience

Yuting Chen

Doctor of Philosophy

Graduate Department of Civil Engineering University of Toronto

2017

Abstract

A safety plateau in safety performance has been observed in many countries or regions. In

order to continuously improve safety performance, the key is to identifying factors affecting

safety performance. This research examined four factors, namely, safety climate, individual

resilience (IR), interpersonal conflicts at work (ICW), and organizational resilience (OR) that

may contribute to explaining safety outcomes. A self-administered survey was used. From 2013

to 2016, 1281 surveys were collected from 180 construction sites of Ontario, Canada.

This thesis composed three papers, which leads to the following conclusions:

Safety climate not only affects physical safety outcomes but also employees’ job stress

level.

ICW is a risk factor for safety performance.

IR has the potential to mitigate post-trauma job stress and interpersonal conflicts of

construction workers.

Management commitment is the key to promoting a good safety culture.

Safety awareness is the most important individual factor affecting construction workers’

safety performance.

Team support, especially the support from coworkers, has a significant positive impact

on construction worker’s safety awareness.

Several original contributions were made:

This study designed and tested questions of individual resilience.

This study is the first empirical study investigating the impact of individual resilience on

safety outcomes.

iii

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 structural equation modeling (SEM) to quantify

organizational resilience.

Accordingly, the following recommendations were provided:

Construction organizations need to not only monitor employees’ safety performance but

also their psychological well-being.

Safety professionals may consider adding coping skill training programs to improve the

individual resilience of their workforce and reduce conflict-related safety outcomes.

Construction organizations can improve employees’ safety awareness by promoting a

good team-level safety culture, and by building a good reporting and learning culture.

iv

Acknowledgments

I have been enjoying my life in the University of Toronto, where I met lots of genius professors,

kind friends, and smart colleagues. However, life is not always easy. I started my Ph.D. in 2012,

and I was lost in the beginning. I did not know where my research could go, and I was not sure

whether I am a person who can do research and who can do research well. Language was also

a problem for me then.

Fortunately, life is beautiful in spite of bumps. In 2014 June, I was fortunate enough to join Dr.

McCabe’s group when I was on the edge of quitting. Since then, suddenly, the door of a new life

was opened for me. I worked with Dr. McCabe on a safety research project, where my Ph.D.

thesis was based. Dr. McCabe has been teaching me how to write proposal, how to write

papers, and demonstrating how to be a good teacher. She works so hard and always used her

weekend and vacation time to modify my papers and thesis. Her engagement, encouragement

and patience has helped me not only progress my research but also build my confidence. There

are so many things I want to list but cannot exhaust. I really want to express my sincere

gratitude to Dr. McCabe for her generous support and for her love to students. Without her, I

wouldn’t have finished my Ph.D.

My special thanks also go to Prof. Douglas Hyatt. As a co-investigator of my research project

and a member of my doctoral committee, he provided so many constructive and valuable

suggestions, especially on quantitative research methods. I am also very grateful for the advice

received from Prof. Kim Pressnail and Prof. Daman Panesar. As members of my doctoral

committee, they reviewed my thesis very thoroughly and provided their insights into my

research from different perspectives. I would also like to thank Prof. Aminah Robinson Fayek

from the University of Alberta for her thoughtful advice, especially her advice on safety

performance measures.

I am very grateful for the support of colleagues and friends in our research group: Hesam

Hamledari, Patrick Marquis, Hiba Ali, Pouya Zangeneh, Farid Mirahadi, Arash Shahi, Kamelia

Shahi, and Eric Junting Li. Especially, I would like to thank Hesam for his encouragement and

inspiration. Hesam is a great friend and sometimes a good teacher. He is very talented and

productive. I am impressed by his hard work. I always learned a lot from him during our

discussion of logic development in academic research. He is studying in Standford as a Ph.D.

student now. I wish him best wishes for his bright future. I would also like to thank Patrick for his

support and efforts to collect safety data from Ottawa, and I would also like to thank Hiba for her

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great advice on my defense presentation slides. I am also very grateful that I always learn a lot

from Farid and Pouya during our discussion of working conditions on remote construction sites.

My special thanks also go to Bangbiao Wu, Ze Wang, and Ruochen Nan. They are great

friends. I am very happy and lucky to meet them in Toronto and have them being with me. I am

very grateful for the help and support from them. I really enjoyed the time when we played board

games together, when we had hot pots together, and when we played in Treetop treckking

zipline parks, etc. My dull Ph.D. life becomes more colorful because of them.

A deep and heartfelt thank you go to my families, especially my parents. They always support

me for any decisions I made. I am very happy to live in such a love family!

Finally, I would like to thank Sheng, my love and friend. His encouragement and support made

me overcome all bumps and difficulties in the past four years. I also wish him best wishes for his

fascinating research journey.

vi

Table of Contents

Acknowledgments ...................................................................................................................... iv

Table of Contents ....................................................................................................................... vi

List of Tables .............................................................................................................................. x

List of Figures ........................................................................................................................... xii

List of Acronyms and Abbreviations ......................................................................................... xiii

List of Symbols.......................................................................................................................... xv

Chapter 1 Introduction ................................................................................................................ 1

1.1. Research objectives ..................................................................................................... 3

1.2. Thesis overview ........................................................................................................... 4

1.2.1. Data collection ...................................................................................................... 7

1.2.2. Data analysis ........................................................................................................ 8

1.3. Chapter Summaries ....................................................................................................12

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” .........................................................................................................13

1.3.2. Chapter 3 “The relationship between individual resilience, interpersonal conflicts at

work, safety outcomes of construction workers” .................................................................14

1.3.3. Chapter 4 “Resilience on construction sites: testing a structural equation model” 15

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

.................................................................................................................................................20

2.1. Introduction .................................................................................................................21

2.1.1. Safety climate dimensions ...................................................................................23

2.1.2. Safety climate and safety outcomes .....................................................................24

vii

2.1.3. Individual resilience, safety outcomes, and psychological stress..........................24

2.1.4. Injuries and psychological stress ..........................................................................25

2.2. Methods ......................................................................................................................25

2.2.1. Data and procedure .............................................................................................25

2.2.2. Measures .............................................................................................................29

2.2.2.1. Individual resilience ..........................................................................................29

2.2.2.2. Safety climate ...................................................................................................30

2.2.3. Data analysis .......................................................................................................31

2.3. Results ........................................................................................................................33

2.3.1. Measurement model ............................................................................................33

2.3.2. Inter-correlations among the variables .................................................................34

2.3.3. Structural model ...................................................................................................38

2.4. Discussion ..................................................................................................................40

2.5. Conclusions ................................................................................................................41

Chapter 3 The relationship between individual resilience, interpersonal conflicts at work, safety

outcomes of construction workers .............................................................................................42

3.1. Introduction .................................................................................................................42

3.1.1. ICW, safety outcomes, and stress ........................................................................44

3.1.2. Antecedents of ICW .............................................................................................45

3.2. Methods ......................................................................................................................46

3.2.1. Data and collection procedures ............................................................................46

3.2.2. Data analysis .......................................................................................................51

3.3. Results ........................................................................................................................53


viii

3.3.2. Descriptive statistics ............................................................................................54


3.4. Discussion ..................................................................................................................58

3.5. Conclusions ................................................................................................................60

Chapter 4 Resilience on construction sites: testing a structural equation model ........................61

4.1. Introduction .................................................................................................................61

4.1.1. Resilience indicators ............................................................................................63

4.1.2. Hypotheses ..........................................................................................................64

4.2. Methods ......................................................................................................................65

4.2.1. Data and procedures ...........................................................................................65

4.2.2. Measures .............................................................................................................69

4.2.3. Data analysis .......................................................................................................70

4.3. Results ........................................................................................................................72


4.3.2. Descriptive statistics ............................................................................................75


4.4. Discussion ..................................................................................................................81

4.5. Conclusions ................................................................................................................83

4.6. Limitations and future work .........................................................................................83

Chapter 5 Conclusions, recommendations, and future work......................................................85

5.1. Conclusions ................................................................................................................85

5.1.1. Impacts on physical injuries and unsafe events ...................................................85

5.1.2. Impacts on job stress ...........................................................................................86

5.2. Conference paper conclusions ....................................................................................86

ix

5.3. Contributions ...............................................................................................................87

5.4. Recommendations ......................................................................................................87

5.5. Future work .................................................................................................................87

References ...............................................................................................................................89

Appendix A ............................................................................................................................. 100

Appendix B ............................................................................................................................. 111

Appendix C ............................................................................................................................. 112

x

List of Tables

Table 1-1. Focus of this research ............................................................................................... 4

Table 1-2. Survey details ........................................................................................................... 6

Table 1-3. Number of surveys by year and location ................................................................... 7

Table 1-4. SEM model fit indices ...............................................................................................12

Table 1-5. Hypotheses and testing results ................................................................................16

Table 2-1. Demographics of respondents ..................................................................................27

Table 2-2. Data representativeness ..........................................................................................27

Table 2-3. Frequency of safety outcomes .................................................................................28

Table 2-4. Frequency of job stress ............................................................................................29

Table 2-5. Comparisons of the hypothesized six-factor model of safety climate with selected

alternative models .....................................................................................................................35

Table 2-6. Measurement model: squared multiple correlations (SMCs) and factor loadings ......35

Table 2-7. Descriptive statistics and correlations .......................................................................37

Table 2-8. Comparisons of model 1 and model 2 ......................................................................39

Table 2-9. Direct and indirect effect testing of the hypothesized model relationships ................40


Table 3-2. Frequency of physical safety outcomes ....................................................................50


Table 3-4. Fit indices for the measurement models ...................................................................53

Table 3-5. Measurement model: square multiple correlations (SMC) and factor loadings .........53

Table 3-6. Descriptive statistics of variables ..............................................................................54

Table 3-7. ICW individual statement frequency distribution (%) .................................................55

Table 3-8. Direct and indirect effect testing of the hypothesized model relationships ................58

xi

Table 4-1. Hypotheses in the study ...........................................................................................65


Table 4-3. Frequency of safety outcomes .................................................................................68


Table 4-5. Fit indices for the measurement models ...................................................................73

Table 4-6. Measurement model: square multiple correlations (SMCs) and factor loadings ........74

Table 4-7. Descriptive statistics and correlations .......................................................................76

Table 4-8. Indirect effect analysis ..............................................................................................80

Table 4-9. Summary of testing results .......................................................................................81

Table B-1. Scale statements ................................................................................................... 111

Table C-1. Measurement model: square multiple correlations (SMCs) and factor loadings ..... 112

xii

List of Figures

Figure 1-1. Safety plateau phenomenon .................................................................................... 1

Figure 1-2. Safety performance terminology hierarchy ............................................................... 3

Figure 1-3. Organization of the research questions .................................................................... 5

Figure 1-4. Data analysis process .............................................................................................. 9

Figure 1-5. Example of SEM .....................................................................................................11

Figure 1-6. Example of mediation analysis ................................................................................11

Figure 1-7. Safety climate, individual resilience, and safety outcomes ......................................13

Figure 1-8. Individual resilience, interpersonal conflicts at work, and safety performance .........15

Figure 1-9. Resilience model .....................................................................................................17

Figure 2-1. Traumatic Fatality Rate in Ontario Construction (1965-2013)1,2,3 .............................22

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 .....................................................................................................................38

Figure 3-1. Safety performance terminology hierarchy ..............................................................44

Figure 3-2. Frequency of interpersonal conflict..........................................................................56

Figure 3-3. Structural model depicting the relationships between the studied variables ............57

Figure 4-1. Structural model depicting the relationships between the studied variables ............78

xiii

List of Acronyms and Abbreviations

AN anticipation (a subfactor of organizational resilience (OR))

AW awareness (a subfactor of OR)

CFI comparative fit index

C.I. confidence intervals

CS coworker safety perception (factor)

CWB counterproductive work behaviors

DA dominance analysis

DEA data envelopment analysis

GTA Greater Toronto Area

ICI industrial, commercial, and institutional sectors of the construction industry

ICW interpersonal conflicts at work

ICWS interpersonal conflicts at work with supervisors (factor)

ICWC interpersonal conflicts at work with coworkers (factor)

IR individual resilience (factor)

LN learning (a subfactor of OR)

M mean value

MC management commitment to safety (a subfactor of OR)

MOE margin of error

OR organizational resilience

PNFI parsimonious normed fit index

PTSD post-traumatic stress disorder

RA research assistant

RMSEA root mean square error of approximation

xiv

RO role overload (factor)

ROP Research Opportunities Program in the Ministry of Labour Ontario

RP reporting (a subfactor of OR)

SC safety climate

SD standard deviation

SEM structural equation modeling

SK safety knowledge (factor)

SMC squared multiple correlation

SRMR standardized root mean square residual

SS supervisor safety perception (factor)

WP work pressure (factor)

xv

List of Symbols

d.f. degree of freedom

d.f. diff degree of freedom difference

p p value that is defined as the probability of obtaining a result equal to or more

extreme than what was actually observed, when the null hypothesis is true

r bivariate correlation

R2 The percent variance of dependent variables explained by independent variables

in regression models.

β standardized regression coefficient

2 Chi square - a measure of the goodness of fit between observed values and

those expected theoretically

2 diff Chi square difference

Standard deviation of a population

0.025z Critical value when confidence level is 95%

1

Chapter 1 Introduction

Safety is a critical issue in the construction industry. Although it has been broadly researched

from many perspectives, continuous improvement in safety performance is now facing

challenges. A plateau or the stagnating improvement in safety performance has been observed

in many countries or regions. Figure 1-1 shows the construction fatality rate of US (BLS 2014;

NIOSH 2001) and the Ontario construction industry (IHSA 2008; Statistics Canada 2015;

AWCBC 2013) from 1980 to 2013. This figure shows that the rate of US construction fatalities

appears to be reaching an asymptote; and although the fatality rate of the Ontario construction

has more noise due to the smaller population, it also appears unable to reach steady state at

lower fatality rates. So the question is: how can the industry continuously improve safety

performance? The key is identifying factors that affect safety performance. There are many

factors affecting safety performance in the construction industry, including individual factors

(e.g. age and work experience) and organizational factors (e.g. technical and economic factors)

(Sawacha et al. 1999). This thesis investigated the impact of two organizational factors and two

individual factors on the safety outcomes of construction workers. The two organizational factors

are: safety climate and organizational resilience. The two individual factors are individual

resilience (IR) and interpersonal conflicts at work (ICW).

Figure 1-1. Safety plateau phenomenon

2

Safety climate refers to the shared perception of people toward safety in their work environment

(Zohar 1980). Safety climate may be affected by a country’s social culture (Ali 2006), because

management’s decisions on how they manage safety are likely influenced by cultural norms.

Examining the geographic distribution of construction safety climate research, most of the

existing research were conducted in Hong Kong (Fang et al. 2006; Hon et al. 2014), Australia,

Queensland or New Zealand (Guo et al. 2016; Mohamed 2002) and U.S.A. (Cigularov et al.

2013; Dedobbeleer and Béland 1991; Gillen et al. 2002; Probst et al. 2008). However, few

research studies have assessed safety climate and its impact on safety performance in the

Canadian construction industry. While most regulatory jurisdictions regularly publish statistical

reports focused on the historic safety performance of all industries, only McCabe et al. (2008)

investigated safety climate factors specific to safety performance in the Ontario construction

industry. Unfortunately, that study was over a decade ago. Given that all systems tend to

deteriorate (Costella et al. 2009), it is necessary to regularly investigate the safety performance

of the Canadian construction industry and the factors that affect it if the safety plateau is to be

overcome. Therefore, the first research question is whether safety climate can explain the safety

plateau phenomenon in the Canadian construction industry. Secondly, given that safety climate

explained only 23 to 28 percent of the variance in safety outcomes 10 years ago (McCabe et al.

2008), it is necessary to explore other factors and investigate what additional contributions they

make to the overall safety performance. The literature suggested three factors, namely,

individual resilience (IR) (Eid et al. 2012), interpersonal conflicts at work (ICW) (Bruk-Lee and

Spector 2006), and organizational resilience (OR) (Hollnagel 2015) that may contribute to

predicting or explaining safety outcomes. IR is people’s proactive psychological capability that

helps them to deal with adverse events and risks (Stewart et al. 1997). ICW refers to negative

interactions with others in the workplace (Nixon et al. 2011), which can range from momentary

disagreements and disrespectful behaviors from coworkers or supervisors to heated arguments.

OR is regarded as a capacity for positive responses and healing capabilities to maintain normal

operations and a high level of safety during stress and disturbance (Bruyelle et al. 2014; Ross et

al. 2014), which is fundamental for human and organizational functioning and viability (Carmeli

et al. 2013). The hierarchy of safety performance terminology used herein is provided in Figure

1-2.

3

Safety outcomes

Physical safety

outcomes

Job stress

Physical injuries

Unsafe events

Figure 1-2. Safety performance terminology hierarchy

1.1. Research objectives

Four research objectives were identified:

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 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


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

H1©: ICWC is positively associated with physical injuries

H1(d): ICWC is positively associated with unsafe events

H2: ICW is positively associated with job stress

H2(a): ICWS is positively associated with job stress

H2(b): ICWC is positively associated with job stress

H3: number of hours worked in the previous month is positively associated with ICW

H3(a): weekly workhours is positively associated with ICWS

H3(b): weekly workhours is positively associated with ICWC

H4: number of employers in the previous 3 years is positively associated with ICW

H4(a): number of employers is positively associated with ICWS

H4(b): number of employers is positively associated with ICWC

H5: number of projects in the previous 3 years is positively associated with ICW

H5(a): number of projects is positively associated with ICWS

H5(b): number of projects is positively associated with ICWC

H6: IR is negatively associated with ICW

H6(a): IR is negatively associated with ICWS

H6(b): IR is negatively associated with ICWC

15

IR

ICWs ICWc

Weekly workhours Job stress

Physical injuries

Unsafe events

-0.29

0.81 0.24

0.61

0.1

3

0.09

0.39

-0.08

0.15

0.0

9

Figure 1-8. Individual resilience, interpersonal conflicts at work, and safety performance

This chapter leads to several conclusions. Although ICW was reported as quite often or very

often by only 6.3% of respondents, it had a significant effect on both physical safety outcomes

including physical injuries and unsafe events, and job stress. Individual resilience (IR) had a

significant negative correlation with both ICWS and ICWC, which in turn could decrease the

frequency of physical safety outcomes and job stress. The contributions to the Body of

Knowledge are: safety professionals may consider adding coping skill training safety programs

to improve the individual resilience of their workforce and reduce conflict-related safety

outcomes. This chapter has been accepted for publication in Journal of Construction

Engineering and Management.

1.3.3. Chapter 4 “Resilience on construction sites: testing a structural

equation model”

Chapter 4 investigated the impact of organizational resilience on safety performance of

construction workers. Seven factors were used to measure resilience, including management

commitment to safety (MC), supervisor safety perception (SS), coworker safety perception (CS),

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

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


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


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).


2.2.3.1. 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




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






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

Model 2 d.f. 2 diff d.f. diff 2/ d.f. RMSEA SRMR CFI PNFI

Hypothesized six factor model of safety climate

778.37 252 3.09 0.05 0.07 0.94 0.76

Alternative model 1: One factor (MC+SS+CS+WP+RO+SK)

2119.35 267 1340.98 15 7.94 0.09 0.08 0.77 0.66

Alternative model 2: two-factors (MC+SS+CS+WP+RO, SK)

1818.07 266 1039.7 14 6.84 0.09 0.07 0.81 0.69

Alternative model 3: three-factors (MC, SS+CS+WP+RO, SK)

1451.58 264 673.21 12 5.50 0.08 0.06 0.85 0.73

Alternative model 4: four-factors (MC, WP+RO, SS+CS, SK)

1241.55 261 463.18 9 4.76 0.07 0.06 0.88 0.74

Alternative model 5: five-factors (MC, SS, CS, WP+RO, SK)

939.41 257 161.04 5 3.66 0.06 0.07 0.92 0.76

MC: management commitment to safety; SS: supervisor safety perception; CS: coworker safety perception; WP: work pressure; RO:

role overload; SK: safety knowledge

Table 2-6. Measurement model: squared multiple correlations (SMCs) and factor loadings

Scale statements

SMCs Management commitment

to safety

Supervisor safety

perception

Coworker safety

perception

Work pressure

Role overload

Safety knowledge

Individual resilience Estimate

10,000 Bootstrapping

95% C.I.

MC1 0.47 [.39,.56] 0.69

MC2 0.55 [.46,.63] 0.75

MC3 0.58 [.49,.65] 0.76

MC4 0.49 [.39,.58] 0.70

MC5 0.58 [.49,.66] 0.76

MC6 0.48 [.40,.56] 0.69

SS1 0.46 [.38,.54] 0.68

SS2 0.59 [.50,.66] 0.77

SS3 0.68 [.61,.73] 0.82

SS4 0.38 [.31,.44] 0.61

SS5 0.56 [.47,.63] 0.75

36

Scale statements

SMCs Management commitment

to safety

Supervisor safety

perception

Coworker safety

perception

Work pressure

Role overload

Safety knowledge

Individual resilience Estimate


95% C.I.

SS6 0.48 [.38,.56] 0.69

CS1 0.67 [.58,.76] 0.82

CS2 0.18 [.11,.26] 0.42

CS3 0.56 [.46,.66] 0.75

CS4 0.19 [.12,.27] 0.43

WP1 0.44 [.34,.54] 0.66

WP2 0.52 [.40,.64] 0.72

RO1 0.39 [.28,.51] 0.62

RO2 0.50 [.36,.67] 0.71

SK1 0.29 [.19,.40] 0.54

SK2 0.29 [.18,.42] 0.54

SK3 0.59 [.48,.70] 0.77

SK4 0.47 [.37,.56] 0.68

SK5 0.43 [.34,.53] 0.66

IR1 0.36 [.27,.46] 0.73

IR2 0.53 [.45,.61] 0.72

IR3 0.52 [.43,.61] 0.59

IR4 0.35 [.26,.44] 0.72

IR5 0.52 [.44,.59] 0.69

IR6 0.48 [.38,.57] 0.73

All the factor loadings are significant (p<0.01).

37

Table 2-7. Descriptive statistics and correlations

Number of

statements in each scale

M S.D. 1 2 3 4 5 6 7 8 9 10

1. Physical injuries - 6.01 6.12 - 0.59 0.46 -0.29 -0.29 -0.24 0.35 0.16 -0.18 -0.18

2. Unsafe events - 3.59 5.19 - 0.41 -0.23 -0.23 -0.22 0.26 0.14 -0.17 -0.15

3. Job stress - 3.62 4.56 - -0.19 -0.20 -0.25 0.30 0.26 -0.15 -0.20

4. Management commitment to safety

6 3.48 0.54 - 0.87 0.69 0.41 -0.68 -0.25 0.55 0.48

5. Supervisor safety perception

6 2.99 0.51 0.86 0.42 -0.57 -0.24 0.48 0.44

6. Coworker safety perception

4 3.41 0.74 0.72 -0.40 -0.47 0.27 0.23

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.

Table 2-8. Comparisons of model 1 and model 2

Model 2 d.f. 2 diff d.f. diff 2/ d.f. RMSEA SRMR CFI PNFI

Model 1 1459.80 509 2.87 0.05 0.07 0.91 0.79

Model 2 1455.46 507 4.34 2 2.87 0.05 0.07 0.91 0.79

Path analysis of model 1 was conducted to determine whether safety climate has indirect effects

on job stress. As shown in Table 2-9, besides the direct effects displayed in Figure 2-2, safety

climate had significantly indirect negative effects on physical injuries via unsafe events; and

unsafe events had significantly indirect positive effects on job stress via physical injuries. More

interestingly is that safety climate had a significant indirect negative impact (i.e. -0.16) on job

stress via both physical injuries and unsafe events. This indirect effect was achieved through

three paths: SC -> physical injuries -> job stress; SC -> unsafe events -> job stress; SC ->

unsafe events -> physical injuries -> job stress. Thus, the indirect effect was decomposed into

three parts, -0.07, -0.04, and -0.05, respectively. In addition, because AMOS only reported the

total indirect effect from one variable to another variable, the decomposition of the indirect effect

was conducted manually by the authors; therefore, the corresponding bootstrapping results

were not available here.

Moreover, R2 of unsafe events, physical injuries, and job stress were 0.06, 0.41, and 0.29,

respectively. Although it has been argued that it is difficult to decompose the R2 contribution of

correlated predictors, Dominance Analysis (DA) is a well-known approach to determining the

relative importance of each predictor (Budescu and V. 1993). The novelty of DA is that

“predictors are compared in a pairwise fashion across all subset models, and a hierarchy of

levels of dominance can be established” (Azen and Budescu 2006). Table 2-9 gives the

estimates of the R2 contribution from each predictor based on DA. Matlab R2015b was used

and the code employed was from (Broomell et al. 2010). As displayed, safety climate explained

7% and 6% variance of physical injuries and unsafe events, respectively. IR contributed 3%

variance of job stress, physical injuries contributed 17%, and unsafe events contributed 9%.

40

Table 2-9. Direct and indirect effect testing of the hypothesized model relationships

Path R2

contribution Direct effects

Indirect effects


95% C.I.

Direct effects

SC -> physical injuries 0.07 -0.17

SC -> unsafe events 0.06 -0.24

IR -> job stress 0.03 -0.11

Unsafe events -> physical injuries 0.34 0.58

Unsafe events -> job stress 0.09 0.15

Physical injuries -> job stress 0.17 0.39

Indirect effects

SC -> unsafe events -> physical injuries -0.14 [-0.19, -0.09]

SC -> (physical injuries and unsafe events) -> job stress

-0.16 [-0.21, -0.11]

SC -> physical injuries -> job stress -0.07 -

SC -> unsafe events -> job stress -0.04 -

SC -> unsafe events -> physical injuries -> job stress

-0.05 -

Unsafe events->physical injuries ->job stress

0.23 [0.17, 0.29]

Direct effects, indirect effects, and 95% bootstrapped confidence intervals denoting indirect

effects are significant (p<0.01).

2.4. Discussion

The objective of this study was to examine the impacts of safety climate and individual

resilience on safety outcomes and job stress of construction workers. Six dimensions of safety

climate were adapted from previous research. Each of these dimensions was found to be

significant, and to be important components of the latent construct of safety climate including

management commitment to safety climate, supervisor safety perception, coworker safety

perception, work pressure, role overload, and safety knowledge. Our findings validate the H1

hypothesis and confirm that safety climate is a critical factor predicting the occurrence of safety

outcomes in the construction industry (Fang et al. 2006; Hon et al. 2014; Mohamed 2002).

Among the six factors of safety climate, management commitment to safety and supervisor

safety perception had the strongest positive correlations with safety climate; work pressure had

the strongest negative correlation with safety climate This validates that management

commitment to safety and the balance between safety and production are essential aspects of

workplace safety climate (Flin et al. 2000; Glendon and Litherland 2001; Huang et al. 2006;

Zohar 2000; Zohar and Luria 2005).

41

The current study showed that job related stress is a common phenomenon in the Ontario

construction industry. Approximately one third of the respondents reported experiencing at least

four of the six job stress. The strong correlations between job stress and safety outcomes

indicate that more attention needs to paid to the job related stress in the construction industry.

Moreover, it was found that safety climate has the potential to decrease workers’ stress. This

suggests that safety climate can affects not only employees’ physical health but also their

psychological health.

Individual resilience had a significantly negative impact on psychological stress, which validates

the H3 hypothesis. It explained 3% of the variance of stress. Individual resilience improvement

can be taken as a secondary preventer of job stress (Cooper and Cartwright 1997). Hence,

organizations may consider developing training programs, such as awareness activities (Cooper

and Cartwright 1997), to improve workers’ relaxation techniques, cognitive coping skills, and

work/lifestyle modification skills. Failing our expectation, the H2 hypothesis was not confirmed,

i.e. individual resilience was not found to be associated with safety outcomes. More research is

needed to validate this.

There are several limitations of the current study. First, the study was based on a cross-

sectional design, which prevents us from making definitive causal conclusions. Second, work

pressure and role overload had relatively low internal consistency alpha values, which needs to

be enhanced in future. Finally, our data was skewed to larger-size companies, which also needs

to be addressed in future.

2.5. Conclusions

This study demonstrated that besides injuries or unsafe events, job related stress is also very

common in the construction industry. Safety climate was confirmed to be associated with safety

outcomes and with fewer job related stress. Results also suggest that individual resilience

affects one’s ability to manage stress. These findings highlight the role of organizational factors

as well as individual factors in affecting individual safety performance and psychological well-

being. Given these findings, 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 organizations’ safety

performance.

42

Chapter 3 The relationship between individual resilience,

interpersonal conflicts at work, safety outcomes of construction workers

Yuting Chen, Brenda McCabe, and Douglas Hyatt

Abstract

Interpersonal conflicts at work (ICW) has been widely regarded as a job stressor; however, it is

rarely linked to physical safety outcomes. ICW mainly has two forms on a construction site:

conflicts with supervisors (ICWS) and conflicts with coworkers (ICWC). This study examined the

occurrences of ICWS and ICWC on construction sites, and investigated the relationship among

ICWS, ICWS and physical safety outcomes together with job stress. In addition, possible

antecedents of ICWS and ICWC including workhours, mobility, and individual resilience were

also examined. The research was based on 837 surveys collected from more than 100

construction sites in Ontario, Canada between 2015 June and 2016 June. Structural equation

modeling (SEM) techniques were used to test the hypothesized relationships. This paper leads

to the following conclusions: although ICW was reported as quite often or very often by only

6.3% of respondents, it had a significant effect on both physical safety outcomes including

physical injuries and unsafe events, and job stress. Individual resilience (IR) had a significant

negative correlation with both ICWS and ICWC, which in turn could decrease the frequency of

physical safety outcomes and job stress. The contributions to the Body of Knowledge are: safety

professionals may consider adding coping skill training safety programs to improve the

individual resilience of their workforce and reduce conflict-related safety outcomes.

Keywords: interpersonal conflict, safety performance, individual resilience, job stress,

construction industry

3.1. Introduction

The nature of construction work, e.g. working in groups under intense job demand, is associated

with higher job stress and may result in interpersonal conflicts at work (ICW) (Neuman and

Baron 1998; Salin 2003; Zapf 1999). ICW refers to negative interactions with others in the

43

workplace (Nixon et al. 2011), which can range from momentary disagreements and

disrespectful behaviors from coworkers or supervisors to heated arguments. ICW is directly

associated with workplace bullying (Ayoko et al. 2003; Hauge et al. 2009; Spector and Fox

2005), which may cause depressive symptoms (Meier et al. 2014), general health problems (De

Raeve et al. 2009), and counterproductive work behaviors (Bruk-Lee and Spector 2006). It is

also associated with considerable unproductive time and financial cost. The average time and

cost expended to manage a conflict incident in the construction industry in a northern Midwest

state in the United States is 161.25 hours and US $10 948 (Brockman 2014), respectively. As a

risk factor of job performance, however, few research studies have examined the role of ICW in

affecting safety performance of construction workers. In addition, research studies have shown

that conflicts with coworkers and supervisors are conceptually distinct (Bruk-Lee and Spector

2006).

The first aim of this paper is to assess how common ICW is in the construction industry by

examining the frequency of conflicts with supervisors (ICWS) and with coworkers (ICWC) on

construction sites. Secondly, to validate that ICW is a risk factor of safety performance, this

study will investigate the relationship between ICW, physical safety outcomes, and job stress of

construction workers. The final purpose of this study is to examine possible antecedents of ICW,

including mobility, working overtime, and individual resilience (IR), which is the capacity of

individuals to cope with significant change, adversity, or risk (Stewart et al. 1997). The hierarchy

of safety performance terminology used herein is provided in Figure 3-1. Safety outcomes refer

to all outcomes, including job stress and physical outcomes, which are further subdivided into

physical injuries and unsafe events. Stress is defined as a “negative emotional experience

accompanied by predictable biochemical, physiological, and behavioral changes that are

directed toward adaptation either by manipulating the situation to alter the stressor or by

accommodating its effects” (Andrew Baum 1990). It is a lack of fit between the needs and

demands of the individual and his/her environment (Cooper and Cartwright 1997). Stress is very

subjective because it is associated with both the individual’s work environment and his/her

personalities (Lazarus 1966). Physical injuries and unsafe events are the most frequently

mentioned physical safety outcomes in previous research (Clarke 2010; Huang et al. 2004;

Mullen and Kelloway 2009; Nahrgang et al. 2011).

44

Figure 3-1. Safety performance terminology hierarchy

3.1.1. ICW, safety outcomes, and stress

ICW is a widely acknowledged job stressor (Schaufeli and Taris 2014; Spector and Jex 1998;

Zapf 1999). It is one of the most cited sources of stress (Bruk-Lee and Spector 2006). The

existing research on ICW and job performance focused on its relationship with

counterproductive work behaviors (CWB) (Bruk-Lee and Spector 2006). CWB refers to any

intentional behavior on the part of an organization member viewed by the organization as

contrary to its legitimate interests (Sackett 2002). From this point of view, unsafe behaviors on

construction sites (e.g. walking behind heavy equipment without informing the operator) are

defined as CWB due to the indirect cost of injuries or accidents. The most widely used CWB

scales (Spector et al. 2006) include CWB directed against people (i.e. abuse), and four scales

related to CWB directed against organizations, namely, production deviance, sabotage, theft,

and withdraw. Unfortunately, these CWB scales are insufficient to measure safety-related

counterproductive behaviors of employees.

Compared with other widely researched job stressors of safety performance, including risk and

hazards (Goldenhar et al. 2003; Nahrgang et al. 2011), physical demands and work complexity

(Goldenhar et al. 2003; Hemingway and Smith 1999; Li et al. 2013; Nahrgang et al. 2011), role

conflict and role ambiguity (Hemingway and Smith 1999), job certainty or insecurity (Probst and

Brubaker 2001), job control (Goldenhar et al. 2003; Leung et al. 2016), and organizational

factors such as coworker and supervisor support (Goldenhar et al. 2003; Hemingway and Smith

1999; Li et al. 2013), little is known about the impact of ICW on safety performance. Only one

study examined the impact of ICW on safety outcomes (Nixon et al. 2011), which found that

interpersonal conflicts had strong relationships with a set of individual physical injuries. To

examine the relationship between ICW and safety outcomes, the following hypotheses are

proposed.


Safety Outcomes

Physical Safety Outcomes

Physical Injuries

Unsafe Events

Job Stress

45

H1(a): ICWS is positively associated with physical injuries

H1(b): ICWS is positively associated with unsafe events

H1(c): ICWC is positively associated with physical injuries

H1(d): ICWC is positively associated with unsafe events


H2(a): ICWS is positively associated with job stress

H2(b): ICWC is positively associated with job stress

3.1.2. Antecedents of ICW

3.1.2.1. Mobility, working overtime, and ICW

Construction workers are more likely to work longer hours than workers in other sectors, have

irregular work schedules, and change jobs (Dong 2005). Long workhours and irregular work

schedules are risk factors of injury occurrences and psychological health (Dong 2005; Härmä

2006). Construction projects are temporary, so the work typically has high mobility, which varies

by trade depending on the nature of their work. For example, sprinkler fitter and window

installation workers have a relatively higher mobility than electricians. Research has shown that

ICW is directly related to overtime work and external mobility ( i.e. changing employers) (De

Raeve et al. 2008, 2009). In this paper, weekly workhours in the previous month and two

indicators of mobility were used, namely, number of employers and number of projects in the

previous 3 years. The following additional hypotheses are presented.

H3: weekly workhours in the previous month is positively associated with ICW

H3(a): weekly workhours is positively associated with ICWS

H3(b): weekly workhours is positively associated with ICWC


H4(a): number of employers is positively associated with ICWS

H4(b): number of employers is positively associated with ICWC


H5(a): number of projects is positively associated with ICWS

46

H5(b): number of projects is positively associated with ICWC

3.1.2.2. Individual resilience (IR) and ICW

IR is regarded as one type of positive psychological capacity for performance improvement

(Luthans 2002; Youssef and Luthans 2007). It changes over time and is enhanced by protective

factors in the individual and environment (Stewart et al. 1997). IR is associated with higher

coping abilities related to large organizational changes (Wanberg and Banas 2000), and is

regarded as a secondary preventer of job stress (Cooper and Cartwright 1997). Research also

found that positive psychological states can moderate the effect of job stress on rudeness and

disrespectful behaviors in the workplace (Penney and Spector 2005). More recently, it was

hypothesized but not tested that IR may facilitate safety focused behaviors (Eid et al. 2012).

However, little research has examined the impact of IR on ICW in the construction industry.

Thus, the final hypothesis is presented.


H6(a): IR is negatively associated with ICWS

H6(b): IR is negatively associated with ICWC

3.2. Methods

3.2.1. Data and collection procedures

A multi-site data collection strategy was employed. The target sample size was 500, which was

to achieve a sample size large enough to conduct structural equation modeling (SEM). There is

no agreement on the minimum sample size for conducting SEM, e.g. a set value of 500

(MacCallum et al. 1999) and a ratio of 20:1 between sample size and number of model factors

(Hair et al. 1995). In total, 837 surveys were collected from 112 construction sites in Ontario,

Canada between July 2015 and July 2016, thereby meeting the requirements for SEM.

3.2.1.1. Survey instrument

A self-administered questionnaire that comprised demographics, attitude statements, and

incident reporting was used to collect data. With the exception of individual resilience questions,

the questionnaire was adapted from previous research (McCabe et al. 2008). The

demographics section included questions about the individual’s characteristics, such as job

tenure and number of projects in the previous 3 years. The incident reporting section asked the

47

respondents how frequently they experienced safety-related 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. Each category comprises 6 to 11 specific incident types, as shown in Table 3-2

and Table 3-3. Physical injuries, such as a cut or hernia, may be associated with an unsafe

event, but no connection was made by the respondents. Similarly, unsafe events, such as a trip

or fall, comprise events that respondents experienced but may or may not have resulted in an

injury. Job stress relate to one’s ability to concentrate; one example is “felt constantly under

strain”.

In the attitude section, six statements were used to measure IR and six statements were used to

measure ICW. For IR, respondents indicated the degree to which they agree with statements

using a Likert scale between 1 (strongly disagree) and 5 (strongly agree). Three statements of

IR 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 2003) focus on a person’s tolerance of negative

impacts, and positive acceptance of change. An example statement from this category is “When

confronted with a problem, I can usually find several solutions”. The coefficient alpha of IR is

0.83. For ICW, ICWS and ICWC were measured by three statements each (Spector and Jex

1998). It assesses how well respondents get along with their coworkers and supervisors at

work. The questions asked about the frequency of the respondents getting into arguments with

their coworkers and supervisors, and how often their coworkers or supervisors do rude or mean

things to them. Five response choices are given: 1 (never), 2 (rarely), 3 (sometimes), 4 (quite

often), and 5 (very often). High scores represent frequent conflicts with others. The internal

consistence alpha of ICWS and ICWC statements are 0.85 and 0.81, respectively.


Both top-down and bottom-up methods were used to collect surveys (Chen et al. 2015). Top-

down means that the team first contacted the head office management. If top management

were enthusiastic about the project, they would engage their site managers and schedule data

collection visits. The bottom-up method involved engaging the site managers first, who then

worked to gain corporate permission. The process entailed four main steps by our research

assistants (RAs): initiate contact at the site, follow-up and communicate with the site and/or the

corporate management until approval is given, schedule site visits, and collect data.

48

At least two RAs were on site to administer the surveys to workers. A procedure was strictly

followed wherein the RAs arrived at the site and met the workers in a lunch trailer or other

comfortable location. After the consent forms were collected, surveys were distributed. RAs

provided immediate help to workers if they had a question, which improved the reliability and

completeness of the data. Surveys were strictly anonymous and were immediately collected

upon their completion; no unfinished surveys were left behind and no follow-up was undertaken.

As such, the response rate was 100% as all participants submitted a survey. However, 56 or

6.7% of surveys were removed from the sample because the maximum threshold for missing

data (10%) was exceeded.

3.2.1.3. Sample characteristics and validation

The respondents were from a variety of construction sectors, including residential (61.4%),

heavy civil (17.2%), and industrial, commercial, and institutional (ICI) sector (21.4%). 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, extending from

Ottawa to Thunder Bay. Table 3-1 shows demographic information of the respondents. Laborer,

carpenter, and electrician are the top three trades in terms of number of surveys completed

(20%, 14%, and 11%, respectively). The mean age of the respondents was 37 years and 98%

were male; 69% of workers were journeymen or apprentices. The respondents had been

employed by their current employer for just over 6 years on average, but half of them had

worked with their employers less than 3.5 years. Respondents reported relatively high mobility

between projects. The weekly working hours of the respondents were approximately 44 hours,

and 38% worked more than 44 hours, which is considered overtime (Ontario Ministry of Labour

2015). The respondents also reported a very high safety training percentage (97.8%) and 37.6%

reported that they had experience as a safety committee member; 60% of the respondents were

union members.


Demographic factors Response range Mean / median or percent

Gender Male / Female 98% male

Age 16 to 67 36.72 / 35.00

Years in construction 0.01 to 46 14.15 / 11.00

Years with the current employer 0.01 to 45 6.22 / 3.50

Number of construction employers in previous 3 yrs 1 to 100 2.25 / 1.00

Number of projects worked in previous 3 yrs 1 to 300 10.09 / 5.00

49

Average hours worked per week in previous month 9 to 100 44.35 / 42.00

Did respondent have any job-related safety training Yes or No 97.8% yes

Was respondent ever a safety committee member Yes or No 37.6% yes

Was respondent a member of a union Yes or No 60.0% yes

Trade type

Laborer 20.1%

Carpenter 14.2%

Electrician 10.6%

Plumber 8.6%

Ironwork 8.2%

Operator 7.2%

others 31.1%

Job position

Supervisor 30.7%

Journeyman 50.9%

Apprentice 18.4%

To validate the sample, two demographic characteristics were compared to publically available

data. The age distribution of construction workers in Ontario are 11.5% (15 to 24 years)

(Statistics Canada 2015b), 70.2% (25 to 54 years), and 18.4% (55 years or more). The sample

percentages are 7.3%, 74.5%, and 18.1% respectively, indicating that the sample has 4% fewer

young workers and 4% more middle aged workers than those estimated by Statistics Canada.

Employment by size of employer (Statistics Canada 2015c) in Ontario is estimated as 16.7%

micro, 57.1% small, 13.6% medium, and 12.8% large –sized employers. The sample

percentages are 5.1%, 55.7%, 25.7%, 13.5%, respectively. These values indicate that the

sample is skewed away from micro-employers and toward medium-sized employers. The

research team found that accessing the micro-employers was very challenging as they tended

to be paid by unit prices, and as a result they were less willing to take time from their work to

participate in the survey. The effect of this skewness on the survey results is unknown.

3.2.1.4. Incidents


five times’, and ‘more than 5 times’ in the previous 3 months. 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 3-2 shows the frequency of physical safety outcomes. In total, 80.6% and 66.7% of the



injuries and unsafe events included incidents like cuts that are not severe but have very high

50

occurrences. Cut or puncture, headache/dizziness, strains or sprains, and persistent fatigue are

the most frequently experienced physical injuries (symptoms) 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, or

approximately 4% experienced these incidents recently.

Table 3-2. Frequency of physical safety outcomes

Reported at least one occurrence in previous 3 months (%)


cut/puncture 53.4




skin rash/burn 24.7

eye injury 11.8





hernia 4.0

Unsafe events 66.7



pinch 34.3







trapped by something collapsing, caving, or overturning 2.3

Table 3-3 shows the frequency of job stress. 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 work related worries. Relatively fewer incidents of

51

feeling incapable of making decisions and losing confidence were reported (16% and 15%,

respectively).


Reported at least one occurrence in previous 3 months (%)

Job stress 55.2

lost much sleep due to work-related worries 36.7


been unable to concentrate on work related tasks 28.8

been unable to enjoy my normal day-to-day activities 28.6


been losing confidence in myself 15.0


In total, 781 cases were used for the analysis (670 surveys complete and 111 with on average

5% missing information). Missing values were replaced with the variable means. 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). 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. After examination, it is believed that the outliers are the natural variation of

the data, thus, the cases with extreme values were considered important and kept.



23). The first step was to determine whether ICWS and ICWC were conceptually distinct.

Confirmatory factor analysis was 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

52

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). Square multiple correlation (SMC) of the statement variables was provided as a

communality estimate, indicating the amount of variance in the scale variable explained by the

common factor. 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 variables were analyzed.

Finally, structural equation modeling (SEM) techniques were used to examine the relationships

between the studied variables. SEM is a very general statistical modeling technique widely used

in the behavior sciences (Hox and Bechger 1998), and the relationships between the studied

variables are represented by regression or path coefficients in SEM.

3.2.2.3. SEM fit indices

There is no consensus about which index to use to determine the best fit of a model. Hooper et

al. (2008) have suggested reporting multiple indices because they reflect different aspects of

model fit. The fit indices used for SEM included an overall fit statistic 2, the relative 2 (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 is very sensitive to sample size, it should be reported along with its degree of

freedom (d.f.) and associated p value (Kline 2005). The relative 2 (Wheaton et al. 1977) can

address the sample size limitation, and thus it was included. A suggested range for the upper

bound of 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, RMSEA should be between 0 and 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 as low as 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).

53

3.3. Results


A hypothesized three-factor model was examined, composed of ICWC, ICWs, and IR. Two

competing models including a one-factor model and a two-factor model (ICWS and ICWC

combined to one factor: ICW) were also assessed (Table 3-4). Both of the alternative competing

models are nested in the proposed three-factor model, so the hypothesized three-factor model

was compared with each of the two 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 and d.f. for the hypnotized three-factor model and the alternative two-factor model

are 196.99, 49 and 387.68, 51 respectively. The 2 difference between these two models is

190.69 with 2 d.f., which is significant. This suggests that the three-factor model is superior to

the two-factor model. Following this, it was found that three-factor model also performed much

better than the one-factor model. Thus, the hypothesized three-factor model is the best option.

The findings also show that the ICWC and ICWs are conceptually distinct.

Table 3-4. Fit indices for the measurement models

Model 2 d.f. 2 diff d.f. diff

2/ d.f.

RMSEA SRMR CFI PNFI

Hypothesized three-factor model: ICWC + ICWs + IR

196.99 49 4.02 0.06 0.04 0.97 0.71

Alternative two-factor model: ICW+IR

387.68 51 190.69 2 7.60 0.09 0.04 0.92 0.70

Alternative one-factor model

1410.70 52 1213.71 3 27.12 0.18 0.17 0.68 0.53

Table 3-5 shows the square multiple correlation (SMC) of each scale statement and factor

loadings. Note that CS is short for ICWS and CC is short for ICWC. The factor loadings are the

correlation coefficients, ranging from 0.58 to 0.88. SMC of statement CS1 is 0.55, i.e. 55% of

the variance of CS1 was explained by the factor “ICWS”. On the whole, SMC ranged from 0.34

to 0.77, thus, the adequacy of the measurement model was supported.

Table 3-5. Measurement model: square multiple correlations (SMC) and factor loadings

54

No. Scale statements SMC IR ICWS ICWC

IR1 It is so easy for me to stay focused and accomplish my goals

0.38 0.61

IR2 I am confident that I could deal efficiently with unexpected events

0.55 0.74

IR3 I remain calm when facing difficulties because I can rely on my coping abilities

0.56 0.75

IR4 When confronted with a problem, I can usually find several solutions

0.34 0.58

IR5 I can cope with stress 0.43 0.66

IR6 I can focus and think clearly when I am under pressure 0.39 0.62

CS1 How often do you get into arguments with your supervisors (subordinates)?

0.55 0.74

CS2 How often are your supervisors (subordinates) rude to you at work?

0.77 0.88

CS3 How often do your supervisors (subordinates) do mean things to you at work?

0.64 0.80

CC1 How often do you get into arguments with your coworkers? 0.45 0.67

CC2 How often are your coworkers rude to you at work? 0.68 0.83

CC3 How often do your coworkers do mean things to you at work?

0.67 0.82

All the estimates are significant (p<0.001).

3.3.2. Descriptive statistics

Table 3-6 shows the mean and standard deviation of the variables, along with bivariate

correlations (r) between them. The mean of ICWS and ICWC were 1.19 and 1.23, respectively,

which means that, on average, workers believed interpersonal conflicts rarely occurred in their

workplace. For the three demographic variables, only weekly workhours had a very weak but

significant correlation with ICWS (r=0.09, 0.01< p<0.05) and job stress (r=0.07, 0.01< p<0.05).

Individual resilience had significant negative correlations with ICWS and ICWC, physical safety

outcomes, and job stress. Further, ICWS and ICWC had significant correlations with physical

safety outcomes and job stress. It is also noteworthy that ICWS and ICWC had a relatively high

correlation with each other (r=0.81, p<0.01).

Table 3-6. Descriptive statistics of variables Variables M SD 1 2 3 4 5 6 7 8 9

1 Weekly working hours 44.35 8.05 - -

0.05 0.04 0.03 0.09 0.05 -

0.06 -0.05

0.07

2 No. of employers in the past 3 years

2.25 6.34 - 0.16 -0.05

0.02 0.03 0.06 0.03 -0.02

3 No. of projects in the past 3 years

10.09 21.14 - -0.01

0.00 0.02 0.06 0.05 -0.01

55

Variables M SD 1 2 3 4 5 6 7 8 9

4 IR 3.13 0.37 - -

0.28 -0.27

-0.14

-0.12

-0.16

5 ICWS 1.19 0.62 - 0.81 0.24 0.25 0.29

6 ICWC 1.23 0.59 - 0.26 0.27 0.29

7 Physical injuries 6.00 6.03 - 0.58 0.45

8 Unsafe events 3.63 4.53 - 0.40

9 Job Stress 3.55 5.12 -

Where: |r| < 0.07 are non-significant at p > 0.05;

0.07 ≤ |r| ≤ 0.10 are significant at 0.01<p<0.05;

|r| ≥ 0.10 are significant at p<0.01

More ICWC than ICWS were reported. As shown in Table 3-7, approximately 21% of the

respondents reported that they sometimes got into arguments with their coworkers, their

coworkers were sometimes rude to them, and 12% reported that their coworkers sometimes did

nasty things to them. By contrast, only 13%, 12%, and 7% of the respondents reported they

sometimes got into arguments with their supervisors or subordinates, their supervisors or

subordinates were rude to them, or, did nasty things to them. Further, 5-9% of the respondents

reported that they experienced one or more of the three forms of coworker conflicts often, while

5-6% reported that they experienced one or more of the three types of supervisor conflicts often.

This phenomenon is further illustrated in Figure 3-2.

Table 3-7. ICW individual statement frequency distribution (%) Scale Statements never rarely sometimes quite often very often

ICWS

CS1 48.4 33.9 13.0 3.4 1.3

CS2 51.3 30.8 11.8 4.2 1.9

CS3 62.0 25.1 6.8 3.8 2.3

ICWC

CC1 29.2 42.7 21.3 4.7 2.1

CC2 29.1 41.0 21.2 5.6 3.1

CC3 54.6 28.4 11.7 3.0 2.3

56

Figure 3-2. Frequency of interpersonal conflict


To examine the antecedents and the consequences of ICWS and ICWC, a structural model was

built (Figure 3-3). The overall model fit was assessed by 2 (95) =317.30, p<0.01. 2 tends to be

influenced by sample size, thus alternative fit indices were provided. In our model, 2/ d.f.=3.34,

RMSEA=0.06, SRMR=0.04, CFI=0.96, PNFI=0.74. These fit indices all indicate that the

hypothesized model fits the data well. The path coefficients of the model were standardized

regression coefficients. Coefficients of the path from weekly workhours to job stress and the

path from IR to physical injuries were significant to 0.01<p<0.05; all the remaining path

coefficients were significant to p<0.01. R2 for the endogenous variables, i.e. variables that serve

as a dependent variable in at least one regression equation in the SEM, were 0.09 for ICWS,

0.65 for ICWC, 0.06 for unsafe events, 0.38 for physical injuries, and 0.29 for job stress.

57

IR

ICWs ICWc

Weekly workhours Job stress

Physical injuries

Unsafe events

-0.29

0.81 0.24

0.61

0.1

3

0.09

0.39

-0.08

0.15

0.0

9

Figure 3-3. Structural model depicting the relationships between the studied variables

The two mobility variables, number of employers and number of projects worked in the previous

3 years, had no relationship with ICWS, ICWC, physical outcomes or job stress. When building

the model, the square and cubic of the number of employers and projects were calculated to

determine whether there is a linear relationship between them and ICWS, ICWC, physical

outcomes or job stress. However, no relationship was found. Hence, H4 and H5 are not

supported.

The model in Figure 3-3 was compared to a set of models with IR linked to unsafe events and

job stress, and (or) with weekly workhours linked to physical injuries and unsafe events, etc.

Based on the 2 differences and the associated significance, the model in Figure 3-3 was

determined the best option.

Path analysis of the model was conducted to test H1, H2, H3, and H6. As shown in Table 3-8,

ICWS and ICWC had a direct positive impact on job stress and unsafe events, respectively.

ICWS had an indirect positive impact on physical injuries and unsafe events via ICWC; and ICWC

had an indirect positive impact on physical injuries and stress via unsafe events. Weekly

workhours had a direct positive impact on ICWS and an indirect impact on ICWC, but with a

weaker significance (0.01<p<0.05). Finally, IR had a direct negative impact on ICWS, and an

indirect impact on ICWC via ICWS. Moreover, SEM mediation analysis was conducted, where a

hypothesized causal chain in which one variable affects a second variable that, in turn, affects a

third variable was tested. Mediation analysis is used to understand a known relationship by

exploring the underlying mechanism or process by which one variable influences another

58

variable through a mediator variable. It was found that ICWS fully mediated the impact of IR on

ICWC, and partially mediated the impact of IR on job stress. ICWC fully mediated the impact of

ICWS on unsafe events.

Table 3-8. Direct and indirect effect testing of the hypothesized model relationships

Path Direct effects

Indirect effects

10,000 Bootstrapping 95% C.I.

Direct effects

Weekly workhours -> ICWS 0.09* [ 0.01, 0.15]

Weekly workhours -> stress 0.09 [ 0.03, 0.15]

IR -> ICWS -0.29 [-0.37, -0.20]

IR -> physical injuries -0.08* [-0.14, -0.01]

ICWS -> ICWC 0.81 [0.74, 0.87 ]

ICWS -> stress 0.15 [0.07, 0.23]

ICWC -> unsafe events 0.24 [0.17, 0.31]

Unsafe events -> physical injuries 0.61 [0.54, 0.67]

Unsafe events -> stress 0.13 [0.04, 0.23]

Physical injuries -> stress 0.39 [0.31, 0.48]

Indirect effects

Weekly workhours -> ICWC 0.07* [0.01, 0.12]

Weekly workhours -> unsafe events 0.02* [0.00, 0.03]

Weekly workhours -> physical injuries 0.01* [0.00, 0.02]

Weekly workhours -> stress 0.02* [0.00, 0.04]

IR -> ICWC -0.23 [-0.31, -0.16]

IR -> unsafe events -0.06 [-0.09, -0.03]

IR -> physical injuries -0.03 [-0.05, -0.02]

IR -> stress -0.09 [-0.14, -0.05]

ICWS -> unsafe events 0.19 [0.14, 0.25]

ICWS -> physical injuries 0.12 [0.08, 0.16]

ICWS -> stress 0.07 [0.05, 0.10]

ICWC -> physical injuries 0.15 [0.10, 0.19]

ICWC -> stress 0.09 [0.06, 0.13]

Unsafe events -> stress 0.24 [0.18, 0.31]

*: the standardized coefficients are significant with 0.01<p<0.05; the remained coefficients and

95% bootstrapped confidence intervals (C.I.) are significant with p<0.01.

3.4. Discussion

The objectives of this study are to determine whether interpersonal conflict at work (ICW) is

common on construction sites, to investigate its relationships with physical safety outcomes and

job stress of construction workers, and to examine the impact of workhours, mobility, and

individual resilience (IR) on ICW. Although, on average, only 6.3% of respondents reported that

conflicts at work occurred quite often or very often, ICW had a significant impact on all three

types of safety outcomes as demonstrated by the correlation coefficients of 0.24 to 0.29 in Table

59

3-6 and the direct and indirect effect analysis in Table 3-8. It extended what was known about

ICW and injuries in construction, and showed that ICW is also a source of job stress on

construction site. From this, it is concluded that hypotheses 1 and 2 were supported. The

conflict identified herein clearly goes beyond friendly banter. Management and supervisors are

encouraged to address situations of potential conflict amongst workers as quickly as possible to

avoid negative effects on safety.

Individual resilience (IR) had a significant negative correlation with both ICWS and ICWC, which

in turn could decrease the frequency of physical safety outcomes and job stress. Through this

finding, hypothesis 6 was supported. The model in Figure 3-3 provided additional insights to the

pathways of impact between the model factors. The pathway for IR to impact injuries is twofold:

1) a weak but direct path, and, 2) a more circuitous but stronger path that first works to

decrease ICWs and ICWc, which in turn decreases unsafe events and injuries. Job stress was

also found to be affected by IR through ICWs, injuries, and unsafe events. As much as two

decades ago, improving one’s IR was thought to be a secondary preventer of job stress (Cooper

and Cartwright 1997), and the findings herein support it. The mediating effect of ICWS allowed

IR to influence ICWC and safety outcomes, further emphasized the importance of IR on reducing

conflict-related outcomes. As a result, organizations may consider expanding their safety

training programs to include situational awareness activities that improve workers’ relaxation

techniques and cognitive coping skills to weaken the impact of ICW on their safety performance

and psychological health.

Work hours were confirmed to be a risk factor for the occurrence of both ICWS and ICWC in the

construction industry (Brockman 2014), but more research is needed to strengthen this finding.

On the other hand, mobility was not related to ICW, which is not consistent with previous

research (De Raeve et al. 2008, 2009). One possible explanation is that due to the temporary

nature of the work, construction workers may establish relationships and stay with a supervisor

or foreman for years although they change employers or projects. In this case, the impact of

organizational change on ICW may not exist. Future research may further validate this by

investigating the impact of changing supervisors on ICW.

There are several limitations of this study. First, the data were cross-sectional data. Future

researchers may consider using diaries to conduct longitudinal studies to examine the degree to

which one day’s conflict affects conflict, stress, and safety outcomes in subsequent days and

weeks. Second, the current research was based on data from contracting companies. As the job

stressors of people from consulting companies and contracting companies may be different (Yip

60

and Rowlinson 2009), it is possible that the conflict level may be different in consulting

companies even if they work in construction-related activities. Thus, future research may survey

people from consulting companies.

3.5. Conclusions

In this study, 837 surveys were collected from more than 100 construction sites in Ontario,

Canada. It was found that while only 6.3 % of participants reported experiencing interpersonal

conflict quite or very often, it had a significant effect on physical safety outcomes and job stress.

Structural equation modeling (SEM) was used to test the relationships between the antecedents

of interpersonal conflict at work (ICW) i.e. workhours, mobility, and individual resilience (IR), and

its consequences on safety performance. Six major hypotheses were presented, four of which

were supported by the data.



H3: number of hours worked in the previous month is positively associated with ICW




Specially, ICW was validated as being positively associated with physical safety outcomes and

job stress; workhours was positively related to ICW; and, IR was negatively related to ICW. This

study indicated a causal chain between IR, ICW, and self-reported safety performance and

psychological health of construction workers. Safety professionals may consider expanding

safety training to include skill development to improve their employees’ coping abilities and

individual resilience to reduce the occurrence and impacts of interpersonal conflict at work.

61

Chapter 4 Resilience on construction sites: testing a structural

equation model

Yuting Chen, Brenda McCabe and Douglas Hyatt

Abstract

Resilience has been proposed to be a proactive approach to safety management for the next

generation. Qualitative studies on defining resilience and using resilience rules to interpret

safety practices have been widely conducted; however, relatively few quantitative studies have

been done to measure resilience. Further, no empirical studies have investigated the

interactions between resilience factors and safety outcomes. This paper aims to identify the

interactions among resilience factors, and their impact on individual safety performance on

construction sites. The research was based on 431 self-administered surveys from 68 sites in

Ontario, Canada. Structural equation modelling (SEM) techniques were used to test the

hypotheses. This paper leads to several conclusions. First, management commitment is the key

to promoting a strong safety culture, and safety awareness is the most important individual

factor affecting the safety performance of construction workers. Second, support from team

members, especially coworkers, has a significant positive impact on improving the safety

awareness of construction workers. Finally, reporting practices did not perform well, although it

is viewed as a fundamental component of a safety culture. The contributions to the Body of

Knowledge are twofold. The relationship between construction safety outcomes and

organizational resilience is quantified for the first time, and the pivotal role that management has

in the safety performance of workers is demonstrated. Secondly, this is the first empirical study

to validate the direct impact of awareness on construction workers’ safety performance.

Keywords: organizational resilience; safety awareness; safety performance in the construction

industry; proactive safety management

4.1. Introduction

Resilience has been proposed as a new approach for the next generation of safety

improvement (Hollnagel 2015). It is regarded as a capacity for positive response and healing

capabilities to normal operations as well as maintaining high level safety during stress and

disturbance (Bruyelle et al. 2014; Ross et al. 2014), which is fundamental for human and

62

organizational functionality and viability (Carmeli et al. 2013). Compared to traditional safety

methods, it is a proactive approach to safety management that recognizes the complexity in an

ever-changing environment (Bergstrom et al. 2015; Carmeli et al. 2013); and 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

measures and quantifying resilience. Regarding resilience measures, most of the existing

research used the scales summarized by (Woods and Wreathall 2003), which include

management commitment, reporting culture, learning culture, anticipation, awareness, and

flexibility. These scales may vary depending on the industry. For example, (Azadeh et al.

2014a) added self-organization, team work, redundancy, fault-tolerant to the six scales.

Compared with qualitative studies focused on defining resilience measures, relatively few

quantitative studies have been done, providing a gap that needs to be explored. To the

knowledge of the authors, only four papers in the literature 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).

The application of resilience is particularly suitable for high-risk systems with complex

characteristics, namely, (a) a high degree of inter-connection between the components of the

system; (b) uncertainty and variability (Costella et al. 2009). As a complex, dynamic, and

unstable system, a construction site needs resilience to develop prevention strategies (Costella

et al. 2009). There are several qualitative studies applying resilience to the construction

industry, including a safety culture model based on resilience engineering (Han et al. 2010a; b),

re-interpreting safety management practices using resilience principles (Saurin et al. 2008), a

task demand model for construction safety risks (Mitropoulos et al. 2009), and using resilience

principles to help disaster management during construction (Bosher 2011; Bosher et al. 2007).

However, no quantitative studies have been conducted in the construction industry. Further, no

study has investigated the interactions amongst resilience indicators and individual safety

performance. For example, can top management affect accidents on site by supporting a culture

of reporting and learning? This paper aims to investigate the interactions among the resilience

indicators and their impact on individual safety performance on construction sites. The

quantitative approach used in the study is structural equation modeling (SEM). The following

sections define resilience indicators used in this paper and propose hypotheses.

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4.1.1. Resilience indicators

Many resilience indicators have been proposed to measure the adaptive capabilities of

organizations. The most widely acknowledged six scales were summarized from previous work

by (Woods and Wreathall 2003):

Management commitment. The commitment of the management to balance production

and protection is a core measure of resilience.

Reporting culture. The degree to which the workers are openly encouraged to report

safety concerns provides a critical source of resilience within an organization.

Learning culture. How much the organization learns from the events (denial or true

reform) is a key sign of resilience.

Preparedness/anticipation. Proactiveness in detecting or assessing evidence of

developing problems versus only reacting after problems become significant is desired.

Flexibility. The ability of the organization to adapt to varying conditions in a way that

maximizes problem solving without disrupting overall functionality requires that people at

the field level (particularly first-level supervisors) are able to make important decisions

without having to wait unnecessarily for management instructions.

Awareness. The organization monitors safety boundaries and recognizes how close it is

to the “edge”. Awareness is also affected by the extent to which information about safety

concerns are distributed throughout the organization at all levels versus closely held by a

few individuals.

In addition to these six indicators, researchers working in different sectors may vary their focus.

Resilience in rail engineering planning focused on preparedness and anticipation (Ferreira

2011). In a petrochemical plant, the most important resilience factors were found to be

awareness, preparedness, and flexibility (Azadeh et al. 2014a). In a refinery, change

management was highlighted (Shirali et al. 2012).

To achieve resilience, efforts from all organizational levels including organizations, groups, and

individuals are needed (Johnsen and Veen 2013; Patterson et al. 2007). For a construction site,

resilience stakeholders are top management, site supervisors, and construction workers.

Management commitment is achieved by both the top management and site supervisors.

Coworkers’ support is also critical for a safe work environment. Regular safety meetings are the

most common formal approach to promoting a good reporting and learning culture. In this

paper, the resilience indicators include management commitment, supervisor safety perception,

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coworker safety perception, reporting, learning, anticipation, and awareness. The reasons we

did not include flexibility is that measurements found in previous studies were for the

manufacturing and industrial sectors, and are not appropriate for construction industry due to its

continuously changing work environment and stakeholders.

4.1.2. Hypotheses

Top management commitment is widely considered an essential positive factor affecting safety

(Flin et al. 2000). As a core of resilience, management commitment may come in the form of a

higher safety budget, full-time trained first aid personnel, or an enhanced in-house safety

program. It is believed that management commitment plays a positive role in establishing a

good reporting and learning culture; however, to our knowledge, no empirical studies in the

construction industry have tested these hypotheses. Thus, the H1 hypothesis was proposed:

management commitment is positively related to reporting and learning.

The impact of top management on site safety is achieved through supervisors. On a

construction site, supervisors set the work atmosphere and therefore significantly influence the

safety of their site. Safety perceptions of supervisors can have a lasting impact on safety by

including safety in their daily verbal exchanges with workers (Kines et al. 2010). Incident

reporting and learning from past events are considered one of the foundations of a true safety

culture (Wiegmann et al. 2004). Further, incident reporting and learning from past events is the

major safety communication approach on a construction site, which must be affected by safety

perceptions of site supervisors. That is to say, if a site supervisor truly cares about safety, he or

she may hold regular safety meetings; and then it is likely that construction workers will be

encouraged to report incidents without being seen as trouble makers. Likewise, no empirical

studies have validated these hypotheses. Thus, the H2 hypothesis was proposed: supervisor

safety perception is positively related to reporting and learning.

Construction workers are directly involved in the building process, and their ability to anticipate

and maintain a high safety awareness is critical. However, continuous anticipation and

assessment of the work environment may be difficult to achieve, given that construction workers

have multiple goals to satisfy in addition to safety, such as a tight project schedule (Mitropoulos

and Cupido 2009). So a question is raised: under a high work demand, what factors can

positively affect an employee’s anticipation and safety awareness? Sneddon et al. (2006) found

that maintaining a good communication in a rig is the best method of increasing awareness of

offshore drill crews. For construction, little has been reported. Raising safety concerns during

65

safety meetings and getting feedback or finding solutions to problems could mitigate the risk of

incidents. Thus, H3 and H4 were proposed: learning and reporting are positively related to

anticipation and awareness. The social exchange between supervisors and coworkers has an

impact on the communication of safety (Hofmann and Morgeson 1999). A study about the

trackside workers found that perceived coworker support for safety was most important for

keeping employees safe in the face of high job demands (Turner et al. 2010). Therefore, a

construction worker’s supervisor and coworkers’ safety perception likely affect his/her

anticipation and awareness. For example, if a coworker encourages him or her to work safely,

this worker may work more safely than if they didn’t receive the encouragement. Conversely, if a

coworker ignores safety, the worker may be negatively affected. Based on this, hypotheses H5

and H6 were proposed: supervisor and coworker safety perception are positively related to

anticipation and awareness. Finally, anticipation and awareness are related to safety

performance (Sneddon et al. 2013). However, little has been reported in the construction

industry. Thus, H7 and H8 were proposed: anticipation and awareness are negatively related to

unsafe events. Table 4-1 summarizes the hypotheses.

Table 4-1. Hypotheses in the study

Hypothesis number


Hypothesized relation

Potential correlated factors

(a) (b)









MC: management commitment to safety; LN: learning; RP: reporting; SS: supervisor safety

perception; CS: coworker safety perception; AN: anticipation; AW: awareness

4.2. Methods

4.2.1. Data and procedures

A multi-site data collection strategy was employed. In total, 431 surveys were collected from 68

construction sites between May and July 2016. Before performing any analysis, 28 cases were

removed because a high proportion (>10%) of data were missing. Thus, 403 cases were used

for the analysis. Missing values were replaced with the variable means in the remaining cases.

66

A self-administered questionnaire that comprised demographics, attitude statements, and

incident reporting was used to collect data. The demographics and incident reporting were

adapted from the previous research (McCabe et al. 2008). The demographics section included

questions about the individual’s characteristics, such as age and trade type. In the attitudinal

section, respondents indicated the degree to which they agreed with statements using a Likert

scale between 1 (strongly disagree) and 5 (strongly agree). The incident reporting section asked

the respondents how frequently they experienced unsafe 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 describes symptoms of job-related stress. Physical injuries, such as an eye injury,

may be associated with an unsafe event, but no connection is made by the respondent. Unsafe

events, such as struck against something fixed, comprise events that respondents experienced

but may or may not have resulted in an injury. One example of job stress is unable to

concentrate on work related tasks.


At least two research assistants (RA) were on site to administer the surveys to workers. A

procedure was strictly followed wherein the RAs arrived at the site and met the workers in a

lunch trailer or other comfortable location. As each consent form was reviewed, signed, and

collected, the survey was distributed. RAs provided immediate help to workers if they had a

question, which improved the reliability and completeness of the data. The surveys took 15-20

minutes to complete and were collected immediately upon their completion. They were strictly

anonymous and no follow up was undertaken. The number of surveys collected from each site

ranged from 1 to 35, with an average around 6. Each survey required approximately 4 research-

hours, including finding sites, communicating with corporate managers and site

superintendents, transportation to site, and data collection.

4.2.1.2. Demographics of the respondents

The respondents were from a variety of construction sectors, including residential, heavy civil,

institutional, and commercial. Table 4-2 summarizes the demographic information of the

respondents. The mean age of the respondents was 37 years and 98% were male; 68% of

workers were journeymen or apprentices. The respondents had been employed by their current

employer for 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

67

hours of the respondents were approximately 44 hours, and 32% worked more than 44 hours,

which is considered overtime (Ontario Ministry of Labour 2015). Finally, 60% of the respondents

were union members.


Demographic factors Response

range Mean or percent

Median

Gender Male / Female 98% male -

Age 16 to 67 37.00 36.00

Years in construction 0.5 to 46 14.29 11.00

Years with the current employer 0.01 to 45 6.01 4.00

No. of construction employers in previous 3 years 1 to 100 2.62 1.00

No. of projects worked in previous 3 years 1 to 300 9.79 4.00

Average hours worked per week in previous month 17 to 85 43.57 40.00

Was respondent a member of a union Yes or No 65.0% yes -

Job position

Supervisor 32.1% -

Journeyman 50.3% -

Apprentice 17.7% -

Size of employers

Micro (1-4) 5.6 % -

Small (5-99) 51.0% -

Medium (100-499)

28.6% -

Large (500+) 14.8% -

4.2.1.3. Incidents


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 4-3 shows the frequency of physical safety outcomes. In total, 79.2% and 68.2% of the



injuries and unsafe events included incidents like cuts that are not severe but may occur often.

Cut or puncture, strains or sprains, persistent fatigue, and headache/dizziness 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, 44% of

the respondents experienced overexertion, 39% reported being exposed to chemicals, and

68

approximately one third experienced pinch and slip/trip/fall on the same level at least once in the

previous 3 months. With regard to the more severe incidents, such as dislocated or fractured

bones and fall from height, it is very surprising that 20 workers, or approximately 5% of

respondents experienced these incidents recently.

Table 4-3. Frequency of safety outcomes

Reported at least 1 occurrence in previous 3 months (%)


cut/puncture 52.0




skin rash/burn 25

eye injury 12.7





hernia 3.5

Unsafe events 68.2



pinch 34.3







trapped by something collapsing/caving/overturning 1.7

Table 4-4 shows the frequency of job stress. More than a half of the respondents reported at

least one occurrence of job stress. Approximately 30% to 38% of the respondents reported that

they were unable to enjoy daily activities, felt constantly under strain, unable to concentrate on

work, and lost sleep because of the work related worries. Relatively fewer incidents of feeling

incapable of making decisions and losing confidence were reported (17% and 16%,

respectively).

69


Report at least one occurrence in previous 3 months (%)

Job stress 58.3

lost sleep due to work-related worries 38.0

unable to concentrate on work tasks 31.6


unable to enjoy day-to-day activities 30.3


losing confidence in self 15.9

4.2.2. Measures

The suggested alpha values from internal-consistency reliability tests for the scales range from

0.7 to 0.9 (Tavakol and Dennick 2011), although values less than 0.7 can be accepted

(Loewenthal 2001).

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


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


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.

Reporting examines whether people on site are willing to report incidents without fear of

retribution. Three statements were adapted from (Ostrom et al. 1993). An example is “people

who raise a safety concern fear retribution.” The coefficient alpha of the scale is 0.71.

70

Learning is the workers’ perception about whether they are informed of past lessons and

whether they can learn from past events. Four statements were used. Two statements were

from the previous research (Ostrom et al. 1993). An example is “timely feedback is provided

when a safety hazard is reported.” The remaining two statements were adapted from (Carthey

et al. 2001). An example is “safety is discussed at regular meetings, not just after an accident.”

The coefficient alpha of the scale is 0.83.

Anticipation examines whether workers on site assess the potential safety impacts caused by

their decisions and detect the potential failures or errors before they create problems. Two

statements were used to measure this scale (Ferreira 2011). One example is “I detect failures or

errors in my job before they create problems.” The coefficient alpha of the scale is 0.68.

Awareness is about whether people on site are clear about their safety responsibilities and

aware of potential safety hazards. Three statements were adapted from previous research (Cox

and Cheyne 2000; Ostrom et al. 1993; Shirali et al. 2013). One example is “people are careful to

minimize and avoid safety hazards.” The coefficient alpha of the scale is 0.80.


4.2.3.1. Data screening

Regarding the univariate normality of the observed variables, none 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). 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 considered important and kept.



23). Confirmatory factor analysis was used to assess the adequacy of the previously mentioned

scales. Robust maximum likelihood estimation technique was used to handle the multivariate

non-normality (Byrne 2001a) (Brown 2015). In Amos, the robust estimation was achieved by a

71

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). Square multiple correlations (SMCs) of the statement variables were provided as

communality estimates, indicating the amount of variance in the scale variable explained by the

common factor. Internal-consistency reliability tests were also conducted to show how well the

individual scale statements reflected a common, underlying construct, resulting in the coefficient

alpha for each scale. Then descriptive statistics and correlations of the studied variables were

analyzed. Finally, structural equation modeling techniques were used to examine the

relationships between variables.

4.2.3.3. Model fit indices

There is no consensus about which indices to use, but it is suggested to report different types of

indices because they reflect different aspects of model fit (Hooper et al. 2008). 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 is very sensitive to sample size, it should be reported along with its degree of




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






2006; Williams and Holahan 1994)

72

4.3. Results


A hypothesized seven-factor model was examined, composed of management commitment to

safety, supervisor safety perception, coworker safety perception, reporting, learning, anticipation

and awareness. A set of selected alternative competing models were also assessed (Table 4-5).

These alternative models included one-factor model, two-factor model, three-factor model, four-

factor model, five-factor model, and six-factor model.

The hypothesized seven-factor model was compared with 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 seven-factor model is 663.62 with

a degree of freedom is 326 and the 2 value of the alternative model six-factor model (a) is

757.23 with a degree of freedom is 332. The 2 difference between these two models is 93.61

with a degree of freedom of 6, which is significant. This suggests that the seven-factor model is

better than the six-factor model (a). Following this rule, we found that the hypothesized seven-

factor model performed better than all of the alternative models. In the seven-factor model, 2

(381) =663.62, p<0.01. The fit indices have the following values: 2/ d.f.=2.04, RMSEA=0.05,

SRMR=0.05, CFI=0.93, PNFI=0.76. Overall, the fit indices suggest the seven-factor

measurement model fits the data well.

Table 4-6 shows the factor loadings and square multiple correlations (SMCs) of each scale

statement. Table C-1 in the Appendix C shows the detailed scale questions. All the estimates in

Table 4-6 are significant (p<0.001). The factor loadings are the correlation coefficients, ranging

from 0.43 to 0.90. SMCs for the scale statements indicate the amount of variance in the scale

statement explained by the common factor. For example, SMCs of statement MC1 is 0.64, i.e.

64% variance of MC1 was explained by the factor “management commitment to safety”. On the

whole, SMCs ranged from 0.18 to 0.81. Accordingly, the adequacy of the measurement model

was supported.

73

Table 4-5. Fit indices for the measurement models

Model 2 d.f. 2 diff d.f. diff

2/ d.f.

RMSEA SRMR CFI PNFI

Hypothesized seven-factor model: MC+SS+CS+RP+LN+AN+AW

663.62 326 2.04 0.05 0.05 0.93 0.76

Alternative six-factor model (a): MC + RP + LN + AN + AW 757.23 332 93.61 6 2.28 0.06 0.06 0.92 0.76

Alternative six-factor model (b): MC + RP + LN + SS + CS 768.15 332 104.53 6 2.31 0.06 0.05 0.91 0.76

Alternative six-factor model (c): CS + RP + LN + AN + AW 940.90 332 277.28 6 2.83 0.07 0.06 0.88 0.73

Alternative six-factor model (d): MC + SS + CS + AN + AW 970.20 332 306.58 6 2.92 0.07 0.07 0.87 0.72

Alternative five-factor model (a): RP + AN + AW + CS 1084.88 337 421.26 11 3.22 0.07 0.06 0.85 0.71

Alternative five-factor model (b): MC + CS + AN + AW 1098.56 337 434.94 11 3.26 0.08 0.07 0.85 0.71

Alternative five-factor model (c): SS + CS + AN + AW 1184.94 337 521.32 11 3.52 0.08 0.07 0.83 0.70

Alternative five-factor model (d): CS + LN + AN + AW 1212.11 337 548.49 11 3.60 0.08 0.07 0.83 0.69

Alternative four-factor model (a): MC + AN + AW 1184.97 341 521.35 15 3.48 0.08 0.07 0.83 0.71

Alternative four-factor model (b): LN + AN + AW 1310.49 341 646.87 15 3.84 0.08 0.07 0.81 0.69

Alternative four-factor model (c): SS + AN + AW 1311.68 341 648.06 15 3.85 0.08 0.07 0.81 0.69

Alternative four-factor model (d):CS + AN + AW 1355.78 341 692.16 15 3.98 0.09 0.07 0.80 0.68

Alternative three-factor model (a): MC + RP 1177.30 344 513.68 18 3.42 0.08 0.07 0.84 0.71

Alternative three-factor model (b): MC + AW 1269.26 344 605.64 18 3.69 0.08 0.07 0.82 0.70

Alternative three-factor model (c): MC + AN 1352.46 344 688.84 18 3.93 0.09 0.07 0.80 0.68

Alternative three-factor model (d): AN + AW 1452.54 344 788.92 18 4.22 0.09 0.08 0.78 0.67

Alternative two-factor model (a):MC 1431.76 346 768.14 20 4.14 0.09 0.07 0.79 0.68

Alternative two-factor model (b):RP 1464.49 346 800.87 20 4.23 0.09 0.08 0.78 0.67

Alternative two-factor model (c):AW 1541.60 346 877.98 20 4.46 0.09 0.08 0.76 0.66

Alternative two-factor model (d):CS 1649.33 346 985.71 20 4.77 0.10 0.08 0.74 0.64

Alternative one-factor model 1717.31 347 1053.69 21 4.95 0.10 0.08 0.73 0.63

TMC: top management commitment to safety; SS: supervisor safety perception; CS: coworker safety perception; RP: reporting; LN:

learning; AN: anticipation; AW: awareness

74

Table 4-6. Measurement model: square multiple correlations (SMCs) and factor loadings

Scale statement

s

SMCs Top management commitment

to safety

Supervisor safety

perception

Coworker safety

perception Reporting Learning Anticipation Awareness

MC1 0.64 0.80

MC2 0.60 0.77

MC3 0.55 0.74

MC4 0.48 0.69

MC5 0.45 0.67

MC6 0.41 0.64

SS1 0.68 0.83

SS2 0.65 0.81

SS3 0.61 0.78

SS4 0.44 0.66

SS5 0.33 0.57

SS6 0.31 0.56

CS1 0.50 0.71

CS2 0.43 0.65

CS3 0.24 0.49

CS4 0.18 0.43

RP1 0.20 0.90

RP2 0.81 0.82

RP3 0.54 0.45

LN1 0.67 0.82

LN2 0.56 0.75

LN3 0.54 0.74

LN4 0.45 0.67

AN1 0.57 0.76

AN2 0.47 0.69

AW1 0.62 0.79

AW2 0.61 0.78

AW3 0.52 0.72

All the factor loadings are significant (p<0.01).

75

4.3.2. Descriptive statistics

Table 4-7 displays descriptive statistics and the correlations between the studied variables. In

general, management commitment to safety, supervisor safety perception, coworker safety

perception, reporting, learning, and awareness had significant negative correlations with

physical injuries, unsafe events, and job stress. Anticipation is an exception, because it is only

negatively correlated with unsafe events.

Management commitment to safety, supervisor safety perception, coworker safety perception,

reporting, learning, anticipation, and awareness were all positively correlated with each other. It

is also worth noting that management commitment had a relatively high correlation with

supervisor safety perception (r=0.70) and learning (r=0.70); supervisor safety perception had a

relatively high correlation with coworker safety perception (r=0.66), learning (r=0.77), and

awareness (r=0.64). Reporting seemed very distinct from other variables, because it had a

relatively low correlation with the remained scales, with the correlation coefficients ranging from

0.21 to 0.38. In terms of the mean values of all the studied variables, reporting and coworker

safety perception were the lowest.

Finally, management commitment to safety and supervisor safety perception had the strongest

negative correlations with physical injuries; management commitment to safety, supervisor

safety perception, and awareness had the strongest correlations with unsafe events; supervisor

safety perception, coworker safety perception, and awareness had the strongest correlations

with job stress.

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Table 4-7. Descriptive statistics and correlations M S.D. 1 2 3 4 5 6 7 8 9 10

1. Physical injuries 6.08 6.22

2. Unsafe events 3.66 4.34 0.59

3. Job stress 3.65 5.00 0.48 0.43

4. Management commitment to safety

3.65 0.52 -0.20 -0.25 -0.14 -

5. Supervisor safety perception

3.92 0.63 -0.21 -0.24 -0.19 0.70 -

6. Coworker safety perception

2.75 0.42 -0.16 -0.18 -0.19 0.49 0.66 -

7. Reporting 1.86 0.46 -0.15 -0.14 -0.12 0.30 0.33 0.33 -

8. Learning 4.64 0.74 -0.18 -0.20 -0.17 0.70 0.77 0.58 0.21 -

9. Anticipation 3.51 0.51 -0.09 -0.15 -0.07 0.38 0.50 0.39 0.21 0.56 -

10. Awareness 3.89 0.53 -0.18 -0.25 -0.19 0.51 0.64 0.67 0.38 0.63 0.49 -

Absolute values of the correlation coefficients less than or equal to 0.09, non-significant (p > 0.05); absolute values equal to 0.12,

significant (0.01<p<0.05); absolute values larger than 0.12, significant (p<0.01).

77


Figure 4-1 shows the final structural model. The overall model was assessed by 2 (418)

=770.11, p<0.01, 2/ d.f.=1.84, RMSEA=0.05, SRMR=0.05, CFI=0.94, PNFI=0.78. These

indices suggest that the proposed model fits the data well. The path coefficients in the model

are standardized regression coefficients. All the paths except the paths from management

commitment to reporting and the path from anticipation to unsafe events are significant to

p<0.01. R2 for the endogenous variables, i.e. variables that serve as a dependent variable in at

least one regression equation in the SEM, were 0.12 for reporting, 0.50 for supervisor safety

perception, 0.66 for learning, 0.52 for coworker safety perception, 0.60 for awareness, 0.35 for

anticipation, 0.11 for unsafe events, 0.40 for physical injuries, and 0.32 for job stress.

The model in Figure 4-1 was compared to models with management commitment linked to

reporting and (or) with anticipation linked to unsafe events. Based on the 2 differences and the

associated significance, the model in Figure 4-1 was determined the best option.

Figure 4-1 shows that management commitment has a significant positive impact on learning

(β=0.29), thus, H1(a) is supported. The impacts of supervisor safety perception on reporting

(β=0.24) and learning (β=0.58) are significantly positive. Hence, H2(a) and H2(b) are supported.

The effect of learning on anticipation is positively significant (β=0.57). Thus, H3(a) is supported.

The impact of reporting on awareness is also significantly positive (β=0.17). Hence, H4(b) is

supported. Coworker safety perception has a direct impact on awareness. Hence, H6(b) is

supported. Awareness has a direct negative impact on unsafe events (β=-0.16). Therefore, H7

is supported. No direct impact of anticipation on unsafe events was found. Table 4-9

summarizes the testing results, which show that seven out of fourteen hypotheses have been

validated. It is also worth noting that management commitment has a significant negative impact

on unsafe events (β=-0.26).

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Top 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

.58

0.17

0.41

0.57

0.1

5

0.4

3

-0.16

ns

0.63

0.17

-0.26

Figure 4-1. Structural model depicting the relationships between the studied variables

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No direct impact does not mean no impact. In addition to the direct effect analysis, Table 4-8

demonstrates the indirect effect of one variable on another. For example, management

commitment has a significant positive impact on reporting via supervisor safety perception

(β=0.17). Supervisor safety perception has a very strong significant positive impact on

awareness via reporting, coworker safety perception, learning, and anticipation (β=0.54), and a

significant positive impact on anticipation via learning (β=0.35). Anticipation has a significant

negative impact on unsafe events via awareness. Accordingly, all of the hypotheses are

supported, as summarized in Table 4-9.

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Table 4-8. Indirect effect analysis LN AN CS RP AW Unsafe events Physical injuries Job stress

TMC β 0.41 0.42 0.51 0.17* 0.46 ns -0.18 -0.13

95%C.I. [.31, .54] [.31, .53] [.42, .61] [.04, .33] [.36, .55] [-.25, -.11] [-.19, -.08]

SS β 0.35 0.54 ns ns ns

95%C.I. [.24, .48] [.42, .66]

LN β 0.17 ns ns ns

95%C.I. [.08, .33]

AN β -0.05* ns ns

95%C.I. [-.14, -.01]

CS β -0.09* -0.06* -0.04*

95%C.I. [-.19, -.01] [-.13, -.01] [-.09, -.01]

RP β -0.03* -0.02* -0.01*

95%C.I. [-.07, -.01] [-.05, .00] [-.04,.00]

AW β -0.11* -0.08*

95%C.I. [-.22, -.01] [-.16, -.01]

Unsafe events β 0.27

95%C.I. [.20,.36]

ns: non-significant; *: the standardized coefficients are significant with 0.01<p<0.05; the remained coefficients and 95% bootstrapped

confidence intervals (C.I.) are significant with p<0.01.

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Table 4-9. Summary of testing results

Hypothesis number


relation Potential correlated factors

(a) (b)









: direct impact supported

: indirect impact supported

4.4. Discussion

This paper aims to build a resilience model in the context of the construction industry to

investigate the interactions among the resilience indicators and their impact on individual

safety performance. Management commitment had the strongest impact on all the three

types of safety outcomes, which confirmed its central role in affecting safety

performance of workers (Jaselskis et al. 1996; Zohar 1980). Nurturing resilience is a way

to keep workers engaged in their work (Baldoni 2009). When a manager demonstrates

care and concern, people may have higher trust in their managers, which can lead to

more reporting, learning, and fewer unsafe acts.

Awareness is one’s perceptions of the elements in their work environment within a

volume of time and space, which provides employees the comprehension and a

projection of the work environment’s status in the near future (Endsley 1988). It had the

second strongest impact on all three types of safety outcomes, and was the conduit

variable through which the other influences traveled to affect safety outcomes in the

model. Approximately 11% of the variance in unsafe events was explained by

awareness. The implications of situational awareness for safety has been broadly

realized in other sectors, such as aviation (Endsley 1988), chemical processing (Kaber

and Endsley 1998), and offshore drilling industry (Sneddon et al. 2013) in the past 20

years. However, little attention has been paid to the construction industry. This is the first

empirical study to validate the direct and pivotal impact of awareness on construction

workers’ safety performance.

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All of the other six factors, namely, coworker safety perception (β=0.56), supervisor

safety perception (β=0.54), management commitment (β=0.46), anticipation (β=0.29),

reporting (β=0.17) and learning (β=0.17), had a significant positive impact on

awareness. Among the pathways leading to better safety awareness, the link from

supervisor safety perception to coworker safety perception to awareness had the

strongest positive impact. Further, coworker safety perception mediated the impact of

supervisor safety perception on awareness. This means that a supervisor’s safety

perception can positively affect one’s perceptions about the safety behaviors of

coworkers, which in turn increases safety awareness. This suggests not only the

importance of the supervisor serving as mentor and leader on site, but also the

importance of individuals in promoting safety awareness. Literature suggests feedback

and information sharing within or among teams (Kaber and Endsley 1998) can improve

the individual, intra-team, and inter-team levels safety awareness. A structured feedback

system can inform the employees that their suggestions or concerns have been

reviewed and what kind of actions, if any, will be taken to address the problem, which

has the potential to improve workers’ reporting, learning, and safety performance. Our

findings suggest the critical role of team members in one’s safety awareness, and

confirm the role of an efficient communication channel (i.e. reporting and learning) in

promoting a higher level of safety awareness.

The score of coworker safety perception was 2.75 out of 5 (Table 4-7), which reflects

that the participants had a relatively low perception of the safety behavior of their

coworkers. Construction organizations should continuously improve their employees’

safety perception through a higher level of management commitment and engaging

training programs. As one of the foundations of safety culture (Wiegmann et al. 2004),

reporting is very important. However, it had the lowest score (1.86 out of 5 in Table 4-7)

among all the resilience factors, which means that construction workers are not

encouraged to report safety incidents and that they believe that reporting safety

concerns would have negative consequences for them. Considering the positive impact

of reporting on awareness, it is important to ensure that employees will not experience

reprisals or negative outcomes as a result of using the reporting system (Wiegmann et

al. 2004). In addition, the impact of management on reporting was achieved via

supervisor safety perception, which highlights the important role of supervisors in the

operation level of promoting a good reporting culture.

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4.5. Conclusions

In this study, 431 surveys were collected from 68 construction sites in Ontario, Canada.

It is the first study to build a resilience framework in the construction industry and the first

study using structural equation modelling techniques. Fourteen hypotheses were

presented and supported by our data. This study demonstrated that management

commitment is critical to promoting a strong safety culture. As a conduit for the other

factors, awareness is the most important individual factor affecting the safety

performance of construction workers. Support from team members has a significant

positive impact on improving safety awareness of construction workers. Further, it was

found that reporting did not perform well, although it is a fundamental component of a

safety culture. Given these findings, construction organizations may consider focusing

safety programs to improve employees’ safety awareness, and building a better

reporting culture where everyone feels comfortable reporting their safety concerns. A

consistent and demonstrable level of organizational commitment is the key.

4.6. Limitations and future work

The scale of resilience research ranges from global resilience (Hirota et al. 2011),

community resilience (Cutter et al. 2008), plant resilience (Azadeh et al. 2014b), and

team resilience (Carmeli et al. 2013) in different research fields. This work is a multi-site

scale, which may be closer to a community or industry scale. This was based on the

assumption that all the surveyed sites are homogeneous in terms of their resilience level

and safety performance. However, because these sites were from different contracting

companies, they might vary in their resilience level and safety performance. Thus, future

work may focus on site-level analysis to examine whether these sites are indeed

heterogeneous regarding both resilience and safety performance levels.

On the other hand, this paper linked resilience to three types of safety outcomes,

namely, physical injuries, job stress, and unsafe events. However, these safety

outcomes are sometimes referred to lagging indicators of safety performance, whereas

leading indicators, such as safety compliance and safety participation, have been

suggested (Guo et al. 2016). Therefore, future research may explore the link between

resilience indicators, safety compliance, and safety participation. Moreover, this work

was based on cross-sectional data, which prevented making definitive causal

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conclusions. Future research may explore the possibility of conducting longitudinal

research.

85

Chapter 5 Conclusions, recommendations, and future work

In this chapter, the conclusions and contributions of this thesis are summarized. The

directions of future work are also outlined.

5.1. Conclusions

This thesis investigated the impact of safety climate, individual resilience, interpersonal

conflicts at work, and organizational resilience on safety outcomes in the construction

industry. From 2013 to 2016, 1281 surveys were collected from 134 sites in the Ontario

province. Questions of individual resilience and organizational resilience were developed

and tested in the research.

Based on the findings, the following major conclusions were made:

Safety climate not only affects physical safety outcomes but also an employee’s job

stress level.

Although ICW was reported as “quite often” or “very often” by only 6.3% of

respondents, it had a significant effect on both physical safety outcomes including

physical injuries and unsafe events, and job stress.

Individual resilience has the potential to mitigate post-trauma job stress and

interpersonal conflicts of construction workers.

Management commitment to safety is the key to promoting a good safety culture.

Safety awareness is the most important individual factor affecting construction

workers’ safety performance.

Team support, especially from coworkers, has a significant positive impact on

construction worker’s safety awareness.

5.1.1. Impacts on physical injuries and unsafe events

Factors affecting physical injuries and unsafe events were in descending order by

correlation coefficients. Factors that have a positive impact on physical injuries and

unsafe events, from the greatest impact to the smallest impact, are: work pressure, ICW,

and role overload. Factors that have a negative impact on physical injuries and unsafe

events, from the greatest impact to the smallest impact, are: management commitment,

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supervisor safety perception, learning, awareness, coworker safety perception, reporting

and anticipation.

5.1.2. Impacts on job stress

In a similar way, factors affecting job stress were ranked in descending order by

correlation coefficients. Factors that have a positive impact on job stress, from the

greatest impact to the smallest impact, are: work pressure, role overload, and ICW.

Factors that have a negative impact on job stress, from the greatest impact to the

smallest impact, are: coworker safety perception, supervisor safety perception,

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

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(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

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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”.

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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)

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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

1 2 3 4 5

75. Our management conducts frequent safety inspections

1 2 3 4 5

76. Our management investigates safety problems quickly

1 2 3 4 5

77. Our management rewards safe workers 1 2 3 4 5

78. Our management provides safe equipment 1 2 3 4 5

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?

Never Once 2-3

times 4-5

times

More than

5 times

1 2 3

123. Headache or dizziness

124. Persistent fatigue

125. Skin rash / burn

126. Strain or sprain (e.g. back pain)

127. Cut or puncture (open wound)

128. Temporary loss of hearing

129. Eye injury

130. Electrical shock

131. Respiratory injuries (e.g. difficulty breathing)

132. Dislocated / fractured bone

133. Hernia

Never Once 2-3

times 4-5

times

More than

5 times

1 2 3

134. Lost much sleep due to work related worries.

135. Been unable to concentrate on work related tasks.

136. Felt incapable of making decisions.

137. Felt constantly under strain.

138. Been losing confidence in myself.

139. Been unable to enjoy my normal day-to-day activities.

140. Was exposed to chemicals such as gases and fumes

Never Once 2-3

times 4-5

times

More than

5 times

1 2 3

141. Over exerted myself while handling, lifting or carrying

142. Slipped, tripped or fell on the same level

143. Fell from height

144. Was struck by a moving vehicle

145. Was struck by flying/falling object(s)

110

146. Struck against something fixed or stationary

147. Was trapped by something collapsing, caving in or overturning

148. Contacted moving machinery

149. Pinch ( Finger or hand was caught between two objects)

150. Other (Please specify)

111

Appendix B Table B-1. Scale statements


MC1 Our management provides enough safety training programs

MC2 Our management conducts frequent safety inspections

MC3 Our management provides safe equipment

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


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

Scale statements

MC1 Our management conducts frequent safety inspections

MC2 Our management provides safety equipment

MC3 Our management is strict about working safely when work falls behind schedule

MC4 Our management gives safety personnel the power they need to do their job

MC5 After an accident, our management focuses on how to solve problems and improve safety rather than pinning blame on specific individuals

MC6 Our management provides enough safety programs

SS1 My supervisor behaves in a way that displays a commitment to a safe workplace

SS2 My supervisor talks about values and beliefs in the importance of safety

SS3 My supervisor keeps workers informed of safety rules

SS4 My supervisor expresses satisfaction when workers perform their job safely

SS5 My supervisor spends time showing workers the safest way to do things

SS6 My supervisor makes sure that workers receive appropriate rewards for achieving safety targets on the job

CS1 My coworkers encourage others to be safe

CS2 My coworkers keep work area clean

CS3 My coworkers ignore safety rules (R)

CS4 My coworkers take chances with safety (R)

RP1 People hesitate to report minor injuries and incidents in my workplace (R)

RP2 People who raise a safety concern fear of retribution (R)

RP3 People who raise safety concerns are seen as trouble-makers (R)

LN1 Workers’ ideas and opinions on safety are solicited and used

LN2 Timely feedback is provided when a safety hazard is reported

LN3 Safety is discussed at regular meetings, not just after an accident

LN4 Workers are informed about lessons learned from past events

AN1 I assess the potential safety impacts for each of my decision or behaviors

AN2 I detect failures or errors in my job before they create problems

AW1 People are aware of the safety hazards in their work area

AW2 People are clear about their responsibilities for safety in my workplace

AW3 People are careful to minimize and avoid safety hazards in my workplace

R: reverse

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