doi:10.24900/ijss/01034660.2017.1201 advancing safety by ...€¦ · somayeh asadi1, ebrahim...

International Journal of Safety Science

Vol. 01, No. 03, (2017), pp. 46-60

DOI:10.24900/ijss/01034660.2017.1201

46

Advancing Safety by In-Depth Assessment of Workers Attention

and Perception

Somayeh Asadi1, Ebrahim Karan

2, Atefeh Mohammadpour

3

1 Department of Architectural Engineering, Pennsylvania State University, 104

Engineering Unit A, University Park, PA 16802, USA

2Department of Applied Engineering, Safety, and Technology, Millersville

University, Osburn Hall, PO Box 1002, 40 East Frederick Street, Millersville, PA

17551, USA

3Department of Manufacturing & Construction Engineering Technology, Indiana

University – Purdue University Fort Wayne, 2101 East Coliseum Boulevard, Fort

Wayne, IN 46805, USA

Abstract

Considering the complexity and sometimes unpredictability of human behavior, and its

role on workplace injuries, it is surprising that psychological research has contributed

relatively little in studying workplace safety compared to technology development. The

focus of this paper is thus shifted to psychological investigation of human factors to

understand how a hazard is perceived, how a seen hazard is recognized, and how a

decision must be made to avoid the hazard. The study is conducted through a three-step

experiment including a questionnaire, Balloon-Analogue-Risk-Task (BART) test and

safety photos and video clips with an eye tracking system to measure and analyze the

relationship between the worker’s personality and perceptions of risks and hazards. A

very close relationship was found between the length of time a participant spent looking

at a hazard and the number of hazards identified correctly. Participants with a higher

safety score were able to recognize more safety hazards than those with lower safety

score. Moreover, it was found that risk-takers were underestimating a hazard, whereas

more cautious and less risky participants were overestimating a hazard.

Keywords: Construction safety, attention, perception, risk-taking tendency, eye-

tracking

1. Introduction

In the past decade (2005-2014) more than 50,000 U.S. workers have died at work, with

more than 9,600 fatalities in construction it continues to be the most dangerous industry

[1]. That equates to approximately four construction worker deaths every working day.

Despite this high fatality rate, considerable progress has been made in improving

workplace safety and thus injury fatality rates have decreased approximately 25% from

2005 to 2014. The reasons are varied, but some of the more common ones include

technological advances in personal protective equipment (PPE), safety incentive and

disincentive policies and practices, effective safety training programs, involvement of

workers in safety by including them in decision-making, helping them become safety

leaders and trainers, and encouraging their ideas for improvement [2].

Safety research has provided valuable strategies to develop construction worker

competency in hazard recognition and communication [3]. Studies have shown that safety

training can improve hazard identification skills for managers and workers and help

preventing workplace injuries and accidents [4]. There is a significant negative

relationship between the level of safety-focused worker cognitive engagement with

accident rates, therefore safety management systems and worker engagement levels can


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be used individually to predict safety performance outcomes such as accident rates [5]. To

find the reason of unsafe behaviors, individual and environmental conditions that

significantly affect safety awareness and safety behavior must be identified [6]. This

would lead to develop foremost indicators that can be used by workers and contractors to

improve identification and awareness of the most common risk factors in workplace [7].

In recent years, several studies have focused on the application of PPE and engineering

control technologies to improve safety in construction projects. However, the statistics

show that these advances are not fully preventing accidents from happening in

construction projects, despite the major declines in work injuries of other industry sectors

[8]. This can be largely attributed to the uniqueness of construction projects that expose

workers to “non-design” emergencies (i.e. emergencies that could not have been foreseen

by the designers).

A rich volume of existing publications have solely focused on technology development

and neglected the human side of the equation. Examples of technology-oriented solutions

include efforts such as sharing safety knowledge using a mobile device [9], real-time data

collection and visualization technology for construction safety [10] and [11], safety

assessment of construction site using geographic information systems (GIS) [12], vision-

based recognition of construction equipment and hazard detection [13], game technology

combined with visualizing safety assessment [14], autonomous jobsite safety proximity

[15], location-aware sensing technology to determine a worker’s safety situation

regarding proximity to dangers [16], use of digital models to enhance construction safety

[17], enhancing hazard recognition with augmented virtually [18], and the applications of

building information modeling (BIM) to investigate potential fall hazards [19]. Among

these studies few of them have investigated the application of eye-tracking technology in

construction safety [20]. Dzeng et al. [21] used an eye-tracking system to compare how

the experienced and novice workers pay attention to the obvious and unobvious hazards.

Their results showed that experienced workers are able to detect hazards faster than

novice workers. It was also found that there is a significant difference between general

work experience and safety-specific experience, and general work experience may not

necessarily improve workers’ accuracy in identifying hazards. In another study conducted

by Hasanzadeh et al. [22], different scenarios within a real-world construction site were

developed to quantify workers’ situation awareness using a mobile eye-tracker. They

found that some factors such as workload, the state of the area of interest, and the level of

experience of workers have a significant impact on the allocation of situation awareness

and visual attention.

The complexity and sometimes unpredictability of human behavior, and its role on

workplace injuries, direct us to shift our emphasis from technology development

approaches towards psychological investigation of human factors. However, few studies

have applied psychology in the construction domain to understand the role of human

factors in safety of workers. The findings point out that risk perception are often linked to

socio-demographic factors such as personality facets [23], gender, education, income, and

social context [24], training, ethnicity and socio-economic status [25]. Tixier et al. [26],

hypothesized that there are significant differences in construction safety risk perception

among people having different emotional states and found that workers in positive and

neutral emotional states may perform risk perceptions that are notably lower than people

with negative emotions. Hallowell [27] quantified the current level of safety risk and the

risk tolerance of workers and managers and compared the risk perceptions and tolerance

of workers with managers. He found a statistically significant difference in the risk

tolerance between workers and managers showed that the level of current perceived risk is

approximately five times higher than the tolerable risk value.


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The theoretical basis for these methods rests on the assumption that human behaviors

depend largely on environmental factors rather than on personal characteristics. As

opposed to current practices that largely focus on technology development, this study

aims to prevent workplace injuries and fatalities by exploring the human side of the

equation and providing an in-depth understanding of human behavior and actions in terms

of safety. After a brief overview of the human factor aspects of accidents, the possibility

of investigating contributing human factors for failures in the perception of a hazard and

the decision to avoid are discussed. This paper will suggest an innovative use of

psychological measurements, an experimental procedure and quantitative analysis of the

results to understand how people perceive a hazard and then evaluate a perceived hazard.

2. Safety and Human Factor

Ramsay's accident sequence model illustrated in Figure 1 demonstrates how a hazard is

perceived (through sensory inputs), how a seen hazard is recognized (or understood to be

a hazard), and how a decision must be made to avoid the hazard based on risk tendency

and personality [28]. Safety decisions or hazard identification by occupants are made in

very short periods of time and it makes the role of attention very important in the present

study. The perceptual process explains how a person takes sensory information

(information collected from person’s senses such as sight, hearing, and smell), or stimuli,

from the environment. Attention explains how a person selects information for more

extensive processing [29] and depends on two important theoretical concepts, including

mental load and situational awareness. The mental load is defined as the number of things

that are objectively relevant to the task while the concept of attention is applied in a more

subjective sense for situational awareness in order to predict which aspects of the current

situation a person will become aware of [30]. Therefore, attentional process plays a much

greater role when the decision must be made in a short period of time (e.g. in risky and

hazardous situations), usually less than 200 ms.

Figure 1. Accident sequence model representing various stages in the

occurrence or avoidance of accident (adopted from Ramsey et al, 1986)

In a recent study, Chi et al. [31] analyzed 9,358 accidents that occurred in the U.S.

construction industry and found a significant correlation between workers’ behavior and


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their impact on injury severity. Approximately 80 to 90 percent of these accidents were

strongly associated with workers' unsafe behavior and acts. Behavior models and human

factors approach consider human errors as being the main cause of accidents [32]. Quinn

[33] studied the demographic information of field and office personnel who most

frequently are involved in workplace accidents and found that 56.3 percent of accidents

were caused by a lack of attention or awareness. Hutchings et al. [34] visited twenty

jobsites and interviewed eighteen general contractors and subcontractors to determine

both causes of and solutions to accidents among on-site construction workers. The

interviewees agreed that lack of training, lack of attention to safety, and negligence are

the most important factors contributing to job-site accidents among workers. In another

study, Gillen et al. [35] evaluated injured construction workers and found a positive

significant correlation between injury severity sustained by the workers and their

perceptions of workplace safety. This finding was confirmed by the result of another

study [36], in which it was found that shared perceptions of safety conduct at work were

negatively and significantly associated with injury rates.

Humans use information from perceptual systems such as vision, audition, touch, taste,

and smell to make decisions. Although inputs from all the perceptual systems are

important, visual perception strongly influences the thinking and acting of human beings.

Eye tracking is the corresponding computer-aided research method to record and analyze

this behavior. Eye-tracking is the process of measuring the point of gaze (the spot a

participant is looking at), and an eye tracker is the portable hardware device that performs

this measurement by measuring eye movement [37]. It is a method to assess the eye

movement (as a dependent variable) in the anticipation of stimuli (as an independent

variable) [38]. Even though the application of eye-tracking technology has been

recognized in a wide variety of domains and its use has become prevalent in a number of

industries, considerably less attention has been paid to its potential to improve

construction safety. Workers act based on their perceptions of risk at the jobsite, so we

can conclude that safety improvement in the construction industry is the most appreciated

beneficial effect of enhancing worker’s risk perception. Therefore, systematic

understanding of unsafe behavior has great potential to contribute to the reduction and

prevention of injuries and fatalities in construction projects.

3. Research Objective

The objective of this study is to find a proper answer for following questions regarding

the source of human error that have intrigued researchers for decades: Do the construction

workers pay enough attention to what is going on around them (error in attention)? Can

construction workers understand or identify the risk if they see a hazard (error in

perception)? What are the causes of systematic deviations from the sequence of

operations restricted or required in safety standards (error in memory)? The significance

of this study is to apply psychology to answer these questions; accurately identify risk-

takers and individual-level interventions; and ultimately advance safety by reducing

worker exposures to workplace hazards.

To achieve this objective, the present study analyzes and understands the relationship

between participant’s attention, visual perception, and the way she or he interprets the

registered information. In addition, authors examine whether individual differences in on-

the-job risk taking tendencies by participants can be predicted by psychological variables.


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The risk taking behavior of participants is measured to identify workers who are more

likely to engage in risky decision making on the construction site. To achieve this

objective, eye-tracking technology is used to analyze the relationship between the

worker’s personality (i.e. attitude towards risks/safety) and perceptions of risks and

hazards. The need for clarity on these issues thus forms a gap in the current body of

knowledge on the area of human behavior in construction safety to examine whether

individual differences in on-the-job risk taking by construction workers can be predicted

by psychological variables. This study can also be used to identify workers who are more

likely to engage in risky decision making on the construction site. If one can identify

those individuals who are more likely to engage in risky behaviors, then they can be

targeted for specialized training.

4. Research Methodology

At least two challenges remain in studying the relationship between workers’ behavior

and assessment of safety performance at construction sites. The first challenge concerns

the amount of time and effort needed to assess risk or safety perceptions among

construction workers. The second challenge is to understand how personality and

perception can influence risk taking. To address these challenges, a three-step experiment

was designed to measure and analyze the relationship between the worker’s personality

and perceptions of risks and hazards and assess the safety performance at construction

sites. Figure 2 shows the overall architecture of the study. The focus of the present study

is to understand the sources of human error (i.e., attention, perception, and/or memory)

through different experiments including a questionnaire, Balloon-Analogue-Risk-Task

(BART) test as well as safety photos and video clips with the eye tracking system.

Figure 2. Overall architecture of the study

Each step of the present study is designed to simulate construction worker’s general

awareness on recognizing hazards on a construction site. At the first step, a questionnaire


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was designed and conducted with the objective of assessing the subject's working

experience and knowledge on safety information which included age, gender, work

experience, and safety training (e.g. 10-hor or 30-hour Occupational Safety and Health

Administration (OSHA) training). Twenty multiple choice questions, obtained from

OSHA training program, were used to evaluate the participants’ prior safety knowledge.

The score is represented on a scale from zero percent to a maximum of 100 percent and

used as the participant’s safety score. The purpose of designing a questionnaire was to

understand whether participants with higher safety score perform better in identifying

safety hazards than participants with lower score or inexperienced participants.

In the second step, the OSHA safety training photos and video clips were displayed on

the monitor and participants were asked to take a look at the photos for a certain period of

time and identify the actual or potential hazards they see, while their interaction with the

photos and video clips is recorded using eye tracking. These photos and video clips

ranged from near misses to unsafe behaviors/conditions with high potential for accidents.

Video clips of potential hazards were recorded from one of the on-going construction

projects at the Pennsylvania State University campus. The eye movement data and

participants’ responses were used to quantify visual attention and measure participants’

perception of hazard and risk, respectively. Therefore, it was possible to understand

“where participants look” and “what they look for”.

Along with identifying potential hazards, the participants were asked to rate the level of

danger (or level of hazard) presents in the safety photos and video clips to determine the

severity score. The presented study used Safety Assessment Code (SAC) developed by

the National Center for Patient Safety (NCPS) for doing comparative analysis. Three

faculty members at Millersville University - Occupational Safety & Environmental Health

(OSEH) program were interviewed to verify the severity level of hazards and those were

used as the baseline for hypothesis testing. Four different levels of severity including

Catastrophic, Major, Moderate, and Minor were defined. The hypothesis is that the

participants who are taking greater risks (based on the results of the BART experiment)

would underestimate the level of danger while those who are less risky would

overestimate the level of danger present in the safety photos. The following equation is

used to calculate the severity score.

Severity Score =

where, Catastrophic = +4, Major = +3, Moderate = +2, and Minor = +1.

For example, if the correct answer is Major and the subject picked Minor, his/her score

will be +3 – 1 = +2, which means that the hazard is underestimated and if the subject

picked Catastrophic, his/her score will be +3 – 4 = -1, which means that the hazard is

overestimated.

In the third step, the BART test was carried out to identify risk-takers [39]. BART is a

computerized measure of risk taking behavior that models real-world risk behavior

through the conceptual frame of balancing the potential for reward versus loss. In the test,

the participant is presented with a balloon and offered the chance to earn money by

pumping the balloon up by clicking a button. Each click causes the balloon to

incrementally inflate and money to be added to a counter up until some threshold, at

which point the balloon is over inflated and explodes. Thus, each pump confers greater

risk but also greater potential reward.


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5. Experimental Design

In this study a Tobii X2-30 compact eye-tracker was used to record eye movement data.

The technical specification of the eye-tracker includes: gaze accuracy of 0.4 to 0.5 degree

range, precision of 0.32 to 0.45 degrees, data rate 30 Hz, freedom of head movement 50

(width) x 36 (height) x 90 (depth) cm, calibration 9 points (~ 15 seconds to complete),

monocular and binocular tracking capability. To perform a BART test, participants were

asked to pump up a computerized balloon. The maximum number of pumps that can be

made to a balloon is 128, and for each pump, the participant is awarded a small amount of

money that is placed in a temporary bank (¢5 per pump). The participant can pump the

balloon 1 to 128 times, and must decide when to stop pumping. If they stop pumping

before the balloon bursts, the money in the temporary bank is placed in a permanent bank.

However, if the balloon bursts before they stop pumping, the money in the temporary

bank disappears. Thus, people who are willing to pump up the balloon more are

essentially taking greater risks, whereas people who quit early can be viewed as being

more cautious and less risky. Lejuez et al. (2002) has shown that one’s propensity to

pump up the balloon is positively correlated with real-world risk taking behaviors.

In this study a within-subjects experiment is implemented in which every single

participant was subjected to every single safety photo and video clip. The results were

analyzed using 122 data samples collected from 32 participants (24 male and 8 female).

Since the focus of this study is on construction safety, all participants for this experiment

were drawn from construction workers, people who had worked in construction jobsites

or students who were studying construction management or construction engineering.

Table 1 summarizes the descriptive statistics of the subject pool and the safety

questionnaire results. Eleven participants (34%) reported that they had formal safety

training and eight other participants (25%) reported no previous work experience. There is

no doubt that prior safety knowledge plays a vital role in the level of hazard identification

(safety photo score), however, this study examines whether construction workers with

similar prior safety knowledge but higher attentional control are able to recognize and

perceive more safety hazards than those with lower attentional control. The results of the

statistical analysis in the next section provide a quantitative way to answer this question.

Table 1. Summary of the Experimental Designs

N Mean/Percentage Std. Deviation

Age - 29.1 11.0

Gender - male 24 75% N/A

Gender - female 32 25% N/A

Work experience - 5.6 yr. 8.1 yr.

Safety score - 54% 3%

In total, ten safety photos and four safety video clips were created and showed to the

participants. All photos and video clips were taken into consideration for the correlation

analysis. Each participant spent 10 and 17 seconds seeing the photos and video clips

respectively, before the evaluation. To further analyze the eye movement (e.g. fixation)

data, static and dynamic areas of interest (AOI) were defined for each photo and video

clip, respectively. AOIs are relevant areas on the stimulus that are usually defined before

starting the experiment and are closely connected to the research question and the

corresponding research design. To ensure consistency and proper balance between


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selectivity and sensitivity in the results, the AOI was defined as 3% of the total area. The

selected size of the AOI was defined based on the largest hazardous situation in each

photo and video. Figure 3 shows the heat map of two of the studied safety photos. Heat

map (or hot spots) is a widely adopted visualization technique for eye tracking studies.

Heat map shows the areas with a large amount of interest and the aggregate eye fixations

of those areas which are shown in red. As it can be seen in these figures, the Time Spent

in AOI, which is defined based on total duration of all respondent's fixations and

measured in second, is significantly higher in the red area in comparison with green areas.

For example, in Figure 3 the Time Spent in AOI is 2s and 1.8s respectively, while in other

areas they range between 0.1-0.8s. The same trend is seen for fixation and time to first

fixation (TTFF). TTFF is the time stamp of the first fixation inside the AOI.

Figure 3. Heat map and the associated AOIs for the photo experiment

6. Results and Discussions

One assumption is if the participants don’t see the stimuli, they can’t identify the

hazard (error in attention). To test this hypothesis, an independent-samples t-test is used to

understand whether the hazard identification score (HIS) differed based on the fixation

time (time spent looking at a hazard). Thus, the dependent variable would be the “HIS”

and the independent variable would be fixation time, which has two groups: unaware,

participants who don’t see a hazard (their fixation time is less than a specific duration)

and attentive, participants who see a hazard (fixation time is greater than a specific

duration). The HIS for each participant is defined as:

Where, t is the fixation time (measured in seconds) less than a specific duration (d0) for

unaware group and greater than a specific duration (d0) for attentive group, IH is the

number of hazards that are identified correctly, and UH is the number of unidentified

hazards (hazards that are not identified). For example, for unaware

group shows that 3 out of 5 hazards are identified correctly when the time spent looking at

a hazard are less than 2 seconds. The HIS between unaware and attentive groups are

compared for different time periods.

Table 2 summarizes the results of two-sample t-test associated with a 95% significant

level for the safety photo experiment. The significance (2-tailed) value for fixation time

periods equal or less than 3 seconds is less than 0.05 (p>0.05). There is a statistically

significant difference between the mean number of hazards identified in the safety photos

for the attentive and unaware groups. Since the average for the attentive group was greater

AOI

AOI


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than the average for the unaware group, we can conclude that participants who spent more

than 3 seconds at a hazard were able to identify significantly more hazards than

participants who spent less than 3 seconds at a hazard.

Table 2: quantitative analysis of t-test for different fixation time periods (photo experiment)

Fixation time Group Mean score Std. deviation F Sig. (2-

tailed)

0.5 sec Attentive 0.50 0.20

5.22 <0.001 Unaware 0.18 0.29

1 sec Attentive 0.53 0.24

1.412 <0.001 Unaware 0.25 0.29


0.916 <0.001 Unaware 0.25 0.23


0.042 <0.001 Unaware 0.26 0.23


1.379 0.001 Unaware 0.30 0.23


1.525 0.020 Unaware 0.33 0.23


7.155 0.495 Unaware 0.35 0.19

A correlation analysis is performed on the data to measure the strength and direction of

relationships between the fixation duration and the HIS. As shown in Table 3, the Pearson

correlation between the fixation durations and the HIS is remarkably high (correlation

coefficient = 0.806) and statistically significant at the 95% confidence level. Therefore

there is a very close relationship between the length of time a participant spent looking at

a hazard and the number of hazard identified correctly.

Table 3. Comparison of fixation time periods with HIS (photo experiment)

Pearson correlation

Fixation time hazard identification score

Fixation time 1 0.927 (Sig. < 0.001)

hazard identification score 0.806 (Sig. < 0.001) 1

Figure 4 shows the relationship between the fixation duration and HIS for the safety

photo experiment. As can be seen, lower scores (when hazards were not identified) are

associated with shorter fixation duration. For example, when fixation duration is less than

0.5 seconds, 100-23=77% of the time the hazards were not identified correctly and when

fixation duration is less than 2 seconds, 100-31=69% of the time the hazards were not

identified correctly.


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Figure 4: Relationships between fixation duration and hazard identification

score

Similar analysis was performed for the video experiment, where fifteen participants

were asked to watch four different safety video clips. As shown in Table 4, none of the

differences between the attentive and unaware groups were significant and no conclusion

can be drawn between the HIS and the fixation time for the video experiment. This is

most likely a result of the small sample size in the experiment, because only eighteen data

sets could be used from fifteen participants in the safety video experiment, while a total of

ninety two data sets were used from thirty two participants in the safety photo experiment.

Table 4: quantitative analysis of t-test for different fixation time periods (video experiment)

Fixation time Group Mean score Std. deviation F Sig. (2-

tailed)


1.427 0.248 Unaware 0.14 0.38

1, 1.5 sec Attentive 0.23 0.41

0.189 0.669 Unaware 0.33 0.43


2.666 0.120 Unaware 0.38 0.46


0.350 0.563 Unaware 0.35 0.45

3.0 sec Attentive 0.40 0.55 2.304 0.150

After examining the relationship between visual attention and hazard identification, the

eye-movement data classified as “seen” were further analyzed to understand whether the

participants with knowledge and experience in the field of safety perform better in

identifying safety hazards than participants with lower safety score and experience. The

prior knowledge was assessed through a series of multiple-choice questions and is

reported as the participant’s safety score. In order to perform the statistical analysis, the

participants were grouped based on their safety score. The average safety score for the

participants was 58%. The participants with high or above average safety score are listed

as knowledgeable and participants who scored less than 58% in the safety questionnaire

are listed as less knowledgeable.


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Table 5 shows the results of two-sample t-test associated with a 95% significant level

for the participants who spent more than 3 seconds at a hazard (note: fixation time of 3

seconds is classified as “seen”). Although authors fail to reject the null hypothesis (p-

value = 0.086 > 0.05) that when a hazard is seen, participants with higher safety score (or

knowledgeable participants) have significantly higher safety performance in identifying

safety hazards than non-experienced participants, but it was noticed that they are able to

recognize more safety hazards than those with a lower safety score (or less knowledgeable

participants). When the average time in seconds spent looking a hazard is more than 3

seconds, participants with higher safety score could identify 64% of hazards; however,

less knowledgeable participants could only identify 33% of seen hazards.

Table 5: quantitative analysis of t-test for “seen hazards” (photo experiment)

Fixation

time

Group Mean

safety

score

Mean hazard

identification

score

F Sig. (2-

tailed)

3 sec

Knowledgeable 46% 0.60

1.217 0.317 Less knowledgeable 66% 0.41

Even if a safety hazard is seen and recognized, construction workers with less

experience or high risk-taking tendencies may underestimate or neglect to avoid a hazard.

To find the causes of deviations from the sequence of operations required in safety

standards, participants’ experience and BART scores are compared with the severity

scores related to the identified hazards. A correlation analysis is performed to measure the

extent of interdependence between the risk-taking tendencies (based on the results of the

BART experiment) or experience and the severity scores. As shown in Table 6 and Figure

5, there was a strong, negative correlation between “years of experience” and “severity”,

which was statistically significant (r= -0.545, p = 0.002). Thus, the participants with

greater work experience would overestimate the level of danger while those who had no

or little work experience would underestimate the level of danger present in the safety

photos.

Table 6. Comparison of participants’ work experience and severity scores

Pearson correlation


Severity score 1 -0.545 (p = 0.002)

Work experience -0.545 (p = 0.002) 1


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Figure 5: Relationships between participants’ work experience and severity

score

A correlation analysis between the BART and severity scores is performed to test the

hypothesis that participants with higher risk-taking tendencies would underestimate the

level of danger while those who are risk-avoider would overestimate the level of danger

present in the safety photos (see Table 7 and Figure 6). On average, participants pumped

up the computerized balloon 50 times and earned $37.6. Although there is inconclusive

evidence about the significance of the association between the risk taking tendencies and

severity scores (p-value = 0.059 > 0.05), we notice a moderate linear relationship between

the variables. Risk takers (participants who pumped up the balloon more) were

underestimating a hazard (higher severity score), whereas participants who quit early

(more cautious and less risky) were overestimating a hazard (lower severity score).

Table 7. Comparison of participants’ BART score and severity scores

Pearson correlation


Severity score 1 0.400 (p = 0.059)

BART score (average pumps) 0.400 (p = 0.059) 1


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Figure 6: Relationships between the average number of pumps and severity

score

7. Conclusions

Many construction workers are injured because of safety hazards on the job site. This

study shows a 95% statistically significant difference between the mean number of

hazards identified in the safety photos for the attentive and unaware groups. Participants

recognized hazards when they spent more than 3 seconds on a hazard in comparison with

participants who spent less than 3 seconds. As a result, there was a very close relationship

between the length of time a participant spent looking at a hazard and the number of

hazard identified correctly.

This study failed to prove that knowledgeable participants have higher safety

performance in identifying safety hazards than non-experienced participants. But it is

important to note that when participants spent more than 3 seconds looking at hazards,

participants with safety knowledge and experience were able to recognize 66% of hazards

while less experienced participants could identify 46% of the hazards. In correlation

analysis between the BART and severity scores there was a moderate linear relationship

between risk-taker and risk-avoider behaviors. There was a strong, negative correlation

between “years of experience” and “severity”, which was statistically significant. It was

found that the participants with greater work experience would overestimate the level of

danger while those who had no or little work experience would overestimate the level of danger present in the safety photos.

The results of this study can be used to develop an assessment tool to gauge how well

construction workers understand safety hazards in a workplace and determine the sources

of human error. This information will be combined with the workplace hazards identified

through a job hazard analysis to predict how (e.g. who might be involved and what is the

source(s) of his or her error), when, and where an accident occurs. A possible subject to

be left for future research is to couple the eye –tracking technology with an immersive

virtual reality environment in order to expose subjects to more realistic construction

environments in order to actively interact with it.

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doi:10.24900/ijss/01034660.2017.1201 advancing safety by ...€¦ · somayeh asadi1, ebrahim...

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