Measurement for Risk Perception Ability

Zhang, Shu; Hua, Xinyu; Shi, Xiuzhi

doi:10.1061/JCEMD4.COENG-13783

Open access

Technical Papers

Mar 14, 2024

Measurement for Risk Perception Ability

Authors: Shu Zhang https://orcid.org/0000-0002-1037-9435 [email protected], Xinyu Hua https://orcid.org/0000-0002-4379-8126 [email protected], and Xiuzhi Shi [email protected]Author Affiliations

Publication: Journal of Construction Engineering and Management

Volume 150, Issue 5

https://doi.org/10.1061/JCEMD4.COENG-13783

PDF

Abstract

Unsafe behaviors may be aroused by inaccurate risk perception. Rather little is known about the measurement of risk perception ability in the construction industry. Based on the conception of risk perception ability, we proposed a measurement of risk perception ability, which conducted a questionnaire and a behavioral experiment. The specific measurement included an indicators system proposed to measure risk perception ability, which contained hazard identification degree, hazard identification accuracy, accuracy of the risk evaluation, deviation degree of the expected damage, deviation degree of the probability, and deviation degree of the risk evaluation. Based on the six indicators, the weighing values for each indicator were calculated, and a formula calculating risk perception ability (RPA) was proposed. Then, we applied the measurement to 327 construction professionals, which showed RPA differed from different construction professionals, and managers’ RPA was significantly superior to workers (

p = 0.000 * * * < 0.05

). The method can measure construction professionals’ RPA conveniently and easily, which filled the lack of research regarding RPA in construction. Measurement results can help to provide targeted safety training recommendations, which is beneficial to reducing workers’ unsafe behavior and accident rates.

Introduction

Most accidents occur due to individuals’ unsafe behaviors (Seo 2005). The human factors analysis and classification system (HFACS) offered a systematic review of individual factors causing accidents, in which perceptual errors are recognized as crucial errors in unsafe acts (Shappell and Wiegmann 2000). Previous research has also indicated that failure to adequately evaluate risk may be a fundamental factor contributing to unsafe behaviors (Na et al. 2019). In daily life, individuals can vary significantly in their capacity to identify hazards and evaluate risks. These differences can influence their behaviors, and incorrect behaviors may potentially result in accidents. So, research on risk perception is crucial for ensuring safe construction practices.

The concept of risk perception ability (RPA) and its impact on safety have gained significant attention through increased research—particularly concerning drivers. Using dynamic traffic scenarios, Spicer (1964) was the first to measure drivers’RPAthrough situational tests (Farrand and Mckenna 2001). Further, RPA was first defined as the ability to identify potential risks in traffic scenes (Crick and McKenna 1992). Using a questionnaire survey (Vanlaar and Yannis 2006), behavior experiments (Rosenbloom et al. 2015), simulation technology (Casutt et al. 2014), and eye movement technology (Pradhan et al. 2003), subsequent studies explored factors that affect drivers’ RPA and figured out how to measure RPA. Through questionnaires and behavior experiments, Salmon et al. (2013) compared drivers’ RPA and prejudgment ability with hazard identification tasks via videos featuring actual working processes. However, there has been a lack of research exploring the RPA in fields such as public safety, mine safety, and construction safety. Li (2005) assessed students’ RPA and explored how individual differences impact their RPA. Perlman et al. (2014) tested superintendents’ ability to identify risks, perceive risks, and assess risks with behavioral experiments.

Various methods have resulted in two types of measurement indicators: behavioral indicators and physiological indicators. McKenna and his colleagues (McKenna and Crick 1992) have used reaction time data as an indicator of drivers’ RPA. Similarly, Yang has used hazard identification time and reaction time to measure driver’s RPA (Yang et al. 2014). By utilizing questionnaires (Wang 2018) and dangerous scenario videos (Borowsky et al. 2010; Salmon et al. 2013), participants were required to evaluate or describe the risk of hazards within a scenario, and the results were used to assess individuals’ RPA. Prior research by Chen et al. (2016b) has attempted to measure RPA in construction workers by utilizing indicators such as hit rates and false alarm rates. Meanwhile, some scholars have combined eye movement and neuroscience techniques to explore individuals’ risk perception abilities. The indicators included driving speed, risk identification rate, gaze time, gaze frequency, and brainwave indicators (Liang and Lin 2018; Qiao et al. 2022; Tang et al. 2013). Based on a picture-based risk perception experiment of road hazards, Liang and Lin (2018) recorded 42 drivers’ electroencephalography (EEG), electrodermal activity (EDA), and behavioral responses to hazards. The results showed that physiological features were significantly different between risky and safe drivers, indicating that physiological indicators can also be applied to study individuals’ risk perception.

Thus, there are three main characteristics in current research on RPA. First, the research on RPA mainly focused on the field of traffic safety, with a majority focusing on drivers’ risk perception abilities. The construction field still lacked related studies. Second, the main research methods used are questionnaire surveys and behavioral experiments, with some studies utilizing technologies such as eye tracking and event-related potential (ERP). The two main types of measurement indicators were behavioral and physiological indicators. Finally, most related studies concentrate more on hazard identification, which ignores the evaluation of risks, and the related indicators were not comprehensive or systematic.

The construction industry has long been viewed as highly risky due to its complex work environment, overlapping tasks, and long life cycles. According to statistics from 2004 to 2021, national construction had 12,801 accidents and 15,193 deaths. As such, many experts have explored ways to effectively reduce accident rates. Deviations in individual risk perception induce unsafe behaviors (Liang 2022) and wrong decision making (Xiao 2014). Construction workers’ risk perception also plays an important role in improving emergency response and committing to safety regulations (Siegrist and Arvai 2020; Wang et al. 2016). Risk perception was mainly defined as a subjective feeling toward risks, without right or wrong, just low or high. Different from risk perception, RPA was a kind of capacity (Crick and McKenna 1992). We defined RPA as an ability to quickly and correctly identify hazards in the target scenario, to objectively analyze the probability and the damage of potential accidents caused by hazards, and to evaluate the risk level of the hazards relatively accurately. As said before, the construction field lacked studies relating to RPA. So, we reviewed the literature on risk perception in construction. As a stage of risk perception, the failure to identify hazards would result in unsafe behavior (Tixier et al. 2014). Hazard identification is one of the fundamental units of risk management. Currently, scholars have conducted extensive research on the hazard identification stage in construction. Dzeng et al. (2016) compared the hazard search patterns of experienced and novice workers by creating a construction site model. Chen et al. (2016a) utilized a helmet system integrated with electroencephalography to identify individuals who demonstrated weaker abilities in hazard identification. By experimental research, Atefeh et al. (2018) discovered that the time to the peak value of hemoglobin concentration varied with the degree of hazard and the source of energy. Previous studies concentrated more on the hazard identification stage and ignored the importance of the risk evaluation stage. An accurate evaluation of risks is also crucial in shaping safety behavior among construction workers. Underestimating risks can foster a sense of recklessness, which in turn can induce unsafe behaviors. Conversely, overestimating risks can induce feelings of fear and anxiety among workers, ultimately resulting in errors and mistakes in their work. Hence, we studied RPA from two stages: hazard identification and risk evaluation.

Accordingly, this study primarily focused on RPA from two perspectives: hazard identification and risk evaluation. We aimed to design a measurement for RPA. Based on existing indicators measuring RPA, we designed an indicator system and proposed a formula to calculate RPA. Through questionnaires and behavioral experiments, we measured construction professionals’ RPA, and we further studied whether ability discrepancy between managers and workers exists. This study addresses the research gap in RPA within the construction industry. In addition to hazard identification, this study refocuses on the risk evaluation stage of risk perception, and it holds practical significance for construction safety work.

Methodology

Research Framework

According to the content presented, we proposed a method to measure construction professionals’ RPA and then applied the method to a construction project in Guangxi, China. We want to explore two main questions: (1) How can we measure RPA? and (2) What are the differences in RPA between managers and workers? The main content of this paper is as follows.

Propose a method to calculate and measure RPA. Previous studies mainly applied the psychometric paradigm to assess risk perception, which is a subjective approach emphasizing individuals’ sense of risk. The measurement of risk perception reflects the subjective sense of the risks and cannot be definitively labeled as correct or wrong. Besides, the indicators measuring RPA were not systematic, and studies utilized separate indicators from experiments to represent RPA. Hence, the measurement of RPA focuses on the accuracy of hazard identification and the objectivity of risk evaluation in our study. We designed an indicator system to measure RPA comprehensively. Then, we proposed a formula to calculate RPA. Combining a questionnaire and a behavioral experiment, we suggested an all-encompassing approach to assess individuals’ RPA.

Investigating the disparities in RPA between managers and workers. Previous studies have shown that individuals with different identities, such as workers, experts, and managers, have different risk perceptions (Li et al. 2018; Zhao et al. 2016; Song and Zhang 2022). The groups were also classified into architects, experts, engineers, and workers in the previous study. Some groups’ jobs directly relate to construction, such as architects; however, they work at the construction site irregularly. Thus, we have narrowed our focus to construction professionals divided into two main groups: workers (including, but not limited to, cementers, scaffolders, electricians, and plumbers) and managers (including quality control and safety officers) on construction sites.

The research framework is shown in Fig. 1.

Measures

Materials of Measurement

Ten pictures of simple risk scenarios and five pictures of complex risk scenarios were used in the questionnaire and behavior experiment. The simple risk scenarios include one risk, and the complex risk scenarios include three to five risks (Fig. 2). The risks contain kinds of common hazards in construction sites, such as falling risk, risk of being hit by an object, risk of mechanical injury, and risk of collapsing. All pictures were captured at construction sites and were evaluated by 10 experts in company management, 10 safety patrols, 10 experienced builders, and 10 university teachers engaging in risk perception.

Fig. 2. Materials: (a) pictures with complex risk scenario; and (b) pictures with simple risk scenario.

Design of Measurement

In previous studies, the measurement of risk perception mainly focused on the personal subjective feeling of risks. Based on Wickens’ information process model (Wickens et al. 2021), Rasmussen’s eight-stage cognitive model (Rasmussen 1986), the communication-human information processing model (C-HIP) (Wogalter 2018), and other cognitive theories (Wogalter et al. 1993, 1999), generally, risk perception includes two processes, perceiving and recognizing information. Taken with the studies presented, we concluded that in the early stages, individuals were able to intuitively perceive external risk information, exhibiting the ability to subconsciously, rapidly, and sensitively detect the presence of risks through visual and auditory senses; and in the later stages, individuals can further comprehend risk information by analyzing it using their memory, knowledge, and other resources. Accordingly, we divided the RPA into two parts for measurement: hazard identification ability and risk evaluation ability. Hazard identification ability is measured by the number and accuracy of hazard identification. Risk evaluation ability is determined by the accuracy and deviation of risk evaluation.

Design of Indicators

The hazard identification ability was measured by five images displaying complex risk scenarios via behavioral experiments. Each complex risk scenario contained one to five hazards. Participants were asked to mark the hazards they identified and describe the hazards.

The measurement of risk evaluation ability mentioned in this paper refers to previous studies. The evaluation of risks mostly depended on the dimensions of risk (Wilson et al. 2019). The one-dimension approach mostly measured risk by “How risky is

X

,” with intuitional judgment (Safi et al. 2012). The two-dimension measurement mostly defined risk from probability and consequence of risk (Rosen and Kostjukovsky 2015), and other multidimensional measurements focused on probability, exposure, vulnerability, affect, and controllability (Mann and Wolfe 2016; Martin et al. 2007; Wilson et al. 2019). In production safety, probability and consequence were viewed as reliable factors to evaluate risk. Peter and Ryan (1976) indicated the risk degree can be calculated by multiplying probability and consequence. Referring to the presented research, we used two approaches to evaluate risks, one is two dimensions: (1) probability of accident and damage of accident’s consequence, which is derived from the classic risk formula:

R i s k = P * D

, with

P

being the probability of accidents and

D

the expected damage of accidents, for quantitative risk evaluation; and (2) “what is the risky level of

X

.” From this, risk evaluation ability was measured by 10 pictures of simple risk scenarios and five pictures of complex risk scenarios. In the task measuring risk evaluation ability, participants were asked three questions about hazards, respectively “How risky is

X

” (assessed by the Likert 5-point scale where 1 = little risk and 5 = great risk.), “What is the probability of the risk event occurring?” (assessed by the Likert 5-point scale where 1 = unlikely and 5 = utterly predictable), and “How serious is the consequence of accident?” (assessed by the Likert 5-point scale where 1 = attractive and 5 = extremely serious).

Based on the measurement presented, the indicators measuring the RPA included two groups, indicators of hazard identification ability and risk evaluation ability. Indicators measuring hazard identification ability included hazard identification degree (HID) and hazard identification accuracy (HIA). Indicators showing risk evaluation ability contained: risk evaluation (ARE), deviation degree of the expected damage (DD), deviation degree of the probability (DP), and deviation degree of risk evaluation (DRE). The specific meanings of each indicator are shown in Appendix.

Procedure of Measurement

The measuring procedure was divided into two parts based on pictures of simple risk scenarios and complex risk scenarios.

First, participants were asked to fill out a questionnaire to judge the damage, probability, and risk level of the hazards in simple risk scenarios, see Fig. 3. ARE, DD, and DP were calculated by the results of the questionnaire.

Fig. 3. Part of the formal questionnaire.

Then, a behavioral experiment including two parts was conducted. Part 1 included five pictures with complex risk scenarios, and each picture contained one to five hazards. In Part 1, we asked participants to find out hazards in complex risk scenarios one by one and illustrate what the hazards are. HID, and HIA were calculated by the data from this part. In Part 2, we required participants to judge the risk level of hazards they identified from two angles: damage and probability, in which the deviation DRE was calculated.

Calculation of RPA

Weight Calculation

The weighing values for the indicators were computed. Methods confirming weight had two kinds: subjective and objective (Fan et al. 1998). The subjective weighting method mainly depended on policymakers’ subjective preference and direct experience, such as the expert investigation method (Li et al. 2014), the Delphi method (Cheshmidari et al. 2017), the analytic hierarchy process (AHP) (Joukar et al. 2016). The objective weighting method created a decision matrix and enlisted mathematical models to compute weighing values, such as principal component analysis (PCA), multiobjective optimization (MOP), and criteria importance through intercriteria correlation (CRITIC). In this paper, a synthesized method is proposed according to the advantages and disadvantages of subjective and objective ways of giving weight. Based on game theory (Chen 2018), we used Nash equilibrium as the goal coordination method and combined the AHP and CRITIC to lessen errors.

As an objective method to compute weighing values, CRITIC measures a criterion by considering both the importance itself and the conflict caused by intercriteria (Li et al. 2012). The calculation process is as follows.

1.

Data preprocessing: eliminate dimensionality effect, each indicator was normalized.

2.

Evaluate indicators’ volatility and coefficient of friction.

3.

Compute weighing values.

We used a computer algorithm to implement its computation, and the results are as follows:

W_{1} = [0.1679, 0.1614, 0.2148, 0.1446, 0.1513, 0.16]

The AHP is a subjective weighting method that prioritizes the relative importance of each factor via pairwise comparisons among the factors by relevant experts using a nine-point scale (Zhu et al. 2015). The specific steps and calculation process are as follows:

1.

Structure the judgment matrix.

2.

Examine the consistency of the comparison matrix.

3.

Compute the values of weight.

The 32 nine-point scales were distributed to related professors, safety experts, and safety managers, and 30 were valid. The calculated process was conducted by a computer algorithm, and the results are as follows:

W_{2} = [0.1689, 0.1485, 0.1476, 0.1946, 0.1871, 0.1533]

Based on the Nash equilibrium of game theory, Su et al. (2014) combined the subjective weighting method and objective weighting method to obtain more scientific results. The basic idea was to seek balance among weights obtained from different methods, aiming to minimize the deviation between basic weights and final weights. The decision model is shown as follows:

\min \sum_{j = 1}^{N} α_{j} {(w_{j}^{T} - w_{i}^{T})}_{2}, i = 1, 2, \dots, N

(1a)

The condition of optimal first-order derivative is

\sum_{j = 1}^{N} α_{j} \times w_{i} \times w_{j}^{T} = w_{i} \times w_{i}^{T}, i = 1, 2, \dots, N

(1b)

where

α_{i}

= coefficient which meets weight vector

w^{*}

w^{*} = \sum_{i}^{N} α_{i} w_{i}

(1c)

According to the results of the subjective weighting method and objective weighting method,

W_{1}

= [0.1679, 0.1614, 0.2148, 0.1446, 0.1513, 0.16],

W_{2}

= [0.1689, 0.1485, 0.1476, 0.1946, 0.1871, 0.1533], the weight-vectors

W_{i}

were put into the formula, and the integrative model was shown

{(\begin{array}{c} 0.1698 & 0.1650 \\ 0.1650 & 0.1687 \end{array})}_{*} \begin{matrix} α_{1} \\ α_{1} & = \end{matrix} \begin{matrix} 0.1698 \\ 0.1687 \end{matrix}

After calculation,

α_{1} = 0.57

, and

α_{2} = 0.44

.

Consequently, it is clear that the final weight vector was:

W = α_{1 *} W_{1} + α_{2 *} W_{2} = [0.17, 0.157, 0.187, 0.168, 0.168, 0.15]

. The weights of HID, HIA, ARE, DD, DP, and DRE separately are 0.168, 0.168, 0.17, 0.187, 0.157, and 0.15.

As shown, for the hazard identification stage, HID, and HIA had the same weights, yielding that both indicators should be taken seriously. For the risk evaluation stage, the deviation degree of the expected damage had the largest weight, showing that the deviation degree of the expected damage may be more important in risk evaluation.

Calculation of RPA

Based on the content above, weight of the indicators were determined. The formula of RPA was obtained as follows:

RPA = HID \times 0.168 + HIA \times 0.168 + ARE \times 0.17 - SDD \times 0.187 - SDP \times 0.157 - DRE \times 0.15

(2)

Because the range of each indicator varies. Hence, all indicators were classified into five degrees, and every degree was assigned a value. Finally, the formula can be used to calculate RPA by the assigned value of each indicator. Specific assignment criteria can be found in the following calculation examples.

Applications

Participants

The study conducted a questionnaire survey and a behavior experiment on 380 construction professionals working at two construction sites in the same construction company in Guangxi, China. To represent the group of construction workers, the type of construction professionals covers carpenters, bricklayers, concrete reinforcements, scaffolders, and electricians. All participants had good psychological and physiological health, and none reported a history of an injury related to the study.

The purpose and aims of the study were detailed to them, and informed consent was obtained before their participation. Ultimately, 327 valid data were collected, with a valid recovery rate of 86.05%.

Our research mainly intended to investigate the risk perception discrepancy between two groups: workers and managers. Workers included cementers, scaffolders, electricians, and plumbers, whereas managers comprised quality control and safety officers. The basic information of the 327 participants is shown in Table 1.

Table 1. Basic information of the 327 participants

Item	Condition	Participants
Item	Condition	Number	Proportion (%)
Position	Workers	255	78
Position	Managers	72	22
Gender	Woman	5	98.5
Gender	Man	322	1.5
Age	25 or younger	22	6.7
	26–35	83	25.4
	36–45	127	41.6
	46 or older	86	26.3
Education	Elementary school or below	5	1.5
	Junior high school	187	57.2
	Senior high school	92	28.1
	Bachelor’s degree or above	43	13.1
Period of employment	2 years or below	55	16.8
	3–5 years	89	27.2
	6–10 years	89	27.2
	11 years or above	94	28.7

Measurements of RPA

The 327 valid data were obtained, 255 from workers and 72 from managers. The results of indicators included HID, HIA, the ARE, DD, DP, DRE, and RPA. According to the indicators’ meaning and the descriptive statistics, we used five risk ratings to assess all six indicators and risk perception ability. The results of the measurement are shown in two parts: hazard identification ability and risk evaluation ability.

Calculation of Hazard Identification Ability

The indicators of hazard identification ability are HID and HIA. The descriptive statistics are listed in Table 2, and Fig. 4.

Table 2. Descriptive statistics of HID and HIA

Indicators	Average	Standard error	Median	Minimum	Maximum	Skewness
HID	0.3294	0.0071	0.3200	0.04	0.78	0.464
HIA	0.6571	0.0099	0.6667	0.1667	1.0000	$- 0.206$

For HID and HIA, data distribution and rating criteria are shown in Table 3 and Fig. 5. The results showed that 57% of participants had poor HID, ranging from 0.2 to 0.4, and 25% of that were general, ranging from 0.4 to 0.6, meaning the ability for participants to identify hazards need to be improved. For HIA, 40% of participants had good HIA, and 32% of them had general HIA, meaning in identified hazards, the accuracy was at a good level.

Table 3. Rate of HID and HIA

HID	HIA	Rate	Value
$0 < C_{1} \leq 0.2$	$0 < C_{2} \leq 0.2$	Very poor	1
$0.2 < C_{1} \leq 0.4$	$0.2 < C_{2} \leq 0.4$	Poor	2
$0.4 < C_{1} \leq 0.6$	$0.4 < C_{2} \leq 0.6$	General	3
$0.6 < C_{1} \leq 0.8$	$0.6 < C_{2} \leq 0.8$	Good	4
$0.8 < C_{1} \leq 1$	$0.8 < C_{2} \leq 1$	Very good	5

Fig. 5. Data distribution of indicators.

Calculation of Risk Evaluation Ability

The indicators of risk evaluation ability are ARE, DD, DP, and DRE. The descriptive statistics are shown in Table 4, and Fig. 4.

Table 4. Descriptive statistics of ARE, DD, DP, and DRE

Indicators	Average	Standard error	Median	Minimum	Maximum	Skewness
ARE	0.401	0.0096	0.400	0	0.8	0.133
DD	1.2863	0.0362	1.1861	0.2533	2.4558	0.343
DP	1.0028	0.0252	0.9478	0.3233	3.8669	1.618
DRE	0.7237	0.0117	0.7674	0.0639	1.6753	$- 0.598$

Data distribution and rating criteria are shown in Tables 5 and 6 and Fig. 5. The results showed that 46% of participants’ ARE was poor, and 30% of that was general, indicating that the ability of participants to judge risk correctly needed improvement. For the deviation degree of the expected damage, 38% of participants had poor DD, 32% of them had general DD, and 20% had very poor DD, indicating that when judging damage of the hazards, participants had a large bias. During the judgment of the probability, the results showed 49% of participants had a general rate of DP, 42% of participants had a poor rate of DP, and 5% of participants had a very poor rate of DP, yielding participants had a bias in the judgment of probability. For the deviation DRE, 85% of participants’ DRE rates were general, and 11% were good, indicating participants could judge risk level more correctly than damage and probability. As mentioned in 2.4.3, every indicator was classified into five degrees, and every degree was assigned a value (shown in Tables 5 and 6). The value would be used to calculate the RPA.

Table 5. Rate of ARE

ARE	Rate	Value
$0 < C_{3} \leq 0.2$	Very poor	1
$0.2 < C_{3} \leq 0.4$	Poor	2
$0.4 < C_{3} \leq 0.6$	General	3
$0.6 < C_{3} \leq 0.8$	Good	4
$0.8 < C_{3} \leq 1$	Very good	5

Table 6. Rate of DD, DP, and DRE

DD	DP	DRE	Rate	Value
$2 < C_{4}$	$2 < C_{5}$	$2 < C_{6}$	Very poor	$- 1$
$0 < C_{4} \leq 2$	$0 < C_{5} \leq 2$	$0 < C_{6} \leq 2$	Poor	$- 2$
$0.5 < C_{4} \leq 1$	$0.5 < C_{5} \leq 1$	$0.5 < C_{6} \leq 1$	General	$- 3$
$0.25 < C_{4} \leq 0.5$	$0.25 < C_{5} \leq 0.5$	$0.25 < C_{6} \leq 0.5$	Good	$- 4$
$0 < C_{4} \leq 0.25$	$0 < C_{5} \leq 0.25$	$0 < C_{6} \leq 0.25$	Very good	$- 5$

Calculation of RPA

As mentioned earlier, the assigned value was utilized to compute the RPA. Here is an example to illustrate how it functions. For instance, if ARE equals 0.25, the corresponding value would be 2 (shown in Table 5). Subsequently, the value of 2 was inserted into Eq. (2) at the position ARE.

Descriptive statistics of RPA are shown:

average = 2.684

, standard

error = 0.0197

,

median = 2.66

,

minimum = 1.638

,

maximum = 3.682

. According to the results, we used five risk ratings to assess construction professionals’ perception condition of risks (shown in Table 7). The results indicated that 329 construction professionals’ RPA ranged from 1.638 to 3.682, and the average grade was 2.6843. For 327 construction professionals, 32.72% of them had a poor RPA, 50.76% of them had a general RPA, and 15.60% of them had a superior RPA (shown in Fig. 6).

Table 7. Evaluate the standard of RPA

Risk perception ability	Grade
$0 < RPA \leq 1.5$	Very poor
$1.5 < RPA \leq 2.5$	Poor
$2.5 < RPA \leq 3$	General
$3 < RPA \leq 3.5$	Good
$3.5 < RPA \leq 5$	Very good

RPA between Workers and Managers

The Results of Workers’ RPA

The results of workers’ RPA are shown in Table 8. To statistics, 38.43% of workers’ risk perception ability ranged from 1.5 to 2.5, meaning they had poor ability to identify and evaluate hazards; 54.90% of workers’ RPA ranged from 2.5 to 3.0, indicating that their risk perception ability in the medium level; 7.45% of workers’ RPA between 3.0 and 3.5, showing they had the superior risk perception ability.

Table 8. Descriptive statistics of workers’ RPA

Mean	Standard error	Median	Minimum	Maximum	Skewness
2.5981	0.0189	2.6430	1.638	3.187	$- 0.406$

The Results of Managers’ RPA

The results of managers’ RPA are shown in Table 9. The results revealed that 12.5% of managers’ risk perception ability was poor, ranging from 1.5 to 2.5; 36.11% of managers’ RPA was at the medium level, ranging from 2.5–3.0; 44.44% of managers had superior RPA, whose RPA were between 3.0 and 3.5.

Table 9. Descriptive statistics of managers’ RPA

Mean	Standard error	Median	Minimum	Maximum	Skewness
2.982	0.042	3.017	1.961	3.682	$- 0.399$

Differences of Indicators between Workers and Managers

Using SPSS software, we conducted a Mann-Whitney U test to analyze risk perception differences between workers and managers. The results indicated that the significant

p

of HIA, HID, DD, DP, and RPA less than 0.5 yields that the differences in these indicators between workers and managers were significant (Table 10). Workers’ DD (

p = 0.000 * < 0.05

) and DP (

p = 0.000 * < 0.05

) were significantly larger than those of managers. Meanwhile, the HIA (

p = 0.001 * < 0.05

), HID (

p = 0.000 * < 0.05

), and RPA (

p = 0.000 * < 0.05

) of workers were significantly smaller than those of managers. Accordingly, the condition of hazard identification and evaluation differed in different positions, and managers’ RPA was significantly superior to workers’.

Table 10. Independent samples Mann–Whitney U test summary

Items	Indicators	Mann–Whitney U	Wilcoxon $W$	$Z$	Mean rank		Significance
Items	Indicators	Mann–Whitney U	Wilcoxon $W$	$Z$	Worker	Manager	Significance
1	HID	5,068.000	37,708.000	$- 5.812$	147.87	221.11	0.000***
2	HIA	6,885.000	39,525.000	$- 3.244$	155.00	195.88	0.001**
3	ARE	8,864.000	41,504.000	$- 0.454$	162.76	168.39	0.650
4	DD	5,175.000	7,803.000	$- 5.657$	179.71	108.38	0.000***
5	DP	6,250.500	8,878.500	$- 4.136$	175.49	123.31	0.000***
6	DRE	8,747.000	41,387.000	$- 0.611$	162.30	170.01	0.541
7	RPA	3,816.500	36,456.500	$- 7.572$	142.97	238.49	0.000***

Note: Asymptotic significances are displayed. The significance level is 0.050. * $p < 0.05$ , ** $p < 0.01$ , and *** $p < 0.001$ .

Discussion

Design of Indicators

Previous studies explored risk perception mainly from two stages: perceptual and cognitive processes. While exploring differences in risk perception between new drivers and experienced drivers, Tang et al. (2014) divided the processing of risk perception into perceptual and cognitive processes. Applying neuroscience, Ma et al. (2010) explored the identification mechanism of safety signs and established a two-stage model of risk perception, the early automatic attention stage, and the evaluation stage. However, in the field of construction, scholars have primarily focused on hazard identification, with some scholars measuring risk perception abilities solely from this perspective (Crick and McKenna 1992). The critical component of risk evaluation in risk perception should not be overlooked. Extensive research has been conducted to study risk evaluation in risk perception (Li 2005; Perlman et al. 2014), but not in RPA. Building upon this, our study encompasses both hazard identification and risk evaluation in measuring risk perception abilities. We have developed six indicators to measure individuals’ risk perception abilities from two parts: hazard identification ability and risk evaluation ability. To assess the abilities, we have used both questionnaires and behavioral experiments, measuring 327 construction professionals’ risk perception abilities. In our study, a person with higher RPA is characterized as identifying hazards correctly, judging the probability and damage of potential accidents, and the risk level of hazards objectively and rightly. In contrast, those with lower RPA tend to identify hazards wrongly or partially and may overestimate or underestimate the probability, damage, and risk level, leading to a larger deviation in risk perception.

The distributions of indicators and RPA we measured are shown in Table 11. Regarding indicators of the hazard identification stage, 57% of participants’ HID was poor, with 25% being categorized as general; 40% of participants had good HIA; and 32% of them had general hazard identification accuracy. So combined, construction professionals have difficulty in identifying all hazards depicted in pictures; however, they have high accuracy in their identified hazards, reflecting they rarely recognize normal scenes as hazards but could not identify hazards comprehensively.

Table 11. Summary of measurement

Indicators	Measurement (sample size = 327)
Indicators	Very poor	Poor	General	Good	Very good
HID	16%	57%	25%	2%	0%
HIA	2%	7%	32	40	19
ARE	15	46	30	9	0
DD	20	38	32	10	0
DP	5	42	49	4	0
DRE	0	1	85	11	3
RPA	0	33	51	14	2

In terms of indicators in the risk evaluation stage, 46% of participants had poor ARE (S-ARE), while 30% had general ARE; 38% of participants’ deviation degree of the expected damage (S-DD) was poor, and 32% of that were general; 49% participants had a poor rate of deviation DP (S-DP), and 49% participants had general rate; and 85% participants’ deviation DRE (C-DRE) was general. Except for the deviation DRE rate, other indicators were mainly in general or poor level. Construction professionals struggle to evaluate the risk level accurately, the deviation exists when they evaluate risk level. Subsequently, the deviation of expected damage and probability was large, suggesting that construction professionals fail to correctly comprehend the expected harm and probability of hazards.

The results presented show that the measurement indicators for construction professionals can vary. Focused on differences in risk perception, previous studies indicated that managers’ risk perception was significantly different from that of the general public (Li et al. 2018), different groups judged risks differently in the construction industry (Zhao et al. 2016), different genders also had different risk perception (Alsharawy et al. 2021), and risk perception varies from ages (Rosi et al. 2021). From data analysis in our study, the HIA (

p = 0.001 * < 0.05

), HID (

p = 0.000 * < 0.05

), DD (

p = 0.000 * < 0.05

), and DP (

p = 0.000 * < 0.05

) of workers were significantly different from managers. The results revealed that the discrepancy in RPA between workers and managers existed in our study. To some extent, the measurement results of indicators can indicate the rationality of indicator design.

The Calculation of RPA

In the calculation process, the determination of indicator weights considers not only expert recommendations but also objective data, thereby enhancing the persuasiveness of the obtained results in measuring RPA. After that, Eq. (2) was obtained.

The indicators, such as HID, HIA, and ARE, showed the right rate and identification number of participants. So, they are positively correlated with RPA. Indicators, such as deviation degree of the DD, DP, and DRE, which indicated the deviation of risk evaluation, were negatively correlated with RPA. As to the results of RPA, for 327 construction professionals, 32.72% of them had a poor risk perception ability, 50.76% of them had a general risk perception ability, and 15.60% of them had a superior risk perception ability, revealing the differences in risk perception abilities among construction professionals.

Managers’ RPA was larger than workers’ (

p = 0.000 * < 0.05

), meaning managers’ risk perception ability was superior to workers. The results proved the risk perception discrepancy between workers and managers exists, which was the same as Song and Zhang (2022).

In conclusion, the ways of calculating risk perception we proposed can used to measure construction professionals’ RPA. This calculation method effectively demonstrated differences in abilities among construction professionals and between workers and managers. It is scientifically valid to a certain extent.

Implication

The method proposed in this article expands upon previous research on risk perception abilities, specifically focusing on risk perception abilities in construction safety. Our measurement offered a comprehensive and systematic examination of individuals’ risk perception abilities by considering two parts of RPA: hazard identification ability, and risk evaluation ability which was neglected by previous study.

From a practical perspective, the proposed method for measuring risk perception abilities is straightforward and easily implementable. It has broad applicability not limited to the field of construction safety but extends to other industries as well. This method effectively evaluates workers’ ability to identify hazards and evaluate risks, enabling management to assess the effectiveness of safety training. Moreover, by utilizing various types of hazard pictures, workers can undergo testing to identify any misconceptions in hazard identification and pinpoint shortcomings in their understanding of risks. This, in turn, facilitates the development of tailored and targeted safety training programs, which are aimed at enhancing workers’ risk perception abilities and expanding their knowledge of risks.

To achieve this, additional training and resources should be provided to expand their knowledge and understanding of hazards. Safety training should be conducted to increase personnel’s sensitivity to hazards. By watching videos or pictures, hazards ought to be introduced thoroughly to increase construction professionals’ familiarity with hazards. Moreover, construction professionals should strengthen their ability to recognize hazards’ risk levels, expected damage, and probability. Combined with accident cases, a detailed and comprehensive explanation of these factors can help them to understand hazards more deeply. An accurate and objective judgment not only helps workers avoid unsafe behaviors but also assists managers in making the proper decision for safety management.

Limitations and Future Research

We formulated a novel evaluation method to measure construction practitioners’ RPA. The results revealed that managers’ risk perception abilities were superior to workers’. Nevertheless, there were certain shortcomings in the research, which should be addressed in future studies.

Primarily, the RPA covers two parts: a questionnaire survey and a behavioral experiment. The samples selected for this research were representative of front-line construction practitioners, whereas the hazard stimulus materials included various risks associated with construction sites. However, some studies showed that flat stimuli can potentially distort information, consequently biasing subjects’ understanding of risk information. Subsequent studies should consider the use of real-scene videos or VR scene simulations as hazardous stimulus materials. Second, this paper mainly measures subjects’ RPA through subjective judgments, and no objective physiological data were considered or analyzed. Some scholars combined neuroscience techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the risk perception process (Ma et al. 2010; Qin and Han 2009). Scholars such as Zhang et al. (2021) also proposed the necessity of using neuroscience techniques to study risk perception. In future studies, research methods should be broadened, such as using event-related potentials to study brain activity during hazard identification and risk evaluation. Finally, to effectively apply the measurement of RPA, we should consider how to conduct the measurement through automatic processes, such as developing a software program.

Conclusion

A measurement method of RPA was proposed with six indicators. Then, 327 construction professionals’ RPA was measured. The results indicated that RPA varied among construction professionals, and the risk perception discrepancy between workers and managers existed. The main content of the research is presented as follows.

An indicators system used to measure RPA was designed. Indicators concluded HID, HIA, risk ARE, DD, DP, and DRE. Then, we proposed a formula to calculate RPA as shown in Eq. (2).

The measurement proposed can evaluate construction professionals’ risk perception ability scientifically and comprehensively. Also, this measurement expands previous research on risk perception abilities, specifically focusing on risk perception abilities in construction safety.

Appendix. Meanings of Indicators

Items	Indicators	Calculation formula	Explanation
Hazard identification ability	Hazard identification degree	$C_{1} = \frac{n}{N}$	The correct ratio identifying hazards in the total in complex scenarios
Hazard identification ability	Hazard identification accuracy	$C_{2} = \frac{n}{s}$	The correct ratio of identified hazards in complex scenarios
Risk evaluation ability	Accuracy of risk evaluation	$C_{3} = \frac{m}{M}$	The correct ratio evaluating risk level in simple scenarios
	Deviation degree of the expected damage	$C_{4} = \frac{\| h_{1} - h_{0} \|}{h_{0}}$	The deviation when evaluating expected damage of hazards in simple scenarios
	Deviation degree of the probability	$C_{5} = \frac{\| p_{1} - p_{0} \|}{p_{0}}$	The deviation when evaluating the probability of accidents that hazards caused in simple scenarios
	Deviation degree of risk evaluation	$C_{6} = \frac{\| R_{1} - R_{0} \|}{R_{0}}$	The deviation when evaluating the risk level of hazards in complex scenarios

Note: $h_{0}$ = answer of damage level; $h_{1}$ = damage level judged subjectively; $M$ = number of risk pictures; $m$ = number of risk pictures judged risk correctly; $N$ = number of hazards in the picture; $n$ = number of hazards identified correctly; $p_{0}$ = answer of probability; $p_{1}$ = probability judged subjectively; $R_{0}$ = answer of risk level; $R_{1}$ = risk level judged subjectively; and $s$ = number of hazards identified.

Data Availability Statement

Some or all data, models, or codes generated or used during the study are available from the corresponding author by request.

Acknowledgments

This work was supported by the Natural Science Foundation of Hunan Province (No. 2021JJ00).

References

Alsharawy, A., R. Spoon, A. Smith, and S. Ball. 2021. “Gender differences in fear and risk perception during the COVID-19 pandemic.” Front. Psychol. 12 (Aug): 689467. https://doi.org/10.3389/fpsyg.2021.689467.

Google Scholar

Atefeh, M., K. Mehdi, K. Ebrahim, B. Michael, and M. Ann. 2018. “Measures of mental alertness of construction workers to enhance job site safety.” In Proc., Construction Research Congress, 262–271. Reston, VA: ASCE. https://doi.org/10.1061/9780784481288.026.

Google Scholar

Borowsky, A., D. Shinar, and T. Oron-Gilad. 2010. “Age, skill, and hazard perception in driving.” Accid. Anal. Prev. 42 (4): 1240–1249. https://doi.org/10.1016/j.aap.2010.02.001.

Google Scholar

Casutt, G., N. Theill, M. Martin, M. Keller, and L. Jaencke. 2014. “The drive-wise project: Driving simulator training increases real driving performance in healthy older drivers.” Front. Aging Neurosci. 6 (May): 85. https://doi.org/10.3389/fnagi.2014.00085.

Google Scholar

Chen, J., X. Song, and Z. Lin. 2016a. “Revealing the ‘Invisible Gorilla’ in construction: Estimating construction safety through mental workload assessment.” Autom. Constr. 63 (Mar): 173–183. https://doi.org/10.1016/j.autcon.2015.12.018.

Google Scholar

Chen, J. X. 2018. “Study on the theoretical framework of game theory: The construction of the theoretical system.” J. Chongqing Univ. Technol. 32 (1): 88–95. https://doi.org/10.3969/j.issn.1674-8425(s).2018.01.01.

Google Scholar

Chen, S., Y. Chen, J. P. Wang, and Z. Li. 2016b. “Risk perception ability of crane driver in hoisting Bailey truss of Three Gorges ship lift.” China Saf. Sci. J. 26 (1): 22–27. https://doi.org/10.16265/j.cnki.issn1003-3033.2016.01.004.

Google Scholar

Cheshmidari, M. N., A. H. H. Ardakani, H. Alipor, and S. Shojaei. 2017. “Applying Delphi method in prioritizing intensity of flooding in Ivar watershed in Iran.” Spatial Inf. Res. 25 (2): 173–179. https://doi.org/10.1007/s41324-017-0086-6.

Google Scholar

Crick, J., and F. McKenna. 1992. “Hazard perception: Can it be trained?” Accessed July 1, 1992. https://www.semanticscholar.org/paper/HAZARD-PERCEPTION%3A-CAN-IT-BE-TRAINED-Crick-McKenna/d18319e4253b26c5a5f6633333dfc03becc62b9a.

Google Scholar

Dzeng, R.-J., C.-T. Lin, and Y.-C. Fang. 2016. “Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification.” Saf. Sci. 82 (Feb): 56–67. https://doi.org/10.1016/j.ssci.2015.08.008.

Google Scholar

Fan, Z. P., Q. Zhang, and J. Ma. 1998. “An integrated approach to weight determination in multi-attribute decisions.” J. Manage. Sci. China 1 (3): 52–55.

Google Scholar

Farrand, P., and F. Mckenna. 2001. “Risk perception in novice drivers: The relationship between questionnaire measures and response latency.” Transp. Res. 4 (3): 201–212. https://doi.org/10.1016/S1369-8478(01)00024-9.

Google Scholar

Joukar, A., I. Nahmens, and C. Harvey. 2016. “An AHP-Based selection model for ranking potential strategies for managing construction’s cost volatilities.” In Proc., Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan, edited by J. L. Perdomo-Rivera, A. Gonzalez-Quevedo, C. Lopez DelPuerto, F. Maldonado-Fortunet, and O. I. Molina-Bas, 616–626. Reston, VA: ASCE.

Google Scholar

Li, J., S. Zhang, D. D. Li, and X. Z. Shi. 2018. “Research progress and analysis of occupational health and safety risk perception based on risk perception theory.” J. Saf. Sci. Technol. 14 (11): 166–173. https://doi.org/10.11731/j.issn.1673-193x.2018.11.027.

Google Scholar

Li, J. Y. 2005. “Evaluation on public risk perception: A case study on college students.” J. Nat. Disasters 14 (6): 153–156.

Google Scholar

Li, L., Z. X. Xv, W. D. Yu, and S. C. Tian. 2012. “Fuzzy optimization and CRITIC method based initial water right allocation of river basin a case of Zhangweinan Canal Sub-basin.” Water Resour. Hydropower Eng. 43 (7): 32–35. https://doi.org/10.13928/j.cnki.wrahe.2012.07.017.

Google Scholar

Li, R., Y. Li, H. Xu, H. Liu, S. Miao, J. Chen, and J. Cai. 2014. “Assessment on typical power supply mode for important power consumers based on analytical hierarchy process and expert experience.” Dianwang Jishu/Power Syst. Technol. 38 (9): 2336–2341. https://doi.org/10.13335/j.1000-3673.pst.2014.09.004.

Google Scholar

Liang, B., and Y. Lin. 2018. “Using physiological and behavioral measurements in a picture-based road hazard perception experiment to classify risky and safe drivers.” Transp. Res. Part F Traffic Psychol. Behav. 58 (Oct): 93–105. https://doi.org/10.1016/j.trf.2018.05.024.

Google Scholar

Liang, Z. D. 2022. “A study of the influential factors and intervention of the coal miners’ unsafe behaviors retrieved.” Doctor’s thesis, Management Science and Engineering, China Univ. of Mining and Technology.

Google Scholar

Ma, Q., J. Jin, and L. Wang. 2010. “The neural process of hazard perception and evaluation for warning signal words: Evidence from event-related potentials.” Neurosci. Lett. 483 (3): 206–210. https://doi.org/10.1016/j.neulet.2010.08.009.

Google Scholar

Mann, C., and S. E. Wolfe. 2016. “Risk perceptions and terror management theory: Assessing public responses to urban flooding in Toronto, Canada.” Water Resour. Manage. 30 (8): 2651–2670. https://doi.org/10.1007/s11269-016-1308-8.

Google Scholar

Martin, I. M., H. Bender, and C. Raish. 2007. “What motivates individuals to protect themselves from risks: The case of wildland fires.” Risk Anal. 27 (4): 887–900. https://doi.org/10.1111/j.1539-6924.2007.00930.x.

Google Scholar

McKenna, F. P., and J. L. Crick. 1992. “Experience and expertise in hazard perception.” In Behavioural research in road safety, edited by G. B. Grayson and J. F. Lester. Crowthorne, UK: Transport and Road Research Laboratory.

Google Scholar

Na, Y., J. Z. Li, and G. R. Feng. 2019. “A cross-level influence of group cognition on individual unsafe behavior intention.” China Saf. Sci. J. 29 (2): 13–19. https://doi.org/10.16265/j.cnki.issn1003-3033.2019.02.003.

Google Scholar

Perlman, A., R. Sacks, and R. Barak. 2014. “Hazard recognition and risk perception in construction.” Saf. Sci. 64 (Apr): 22–31. https://doi.org/10.1016/j.ssci.2013.11.019.

Google Scholar

Peter, J. P., and M. J. Ryan. 1976. “An investigation of perceived risk at the brand level.” J. Mark. Res. 13 (2): 184–188. https://doi.org/10.1177/002224377601300210.

Google Scholar

Pradhan, A. K., K. R. Hammel, and R. DeRamus. 2003. “The use of eye movements to evaluate the effects of driver age on risk perception in an advanced driving simulator.” In Proc., 82nd Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.

Google Scholar

Qiao, J., X. Xv, C. P. Liu, J. W. Zhao, and Y. T. Liu. 2022. “Review on affecting factors and intervention methods of drivers’ hazard perception ability.” China Saf. Sci. J. 32 (2): 34–41. https://doi.org/10.16265/j.cnki.issn1003-3033.2022.02.006.

Google Scholar

Qin, J., and S. Han. 2009. “Neurocognitive mechanisms underlying identification of environmental risks.” Neuropsychologia 47 (2): 397–405. https://doi.org/10.1016/j.neuropsychologia.2008.09.010.

Google Scholar

Rasmussen, J. 1986. “Information processing and human-machine interaction: An approach to cognitive.” In Engineering. Amsterdam, Netherlands: Elsevier.

Google Scholar

Rosen, L., and I. Kostjukovsky. 2015. “Parental risk perceptions of child exposure to tobacco smoke.” BMC Public Health 15 (Aug): 90. https://doi.org/10.1186/s12889-015-1434-x.

Google Scholar

Rosenbloom, T., R. Mandel, Y. Rosner, and E. Eldror. 2015. “Hazard perception test for pedestrians.” Accid. Anal. Prev. 79 (Jun): 160–169. https://doi.org/10.1016/j.aap.2015.03.019.

Google Scholar

Rosi, A., F. T. van Vugt, S. Lecce, I. Ceccato, M. Vallarino, F. Rapisarda, T. Vecchi, and E. Cavallini. 2021. “Risk perception in a real-world situation (COVID-19): How it changes from 18 to 87 years old.” Front. Psychol. 12 (Apr): 646558. https://doi.org/10.3389/fpsyg.2021.646558.

Google Scholar

Safi, A. S., W. J. Smith, and Z. Liu. 2012. “Rural Nevada and climate change: Vulnerability, beliefs, and risk perception.” Risk Anal. 32 (6): 1041–1059. https://doi.org/10.1111/j.1539-6924.2012.01836.x.

Google Scholar

Salmon, P. M., K. L. Young, and M. Cornelissen. 2013. “Compatible cognition amongst road users: The compatibility of driver, motorcyclist, and cyclist situation awareness.” Saf. Sci. 56 (Jul): 6–17. https://doi.org/10.1016/j.ssci.2012.02.008.

Google Scholar

Seo, D. C. 2005. “An explicative model of unsafe work behavior.” Saf. Sci. 43 (3): 187–211. https://doi.org/10.1016/j.ssci.2005.05.001.

Google Scholar

Shappell, S., and D. Wiegmann. 2000. “The human factors analysis and classification system—HFACS.” Office of Aviation Medicine 72: 1006–1016.

Google Scholar

Siegrist, M., and J. Arvai. 2020. “Risk perception: Reflections on 40 years of research.” Risk Anal. 40 (Jun): 2191–2206. https://doi.org/10.1111/risa.13599.

Google Scholar

Song, Y., and S. Zhang. 2022. “The differences in risk perception between practitioners in the non-coal-mining industry: Miners, managers and experts.” Toxics 10 (10): 623. https://doi.org/10.3390/toxics10100623.

Google Scholar

Spicer, R. A. 1964. Human factors in traffic accidents. Final report USPHS Research Grant No.AC-O0055. May 1964. Honolulu: Dept. of Health.

Google Scholar

Su, G. N., X. Q. Fu, and T. X. Liu. 2014. “Application of improved game theory comprehensive weights in comprehensive evaluation of dam safety.” China Rural Water Hydropower 2014 (11): 82–85.

Google Scholar

Tang, Z. H., J. Chen, and Z. N. Fang. 2013. “Causal mechanism of difference between experienced and novice drivers in hazard perception based on fuzzy signal detection theory.” J. Southwest Jiaotong Univ. 48 (3): 532–538. https://doi.org/10.3969/j.issn.0258-2724.2013.03.022.

Google Scholar

Tang, Z. H., Z. N. Fang, and J. Cheng. 2014. “Research on the difference in hazard perception of new and old drivers.” Chin. J. Ergon. 20 (1): 59–62. https://doi.org/10.13837/j.issn.1006-8309.2014.01.005.

Google Scholar

Tixier, A. J.-P., M. R. Hallowell, A. Albert, L. van Boven, and B. M. Kleiner. 2014. “Psychological antecedents of risk-taking behavior in construction.” J. Constr. Eng. Manage. 140 (11): 04014052. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000894.

Google Scholar

Vanlaar, W., and G. Yannis. 2006. “Perception of road accident causes.” Accid. Anal. Prev. 38 (1): 155–161. https://doi.org/10.1016/j.aap.2005.08.007.

Google Scholar

Wang, J., P. X. W. Zou, and P. P. Li. 2016. “Critical factors and paths influencing construction workers’ safety risk tolerances.” Accid. Anal. Prev. 93 (Aug): 267–279. https://doi.org/10.1016/j.aap.2015.11.027.

Google Scholar

Wang, Z. H. 2018. “Study on the influence of construction workers’ safety cognitive ability on their behavior results Hubei University of Technology.” Master Bachelor’s thesis, School of Civil Engineering Architecture and Environment, Hubei Univ. of Technology.

Google Scholar

Wickens, C. D., W. S. Helton, J. G. Hollands, and S. Banbury. 2021. Engineering psychology and human performance. 5th ed. England, UK: Routledge. https://doi.org/10.4324/9781003177616.

Google Scholar

Wilson, R. S., A. Zwickle, and H. Walpole. 2019. “Developing a broadly applicable measure of risk perception.” Risk Anal. 39 (4): 777–791. https://doi.org/10.1111/risa.13207.

Google Scholar

Wogalter, M., D. DeJoy, and K. Laughery. 1993. “Warnings and risk communication.” In Safety science, 16. London: CRC Press. https://doi.org/10.1016/0925-7535(93)90023-7.

Google Scholar

Wogalter, M. S. 2018. “Communication-human information processing (C-HIP) model.” In Forensic human factors and ergonomics, 33–49. London: CRC Press. https://doi.org/10.1201/9780429462269-3.

Google Scholar

Wogalter, M. S., M. J. Kalsher, and R. Rashid. 1999. “Effect of signal word and source attribution on judgments of warning credibility and compliance likelihood.” Int. J. Ind. Ergon. 24 (2): 185–192. https://doi.org/10.1016/S0169-8141(98)00025-0.

Google Scholar

Xiao, Z. Y. 2014. “Research on the relationship between miners’ risk perception and unsafe behavior.” Master Bachelor’s thesis, College of Safety Science and Engineering, Xi’an Univ. of Science and Technology.

Google Scholar

Yang, J. S., W. L. Wang, W. Su, D. T. Yang, and Z. Y. Song. 2014. “Drivers’ hazard perception modeling and experimental analysis.” J. Southeast Univ. 44 (6): 1304–1308.

Google Scholar

Zhang, S., Y. F. Huang, and X. Z. Shi. 2021. “Background and foundation analysis of construction of neuro-safety science.” China Saf. Sci. J. 31 (5): 98–105. https://doi.org/10.16265/j.cnki.issn1003-3033.2021.05.015.

Google Scholar

Zhao, D., A. P. McCoy, B. M. Kleiner, T. H. Mills, and H. Lingard. 2016. “Stakeholder perceptions of risk in construction.” Saf. Sci. 82 (Jun): 111–119. https://doi.org/10.1016/j.ssci.2015.09.002.

Google Scholar

Zhu, G.-N., J. Hu, J. Qi, C.-C. Gu, and Y.-H. Peng. 2015. “An integrated AHP and VIKOR for design concept evaluation based on rough number.” Adv. Eng. Inf. 29 (3): 408–418. https://doi.org/10.1016/j.aei.2015.01.010.

Google Scholar

Information & Authors

Information

Published In

Journal of Construction Engineering and Management

Volume 150 • Issue 5 • May 2024

Copyright

This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.

History

Received: Mar 22, 2023

Accepted: Dec 5, 2023

Published online: Mar 14, 2024

Published in print: May 1, 2024

Discussion open until: Aug 14, 2024

Authors

Affiliations

Shu Zhang https://orcid.org/0000-0002-1037-9435 [email protected]

Professor, School of Resources and Safety Engineering, Safety and Security Theory Innovation and Promotion Center, Central South Univ., Changsha, Hunan 410083, PR China (corresponding author). ORCID: https://orcid.org/0000-0002-1037-9435. Email: [email protected]

View all articles by this author

Xinyu Hua https://orcid.org/0000-0002-4379-8126 [email protected]

School of Public Administration, Central South Univ., Changsha 410083, PR China; Center for Social Stability Risk Assessment, Central South Univ., Changsha, Hunan 410075, PR China. ORCID: https://orcid.org/0000-0002-4379-8126. Email: [email protected]

View all articles by this author

Xiuzhi Shi [email protected]

Professor, School of Resources and Safety Engineering, Central South Univ., Changsha, Hunan 410083, PR China. Email: [email protected]

View all articles by this author

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Abstract

Introduction

Methodology

Research Framework

Measures

Materials of Measurement

Design of Measurement

Design of Indicators

Procedure of Measurement

Calculation of RPA

Weight Calculation

Calculation of RPA

Applications

Participants

Measurements of RPA

Calculation of Hazard Identification Ability

Calculation of Risk Evaluation Ability

Calculation of RPA

RPA between Workers and Managers

The Results of Workers’ RPA

The Results of Managers’ RPA

Differences of Indicators between Workers and Managers

Discussion

Design of Indicators

The Calculation of RPA

Implication

Limitations and Future Research

Conclusion

Appendix. Meanings of Indicators

Data Availability Statement

Acknowledgments

References

Information

Published In

Copyright

History

Authors

Affiliations

Metrics

Citations

Download citation

Figures

Other

Share

Copy the content Link

Share with email

Share

Request Username

Create a new account

Change Password

Password Changed Successfully

Verify Phone

Congrats!