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A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm

Mahmaod Alrawad
Abdalwali Lutfi
Mohammed Amin Almaiah
Adi Alsyouf
Hussin Mostafa Arafa
Yasser Soliman
10 and
Ibrahim A. Elshaer
Quantitative Method, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
College of Business Administration and Economics, Al-Hussein Bin Talal University, Ma’an 71111, Jordan
Department of Accounting, College of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
Department of Computer Networks, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
Department of Computer Science, King Abdullah the II IT School, The University of Jordan, Amman 11942, Jordan
Department of Managing Health Services and Hospitals, Faculty of Business Rabigh, College of Business (COB), King Abdulaziz University, Jeddah 21991, Saudi Arabia
Department of Statistics, Mathematics and Insurance Faculty of Commerce, Assiut University, Assiut 71515, Egypt
Applied College, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Management Department, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Faculty of Tourism and Hotel Management, Suez Canal University, Ismailia 41522, Egypt
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(13), 9965;
Submission received: 16 May 2023 / Revised: 18 June 2023 / Accepted: 21 June 2023 / Published: 22 June 2023


Understanding how the public perceives various risks and hazards associated with our well-being and health is crucial for governments and policymakers. The present research aimed to assess the public perception of various risks and hazards associated with well-being and health. The study combined two well-known risk assessment approaches: the analytical hierarchical process (AHP) and the psychometric paradigm. Seven risk attributes were chosen from the risk perception literature to evaluate 27 risks and hazard activities using a survey questionnaire developed based on the psychometric paradigm literature. The collected data were then analyzed using the AHP to determine the priority weight for each risk attribute. The results showed that the most crucial risk attribute was voluntariness of risk, followed by chronic catastrophic and newness of risk. Furthermore, the study found that natural hazards were ranked the highest, followed by refugee influx and fire hazards. In contrast, the mobile phone was perceived as posing the lowest type of risk. Policymakers can use these findings to develop effective and sustainable risk communication strategies to help the governments to inform and educate the public about potential risks, improve coordination among agencies and stakeholders, and enhance public trust in government decision making.

1. Introduction

In recent years, the field of risk analysis has received growing attention from academics and practitioners. Researchers from various disciplinary backgrounds (e.g., management, psychology, engineering, environmental sciences, and statistics) have been performing risk analysis from two main perspectives: risk assessment and risk management. Researchers within the risk assessment stream have focused on identifying and quantifying risks and hazards associated with induvial and organizational activities [1,2,3,4,5]. In comparison, the latter stream of research is focused on mitigating and communicating risks and decision making under uncertainty [6,7,8,9,10]. A growing body of literature recognizes the critical role of risk communication in the success of risk management [6,11,12,13,14,15,16,17,18,19,20,21,22,23,24].
The World Health Organization (WHO) defines risk communication as the exchanging of real-time information, advice, and opinions between experts and individuals facing health, economic, or social threats. In order to have an effective and sustainable risk communication strategy, it is essential to deal with all the issues and problems related to the key components of message, source, channel, and receiver. However, according to Covello et al.’s research [25], the most critical issues that policymakers need to address while designing and executing an effective risk communication strategy are related to the receiver component. This component includes individuals’ perception of risks and ability to assess risks’ severity accurately. Communication problems from the receiver side can often arise due to misconceptions, misinformation, or a lack of understanding of the risks involved. These factors can significantly impact the effectiveness of any risk communication strategy. Therefore, addressing these issues related to the receiver component is crucial to ensure that people receive accurate and reliable information on time, which can help them make informed decisions to protect themselves and their communities [6,25].
The way individuals perceive risks has a significant impact on the decisions they make. Disagreement about the best course of action often arises between experts and laypeople due to their differences in risk perception [5,26,27,28,29,30]. Studies have shown that individual and group differences in risk perception are associated with how people perceive the relative risk of different options rather than their general attitude towards risk or their tendency to approach or avoid risky options [9,10]. Policymakers have been interested in understanding laypeople’s risk perceptions for several decades, as it is central to political agendas in many countries. Risk perception is crucial for understanding the public’s involvement in environmental issues and the opposition to new technologies [20].
The main focus of this research was to investigate the public’s perceptions of various activities and technologies concerning risks using a psychometric paradigm and an analytical hierarchical process. The research examined the possibility of utilizing expert risk assessment methods to gain a deeper understanding of public risk perception to provide valuable insights into how individuals perceive risks associated with various activities and technologies. By using the psychometric paradigm and the analytical hierarchical process, this study hoped to shed light on the factors influencing public risk perception and how these perceptions influence the public’s behavior. This research will be outlined as follows: Section 1 presents an introduction; Section 2 includes a review of previous studies on risk perception, highlighting different approaches used in these studies; Section 3 describes the collection of the necessary information for the study, including details about the research instrument used; Section 4 presents the results; Section 5 discusses the research findings. Finally, the conclusion, research limitations, and contributions of the study suggestions for future research are reported in Section 6.

2. Background

2.1. Risk Perception

The study of risk perception originated in the 1960s during the nuclear debate, as noted by Sjöberg [20]. During this time, Sowby [31] developed risk comparisons initially considered crucial for effective risk communication. However, in 1969, Starr demonstrated that risk acceptance was related to technical estimations of risks and benefits and to subjective factors such as risk voluntariness or individual knowledge of exposure. While Starr’s empirical data were later criticized, his research opened up a new field of study, highlighting the importance of perceived risks. This curious phenomenon prompted social scientists to explore and explain the concept of perceived risk further. Various approaches have been used in studying risk perception, with the revealed-preferences approach by Starr [32] being the first and most influential model. However, Starr’s work suffered from several limitations. For instance, Fischhoff et al. [33] noted that Starr’s attempt measured risk perception while asserting that past behavior indicates present preferences. However, given the rapid advancements in technology and the improvements in our life, it may not be accurate to suggest that future preferences reflect the past behavior. Moreover, market prices may not accurately reflect the costs or risks involved. For example, cigarette prices do not account for smokers’ increased likelihood of developing heart disease or cancer [34]. Accordingly, the limitations of this approach led to the development of other methods, including the heuristic and biases, psychometric, and cultural paradigms.
The revealed-preferences model assumes people evaluate risk based on costs and benefits. However, Tversky and Kahneman [35] challenged this assumption in 1974 by identifying three heuristics (representativeness, availability, and anchoring) that often lead to biases in decision making. Sjoberg [20] argued that the availability of heuristic was most important for understanding risk perception, as frequent media exposure can create a high level of perceived risk. For instance, Plous [36] found that respondents were more likely to choose death from shark attacks over that from a falling airplane, despite the latter being 30 times more likely. This suggests that recent events such as the movie Jaws affected the respondents’ judgment. However, heuristics and biases in research have also been criticized for their experimental design. For example, using undergraduate students as decision-makers raise questions about their probability estimation skills, access to relevant information, and motivation to complete tasks with concern [37]. Additionally, Fischhoff et al. [33] showed that subjective probability is only one of many factors contributing to the public’s risk perception.
The cultural theory of risk perception by Douglas and Wildavsky [38], on the other hand, examines the influence of culture on people’s perceptions and acceptability of risks. The theory suggests that conflicts over risk are best understood through different social constructions of meaning, and competing cultures can give different meanings to the same events [10]. According to this theory, individuals can be classified based on two dimensions—grid and group—resulting in four worldviews: egalitarian, individualistic, hierarchic, and fatalistic, which are defined based on the extent to which individuals incorporate themselves into relationships with others and the rules and customs that maintain distinctions between them. However, as with the previous approaches, this approach has been criticized by authors who view attitudes toward risk as the result of conflict and interaction of different cultural influences on an individual or group; therefore, they consider this approach too simple [39]. Furthermore, the cultural theory does not consider how worldviews might change over time [40,41,42,43,44].
Despite the early criticisms, Starr’s work was seminal in initiating research into understanding how individuals perceive and evaluate different risks [33]. Subsequent developments in research methods have allowed for deeper insights into how people process and respond to risk information, which is essential for developing effective risk communication strategies that consider individuals’ and communities’ diverse perspectives and values [25,45]. Therefore, research into risk perception remains an essential area of study, enabling policymakers and organizations to understand public perceptions of risk better and develop more informed decision-making processes [28].

2.2. The Psychometric Paradigm

The psychometric paradigm is a well-established and widely used approach in risk perception studies. It was developed by Paul Slovic and his team of researchers from Oregon, who sought to build upon the research conducted by Starr [32,46]. In their study, they utilized the mimic of the personality theory. They created the personality of hazards by asking respondents to rate various risks based on different qualities or characteristics that could influence their risk perception [33,46,47,48,49,50]. The risk characteristics commonly used by risk perception, according to Bronfman and Cifuentes [51] and Bronfman et al. [13], listed in Table 1, were carefully chosen as they had been hypothesized to affect people’s perceptions of risk. After gathering responses from participants using various psychometric scaling methods, the researchers analyzed the data using factor analysis [52]. This process identified several factors or dimensions related to people’s perceptions of risk sources. One of the key factors identified as the frequency and probability of risks suggests that people may be more likely to perceive more common risks as more severe than those that occur less frequently [10,52,53,54,55,56]. Other factors that influence risk perception included the nature of the hazard [54], its potential consequences, and the extent to which it is controllable.
Slovic’s work in 1992 highlighted the importance of considering multiple dimensions in shaping people’s perceptions of risk [47]. By incorporating the personality theory into their study, the researchers could better understand how these dimensions influence risk perception and acceptance. The psychometric paradigm has been valuable in risk perception studies, demonstrating that perceived risks can be measured and anticipated [14,33,46,57,58,59,60]. This approach uses psychometric scaling techniques to develop quantitative measures of perceived risk based on various qualities or characteristics suggested by Slovic [46] (e.g., voluntary, chronic, common, certainly not fatal, knowledge of exposure, immediate, known to science, not controllable, and new).
Through this approach, researchers have identified variations in risk perceptions between different groups, making it particularly useful for examining cross-cultural differences in risk perception [57,61]. These findings are crucial in developing effective risk communication strategies considering different groups’ unique perspectives and biases. In addition to uncovering variations in risk perception, studies utilizing the psychometric paradigm have also highlighted the complexity of the term “risk” itself. For instance, experts’ risk assessments tend to focus on technical estimates of annual fatalities, whereas laypeople’s judgments of risk center more on other characteristics of hazards, such as their potential for catastrophic events and their impact on future generations.
This distinction is particularly relevant when considering the potential emotional responses that different types of risk may elicit. For example, a hazard with a low probability of fatality but high catastrophic potential may produce stronger emotional reactions than a hazard with a higher probability of fatality but lower catastrophic potential. Overall, the psychometric paradigm has advanced our understanding of risk perception by demonstrating its quantifiability and predictability while highlighting the importance of considering the diverse perspectives that shape our risk judgments. However, the psychometric paradigm has received criticism for its tendency to adopt a top-down approach. This approach involves applying preconceived theoretical models to empirical data, sometimes leading to results reflecting the researcher’s initial assumptions or beliefs. Scholars have argued that this limits the scope of a study and makes the findings less objective [53]. Overall, while the psychometric paradigm has helped to understand human behavior and mental processes, it is essential to acknowledge its limitations and strive towards more objective and representative research practices. However, the psychometric paradigm investigates risk perception on an individual level through the implementation of principle component analysis (PCA). It is essential to note that PCA reduces the number of factors into fewer dimensions, eventually providing less information about an individual’s risk perception. Therefore, using AHP, we aimed to measure public risk perception without losing vital information.

2.3. Analytical Hierarchical Process

The analytical hierarchy process (AHP) is one of the multiple-criteria decision-making methods (MCDM) that Thomas Saaty developed in the early 1980s as a means of dealing with situations that required the consideration of multiple criteria or features [48,62,63,64,65,66,67,68,69,70]. Risk managers have used this technique widely to evaluate experts’ assessment of various risks and hazards in various fields. Alrawad et al. [56] used AHP to assess managers’ perceptions of financial risks. The study evaluated six financial and cash flow risk types using two risk criteria: probabilities and consequences. Similarly, AHP was used to evaluate occupational health risks [71], investment risks [72], and project management risks [30,73,74,75]. Many of these studies have employed a list of risks as decision alternatives, with probability and consequence serving as decision criteria. By applying the AHP, these studies could rank and prioritize the studied risks. The present study followed the previous literature using the AHP to assess public risk perception. Instead of relying on expert risk evaluation methods such as probability and consequence, the present study used seven risk attributes (e.g., voluntary, chronic, common, certain not fatal, knowledge of exposure, immediate, known to science, not controllable, and new) as decision criteria for evaluating 27 different types of risks.
The AHP compares and ranks a set of options based on predetermined criteria, and their relative importance based on respondents’ preferences. This generates a hierarchical structure through pairwise comparisons of decision alternatives, which can be used in complex decision-making scenarios. Accordingly, the AHP is particularly useful when both qualitative and quantitative criteria, such as risk assessment and management, must be addressed. However, it has been criticized for being time-consuming and difficult to manage when considering many alternatives. For instance, making a decision with 10 options would require 45 pairwise comparisons (i.e., 45 = 10 (10−1)/2) to arrive at a final list of priorities or weights. The AHP analysis goes through four major stages, which are explained in detail in the analysis Section. Despite criticisms, the AHP simplicity has made it popular in many applications that require a multi-criterion decision-making process.

3. Materials and Methods

A questionnaire based on previous literature was adapted [6,13,14,47,70,76,77,78]. The questionnaire included 14 questions distributed into 8 main sections. The first section of the questionnaire contained questions aimed at obtaining the socio-demographic characteristics of the study sample, including age, gender, and level of education. At the same time, the other seven sections comprised seven questions aimed at measuring the respondent’s perception of the 27 risks and hazards using a seven-point Likert scale, as shown in Table 1. The questionnaire items were translated into Arabic using the Back translation technique recommended by Brislin [79]. The participants were recruited using a simple random sample from two cities in Saudi Arabia (Jeda and Alhasa) between November 2022 and January 2023. Seven hundred questionnaires were distributed with the help of six research assistants. A total of 487 responses were collected, and 57 of them were eliminated for missing values. A t-test was conducted to see if the collected questionnaires had any non-response bias.
Consequently, two groups of respondents were chosen based on their response time (early and late respondents). Both groups were compared based on their risk average score. According to Armstrong and Overton [80], a non-response bias exists if the mean difference between two groups is significant. The test showed no issue of nonresponse bias [44,81,82].
The data were gathered and monitored to identify any missing data or outliers. Each participant was asked to assess a list of risks and hazards, shown in Figure 1, based on seven risk characteristics (e.g., knowledge of exposure, newness of risk, risk commonness, risk controllability, risk immediacy of consequence, catastrophic chronic, and activity voluntariness). The evaluation was performed through individual judgment considering these risk characteristics and the participants’ prior experience with risk. The data were then analyzed using the AHP procedure outlined in Section 2.
As shown in Table 2, regarding the respondents’ gender, around 32% of the respondents were female, while 35% were male. Regarding the respondents’ age, 18% of the participants were in the age group of 20–24 years, 42% were between 26 and 34 years old, 32% were between 34 and 45 years old, and 8% were older than 44. Considering education, around 17% of the participants had a high school certificate, 73% held an undergraduate degree, and only 10% of the respondents held a post-graduate degree.

4. Application of the AHP in Measuring Public Risk Perception

4.1. Step 1: Hierarchy Construction

The structure of the assessment process can be visualized in the hierarchy diagram presented in Figure 1. The first level of the hierarchy outlines that the primary objective of the analysis was to evaluate the proposed list of risks and hazards. Level two of the structure comprises seven criteria or risk attributes used in the assessment: knowledge of exposure, newness of risk, common dread, control over risk, immediacy of effect, chronic catastrophic, and voluntariness of risk. The third level represents the decision alternatives, including a list of proposed risks and hazards commonly encountered by individuals.

4.2. Step 2: Computing the Pairwise Comparison Matrix

The second stage of the process involved gathering data from a representative sample by the mean of a survey or a questionnaire. During this phase, the participants were asked to rank each type of risk and hazard based on predetermined criteria using a 7-point Likert scale. Then, the collected data were transformed to facilitate the risk comparison based on a subjective scale developed by Saaty [64], outlined in Table 3.
After comparing the complete list of risks and hazards, the geometric mean of all participants’ ratings was calculated using Equation (1) and used to fill the comparison matrix indicated by Equation (2). The values in the comparison matrix (At_1 vs. At_2) displayed in Equation (2) represent the geometric mean of the participants’ preferences when reflecting the alternative (At_1) versus the alternative (At_2). The matrix was then utilized to determine the average weight of the chosen criteria and the options by applying Equation (2).
Geometric   Mean :   a i j = a i j 1 a i j 2 . a i j n n
A = a 11 a 12 a 1 j a 21 a 22 a 2 j a i 1 a i 2 a i j , A = A t _ 1   v s   A t _ 1 A t _ 1   v s   A t _ 2 A t _ 1   v s   A t _ 7 A t _ 2   v s   A t _ 1 A t _ 2   v s   A t _ 2 A t _ 7   v s   A t _ 7 A t _ 7   v s   A t _ 7 A t _ 7   v s   A t _ 2 A t _ 7   v s   A t _ 7
Table 4 shows the pairwise comparison for the previously mentioned seven risk attributes.
Next, as a part of the AHP analysis, the values in the comparison matrix for all seven risk attributes listed in Table 4 were normalized. All values in the comparison matrix were divided by the sum of the column values using Equation (3), a normalization formula.
b i j = a i j i = 1 n a i j
Once the comparison matrix had been normalized, both Eigenvalue and Eigenvector were computed for the pairwise comparison matrix. The criteria weights for all risk attributes were calculated by determining the values mean for each raw using Equation (4) and are presented in Table 5. For instance, the criteria weightings for the risk attribute Knowledge of Exposure in Table 5 were obtained by averaging all the elements in the first row and dividing the result by the total number of risk attributes (e.g., 0.061 + 0.059 + 0.053 + 0.053 + 0.052 + 0.069 + 0.071/7 = 0.060).
w i = j = 1 n c i j n

4.3. Step 3: Consistency Vector

The next step in the process entailed creating a weighted sum matrix, which was achieved by multiplying the comparison matrix by the criteria weight to produce matrix D. Once the comparison matrix had been normalized, the Eigenvalue and Eigenvector were calculated for the pairwise comparison matrix.
W = W n 1 W n 2 W n n
D = a 11 a 12 . . a 1 n a 21 a 22 . . a 2 n . . . . . . . . a n 1 a n 2 . . a n n   ×   W 1 W 2 W n
E i = d i w i , ( i = 1,2 , 3 , , n )
D = 1.000 0.353 0.350 0.563 0.464 0.372 0.316 2.832 1.000 0.944 1.609 1.370 0.971 0.848 2.857 1.059 1.000 1.428 1.219 0.726 0.692 1.775 0.622 0.700 1.000 0.757 0.470 0.373 2.157 0.730 0.820 1.321 1.000 0.515 0.462 2.686 1.030 1.378 2.129 1.942 1.000 0.758 3.169 1.179 1.445 2.678 2.167 1.319 1.000   ×   0.060 0.165 0.152 0.095 0.116 0.188 0.224 = 0.419 1.162 1.066 0.670 0.816 1.325 1.582
After that, we obtained the consistency vector using Equation (5).
λ m a x = 1 n i = 1 n ( A w ) i w i
where lambda max ( λ m a x ) is the maximum eigenvalue of the comparison matrix. Next, lambda max was calculated using Equation (5) for all risk attributes to generate the analysis consistency vector (Wi/w). W signifies the matrix eigenvalue.
λ m a x = 7.037 C I = 0.00623 C R = 0.0047 < 10 %

4.4. Step 4: Estimating the Consistency Index

The concluding step in conducting an AHP analysis involves assessing the results’ accuracy [83]. This is achieved by checking whether the expert judgments in the pairwise comparisons are consistent. The AHP methodology outlines several steps for testing consistency. The relative weight or highest Eigenvector for all criteria must be computed as in the previous stage. Secondly, the consistency index (CI) value can be determined using Equation (6).
C I = λ m a x n n 1
In the third step, it is required to calculate the final solution consistency ratio (CR) [84]. To do so, we must determine the random consistency value based on the alternatives used in this study using Equation (7). Table 6 illustrates that the Random Index number (RI) for an AHP analysis with seven alternatives is RI = 1.32 [62].
C R = C I R I
The outcomes of the consistency examination for all criteria are presented in Table 7. As seen in the table, the CR value was less than (<0.10) [63], indicating that the current AHP analysis attained an adequate level of consistency.

4.5. Public Risk Perception

The tested list of hazards and risks was then ranked based on the respondent’s average score for each risk attribute and the criteria weights obtained through the AHP. These criteria weights are presented in Table 5. The risk scores for all hazards and risks tested were calculated using Equation (8). For example, the Risk Score (RS) for natural hazards was determined by adding the product of each criterion weighting (CW) to the corresponding risk average (RA)
R i s k   s c o r e = n = 1 R A n C W n
Risk Score for Natural Hazards = 2.58 × (0.06) +5 × (0.165) + 5.35 × (0.152) + 1.72 × (0.095) + 2.53 × (0.116) + 5.35 × (0.188) + 5.64 × (0.224) = 4.518.
Accordingly, Table 8 shows all risk scores for all studied risks and hazards alongside their ranking. Figure 2 also shows the final ranking for all risks and hazards. As can be seen in Table 8 and Figure 2, the priority weight for risk attributes was as follows; Voluntariness of Risk (22.4%), Chronic Catastrophic Risk (18.8%), Newness of Risk (16.5%), Control Over Risk (9.5%), Knowledge of Exposure (6%), Common Dread (15.2%), Immediacy of Effect (11.6%). Furthermore, the risk score for natural hazards was ranked as the highest, with an average risk score of 4.518, followed by those of refugee influx (4.427) and fire (4.422). At the same time, the risks linked to aircraft travel (3.377) and mobile phones (3.267) ranked as the lowest types of risk for the present respondent sample.

5. Discussion

In order to fulfill the study objectives, which were to investigate the public’s risk perceptions linked to various activities and technologies using a psychometric paradigm and an analytical hierarchical process, we examined the possibility of utilizing expert risk assessment methods for a more in-depth understanding of public risk perception. Empirical data were collected using a questionnaire and analyzed using the analytical hierarchical process method (AHP). By undertaking this analysis, the researchers aimed to identify the structure of risk perception. As pointed out by Slovic [34], using factor analysis to analyze multiple risk attributes, early research conducted within the psychometric paradigm resulted in a two-dimensional representation of hazards. The produced factors reflected the degree to which the risk from a particular hazard is understood and the degree to which the risk evokes a feeling of dread. However, reducing the risk attributes into only two factorial dimensions (e.g., knowledge and dread) narrows our understanding of how individuals form their perceptions. Therefore, preserving risk attributes through the AHP will expand our understanding of how individuals perceive risk. Accordingly, seven risk attributes were evaluated using data collected from 430 respondents. The data were analyzed following analytical hierarchical process traditions. The following is the most prominent finding emerging from the analysis.
Concerning the initial research objectives, the outcomes of this AHP analysis indicated the order by which risk attributes were prioritized: voluntariness of risk, chronic catastrophic risk, newness of risk, control over risk, and knowledge of exposure. The study also found that natural hazards were ranked as having the highest risk, with a risk score of 4.518, followed by refugee influx, with a risk score of 4.427, and fire (RS = 4.422). At the same time, the risks linked to aircraft travel (risk S = 3.377) and mobile phones (risk score = 3.267) ranked as the lowest types of risk for the present respondent sample. Furthermore, previous literature on public risk perception established knowledge’s fundamental function in shaping individuals’ risk perceptions for different technologies and activities [12,85,86,87,88]. Our findings align with the existing literature, indicating that the average weights of chronic, catastrophic events (0.188) and voluntariness (0.224) significantly shape an individual’s perception of risk. For instance, Van Schaik et al. [88] found that voluntariness to risk exposure and chronic, catastrophic risks are significant factors in students’ perception of cyber security hazards. Similar results were also reported by Alrawad et al. [6] in their attempt to evaluate miners’ risk perception. They found that workers’ voluntariness to exposure to some occupational and environmental risk and hazard significantly influenced their level of perceived risk. Similar results were also reported by Al-Rawad and Al Khattab [86].
Another important finding of the present study is the respondents’ ranking of natural hazards and how they perceived the associated risks. Accordingly, public perception can vary based on several risk attributes. The high rank of involuntariness of risk for natural hazards (1.264) tells us that individuals and communities have little control over the occurrence of these hazards. Unlike some other risks, such as those associated with mobile phones, aircraft travel, or smoking, individuals cannot avoid or prevent natural hazards from occurring. Similarly, natural hazards scored high as regards their chronic and catastrophic (1.005) attributes. The chronic and catastrophic attributes evaluate the scale of the damage a risk or hazard can cause. Although natural hazards have the potential to be both chronic (i.e., ongoing) and catastrophic (i.e., causing massive destruction and loss of life), the present findings showed that the respondents tended to worry more about the sudden and devastating impacts of natural hazards, such as the loss of life and property caused by earthquakes, tsunamis, and hurricanes.
Effective risk communication is vital to addressing these risk attributes, as it can help overcome the perception of voluntariness and the tendency to underestimate chronic, catastrophic risks. By providing accurate information about the likelihood and potential consequences of natural hazards, communities can make informed decisions about preparing and responding to these events. Additionally, by emphasizing the long-term impacts of natural hazards, stakeholders can be motivated to take proactive steps to reduce their vulnerability and build resilience.
Although this study addressed some of the psychometric limitations and attempted to propose an alternative approach to the analysis of public risk perception, it is crucial to understand that risk communication is an essential component to achieve sustainability in any organization or community [89] and support sustainable behavior changes during hazardous events [90]. An effective risk communication strategy ensures that risks and potential hazards are identified, assessed, and communicated to the relevant stakeholders promptly and transparently. This enables organizations to make informed decisions on managing risks, minimizing their impact on the environment, society, and economy and eventually meeting their sustainability goals. Accordingly, the research findings offer valuable insights for policy- and decision-makers to develop effective risk communication strategies and policies pertaining to risk management and public safety. For instance, the present study results confirmed that risks such as natural hazards, refugee influx, industrial pollution, and nuclear power are of the highest concern for the society. Although an expert may have a different opinion about these risks, decision-makers must address them by designing and launching awareness campaigns that provide accurate information to the public and help community engagement.
Furthermore, this study highlights the importance of considering the public’s perspective when evaluating risks and hazards associated with their well-being and health. Understanding how the public perceives various risks and hazards associated with their well-being and health is crucial for governments and policymakers. By taking a more sustainable perspective, policymakers and governmental agencies can consider the immediate impacts of risks and hazards on public health and their long-term implications for the environment and future generations.

6. Conclusions

This study is the first to evaluate public risk perception using the psychometric paradigm and the analytical hierarchical process, providing a comprehensive understanding of how people perceive different risks and hazards. Therefore, comparing this study’s findings with previous ones is challenging, since many prior studies only used the psychometric paradigm [6,11,13,33,54,57,91]. Finally, since our study was grounded on the psychometric paradigm, it is essential to acknowledge that the well-established limitations of this approach may apply. One notable limitation is the underlying assumption that individuals can provide meaningful responses to complex and sometimes unanswerable questions (e.g., what are the risks of gene technology?). This study focused on the risks inherent in 27 items or activities and technologies. Those are regarded as the most relevant to people in the literature. Furthermore, conducting a comparative analysis of different risk assessment methods and exploring sub-indicators that may expand the findings on different natural hazards can improve our understanding of natural hazards and help develop effective risk management strategies to mitigate their impact. It must be acknowledged that many more risks could have been considered. For instance, future research can explore sub-indicators that may expand our findings to different types of natural hazards (e.g., avalanche, earthquake, flooding, tsunami, tornado) using risk attributes such as knowledge of exposure.
Moreover, certain limitations are due to the size of the studied sample. Although the AHP method does not provide a statistical confidence level, usually, in order to have a statistically significant result, a larger sample size is required.

Author Contributions

Conceptualization, M.A. and A.L.; methodology, M.A. and M.A.A.; software, M.A. and M.A.A.; validation, H.M.A., M.A. and I.A.E.; formal analysis, M.A. and A.A.; investigation, M.A., A.L., M.A.A. and Y.S.; resources, M.A. and H.M.A.; data curation, Y.S.; writing—original draft preparation, M.A.; writing—review and editing, A.L., M.A.A. and Y.S.; visualization, M.A. and I.A.E.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. and H.M.A. All authors have read and agreed to the published version of the manuscript.


The authors extend their appreciation to the Deputyship of Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research through project number INST109.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Deputyship of Research and Innovation, Ministry of Education in Saudi Arabia, project number INST109.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Burstein, G.; Zuckerman, I. Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning. J. Risk Financ. Manag. 2023, 16, 97. [Google Scholar] [CrossRef]
  2. Kasap, Y.; Subaşı, E. Risk Assessment of Occupational Groups Working in Open Pit Mining: Analytic Hierarchy Process. J. Sustain. Min. 2017, 16, 38–46. [Google Scholar] [CrossRef]
  3. Moosavi, S.; Namdar, P.; Moghaddam Zeabadi, S.; Akbari Shahrestanaki, Y.; Ghalenoei, M.; Amerzadeh, M.; Kalhor, R. Healthcare Workers Exposure Risk Assessment in the Context of the COVID-19: A Survey among Frontline Workers in Qazvin, Iran. BMC Health Serv. Res. 2023, 23, 155. [Google Scholar] [CrossRef] [PubMed]
  4. Rizkiani, D.O.; Modjo, R. Health Risk Assessment of Workers at the Mining Company PT. HIJ Site in South Kalimantan: An Overview. KnE Life Sci. 2018, 4, 616. [Google Scholar] [CrossRef]
  5. Rivers, L.; Arvai, J.; Slovic, P. Beyond a Simple Case of Black and White: Searching for the White Male Effect in the African-American Community. Risk Anal. 2010, 30, 65–77. [Google Scholar] [CrossRef] [PubMed]
  6. Alrawad, M.; Utfi, A.; Alyatama, S.; Elshaer, I.A.; Almaiah, M.A. Perception of Occupational and Environmental Risks and Hazards among Mineworkers: A Psychometric Paradigm Approach. Int. J. Environ. Res. Public. Health 2022, 19, 3371. [Google Scholar] [CrossRef] [PubMed]
  7. Jenkins, S.C.; Harris, A.J.L.; Osman, M. What Drives Risk Perceptions? Revisiting Public Perceptions of Food Hazards Associated with Production and Consumption. J. Risk Res. 2021, 24, 1450–1464. [Google Scholar] [CrossRef]
  8. Khalid, U.; Sagoo, A.; Benachir, M. Safety Management System (SMS) Framework Development—Mitigating the Critical Safety Factors Affecting Health and Safety Performance in Construction Projects. Saf. Sci. 2021, 143, 105402. [Google Scholar] [CrossRef]
  9. Slovic, P.; Weber, E.U. Perception of Risk Posed by Extreme Events. In Regulation of Toxic Substances and Hazardous Waste, 2nd ed.; Applegate, J.S., Gabba, J.M., Laitos, J., Sachs, N., Eds.; Foundation Press: St. Paul, MN, USA, 2013. [Google Scholar]
  10. Weber, E.U.; Milliman, R.A. Perceived Risk Attitudes: Relating Risk Perception to Risky Choice. Manag. Sci. 1997, 43, 123–144. [Google Scholar] [CrossRef]
  11. You, M.; Ju, Y. A Comprehensive Examination of the Determinants for Food Risk Perception: Focusing on Psychometric Factors, Perceivers’ Characteristics, and Media Use. Health Commun. 2017, 32, 82–91. [Google Scholar] [CrossRef]
  12. Bassarak, C.; Pfister, H.-R.; Böhm, G. Dispute and Morality in the Perception of Societal Risks: Extending the Psychometric Model. J. Risk Res. 2017, 20, 299–325. [Google Scholar] [CrossRef]
  13. Bronfman, N.C.; Cifuentes, L.A.; deKay, M.L.; Willis, H.H. Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus. J. Risk Res. 2007, 10, 527–554. [Google Scholar] [CrossRef]
  14. Marris, C.; Langford, I.; Saunderson, T.; O’Riordan, T. Exploring the “Psychometric Paradigm”: Comparisons between Aggregate and Individual Analyses. Risk Anal. 1997, 17, 303–312. [Google Scholar] [CrossRef] [PubMed]
  15. Valente, J.-P.; Gouveia, C.; Neves, M.-C.; Vasques, T.; Bernardo, F. Small Town, Big Risks: Natural, Cultural and Social Risk Perception (Ciudad Pequeña, Grandes Riesgos: Percepción Del Riesgo Natural, Cultural y Social). PsyEcology 2021, 12, 76–98. [Google Scholar] [CrossRef]
  16. Liu, H.; Li, J.; Li, H.; Li, H.; Mao, P.; Yuan, J. Risk Perception and Coping Behavior of Construction Workers on Occupational Health Risks—A Case Study of Nanjing, China. Int. J. Environ. Res. Public. Health 2021, 18, 7040. [Google Scholar] [CrossRef]
  17. Jensen, M.; Combariza Bayona, D.A.; Sripada, K. Mercury Exposure among E-Waste Recycling Workers in Colombia: Perceptions of Safety, Risk, and Access to Health Information. Int. J. Environ. Res. Public. Health 2021, 18, 9295. [Google Scholar] [CrossRef]
  18. Siegrist, M.; Árvai, J. Risk Perception: Reflections on 40 Years of Research. Risk Anal. 2020, 40, 2191–2206. [Google Scholar] [CrossRef]
  19. Namian, M.; Albert, A.; Feng, J. Effect of Distraction on Hazard Recognition and Safety Risk Perception. J. Constr. Eng. Manag. 2018, 144, 04018008. [Google Scholar] [CrossRef]
  20. Sjoberg, L. Factors in Risk Perception. Risk Anal. 2000, 20, 1–12. [Google Scholar] [CrossRef]
  21. Alaiah, M.A.; Al-Otaibi, S.; Lut, A.; Almomani, O.; Awajan, A.; Alsaaidah, A.; Aawad, M.; Awad, A.B. Employing the TAM Model to Investigate the Readiness of M-Learning System Usage Using SEM Technique. Electronics 2022, 11, 1259. [Google Scholar] [CrossRef]
  22. Alaiah, M.A.; Al-Rahmi, A.; Alturise, F.; Hassan, L.; Lut, A.; Aawad, M.; Alkhalaf, S.; Al-Rahmi, W.M.; Al-sharaieh, S.; Aldhyani, T.H.H. Investigating the Effect of Perceived Security, Perceived Trust, and Information Quality on Mobile Payment Usage through Near-Field Communication (NFC) in Saudi Arabia. Electronics 2022, 11, 3926. [Google Scholar] [CrossRef]
  23. Almaiah, M.A.; Al-Rahmi, A.M.; Alturise, F.; Alrawad, M.; Alkhalaf, S.; Lutfi, A.; Al-Rahmi, W.M.; Awad, A.B. Factors Influencing the Adoption of Internet Banking: An Integration of ISSM and UTAUT with Price Value and Perceived Risk. Front. Psychol. 2022, 13, 919198. [Google Scholar] [CrossRef]
  24. Lutfi, A.A.; Idris, K.; Mohamad, R. AIS Usage Factors and Impact among Jordanian SMEs: The Moderating Effect of Environmental Uncertainty. J. Adv. Res. Bus. Manag. Stud. 2017, 6, 24–38. [Google Scholar]
  25. Covello, V.T.; von Winterfeldt, D.; Slovic, P. Risk Communication. In Carcinogen Risk Assessment; Travis, C.C., Ed.; Contemporary Issues in Risk Analysis; Springer: Boston, MA, USA, 1988; pp. 193–207. ISBN 978-1-4684-5484-0. [Google Scholar]
  26. Slovic, P. The Perception of Risk. In Scientists Making a Difference: One Hundred Eminent Behavioral and Brain Scientists Talk about Their Most Important Contributions; Cambridge University Press: Cambridge, UK, 2016; pp. 179–182. [Google Scholar]
  27. Sjöberg, L. Political Decisions and Public Risk Perception. Reliab. Eng. Syst. Saf. 2001, 72, 115–123. [Google Scholar] [CrossRef]
  28. Almaiah, M.A.; Alfaisal, R.; Salloum, S.A.; Al-Otaibi, S.; Al Sawafi, O.S.; Al-Maroof, R.S.; Lutfi, A.; Alrawad, M.; Mulhem, A.A.; Awad, A.B. Determinants Influencing the Continuous Intention to Use Digital Technologies in Higher Education. Electronics 2022, 11, 2827. [Google Scholar] [CrossRef]
  29. Aaiah, M.A.; Alfaisal, R.; Salloum, S.A.; Al-Otaibi, S.; Shishakly, R.; Lutfi, A.; Alra, M.; Mulhem, A.A.; Awad, A.B.; Al-Maroof, R.S. Integrating Teachers’ TPACK Levels and Students’ Learning Motivation, Technology Innovativeness, and Optimism in an IoT Acceptance Model. Electronics 2022, 11, 3197. [Google Scholar] [CrossRef]
  30. Idris, K.M.; Mohamad, R. The Influence of Technological, Organizational and Environmental Factors on Accounting Information System Usage among Jordanian Small and Medium-Sized Enterprises. Int. J. Econ. Financ. Issues 2016, 6, 240–248. [Google Scholar]
  31. Sowby, F.D. Radiation and Other Risks. Health Phys. 1965, 11, 879–887. [Google Scholar] [CrossRef]
  32. Starr, C. Social Benefit versus Technological Risk: What Is Our Society Willing to Pay for Safety? Science 1969, 165, 1232–1238. [Google Scholar] [CrossRef]
  33. Fischhoff, B.; Slovic, P.; Lichtenstein, S.; Read, S.; Combs, B. How Safe Is Safe Enough? A Psychometric Study of Attitudes towards Technological Risks and Benefits. Policy Sci. 1978, 9, 127–152. [Google Scholar] [CrossRef]
  34. Slovic, P. The Perception of Risk; Earthscan: London, UK, 2000. [Google Scholar]
  35. Tversky, A.; Kahneman, D. Judgment under Uncertainty: Heuristics and Biases: Biases in Judgments Reveal Some Heuristics of Thinking under Uncertainty. Science 1974, 185, 1124–1131. [Google Scholar] [CrossRef] [PubMed]
  36. Plous, S. The Psychology of Judgment and Decision Making; Mcgraw-Hill Book Company: New York, NY, USA, 1993. [Google Scholar]
  37. Beach, L.R.; Smith, B.; Lundell, J.; Mitchell, T.R. Image Theory: Descriptive Sufficiency of a Simple Rule for the Compatibility Test. J. Behav. Decis. Mak. 1988, 1, 17–28. [Google Scholar] [CrossRef]
  38. Douglas, M.; Wildavsky, A. Risk and Culture: An Essay on the Selection of Technological and Environmental Dangers; University of California Press: Berkeley, CA, USA, 1983. [Google Scholar]
  39. Guillaume, B.; Charron, S. Exploring Implicit Dimensions Underlying Risk Perception of Waste from Mining and Milling of Uranium Ores in France; Institute for Protection and Nuclear Safety: Fontenay-aux-Roses, France, 1999. [Google Scholar]
  40. Henwood, K.L.; Pidgeon, N.F. Qualitative Research and Psychological Theorizing. Br. J. Psychol. 1992, 83, 97–111. [Google Scholar] [CrossRef] [PubMed]
  41. Pidgeon, R.T. Recrystallisation of Oscillatory Zoned Zircon: Some Geochronological and Petrological Implications. Contrib. Mineral. Petrol. 1992, 110, 463–472. [Google Scholar] [CrossRef]
  42. Aiah, M.A.; Alfaisal, R.; Salloum, S.A.; Hajjej, F.; Shishakly, R.; Lutfi, A.; Awad, M.; Al Mulhem, A.; Alkhdour, T.; Al-Maroof, R.S. Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate. Electronics 2022, 11, 3291. [Google Scholar] [CrossRef]
  43. Aiah, M.A.; Alfaisal, R.; Salloum, S.A.; Hajjej, F.; Thabit, S.; El-Qirem, F.A.; Lut, A.; Awad, M.; Mulhem, A.A.; Alkhdour, T.; et al. Examining the Impact of Artificial Intelligence and Social and Computer Anxiety in E-Learning Settings: Students’ Perceptions at the University Level. Electronics 2022, 11, 3662. [Google Scholar] [CrossRef]
  44. Alsharif, A.H.; Salleh, N.Z.M.; Alrawwa, M.; Ltfi, A. Exploring Global Trends and Future Directions in Advertising Research: A Focus on Consumer Behavior. Curr. Psychol. 2023. [Google Scholar] [CrossRef]
  45. Cox, D.F. The Audience as Communicators. In Risk Taking and Information Handling in Consumer Behavior; Cox, D.F., Ed.; Graduate School of Business Administration, Harvard University: Boston, MA, USA, 1967; pp. 172–187. [Google Scholar]
  46. Slovic, P.; Fischhoff, B.; Lichtenstein, S. The Psychometric Study of Risk Perception. In Risk Evaluation and Management; Covello, V.T., Menkes, J., Mumpower, J., Eds.; Springer: Boston, MA, USA, 1986; pp. 3–24. ISBN 978-1-4612-9245-6. [Google Scholar]
  47. Slovic, P. Perception of Risk: Reflections on the Psychometric Paradigm; Praeger: New York, NY, USA, 1992. [Google Scholar]
  48. Saaty, T.L. A Scaling Method for Priorities in Hierarchical Structures. J. Math. Psychol. 1977, 15, 234–281. [Google Scholar] [CrossRef]
  49. Nunnally, J.C. Psychometric Theory 3E; Tata McGraw-Hill Education: New York, NY, USA, 1994. [Google Scholar]
  50. Khassawneh, A.A.L. The Influence of Organizational Factors on Accounting Information Systems (AIS) Effectiveness: A Study of Jordanian SMEs. Int. J. Mark. Technol. 2014, 4, 36. [Google Scholar]
  51. Bronfman, N.C.; Cifuentes, L.A. Risk Perception in a Developing Country: The Case of Chile. Risk Anal. Off. Publ. Soc. Risk Anal. 2003, 23, 1271–1285. [Google Scholar] [CrossRef]
  52. Slovic, P. Perception of Risk. Science 1987, 236, 280–285. [Google Scholar] [CrossRef]
  53. Otway, H.; Thomas, K. Reflections on Risk Perception and Policy. Risk Anal. 1982, 2, 69–82. [Google Scholar] [CrossRef]
  54. Keown, C.F. Risk Perceptions of Hong Kongese vs. Americans. Risk Anal. 1989, 9, 401–405. [Google Scholar] [CrossRef] [PubMed]
  55. Almaiah, M.A.; Hajjej, F.; Lutfi, A.; Al-Khasawneh, A.; Shehab, R.; Al-Otaibi, S.; Alrawad, M. Explaining the Factors Affecting Students’ Attitudes to Using Online Learning (Madrasati Platform) during COVID-19. Electronics 2022, 11, 973. [Google Scholar] [CrossRef]
  56. Alrawad, M.; Lutfi, A.; Almaiah, M.A.; Alsyouf, A.; Al-Khasawneh, A.L.; Arafa, H.M.; Ahmed, N.A.; AboAlkhair, A.M.; Tork, M. Managers’ Perception and Attitude toward Financial Risks Associated with SMEs: Analytic Hierarchy Process Approach. J. Risk Financ. Manag. 2023, 16, 86. [Google Scholar] [CrossRef]
  57. Fife-Schaw, C.; Rowe, G. Research Note: Extending the Application of the Psychometric Approach for Assessing Public Perceptions of Food Risk: Some Methodological Considerations. J. Risk Res. 2000, 3, 167–179. [Google Scholar] [CrossRef]
  58. Lutfi, A.; Al-Khasawneh, A.L.; Almaiah, M.A.; Alsyouf, A.; Alrawad, M. Business Sustainability of Small and Medium Enterprises during the COVID-19 Pandemic: The Role of AIS Implementation. Sustainability 2022, 14, 5362. [Google Scholar] [CrossRef]
  59. Lutfi, A.; Al-Khasawneh, A.L.; Almaiah, M.A.; Alshira’h, A.F.; Alshirah, M.H.; Alsyouf, A.; Alrawad, M.; Al-Khasawneh, A.; Saad, M.; Ali, R.A. Antecedents of Big Data Analytic Adoption and Impacts on Performance: Contingent Effect. Sustainability 2022, 14, 15516. [Google Scholar] [CrossRef]
  60. Lutfi, A.; Al-Okaily, M.; Alsyouf, A.; Alrawad, M. Evaluating the D&M IS Success Model in the Context of Accounting Information System and Sustainable Decision Making. Sustainability 2022, 14, 8120. [Google Scholar] [CrossRef]
  61. Milfont, T.; Fischer, R. Testing Measurement Invariance across Groups: Applications in Cross-Cultural Research. Int. J. Psychol. Res. 2010, 3, 111–130. [Google Scholar] [CrossRef] [Green Version]
  62. Saaty, T.L.; Vargas, L.G. Decision Making with the Analytic Network Process; Springer: New York, NY, USA, 2006; Volume 282. [Google Scholar]
  63. Saaty, T.L. Decision-Making with the AHP: Why Is the Principal Eigenvector Necessary. Eur. J. Oper. Res. 2003, 145, 85–91. [Google Scholar] [CrossRef]
  64. Saaty, T.L. Some Mathematical Concepts of the Analytic Hierarchy Process. Behaviormetrika 1991, 18, 1–9. [Google Scholar] [CrossRef]
  65. Saaty, T.L. What Is the Analytic Hierarchy Process? In Mathematical Models for Decision Support; Springer: Berlin/Heidelberg, Germany, 1988; pp. 109–121. [Google Scholar]
  66. Saaty, R.W. The Analytic Hierarchy Process—What It Is and How It Is Used. Math. Model. 1987, 9, 161–176. [Google Scholar] [CrossRef] [Green Version]
  67. Lauqt, A.; Alkelani, S.N.; Al-Khasawneh, M.A.; Alshira’h, A.F.; Alshirah, M.H.; Almai, M.A.; Alrawad, M.; Alsyouf, A.; Saad, M.; Ibrahim, N. Influence of Digital Accounting System Usage on SMEs Performance: The Moderating Effect of COVID-19. Sustainability 2022, 14, 15048. [Google Scholar] [CrossRef]
  68. Lutfi, A.; Alkelani, S.N.; Alqudah, H.; Alshira’h, A.F.; Alshirah, M.H.; Almaiah, M.A.; Alsyouf, A.; Alrawad, M.; Montash, A.; Abdelmaksoud, O. The Role of E-Accounting Adoption on Business Performance: The Moderating Role of COVID-19. J. Risk Financ. Manag. 2022, 15, 617. [Google Scholar] [CrossRef]
  69. Utfi, H.; Alkilani, S.Z.; Saad, M.; Alshirah, M.H.; Alshirah, A.F.; Awad, M.; Al-Khasawneh, M.A.; Ibrahim, N.; Abdelhalim, A.; Ramadan, M.H. The Influence of Audit Committee Chair Characteristics on Financial Reporting Quality. J. Risk Financ. Manag. 2022, 15, 563. [Google Scholar] [CrossRef]
  70. Utfi, H.; Awad, M.; Alsyouf, A.; Aiah, M.A.; Al-Khasawneh, A.; Al-Khasawneh, A.L.; Alshira’h, A.F.; Alshirah, M.H.; Saad, M.; Ibrahim, N. Drivers and Impact of Big Data Analytic Adoption in the Retail Industry: A Quantitative Investigation Applying Structural Equation Modeling. J. Retail. Consum. Serv. 2023, 70, 103129. [Google Scholar] [CrossRef]
  71. Vásquez, J.A.; Escobar, J.W.; Manotas, D.F. AHP–TOPSIS Methodology for Stock Portfolio Investments. Risks 2021, 10, 4. [Google Scholar] [CrossRef]
  72. Mohsin, M.; Yin, H.; Huang, W.; Zhang, S.; Zhang, L.; Mehak, A. Evaluation of Occupational Health Risk Management and Performance in China: A Case Study of Gas Station Workers. Int. J. Environ. Res. Public. Health 2022, 19, 3762. [Google Scholar] [CrossRef]
  73. Al-Harbi, K.M.A.-S. Application of the AHP in Project Management. Int. J. Proj. Manag. 2001, 19, 19–27. [Google Scholar] [CrossRef]
  74. Zayed, T.; Amer, M.; Pan, J. Assessing Risk and Uncertainty Inherent in Chinese Highway Projects Using AHP. Int. J. Proj. Manag. 2008, 26, 408–419. [Google Scholar] [CrossRef]
  75. Aminbakhsh, S.; Gunduz, M.; Sonmez, R. Safety Risk Assessment Using Analytic Hierarchy Process (AHP) during Planning and Budgeting of Construction Projects. J. Safety Res. 2013, 46, 99–105. [Google Scholar] [CrossRef]
  76. Lfi, A.; Alsyouf, A.; Maiah, M.A.; Awad, M.; Abdo, A.A.K.; Al-Khasawneh, A.L.; Ibrahim, N.; Saad, M. Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs. Sustainability 2022, 14, 1802. [Google Scholar] [CrossRef]
  77. Lutfi, A.; Ashraf, M.; Watto, W.A.; Alrawad, M. Do Uncertainty and Financial Development Influence the FDI Inflow of a Developing Nation? A Time Series ARDL Approach. Sustainability 2022, 14, 12609. [Google Scholar] [CrossRef]
  78. Lfi, A.; Saad, M.; Maiah, M.A.; Alsaad, A.; Al-Khasawneh, A.; Awad, M.; Alsyouf, A.; Al-Khasawneh, A.L. Actual Use of Mobile Learning Technologies during Social Distancing Circumstances: Case Study of King Faisal University Students. Sustainability 2022, 14, 7323. [Google Scholar] [CrossRef]
  79. Brislin, R.W. Back-Translation for Cross-Cultural Research. J. Cross-Cult. Psychol. 1970, 1, 185–216. [Google Scholar] [CrossRef]
  80. Armstrong, J.S.; Overton, T.S. Estimating Nonresponse Bias in Mail Surveys. J. Mark. Res. 1977, 14, 396. [Google Scholar] [CrossRef] [Green Version]
  81. Alsharif, A.H.; Salleh, N.Z.M.; Abdullah, M.; Khraiwish, A.; Ashaari, A. Neuromarketing Tools Used in the Marketing Mix: A Systematic Literature and Future Research Agenda. SAGE Open 2023, 13, 215824402311565. [Google Scholar] [CrossRef]
  82. Alsharif, A.H.; Salleh, N.Z.M.; Hashem E, A.R.; Khraiwish, A.; Putit, L.; Arif, L.S.M. Exploring Factors Influencing Neuromarketing Implementation in Malaysian Universities: Barriers and Enablers. Sustainability 2023, 15, 4603. [Google Scholar] [CrossRef]
  83. Milošević, D.M.; Milošević, M.R.; Simjanović, D.J. Implementation of Adjusted Fuzzy AHP Method in the Assessment for Reuse of Industrial Buildings. Mathematics 2020, 8, 1697. [Google Scholar] [CrossRef]
  84. Chou, T.-Y.; Chen, Y.-T. Applying Fuzzy AHP and TOPSIS Method to Identify Key Organizational Capabilities. Mathematics 2020, 8, 836. [Google Scholar] [CrossRef]
  85. Renn, O.; Benighaus, C. Perception of Technological Risk: Insights from Research and Lessons for Risk Communication and Management. J. Risk Res. 2013, 16, 293–313. [Google Scholar] [CrossRef]
  86. Al-Rawad, M.; Al Khattab, A. Risk Perception in a Developing Country: The Case of Jordan. Int. Bus. Res. 2014, 8, p81. [Google Scholar] [CrossRef] [Green Version]
  87. Martinez-Fiestas, M.; Rodríguez-Garzón, I.; Delgado-Padial, A.; Lucas-Ruiz, V. Analysis of Perceived Risk among Construction Workers: A Cross-Cultural Study and Reflection on the Hofstede Model. Int. J. Occup. Saf. Ergon. 2017, 23, 307–317. [Google Scholar] [CrossRef]
  88. Van Schaik, P.; Jeske, D.; Onibokun, J.; Coventry, L.; Jansen, J.; Kusev, P. Risk Perceptions of Cyber-Security and Precautionary Behaviour. Comput. Hum. Behav. 2017, 75, 547–559. [Google Scholar] [CrossRef] [Green Version]
  89. Lindenfeld, L.; Smith, H.M.; Norton, T.; Grecu, N.C. Risk Communication and Sustainability Science: Lessons from the Field. Sustain. Sci. 2014, 9, 119–127. [Google Scholar] [CrossRef]
  90. Porat, T.; Nyrup, R.; Calvo, R.A.; Paudyal, P.; Ford, E. Public Health and Risk Communication During COVID-19—Enhancing Psychological Needs to Promote Sustainable Behavior Change. Front. Public Health 2020, 8, 573397. [Google Scholar] [CrossRef] [PubMed]
  91. Khan, H.U.; Ali, Y.; Khan, F. A Features-Based Privacy Preserving Assessment Model for Authentication of Internet of Medical Things (IoMT) Devices in Healthcare. Mathematics 2023, 11, 1197. [Google Scholar] [CrossRef]
Figure 1. The proposed AHP structured model for evaluating public risk perception.
Figure 1. The proposed AHP structured model for evaluating public risk perception.
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Figure 2. Public risk ranking.
Figure 2. Public risk ranking.
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Table 1. Risk Attributes.
Table 1. Risk Attributes.
AttributeScale DescriptionLow (1)High (7)
Knowledge of exposureHow well do you understand the level of risk involved with each activity, substance, or technology?KnownUnknown
Newness of riskIs the risk associated with the following activities, substance, or technology something unfamiliar and new to you or not?OldNew
Common, dreadDo these activities, substances, and technology pose a common or severe risk and hazard?CommonDreaded
Control over riskHow can individuals or groups avoid the risk linked with each activity, substance, or technology?ControllableUncontrollable
Immediacy of effectDo the consequences of engaging in any particular activity, using a specific substance, or adopting a technology occur immediately, or are they delayed?DelayedImmediate
Chronic, catastrophicDoes the risk linked to the following activities, substances, or technologies pertain to a novel and unfamiliar situation, or does it involve chronic or catastrophic consequences?ChronicCatastrophic
Voluntariness of riskTo what extent do individuals willingly confront this risk involved in engaging in a particular activity, using a particular substance, or adopting a specific technology?VoluntaryInvoluntary
Source: Bronfman and Cifuentes [13,51].
Table 2. Respondents’ Profile.
Table 2. Respondents’ Profile.
Gender Male29268
Age group20–247718
45 and above358
EducationHigh school certificate7317
Undergraduate degree31373
Post Graduate degree4410
Table 3. Pairwise comparison scale for AHP preferences.
Table 3. Pairwise comparison scale for AHP preferences.
Rating Definition Explanation
1Equally preferredAlternative i and j are of equal value.
3Moderately preferredAlternative i has a slightly higher value than j.
5Strongly preferredAlternative i has a strongly higher value than j.
7Very strongly preferredAlternative i has a very strongly higher value than j.
9Extremely preferredAlternative i has a higher value than j.
2,4,6,8Intermediate scaleThe intermediate scale between two adjutant judgment
ReciprocalReverence the preferenceIf alternative, i has a lower value than j
Source: Saaty [48,66].
Table 4. Comparison matrix for perceived risk attributes.
Table 4. Comparison matrix for perceived risk attributes.
Knowledge of Exposure (KE)10.3530.3500.5630.4640.3720.316
Newness of Risk (NR)2.83210.9441.6091.3700.9710.848
Common Dread (CD)2.8571.05911.4281.2190.7260.692
Control Over Risk (CR)1.7750.6220.70010.7570.4700.373
Immediacy of Effect (IE)2.1570.7300.8201.32110.5150.462
Chronic Catastrophic (CC)2.6861.0301.3782.1291.94210.758
Voluntariness of Risk (VR)3.1691.1791.4452.6782.1671.3191
Table 5. Normalized matrix for risk attributes.
Table 5. Normalized matrix for risk attributes.
Knowledge of Exposure (KE)0.0610.0590.0530.0530.0520.0690.0710.060
Newness of Risk (NR)0.1720.1670.1420.1500.1540.1810.1910.165
Common Dread (CD)0.1730.1770.1510.1330.1370.1350.1560.152
Controls Over Risk (CR)0.1080.1040.1050.0930.0850.0870.0840.095
Immediacy of Effect (IE)0.1310.1220.1240.1230.1120.0960.1040.116
Chronic Catastrophic (CC)0.1630.1720.2080.1980.2180.1860.1700.188
Voluntariness of Risk (VR)0.1920.1970.2180.2500.2430.2460.2250.224
Table 6. Average random consistency (RI).
Table 6. Average random consistency (RI).
Size 12345678910
Random consistency000.580.901.121.241.321.411.451.49
Source: Saaty [62].
Table 7. Consistency test results.
Table 7. Consistency test results.
N = 7Risk Attributes
Lambda max (λmax)7.037
Consistency Index (CI)0.0062
Consistency Ratio (CR)0.0047
Random Index (RI)1.32
Table 8. Public risk assessment ranking.
Table 8. Public risk assessment ranking.
RiskKnowledge of ExposureNewness of RiskCommon DreadControls Over RiskImmediacy of Effect Chronic Catastrophic Voluntariness of Risk Risk ScoreRank
Natural Hazards0.1550.8250.8130.1630.2931.0051.2644.5181
Refugee influx0.1530.8210.6790.2360.4310.9241.1834.4272
Industrial pollution0.1590.7280.7100.2470.4790.8811.1664.3704
Nuclear power0.2000.6350.7900.2090.3630.8681.2544.3195
Terrorist attack0.1580.5630.8290.2020.3090.8971.2954.2536
Extreme poverty0.1550.8380.7250.3280.3820.8600.9334.2217
Warfare 0.1910.5170.8130.1940.3220.8191.3004.1569
Pesticides 0.1750.7260.5800.3300.4070.8170.9694.00412
Feeling depressed0.1730.7770.6200.4450.4610.7520.7523.98013
Food preservatives0.1900.6280.5470.3260.4940.8600.9113.95614
Gene technology0.2620.5360.6010.2920.4690.8320.9283.92016
Being bullied0.1750.8200.5010.3490.3950.7830.8793.90218
Side effect: drugs0.1810.6380.6050.2950.4260.8750.8773.89719
Car Accident0.1170.8550.3750.4560.4960.7110.7843.79420
Falling share prices0.2250.5850.6430.2470.3580.8070.9253.79021
Illegal drugs 0.1940.6620.6590.3340.3760.4760.9893.69023
Surgery 0.1600.7710.4100.3660.3850.8300.7133.63524
Aircraft travel0.1450.7390.4000.3550.4090.5210.8083.37726
Mobile phones0.1450.5700.3320.4650.5380.5890.6283.26727
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MDPI and ACS Style

Alrawad, M.; Lutfi, A.; Almaiah, M.A.; Alsyouf, A.; Arafa, H.M.; Soliman, Y.; Elshaer, I.A. A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm. Sustainability 2023, 15, 9965.

AMA Style

Alrawad M, Lutfi A, Almaiah MA, Alsyouf A, Arafa HM, Soliman Y, Elshaer IA. A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm. Sustainability. 2023; 15(13):9965.

Chicago/Turabian Style

Alrawad, Mahmaod, Abdalwali Lutfi, Mohammed Amin Almaiah, Adi Alsyouf, Hussin Mostafa Arafa, Yasser Soliman, and Ibrahim A. Elshaer. 2023. "A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm" Sustainability 15, no. 13: 9965.

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