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Article

Derivation of Risk Factors to Quantify the Risk of Safety Accidents for Small and Medium-Sized Enterprises in Construction Industry

by
Sungjin Ahn
Department of Architectural Engineering, Keimyung University, Daegu 42601, Korea
Sustainability 2022, 14(12), 7306; https://doi.org/10.3390/su14127306
Submission received: 17 April 2022 / Revised: 6 June 2022 / Accepted: 8 June 2022 / Published: 15 June 2022
(This article belongs to the Section Sustainable Engineering and Science)
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Abstract

:
The accident rate in the construction industry is much higher than in other industries. In particular, small- and medium-sized construction sites need to be managed by differentiating them from large construction sites. In order to create and manage a separate management guideline, a quantitative study on the difference between the two groups should be preceded. However, in previous studies, research particularly based on empirical and quantitative data is insufficient and somewhat inadequate. In this study, through statistical analysis of small- and medium-sized construction enterprises and large-scale construction enterprises, this research statistically proves the difference between the risk of occupational accidents. Furthermore, through multiple regression, safety accidents and significant factors of small- and medium-sized construction sites and large-scale construction companies have been verified and considered. The result shows the day of the week, accident time, and workers’ contract type are significant factors affecting construction workers’ accident risk for SMEs, while only the contract type was identified as a factor influencing accident risk for large construction companies. As this study aims at recognizing the risk factors of small-sized construction companies, the findings can provide effective references for assessing and managing the risks particular to the small- and medium-sized construction enterprises.

1. Introduction

Construction safety accidents occur at a higher frequency at smaller construction sites compared to large construction sites [1,2,3,4,5]. According to statistics from the Korean Ministry of Employment and Labor, 26,799 construction workers were injured during the work in 2020, of which 458 died. Among this number, 70 percent of the accidents were at small construction sites that were with fewer than 100 workers [6]. Such the fact that many small construction companies tend to be negligent in safety management compared to large construction companies stems from poor financial structure and insufficient safety management capabilities and conditions [7,8,9,10,11]. Mid- to long-term construction disaster prevention plans and strategies have been continuously established and implemented by many countries and agencies, but a significant decrease in the safety accident rate has not been seen because they tend not to reflect and overlook the characteristics of small construction sites, which are usually composed of day workers with frequent relocations and changes [12,13,14]. Recently, as part of the construction safety reinforcement policies aimed at preventing industrial accidents and improving safety management at small construction sites, the Korean Ministry of Land, Infrastructure, and Transport and the Ministry of Employment and Labor have proposed measures for (1) ensuring substantiality of risk assessment on sites and (2) developing effective safety training materials and safety and health training inspection. In this process, the voluntary participation of construction entities included in the entire construction phase is induced. Therefore, the prompt progress of research for providing direct solutions to small-scale construction safety disaster prevention has been called to support the government’s development of these policies.
In the case of the U.S., the Occupational Safety and Health Administration (OSHA) supports the safety and health of small- and medium-sized construction companies in more diverse and detailed ways, and the federal government and each state-level OSHA focus on securing safety and health at small- and medium-sized workplaces through mutual cooperation [15]. OSHA especially has been carrying out various disaster prevention activities for more than 30 years for small businesses. For example, a Voluntary Protection Program (VPP) is in place to ensure the confidentiality of the workplace and to prevent fines and summons. As part of this, on-site consulting has been provided to specifically identify and eliminate hazards to safety and health, resulting in the voluntary participation of many businesses in support programs. For compliance with the OSHAct 1970 Act, it also provides a Small Business Handbook, which is an exclusive guide tailored to the circumstances and characteristics of a small business establishment and includes a variety of self-survey safety health checklists for small business owners. Consequently, the number of deaths from construction site disasters has decreased by 50%, and the number of industrial and occupational diseases has decreased by 40% over the past 30 years [16,17]. This example of OSHA suggests that effective safety management and accident prevention are possible without relying solely or heavily on safety inspection and monitoring by legal and institutional regulations. In other words, active and voluntary improvement in safety awareness and behavior is needed, and the basis should be a concrete identification of small construction site characteristics and the corresponding improvement of the site environment and the development of safety education development of safety training. That is, there is a strong need for improvements in the environmental and educational aspects that be actively applied to small construction sites, while governments, health and safety agencies, and small construction sites maintain cooperative relationships with each other. This should take precedence over compulsory safety management by legal/institutional management, and it is crucial to verify the economic/effectiveness of such measures, and the economic and practical effectiveness of such measures should also be urgently assessed.
Small construction sites have different characteristics and circumstances compared to larger sites. Therefore, instead of applying general risk factors leading to accidents at larger sites, it is necessary to apply differentiated risk factors to small site management while distinguishing them from large-scale construction. To this end, it is necessary to first identify the risk factors reflecting the size of the construction and its characteristics through data-based statistical analysis, and then a risk response plan should be prepared from the perspective of using the risk as an accident prevention opportunity rather than a threat of an accident. This study aims first to statistically demonstrate the difference in the risk of safety accidents between small- and medium-sized construction sites and large-scale construction sites through a t-test and through multiple regression; this study also seeks to derive and explain the significant factors of safety accidents at small- and medium-sized construction sites and large-scale construction sites.

2. Literature Review

A review of safety and risk management literature on the small- and medium-sized enterprises in the construction industry includes a variety of studies with various perspectives and focuses. Many studies argue small- and medium-sized enterprises have problematic work environment, in which the risk is high and risk control ability is low, and exposure to chemical and physical hazards are larger [18,19,20,21].
Gunduz and Laitinen (2016) [22] suggest an occupational health and safety management system (OHS MS) and introduce a quantitative OHS MS indexing method for SMEs; they also indicate that a positive organizational culture is strongly required to promote the safety management framework Ozmec et al. (2015) [23] carried out a qualitative multi-case study on small-sized construction enterprises focusing on the small construction workers’ duties according to their specific occupation and examined safety practices from the workers’ perspective considering the role of the owners.
The methods used to address the safety concerns of large corporations tend not to apply to most small- and medium-sized enterprises; Legg et al. (2014) [24] also report that small-sized enterprises suffer from “ informal management structures, unstructured approaches to OSH management, little or no internal health and safety expertise, or access to external sources of assistance”, which Ram et al. (2001) [25] also suggested as issues directly related to the enterprise sizes,” as well as provided several related studies such as Staniewski (2016) [26], and Mallet and Wapshott (2014) [27].therefore, the methods to deal with occupational health and safety management in SMEs should develop and apply tools specifically designed for them. Nyirendaavwil et al. (2015)’s study [28] noted that effective worker safety management in SMEs should consider the following factors: accident types, workers’ age, workers’ gender, experience length, the industrial activity of the enterprise, part of body affected, primary sources of injury occupation of the worker in the enterprise, and time of day the accident happened.
Holte et al. (2015) [29] investigated injury risk among young workers in different sized enterprises within different construction trades, and they found that the nature of work and associated exposures, along with other characteristics that may vary by the enterprise’s size, should be considered when considering injury risk among young workers. Cheng et al. (2010) [30] found characteristic factors for safety accident occurrence for small construction enterprises using various statistical analyses. The factors affecting occupational accident prevention for small construction enterprises included the health and safety management capacities and the degree of compliance with safety regulations for laborers. Furthermore, they found that safety accidents tend to occur (1) on the first day of a worker’s workday, (2) when the risk management skill in the construction project site is too low, and (3) when the workers have not provided the worker with personal protective equipment, (4) when workers fail to adopt the safety tools or did not recognize the hazard warning sign.
Through small construction and metal industry accident investigations and interviews, one can recognize that the accidents are not caused by the poor occupational environment under the owner’s control but by unpredictable circumstances. This research also indicates that safety experts such as researchers and consultants should consider the better ways for small business owners to deal with health and safety, which can be quite different from those of large business owners [31].
Tang et al. (2010) [32] developed an entropy-based decision-making system on the multi-criterion risk and decision analysis for SMEs in the construction industry. The case study verified the use of entropy in analyzing multi-risk criteria in the project development stage of the small- and medium-sized enterprises.
In small- and medium-sized construction sites, in which many foreign and/or younger workers are hired [33], it is more critical to address the issues of safety. Accordingly, to face such challenges, much effort and alternatives have been made; for example, a training program called WISE (Work Improvement in Small Enterprises) in small- and medium-sized enterprises has been developed and adopted [30], and in 2013, “Training package on workplace risk assessment and management for small- and medium-sized enterprises” has also been published by The International Labour Office [34].
There are many studies looking into construction safety and risk management targeting SMEs from various perspectives. This paper aims to contribute to the current literature in the field of safety management by devising a construction safety management system that can be easily applied to small- and medium-sized enterprises and is easy to understand and use for users.
As mentioned, the previous research and statistical data have suggested that SMEs are more exposed to occupational accidents compared to large enterprises. However, more quantitative research on the accident risk in SMEs in the construction industry is still unassessed. Moreover, systematic safety management specified for each factor of the safety accident has not yet been implemented. Consequently, this study aims to provide an understanding of risk management from a new perspective by recognizing the risk factors that affect occupational accidents in SME construction sites. More specifically, this study identifies the key risk factors related to SMEs in Korea, using a quantitative data set, ultimately aiming at sustainable and systematic safety management for SMEs.

3. Data Collection

The data set of this study was collected from KOSHA’s (Korea Occupational Safety and Health Agency) accidental injuries at construction project sites from 2010 to 2019 in South Korea. KOSHA is a government agency under the Korean Ministry of Employment and Labor, which provides technical guidelines helping workers to protect themselves as they inspect hazardous equipment and eliminate hazards in their workplaces. Furthermore, KOSHA provides and develops training programs and materials for safety and health, which are customized to each workplace to create a better work environment. The data in this study involve accident details, i.e., accident type, the project progress rate when the accidents happened, construction project scale, citizens of the accident victim, accident time and dare, construction site address, and the number of days for medical treatment. The victims’ treatment days were considered as a dependent variable in the regression model in this study for quantifying the accident severity.

4. Analysis

4.1. Comparison between SME and Large Enterprise

This study used a t-test to compare small- and medium-sized enterprises and large-sized enterprises. If there is a statistically significant difference between the two in terms of the treatment days, it can logically explain the difference in risk management between SMEs and large construction enterprises.
Table 1 illustrates the t-test values. The p-value is smaller than 0.05, which means the treatment days of small-to-medium-sized enterprises and large-sized enterprises in construction fields are significantly different. The mean value indicates that small-to-medium-sized enterprises have 14.3% more treatment days than large construction enterprises.

4.2. Normality

A normal Q-Q plot and histogram of dependent variables were tested for the regression. The histogram shows a bell shape, and the points in a Q-Q plot will lie on a straight diagonal line. Conversely, the more the points in the plot deviate significantly from a straight diagonal line, the less likely the set of data follows a normal distribution (see Figure 1).

4.3. Multiple Regression Analysis

This research used multiple regression in order to explain the relationship between the treatment days and suggested accident risk factors in this research. The dependent and independent variables are described in Table 2. The dependent variable in this model is the treatment days indicated in the medical billing statement in order to quantify the severity of the accident. In this study, the accident severity of the corresponding variable element, that is, the average of medical treatment days was sequentially sorted and expressed as an ordinal scale. The influence of the increase in level is corrected by the coefficient value.
The term progress rate indicates how far the construction had progressed at the time when the accident happened. Previous studies have found that the risk and complexity of construction projects increase as well as the progress rate, and therefore, the risk and the progress rate are significantly related [35,36].
The scale of construction by contract amount is adopted from KOSHA, and building scale and risk are frequently used indicators to express risk amounts, as they have statistically significant relations [37,38]. In addition, small- and medium-sized construction sites tend to be rather vulnerable to safety accidents [39].
In this study, large-scale construction sites were designated as construction sites worth more than 12 billion KRW (Korean Won), and small- and medium-sized construction sites were designated as construction sites worth less than 12 billion won, based on the construction scale set by the KOSHA.
Occupations were categorized using Korean standards. Statistical surveys related to employment were adopted in the process, using the Korean standard occupation classification. It also characterizes risks in the health sector by task and provides guidelines for comprehensive management of related occupations [40].
Variables include days of the week and accident times, and these were selected to illustrate the risks associated with the working environment at construction sites. Workers are exposed directly to risks in the environment of the construction work site. Construction works also change the nature and intensity of their work depending on the time or day of the week. For this reason, distinguishing the day and time of the week when accidents are most frequent will contribute to the prevention of accidents [41,42]. For instance, occupational accidents in construction work occur concentratedly in the mid-morning and early afternoon, while work is usually intensive. As such, the incidence of accidents can be inferred to be high at times when the amount of work is high. However, differences in the incidence rate of accidents depending on the task difficulty of each period may also arise. Thus, in this study, days of the week were used as variables, but time zones were categorized into four; morning, afternoon, evening, night, and dawn.
Employment contracts were also categorized into two: regular contracted workers and contingent workers. Foreign workers tended to have relatively few full-time employment contracts compared to domestic workers, which can have a negative impact on migrant workers. This seems to be the case not only in Korea, where this study was conducted, but also in other countries as well [43,44].
According to Lim (2015) [45], non-regular workers with less than a half year of experience accounted for about 89.5% of construction workers. Due to the nature of the construction industry, the movement of the site is made on a team basis, and the workgroup head tends to prioritize the schedule of the construction project over safety. In addition, the shorter the construction period, the more workers tend to migrate, neglect supervision of safety management, and the higher the accident numbers among contingent and immigrant workers. Therefore, the patterns of employment should be a very large indicator of the risk of occupational accidents as well.

5. Result

Table 3 displays the treatment projects and accident information to the dependent variable (the treatment days) for accident factors of SMEs and large companies as assessed through regression analysis. In the regression model of the SMEs, the adjusted R2 is 0.218, which suggests that 21.8% of the variant of the treatment days can be described by this model. Three significant variables, day of the week, accident time, and contract type, are considered as meters of safety accidents. By contrast, the other variables are not related to the days of treatment of SMEs. The VIF (Variance Inflation Factors) ranged from 1.003 to 1.031. VIF suggests that there is no multicollinearity between the variables. Beta coefficient is regression coefficients that are standardized against one another. This standardization means that they are ‘on the same scale or unit’, which allows you to compare the magnitude of their effects directly. The beta coefficient, i.e., standardized coefficient, refers to the relationship value between the dependent and the independent variables, which illustrates the influence of the independent variable on the dependent variable. As such, the standardized regression coefficient represents the importance of the regression coefficient, and thus the higher the value of the variable’s beta coefficient, the greater the effect on the dependent variable. The descending order by the beta coefficient of the variable is as follows. (1) the accident time (beta coefficient = 0.58), (2) day of week (beta coefficient = 0.56) and (3) contract type (beta coefficient = 0.55).
In the regression model of the large company, the Adjusted R2 is 0.244, which indicates that 24.4% of the variant of the day of treatment can be described by this regression model. One variable, contract type (beta coefficient = −0.267), is identified as a significant indicator of the accident occurrence. Furthermore, there is no multicollinearity between the variables; the values of the VIF ranged from 1.007 to 1.069. In the case of accident severity, the severity was identified using the number of treatment days for the accident that occurred. As a result of calculating the average number of treatment days for each day of the week, it was found that the severity increased in the order of Monday, Tuesday, Sunday, Wednesday, Friday, Saturday, and Thursday. The severity of the accident with respect to the accident time was also identified in a similar way. In other words, as a result of securing the average number of treatment days for accidents that occurred at each time of dawn, morning, afternoon, evening, and night, the average of the number of treatment days, that is, the severity of accidents, in the order of dawn, evening and night, afternoon, and morning. Based on the ranking identified in this way, the independent variables’ ordinal scale was shown, and as a result of the regression analysis, it was verified that these independent variables by ordinal scale were statistically significant.

6. Discussion

This study analyzes risk factors related to occupational accidents that occur at construction sites, especially in small- and medium-sized enterprises in the Korean construction industry, based on quantitative data.
The t-test clearly shows that the workers’ treatment days for SMEs and large enterprises have a significant difference. The days of treatment for victims affiliated with SMEs are 2.2% longer than the days of large enterprises’ treatment days. This is coherent with the previous research in the sense that SMEs’ workers are more exposed to occupational accidents compared to large company workers. In other words, this supports the existing research results that small- and medium-sized companies are more likely to be exposed to safety accidents than large companies at construction sites [46,47,48].
In many countries, including the majority of developed countries, the industry that has contributed the most to the nation’s economy is the construction industry [49,50]. Nevertheless, the construction industry has not changed rapidly in nature, which can be attributed to an atmosphere in which construction companies do not welcome the adoption of new technologies [51]. Therefore, innovative changes in the construction industry have been relatively slow compared to other industries [52,53,54], and the construction industry has also raised many critical views on adopting and applying innovative products and technologies [55]. Identifying risks based on the least available information and developing a response plan through quantitative analysis will provide a fundamental solution to prevent safety accidents at construction sites. Small construction companies are no exception. Compared to larger companies, the advantage of SMEs is that owners or managers tend to demonstrate more influence and flexibility in promoting innovation, such as introducing new technologies and systems [56]. This is because the smaller the size of the company, the less bureaucracy, the horizontal organizational structure [57], the more flexible organizational culture exists, and fewer institutional bureaucratic systems and procedures [58].
As a result, it is essential to consider the risks especially relevant to small construction sites for more effective management of safety and to also focus further on SMEs’ workers to observe a substantial decrease in safety accidents. Multiple regression analysis in this study identifies the dependent variables and significant indicators among independent variables. Significant variables regarding occupational accidents of small- and medium-sized companies and large companies were clearly different. There are three important factors in the SMEs’ worker model: day of the week, accident time, and employment. On the contrary, the contract type is the sole significant variable in the large enterprises model. The construction workers at small- and medium-sized construction companies are obviously affected by safety accidents due to difficulties in concentrating and understanding safety training contents. Therefore, it can be inferred that small- and medium-sized construction workers require more additional safety training and reinforcement based on their level of skill and experience.
As mentioned, this study also suggests additional proof that construction workers may be directly vulnerable to risk depending on the day of the week, among many environmental factors at the sites. This study’s findings could be applied to small- and medium-sized businesses and their construction supervisors in developing measures to prevent worker safety accidents by reducing the risk of accidents. In other words, this study has theoretical and realistic contributions to safety across the construction industry as well as to understanding and improving concentration on workers in small- and medium-sized enterprises. More specifically, SMEs and large construction companies’ worker group has characteristics very much different from each other and thus requires different safety accident management. For example, not only improving the overall working environment and preventing risk factors are important to decrease the occurrence of occupational accidents at construction sites of small- and medium-sized enterprises but also providing appropriate and detailed information to suit the characteristics of each small- and medium-sized enterprise is necessary.
This is because it may be more important for workers at small- and medium-sized business construction sites to be provided with health and safety information and then familiarize themselves with such information. This is because these workers tend to have a lack of complete understanding of regulatory requirements, risk management systems, the safety environment of construction sites, and the systematic cooperation system with officials.
In small- and medium enterprise’ cases, there is a possibility that data from safety accidents can be manipulated, resulting from the unfavorable and disadvantaged status. In order to prevent such situations, it is crucial to establish a system that transparently and systematically collects and manages the data on construction accidents. Furthermore, it is essential to develop an integrated risk assessment that estimates and prevents elements of the accident environment or the possibility of accidents suitable for the characteristics of each construction site based on such data collection. In addition, it will be crucial to use the fourth industrial technology to share decisions related to safety management and manage the process of carrying them out so that a new paradigm of a safe environment can be created.
In this study, day of the week, incident time, and contract type were found to be the significant factors for the occurrence of accidents at small construction sites. In terms of day of the week, other previous has also noted the factor as important and influential.
In the course of conducting the study, the field safety manager and supervisor consistently answered that they had a sense that working hours could be related to safety accidents but that the days of the week were irrelevant. However, not only the data analyzed by this study but also past studies have shown that the day of the week is a factor that should be specially managed. For example, Vwila Nyirenda, 2015, revealed that the day of the week is an important factor in accident management [28], and Amiri, through the analysis of more than 20,000 construction safety accident cases, found that the accident intensity was high on Thursday and Friday [59].
In the study of Camino Ropez et al. (2011) [60], accident time has also been reckoned to be an important accident prevention management factor. This research, in which this accident factor was named “The Lunch Effect,” noted that construction industry accidents occurring around lunchtime (13:00–17:00) tend to be more severe and involve more deaths than accidents occurring at other times of the day. The term “lunch effect” indicates that the time between 13:00 to 18:00 tend to affect construction workers and that the rate of serious accidents is higher during that period of time. This study also found that many accidents occurred after lunch based on statistical significance. This is an important indication that there are times during the day when the severity and mortality of accidents are particularly high and suggests that the task manager should inform workers about this particular risk. It can also mean that special attention should be paid to site risk prevention during13:00–18:00 h. Other studies have also viewed this as due to the possibility of drinking [61], while it may also be caused by a decrease in concentration due to accumulation of fatigue [62,63,64].
Employment type was analyzed as a significant risk factor for accidents in both the SME and large construction enterprises. Cheng et al. (2010) [30] reported that daily workers in their 40s and 50s are reported to be the most vulnerable in construction accidents. Considering that a large proportion of workers in this age group tend to be working temporarily and have a relatively low level of education and training, this finding is consistent with other studies on the work risks faced by temporary workers [65,66]. Furthermore, Son (2017) [67] suggested the links between contingent workers and industrial accidents. For the analysis of this study, a total of 5-year survey data, conducted every other year from 2005 to 2013 by the Korea Labor Institute, were used. As a result of the analysis, the employment ratio of non-regular workers was not statistically significant at the significance level of 0.05, but the direction of influence was consistent with the hypothesis. However, statistically significant effects were found in the industrial accident disease rate and the presence or absence of industrial accidents. In other words, it has been empirically confirmed that as the employment of non-regular workers increases, the worker’s industrial disease rate and the frequency of industrial accidents increase. As a result of empirical analysis of how the labor union acts as a controlling variable, both the industrial accident rate and the industrial accident disease rate were statistically significant and showed a positive (+) direction. As a result, it was also confirmed that the labor union has a moderating effect that affects industrial safety.
The risks and threats are not the same. In order to carry out a successful construction project, risk taking is inevitable, and for this reason, risk must be calculated and predicted as precisely as possible. Nevertheless, not all risk-related decisions are made accurately, and therefore not all risks can be eliminated. Therefore, each small- or large-scale construction organization should understand what risks are acceptable in its current capacity and also understand whether the risks accepted can be adequately compensated.
In the case of small- and medium-sized enterprises, many factors related to worker safety should be identified, such as accident types, workers’ age, workers’ gender, experience length, the industrial activity of the enterprise, part of body affected, primary sources of safety, and enterprise. In addition to these factors, this study also includes the progress rate of the project, number of employees, citizenship, work contents, day of the week, and contract type in the analysis, in order to examine the relationship between the factors and incident severity. Among the factors, the analysis of this study confirmed that the factors of the day of the week, accident time, and contract type had a statistical significance that reflected the characteristics of SME construction companies and thus could be used as a reasonable basis for safety risk management. Therefore, if a systematic management system for these factors, which reflects the characteristics of small- and medium-sized construction companies, is provided and applied to the construction site through academia or the government, the possibility of reducing accidental losses can be expected in advance. This is because risk includes not only threats but also opportunities. Therefore, identifying unknown risks other than known risks may be at the heart of the risk management process.

7. Conclusions

In this study, the risk factors of construction in large companies and SMEs were analyzed based on the loss caused by construction site accidents among the actual data on compensation payments of the actual domestic insurance companies in Korea. Previous studies included the risk factor analysis in construction projects by size and the quantitative risk evaluation method accordingly. However, because of the lack of data due to detailed actual records of losses and construction companies’ tendency to hesitate to disclose records related to accidents and losses, it could be difficult to exclude the limitations of subjective judgment based on expert surveys and these basic qualitative data. On the other hand, the payment of claims for losses by insurance companies can be the most specific and precise numerical data of losses and can be used as robust data for evaluating and predicting potential project risks by utilizing the characteristics included in each construction.
This study analyzed 1608 accident data from 2000 to 2018 in the Korean construction industry. The quantitative data based on the regression analysis allowed identifying the potential correlations among several factors that play an important role in the occurrence of accidents. In this study, it was revealed that while employment type is a significant factor for large construction companies, employment type, working hours, and day of the week are important management factors for small- and medium-sized construction sites. What is noteworthy in this study is that most industrial accidents are easily recognized as due to worker carelessness, but it was found that factors by date, time, and contract type also had a significant effect. Therefore, in order to minimize field accidents, it is strongly required to develop a safety management system that comprehensively considers inherent risk factors as well as safety training and management for existing workers. However, even though the suggested risk factors have a statistical correlation with the accident severity, the causal relationship cannot be explained, and therefore further research is required to prepare detailed risk prevention measures through further investigation.
The findings of this study can contribute to establishing more effective risk response measures reflecting the characteristics of small- and medium-sized construction sites, rapid risk management, and the prevention and minimization of losses. By using the indicators and loss function development frames presented in this study, it will be possible for construction companies and insurance companies to predict financial losses based on the minimum information they have. In particular, insurance companies and reinsurance companies could utilize the risk indicators of this study in reconstructing the risk prediction model. That is, insurers can also develop their own premium calculation model that measures the potential risks for small builders to estimate the maximum foreseeable loss to assess the risk and use the results as key data for calculating the underlying rate of premiums.
Ultimately, the model development frame of this research and indicators of the risk found from it can be effectively used to distribute labor and costs according to the risk indicators in the field and to diagnose the risk of loss in advance. In order to develop more various risk indicators and improve the preciseness of quantitative risk prediction models, government agencies and construction entities in charge of designing and engineering are required to make ongoing efforts to establish a wider and more detailed and sophisticated loss record database construction system and accumulate the data accordingly.

Limitation and Future Research

Integrating the factors affecting worker safety, including workers’ age, workers’ gender, experience length, the industrial activity of the enterprise, part of body affected, primary sources of occupation of worker, and time, and workers with the factors identified in this study can improve the explanatory power (R2) of risk predictions. As such, in future studies, analysis with data sets including more diverse risk factors will help more effective risk management. In this study, it was confirmed that the derived risk factors have a correlation between the dependent and independent variables, and yet they do not explain whether there is a causal relation between them. For example, a correlation was identified where the differences in the day of the week, accident time, and contract type impact accident severity, while the causal relationship, such as whether the reason for the decrease in the accident severity was due to decent risk management or disaster prevention measures within the construction company, is still unexplained.
This study analyzes the risk factors of construction accidents due to the difference in terms of the sizes of construction companies. Many previous studies have pointed out the limitations of small construction companies on risk management. Examples include the cases of various countries such as Malaysia, China, Iran, and so on [68,69,70]. This study confirmed many factors, including not only human factors that directly affect accidents but also contract type and nationality, are related to the occurrence of safety accidents. These factors could also be applied to risk management in many other countries. However, adapting the methodology used in this study to other countries may require further research and verification because the composition of the same variables could produce different results in different cultures.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The author declares no conflict of interest.

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Sustainability 14 07306 g001 550
Figure 1. Normal Q-Q plot and histogram for the dependent variables.
Figure 1. Normal Q-Q plot and histogram for the dependent variables.
Sustainability 14 07306 g001
Table
Table 1. Results of the t-test.
Table 1. Results of the t-test.
Construction SiteNMeanStandard Deviationtp
SMEs148399.6937.75−3.9190.000
Large Enterprises125107.2834.190
Table
Table 2. Variables for regression analysis.
Table 2. Variables for regression analysis.
VariablesFactorsExplanationUnit
DependentTreatment daysDays of treatment indicated in the medical billing statement in order to quantify the severity of accidentLn(Days)
IndependentProgress rate of projectthe completed construction progress at the time when the accident happened%
Employees numberTotal number of employees at the construction site(Company size)Number
CitizenshipThe citizenship of the victims0: Locals (Korean)
1: Foreigners
Work contentsApplication of Korean Standard Classification of Occupations1: Equipment, machine operating and assembling worker
2: Professionals and related workers
3: Craft and related trades workers
4: Manager
5: Elementary workers
Day of weekThe day of the week when the accident occurred.1: Monday 2: Tuesday 3: Sunday 4: Wednesday 5: Friday 6: Saturday 7: Thursday
Accident timeThe time when the accident occurred.1: Dawn (0~6)
2: Evening and Night (18~24)
3: Afternoon (13~18)
4: Morning (6~12)
Contract typeEmployment contract type.0: Regular contract workers
1: Contingent workers
Table
Table 3. Results obtained with the regression models.
Table 3. Results obtained with the regression models.
Small and Medium Construction SiteLarge Contruction Site
VariablesCoef.Beta Coef.p > |z|VIFCoef.Beta Coef.p > |z|VIF
Constant4.349 0.000 4.938 0.000
Progress rate0.000−0.0090.7391.0260.0010.0690.4791.041
Number of employee0.000−0.0160.5711.0310.0000.1490.1341.069
Nationality0.0260.0160.5771.0260.0610.0820.3931.008
Work Contents0.0200.0550.1661.012−0.064−0.2670.2451.007
Day of week0.0100.0560.046 *1.0030.0040.0330.7361.022
Accident time0.0370.0580.040 *1.0150.0050.0110.9111.030
Contract type0.0630.0390.050 *1.0040.1410.1140.006 *1.049
F2.1911.748
Adj-R20.2180.244
*: p < 0.05.
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Ahn, S. Derivation of Risk Factors to Quantify the Risk of Safety Accidents for Small and Medium-Sized Enterprises in Construction Industry. Sustainability 2022, 14, 7306. https://doi.org/10.3390/su14127306

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Ahn S. Derivation of Risk Factors to Quantify the Risk of Safety Accidents for Small and Medium-Sized Enterprises in Construction Industry. Sustainability. 2022; 14(12):7306. https://doi.org/10.3390/su14127306

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Ahn, Sungjin. 2022. "Derivation of Risk Factors to Quantify the Risk of Safety Accidents for Small and Medium-Sized Enterprises in Construction Industry" Sustainability 14, no. 12: 7306. https://doi.org/10.3390/su14127306

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