Knowledge,attitude,and perception of risk management of steam boilers among workers in palm oil mills

Abstract

BACKGROUND:

People who work with the steam boilers in palm oil mills are vulnerable to accidents, notably to explosions, whenever failures of any kind occur during their operation.

OBJECTIVE:

This cross-sectional study was conducted in palm oil mills to determine the knowledge, attitude, and perception (KAP) concerning the risk management of steam boilers among workers in palm oil mills.

METHODS:

A total of 50 workers who were working with steam boilers and/or involved in managing the operation of steam boilers with at least three years of working experience in the palm oil industry were purposively sampled. A self-administrated questionnaire consisting of four main parts – socio-demographic and occupational information, knowledge, attitude, and perception of the risk management of steam boilers was distributed. The knowledge, attitude, and perception were calculated based on a Likert-type scale.

RESULTS:

This study found that 56% of boiler workers had a good level of knowledge (mean score = 77.22±19.6), 72% had a good attitude toward risk (mean score = 83.17±5.85), and 64% had a good perception of risk (mean score = 79.50±11.22). The prevalence of accidents was 16%, and, for near misses, it was 24%. The cause of the reported accidents was largely attributed to carelessness, specifically at 80%. Apart from that, there was a positive association (χ² = 6.56; p = 0.010) between attitude and accidents.

CONCLUSION:

This study found that the workers had good KAP levels of risk management of steam boilers in palm oil mills. However, there is a need for the employers to revise the training of workers and ensure its effectiveness in heightening the awareness of risks and accidents despite the high level of KAP.

Keywords

Hazards occupational health prevalence training

1 Introduction

The palm oil industry is one of the world’s most rapidly expanding industries [1]. Malaysia is currently one of the largest producers and exporters, accounting for 12% of the world production of oils and fats, and 27% of the export trade of oils and fats. In this context, Malaysia currently generates outputs constituting 39% of the world palm oil production and 44% of world exports [2].

All parts of the oil palm fruit are useful, including the byproducts of several processes. Specifically, the solid wastes from the palm oil mill comprise empty fruit bunches (EFB), mesocarp fruit fibers (MF), and palm kernel shells (PKS) [3]. The solid waste, such as fibers and kernels, can be used as an alternative fuel source [4] to produce superheated steam, which can generate electricity for the entire mill. The most common hazards associated with steam are contact with heated equipment due to high temperature, physical exposure to live steam, non-uniform heat transfer, and over-pressuring the equipment [5].

A steam boiler is an enclosed vessel, in which the heat produced is transferred to water until it becomes steam. In a technical context, a steam boiler includes the entire complex system to provide steam that will be used as a steam turbine [6].

In Malaysia, the Department of Safety and Health (DOSH) [7] have recorded many cases of accidents related to palm oil mills, in which boiler operation has been identified as one of the causes. Deficiencies in the strength of steam boilers may be due to an original defect, bad workmanship or deterioration from use or mismanagement [8, 9]. Even though the boilers are equipped with automated control systems and a set of safety interlocks, boiler accidents still occur frequently due to the lack of proper attention by the operators in following the procedures and maintenance schedules [10].

Moreover, from the preliminary survey among boiler workers in palm oil mills, most of them solely rely on the automated system of the boiler operation and believe that the automated boiler will not have any impact on the workers and mills because the system will shut down should any failure occur [11, 12].

In a review paper by Innes-Jones and Scandpower [13], complacency was associated with boredom, over-reliance, and over-confidence, where the respondents were typically monitoring the automated systems, which were perceived to be more reliable than manual operations. In a similar research, Chiejile [14] found that incidents of burns, slips, trips, and falls in palm oil mills were frequent, despite the fact that accident and injury prevention mechanisms were already in place.

According to the health belief models (HBM), workers’ behavior can be changed with an increase in knowledge followed by skill development and conscious effort as well as the provision of a safer work environment [15, 16]. The HBM has been used in various intervention studies using knowledge, attitude, and practices as a means to evaluate the effectiveness of the intervention program. At the same time, several studies concerning the knowledge, attitude, and practice (KAP) of workers engaged in different occupations have been published [17]. However, the KAP of boiler workers working in the palm oil industry has not been well studied. Therefore, this study could serve as an input to the management of palm oil mills by understanding the fundamental knowledge, awareness, attitude, and perception of their workers concerning the use of boilers in their daily work routine.

Due to the increasing demand from the global market for palm oil, this study could serve as a preliminary study to highlight the health and safety problems emerging from this upcoming industry, particularly in the developing countries of Southeast Asia. Thus, this study intends to determine the association between knowledge, attitude, and the perception of risks, and the prevalence of accidents and near misses among steam boiler operators.

2 Method

2.1 Subjects

This cross-sectional study was conducted from January to March 2016 among male steam boiler workers from four palm oil mills aged between 20 and 50 years old, who worked with a water tube boiler. The water tube boiler is by far the most common type of boiler used in palm oil mills for generating steam to be used in the sterilization process of fresh fruit bunches as well as to provide power for the mill in other processes. Therefore, workers who worked with the same type of boiler were recruited in this survey to enable the evaluation of similar hazards and work environments.

Ethical approval for this research was obtained from the Ethics Committee for Research Involving Human Subjects (UPM/TNCPI/RMC/JKEUPM/1.4.18.1/F1). This study was conducted in accordance with the Helsinki Declaration [18]. Informed consent was obtained from each respondent prior to data collection. During data collection, respondents were given a set of questionnaires with instructions concerning how to complete them, accompanied by a briefing conducted by the researcher. The respondents were required to complete their particular information and answer the questions given.

2.2 Sampling

Purposive sampling was employed for this study to focus on workers engaged in the operation of steam boilers. The sampling frame of this study was the list of names of the persons in charge of boiler operations provided by the Department of Human Resources (administration office) for each of the palm oil mills. Based on the sample size calculation obtained from Lemeshow et al. [19] a total of 55 workers who fulfilled the inclusion criteria; for example, gender (male) and occupation (person in charge of water tube boilers from the four selected palm oil mills), were recruited to participate in this study. The details of the calculation of the sample size are shown below. $n = \frac{{Z_{1 - \frac{α}{2}} [\sqrt{2 \bar{P} (1 - \bar{P})} + Z_{1 - β} \sqrt{P_{1} (1 - P_{1}) + P_{2} (1 - P_{2})}]}^{2}}{{(P_{1} - P_{2})}^{2}}$ (2.1)

Where:

N = sample size

$Z_{1 - \frac{α}{2}}$ set at 95% = 1.96

Z_1-β set at 80% = 0.842

According to a previous study that related to knowledge, attitude, and practice (KAP) in other industries, the highest proportion score was 0.47, and the lowest proportion score was 0.217; as reported by Jamiluddin [20]. These values were used as inputs in Equation 2.2. $\bar{P} = \frac{(P_{1} + P_{2})}{2}$ (2.2) $\begin{matrix} P_{1} & = & estimated proportion (larger) = 0.47 \\ P_{2} & = & estimated proportion (smaller) = 0.217 \\ \bar{P} & = & \frac{(P_{1} + P_{2})}{2} \\ \bar{P} & = & \frac{(0.47 + 0.217)}{2} \\ \bar{P} & = & 0.344 \end{matrix}$

Therefore, the sample size is:

$n = \frac{{Z_{1 - \frac{α}{2}} [\sqrt{2 \bar{P} (1 - \bar{P})} + Z_{1 - β} \sqrt{P_{1} (1 - P_{1}) + P_{2} (1 - P_{2})}]}^{2}}{{(P_{1} - P_{2})}^{2}}$ $\begin{matrix} n & = & \frac{{1.96 [\sqrt{2 \times 0.344 (1 - 0.344)} + 0.842 \sqrt{0.47 (1 - 0.47) + 0.217 (1 - 0.217)}]}^{2}}{(0.47 - 0.217)^{2}} \\ n & = & 54 \end{matrix}$

By referring to ES_p (effect size population) and power of study (80%) the sample size required was 55 respondents.

Effect size: ${ES}_{P} = \emptyset_{1} - \emptyset_{2}$ ES_p= Arcsine transformation for proportion

ES_p= 0.535

80% = 55

Thus, 55 subjects were selected purposively.

2.3 Instrument

The questionnaire was prepared in two languages; namely, Malay and English, in order to avoid language bias among the respondents. The questionnaire consists of four sections:

Section A: Socio-demographic and occupational information

This section was used to collect information concerning the basic socio-demographic background of the respondents, such as age and education level. Besides that, the occupational information was also required; namely, working experience, boiler-related training background, and frequency of past training program, as well as the prevalence of accidents and near misses experienced throughout their career in operating steam boilers.

Section B: Knowledge about risk management of boilers

The KAP questionnaires were prepared based on the Hazard and Operability (HAZOP) analysis conducted at the four selected palm oil mills. Based on the results obtained from the HAZOP analysis, the potential hazards affecting the workers were outlined, and the KAP questionnaire was prepared to further examine the knowledge, attitude, and perception of risk management of the boilers used by the workers.

Tables 1 to 3 below summarize the risk items following the HAZOP analysis.

There were nine (9) questions in this section. These questions generally revolved around the knowledge of the boiler operations and related risks. Positive responses were indicated by one (1) point each, whereas zero (0) points stood for negative responses. The score varied from one (1) to nine (9) points. For example, in the first question, the respondents were asked if risk management is important to prevent accidents in the workplace and they could choose either yes or no; the correct answer would be yes.

Table 1
Knowledge of risk management of boilers

Items Optional answer

Risk management is important to prevent accidents in the workplace a) Yes

b) No

Low water level inside the steam drum can cause an explosion a) No regular maintenance

b) Impossible due to automatic system

c) Not sure

Safety valve produces a sound a) Yes

b) No

Reasons sound is produced by safety valve a) Exceeds safe working pressure (TKS)

b) Warning

c) Normal operation

d) Not sure

Steam boiler can explode a) Yes, possible

b) Yes, due sabotage

c) Yes, due too long operation

d) Impossible

e) Not sure

Smoke from chimney a) Environmental problem

b) Workers problem

c) No problem

Inspection by DOSH a) Disturb my job

b) Perform their duties

c) Prevent accidents

d) Learn about the boiler

Good combustion of furnace a) No black smoke

b) Slow fire

c) Less steam production

d) Not sure

Excessive steam delivered to the sterilizer a) Danger

b) The system will give warning

d) Not sure

Items	Optional answer
Risk management is important to prevent accidents in the workplace	a) Yes
	b) No
Low water level inside the steam drum can cause an explosion	a) No regular maintenance
	b) Impossible due to automatic system
	c) Not sure
Safety valve produces a sound	a) Yes
	b) No
Reasons sound is produced by safety valve	a) Exceeds safe working pressure (TKS)
	b) Warning
	c) Normal operation
	d) Not sure
Steam boiler can explode	a) Yes, possible
	b) Yes, due sabotage
	c) Yes, due too long operation
	d) Impossible
	e) Not sure
Smoke from chimney	a) Environmental problem
	b) Workers problem
	c) No problem
Inspection by DOSH	a) Disturb my job
	b) Perform their duties
	c) Prevent accidents
	d) Learn about the boiler
Good combustion of furnace	a) No black smoke
	b) Slow fire
	c) Less steam production
	d) Not sure
Excessive steam delivered to the sterilizer	a) Danger
	b) The system will give warning
	d) Not sure

Table 2

Attitude to risk management of boilers

Items	Statement	Optional answer with Likert-type scale score
Wear PPE	Positive statement	Strongly agree = 5
Safety helmet		Agree = 4
Safety shoes		Don’t know = 3
Earplug		Disagree = 2
Glove		Strongly disagree = 1
I always check the logbook before the start of my shift duties.
Before I start working, I am given a short briefing about the boiler performance from the previous shift.
I have checked all the parameters of the boiler to ensure it is under control.
Every failure or damage that occurs must be reported quickly to the employer.
I will keep all the records about the parameters of the boiler’s performance.
I had to attend training before I started working.
Steam boiler can be operated without boilerman.	Negative statement	Strongly disagree = 5
I don’t have to check the boiler’s performance as an automatic system is used.		Disagree = 4
		Don’t know = 3
I don’t have to worry because boiler will shut down if there is any failure.		Agree = 2
		Strongly agree = 1

Table 3

Perception of risk management of boilers

	Items	Statement	Optional answer with Likert-type scale score
1.	Steam boilers have the potential to explode.
2.	Low water level inside water drum can cause explosion.
3.	Sound of safety valve indicates a high level of pressure.
4.	Regular maintenance is important to prevent boiler explosion.		No = 1
5.	Loss of water inside the water tube can cause the boiler to become dry and lead to an explosion	Positive statement	Low = 2
6.	Training of the workers is important to prevent any accidents in the workplace.		Moderate = 3
7.	Records about the operation of the boiler need to be kept.		High = 4
8.	The inspection from DOSH officer is important to prevent potential accidents.		Very high = 5
9.	Logbook of boiler needs to be updated to prevent operational problems.
10.	Boilerman and other workers need to monitor the boiler control system.
11.	Steam boilers may be left without supervision from boilerman and other employees
12.	An automatic system for the boiler is used nowadays.

Section C: Attitude to risk management of boilers

There were 14 questions in this section concerning the attitudes of the workers to boiler operations and management. The questions on attitude were designed to be answered using a Likert-type scale (Strongly agree/Agree/Do not know/Disagree/ Strongly Disagree). For positive attitude items, the scores were denoted from 5, 4, 3, 2 to 1. For example, respondents were asked if they always check the logbook before starting their respective duty shifts, and they were given a choice of strongly agree, agree, do not know, disagree, or strongly disagree. For negative attitude items, the scoring system was the opposite, whereby 5 indicated strong disagreement, while 1 represented strong agreement. As an example, the respondents were asked whether they have any reason to worry should the boilers shut down due to a failure of any sort, and they were given the same options for answering (Table 2).

Section D: Perception of risk management of boilers

There were 12 questions provided in this section. This section used a scale of 1 (‘No risk’), 2 (‘Low risk’), 3 (‘Moderate Risk’), 4 (‘High Risk’) and 5 (‘Extremely High Risk’), as shown in Table 3. All questions were asked in a positive statement. The total scores were calculated using mean scores. The score in each KAP category is shown in Table 4. This study is used as the reference guide (reference to the number of questions and scale) to construct knowledge, attitude, and practice questionnaires related to occupational safety and health performance among workers. For example, the respondents were asked if steam boilers have the potential to explode, and they should choose from 1 to 5.

A pilot study was conducted among 15 boiler workers from other industries. For content validity, two experts (Ph.D. holders) in Occupational Health and Safety reviewed the questionnaire. This survey was conducted to determine if the questions given could provide answers relevant to the underlying concepts that have been designed. Based on the opinion of the experts, they were strongly (+90%) in agreement with the ability of this questionnaire to determine the attitude, perception, and knowledge about risk management among steam boiler workers.

Table 4

Score for knowledge, attitude, and perception categories

Variables	Score
Knowledge
Poor knowledge	0–3 (0–33.3%)
Moderate knowledge	4–6 (44.4–66.7%)
High knowledge	7–9 (77.8–100%)
Attitude
Good attitude	0–42 (less than 60%)
Moderate attitude	43–55 (61–79%)
Poor attitude	56–70 (80–100%)
Perception
Poor perception	0–36 (less than 60%)
Moderate perception	37–47 (61–79%)
Good perception	48–60 (80–100%)

In order to measure the internal consistency and reliability of the questionnaire, participants were required to fill in and complete the questionnaire twice. The interval between testing was 8 days, which is within the range of seven to ten days suggested by Shariat et al. [21]. Next, the Kappa coefficient was utilized to assess the test’s reliability, while the Cronbach’s alpha statistic was used to measure the internal consistency (recommended value ≥0.70) of the results. It was found that the scales of Cronbach’s alpha for attitude, knowledge, and perception were 0.842, 0.868, and 0.852, respectively, which indicated a high level of internal consistency for the questionnaire.

2.4 Statistical analysis

Statistical Packages for Social Science (SPSS), version 22.0, was used for data analysis. The level of significance of study was set at p < 0.05. The data were analyzed descriptively using frequencies, percentages, means and standard deviations for knowledge, attitude, and the perception of risk management of steam boilers. The chi-square was used to determine the association between knowledge, perception, and attitude among palm oil mill workers, and the association between knowledge and perception, and attitude to steam boiler risk management, and the frequency of accidents among workers in palm oil mills.

3 Results

3.1 Socio-demographic and occupational information of respondents

Out of 55 selected respondents, two were not interested in participating in this study (due to non-medical reasons), and three respondents were absent during data collection. As a result, only 50 respondents were involved in this survey study. Table 5 shows the socio-demographic and occupational information of the respondents. It can be observed that 19 (38%) respondents were aged between 21 and 30 years old. The majority of the respondents were Malay (90%), followed by Indian (4%) and other ethnicities (6%). In terms of education level, the majority of the respondents (72%) had attended secondary school. Three respondents had only completed their primary school education, whereas 36 (72%) respondents were educated up to tertiary level. In addition, only eight (16%) respondents had the certificate of boilerman Grade 2. The job positions of the respondents were fireman (54%), boilerman (22%), supervisor (6%), and steam engineer (4%), in addition to other related jobs, such as managers and mill assistants (14%).

Table 5
Socio-demographic and occupational information for palm oil mill workers

Characteristic Study Group (N = 50)

N (%)

Age 21–30 years old 19 38

31–40 years old 18 36

41–50 years old 9 18

More than 50 years old 4 8

Race Malay 45 90

Indian 2 4

Others 3 6

Educational level Primary 3 6

Secondary 36 72

Tertiary 3 6

Certificate of steam boiler 8 16

Job title Boilerman 11 22

Fireman 27 54

Steam engineer 2 4

Supervisor 3 6

Others 7 14

Working experience 3 months – 5 years 22 44

5–10 years 14 28

More than 10 years 14 28

Training for Job Task Yes 42 84

No 8 8

Training frequency Once a week 1 2.38

Once a month 2 4.76

Once a year 9 21.43

Once since the first time working 30 71.43

	Characteristic	Study Group (N = 50)
Age	21–30 years old	19	38
	31–40 years old	18	36
	41–50 years old	9	18
	More than 50 years old	4	8
Race	Malay	45	90
	Indian	2	4
	Others	3	6
Educational level	Primary	3	6
	Secondary	36	72
	Tertiary	3	6
	Certificate of steam boiler	8	16
Job title	Boilerman	11	22
	Fireman	27	54
	Steam engineer	2	4
	Supervisor	3	6
	Others	7	14
Working experience	3 months – 5 years	22	44
	5–10 years	14	28
	More than 10 years	14	28
Training for Job Task	Yes	42	84
	No	8	8
Training frequency	Once a week	1	2.38
	Once a month	2	4.76
	Once a year	9	21.43
	Once since the first time working	30	71.43

The distribution of working experience was divided into three; namely, three months to five years (44%), which was the highest, followed by 5 to 10 years (28%) and more than 10 years (28%). Most of the respondents obtained general training for their respective jobs in the workplace itself (84%) once, and during their first day of work (71.43%).

3.2 Knowledge of risk management of steam boilers

The mean score for knowledge was 77.22±19.60 (mean±SD). Among the respondents, 28 had a good level of knowledge. Another 19 respondents had a moderate level of knowledge, and only 3 respondents exhibited a poor level of knowledge.

3.3 Attitude to risk management of steam boilers

The mean score for attitude was reported to be 83.17±5.85 (mean±SD). Specifically, 37 respondents had a good attitude, while 13 of the respondents had a moderate attitude to risk and no respondent scored poor attitude to risk management.

3.4 Perception of risk management of steam boilers

The mean for perception was recorded as 79.50±11.22 (mean±SD). In this context, a total of 32 respondents had a good perception of risk, 15 respondents had a moderate level of perception, and only 3 of the respondents showed poor perception.

3.5 Prevalence of accidents and near misses

The prevalence of accidents reported was 8 times, while near misses exhibited a frequency of 12 times. The cause of accidents was highly reported as being due to carelessness (40 respondents), compared to operational failure (3 respondents), lack of knowledge (6 respondents) and miscellaneous causes (1 respondent). No respondent chose overconfidence as the cause of accidents in the workplace.

3.6 Association between knowledge and perception, and attitude

The association between knowledge and perception, and attitude is presented in Table 6. From the results, no significant association between knowledge and attitude (χ² = 4.249; p = 0.119) or between perception and attitude (χ² = 0.440; p = 0.803) was found.

Table 6
The association between knowledge and perception, and attitude (N = 50)

Category of attitude χ²(df) p-value

Moderate N (%) Good N (%)

Category of knowledge

Poor 2 (4) 13 (26) 4.249 (2) 0.119

Moderate 8 (16) 11 (22)

Good 3 (6) 13 (26)

Category of perception

Poor 1 (2) 2 (4) 0.440 (2) 0.803

Moderate 3 (6) 12 (24)

Good 9 (18) 23 (46)

	Category of attitude	χ²(df)	p-value
Category of knowledge
Poor	2 (4)	13 (26)	4.249 (2)	0.119
Moderate	8 (16)	11 (22)
Good	3 (6)	13 (26)
Category of perception
Poor	1 (2)	2 (4)	0.440 (2)	0.803
Moderate	3 (6)	12 (24)
Good	9 (18)	23 (46)

χ² = Chi-square test. *p-value is significant at < 0.05 level.

3.7 Association between knowledge, attitude, and perception, and accidents

Table 7 shows the results of the association between knowledge, attitude, and perception of risk management, and accidents. There was no significant association between knowledge and perception, and accidents with χ² = 0.75; p = 0.686 and χ² = 2.91; p = 0.233, respectively. In contrast, there was a significant association between attitude and accidents (χ² = 6.56; p = 0.010).

Table 7
The association between knowledge, attitude, and perception, and accidents (n = 50)

Accidents χ²(df) p-value

Yes No

N (%) N (%)

Knowledge

Poor 2 (4) 1 (2) 0.75 (2) 0.686

Moderate 18 (36) 1 (2)

Good 22 (44) 6 (12)

Attitude

Poor – – 6.56 (1) 0.010*

Moderate 8 (16) 5 (10)

Good 34 (68) 3 (6)

Perception

Poor 3 (6) – 2.91 (2) 0.233

Moderate 12 (24) 3 (6)

Good 27 (54) 5 (10)

	Accidents	χ²(df)	p-value
Knowledge
Poor	2 (4)	1 (2)	0.75 (2)	0.686
Moderate	18 (36)	1 (2)
Good	22 (44)	6 (12)
Attitude
Poor	–	–	6.56 (1)	0.010*
Moderate	8 (16)	5 (10)
Good	34 (68)	3 (6)
Perception
Poor	3 (6)	–	2.91 (2)	0.233
Moderate	12 (24)	3 (6)
Good	27 (54)	5 (10)

χ² = Chi-square test. *p-value is significant at < 0.05 level.

4 Discussion

Boilers are hazardous installations that require operation and maintenance by a registered competent person in Malaysia [22]. In ensuring that the boiler is operated safely, it is important that the competent person has the necessary knowledge, correct attitude and capability of perceiving the hazards and risks in the event that any deviation occurs [17 , 24].

In this study, despite having sufficiently high or good levels of knowledge, attitude, and perception of risks, the workers were still reported as being involved in boiler-related accidents. This is generally in tandem with the theoretical construct of perceived susceptibility in the Health Belief Model. Although, logically, preventive actions are taken when a person perceives that he is at risk of a disease [21].

The results of this study were equivalent to that of a previous study by Arezes et al. [25] in which there was an association between risk perception and safety behavior. The findings of this study can also be supported by the study of KAP among workers in the printing industry who reported that a good level of knowledge and attitude facilitated in practicing the preventive measures appropriately [26, 27]. A good perception of risk can lead to better behavior and attitude. This, however, was in contrast to a previous study by Ismail et al. [28], which revealed that poor KAP was scored among quarry workers. Another study on KAP reported different scores among abattoir workers in relation to compliance with the abattoir laws in Malaysia [8].

According to the Heinrich Law [30], one major and injurious accident can cause 29 cases of accidents that lead to minor injuries and 300 accidents that cause no injury (proportion of 1-30-300). The axioms of the Heinrich Law applied to this day highlight the cause of accidents resulting from an unsafe act. The axioms of the Heinrich Law explain the cause of accidents found in this study. Throughout this study, carelessness was found to be the main cause of accidents. Moreover, Khanzode et al. [9] agreed that accidents are caused by subconscious processes, which include guilt, aggression, anxiety, ambition, and conflict. Besides that, Azlina et al. [31], in their study, also indicated that the occurrence of accidents increased due to the lack of knowledge and poor awareness of workers. The increase in age also increased the risk of injury [9]. In this study, most of the respondents were young adults (aged less than 30 years old). Therefore, due to a small sample size, the prevalence cannot be generalized to the whole population of workers.

A study of KAP on occupational hazards and safety among textile mills in India showed a statistically significant association between knowledge and attitude [32]. However, in this study, knowledge, and attitude were not significantly associated with each other, which was similar to a study by Khoso and Nafees [27] that was conducted among workers in a textile mill. Nevertheless, a different finding was found for civil and structural engineers in terms of KAP in that safety was deemed as being more critical than knowledge [17, 33].

The association between knowledge and accidents was clearly explained by Kalatpour [34], who posited that major accidents could be prevented by applying safety knowledge. Indeed, knowledge could be used as a tool to educate people to prevent accidents. They also stated that the cognitive and emotional components of risk perception were related differently to risk behavior. In contrast, Goh and Chua [33] found that the prevalence of accidents was dependent on the attitudes of workers.

As a limitation of this study, it should be noted that the palm oil industry is a developing industry that is highlighted by ASEAN countries to increase the demand for palm oil products. Although this study was conducted based on a small sample size of workers from Malaysian palm oil mills, the significant output of this study highlights the necessity to recruit a larger and randomized sample size with the developed method to answer the hypothesis in a nationwide-based study. In addition, related studies on a similar topic were scarce, which increased the possibility of self-reporting results.

5 Conclusion

In conclusion, the knowledge, attitude, and perception of a worker play an important role in reducing any accidents in the workplace. In addition, steam boilers have a high tendency of explosions and accidents. Therefore, boiler workers need to have high levels of knowledge and awareness before performing their duties. This study found that the respondents have good levels of knowledge, perception, and attitude to risk management of steam boilers in palm oil mills. Nevertheless, the management of the palm oil mills should increase the frequency of training regarding risk management. This is important to ensure that the workers are always aware of the risks around them. Moreover, the prevalence of near misses was higher than accidents among workers due to unsafe acts, such as carelessness at work. The workers need to report any near misses and minor accidents to prevent major accidents from occurring in future. Lastly, there was a significant association between the attitude to risk management and accidents. Accidents can occur at any time that any condition goes unnoticed by the workers.

Conflict of interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The research was funded by the Ministry of Higher Education (MOHE), Malaysia under the Long Research Grant Scheme (LRGS).

Footnotes

Acknowledgments

The authors would like to thank the Department of Safety and Health, Negeri Sembilan for helping with the data collection, as well as all the respondents from the palm oil mills.

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