Determinants of Workers Health and Safety in the Malaysian Wooden Furniture Industry

MATERIALS AND METHODS

The study was conducted in 30 large (employing more than 100 workers, with an annual turnover in excess of US$ 10 million) wooden furniture-manufacturing companies in Malaysia, using a five-part structured questionnaire. The companies were selected according to the following criteria: (1) fifteen companies with accident rates below the industrial average and (2) fifteen companies with accident rates above the industrial average. In this study, accident rate refers to number of accidents at the workplace per million work hours and the accident leads to at least an hour of production loss. All the companies had consented to participate in the study, which was carried out during the period of February to August 2009. The study was conducted in 5 parts.

Part I: This part related to the measurements of safety climate in the factories using a questionnaire, which had 34 variables (Table 1, 2). The variables were selected based on the previous study by Varonen and Mattila (2000) and Zhou et al. (2009) as well as after discussions with industrial health and safety experts. The variables were divided into two groups: (1) 20 variables related to safety practices in the factory and the motivational and attitude factors of the management and workers and (2) 14 variables concerning safety precautions in the factory. All the variables were rated based on the Likert’s 5 point rating scale where higher rating indicated stronger positive opinion. The questionnaire were distributed to every worker in the thirty factories by the respective factory manager and collected in sealed envelopes one week later. The total number of workers who had participated in the study from all fifty factories was 2893, which provided a response rate of 87%. The structure of the safety climate was determined by means of factor analysis. Factor analysis is a method that enables a large number of variables to be described in a compact manner, using fewer new variables (Brown and Holmes, 1986). Through the use of rotation, it is possible to arrange the factor matrix in such a way that it is easier to interpret. The Kaiser-Meyer-Olkin test was used to estimate whether the data was suitable for analysis, i.e., factor analysis cannot be used if the Kaiser-Meyer-Olkin test index is below 0.5. The maximum likelihood method and varimax rotation were used in the analysis. All factors whose eigen value was greater than 1.0 were accepted, while factors that consisted of three or less variables were rejected, because according to Brown and Holmes (1986), it takes at least three variables to define a factor.

Part II: This part related to the measurement of the safety practices in the factories, in which 14 variables were evaluated based on the Likert’s five-point rating scale (Table 3). The senior managers at the respective factories were interviewed to determine the ratings for the variables.

Table 1:	Six-factor solution for safety practices of the company and the attitudes and motivation of the company and workers

Table 2:	One-factor solution for company safety precautions

Table 3:	Variables used in company safety practices

These variables were then grouped into groups, namely: (1) the safety activities of factory management, (2) anticipation of hazards and (3) safety training. The means of factors were calculated as the means of all those variables that were included in each factor.

Part III: This part focused on the measurement of the safety level in the factories, which was carried out based on correlation between the drivers of safety climate and the safety level of the work environment. The safety level of the work environment was evaluated on the degree of compliance of each of the factories to the 45 safety checkpoints, as reported by Ratnasingam and Bennet (2009).

Part IV: This part focused on establishing the relationship between the drivers of safety climate and the accident rate in the factories surveyed. The work accidents that had occurred during the period 2007-2008 were analyzed based on the insurance claims made. This information was regarded as accurate as physicians’ statements supported the claims and the compensation paid by the insurance companies depended on these reports (NIOSH, 2009).

Part V: In this part of this study, a total of 20 workers chosen randomly from each factory, were asked to list down their primary health and safety concerns related to their workplace.

RESULTS

Part I: Safety climate and safety precautions: When the correlation matrix that represent the variables concerning daily safety practices of the company and attitudes and motivational factors of the organization and workers received a Kaiser-Meyer-Olkin test index of 0.86, the results was deemed to be suitable for analysis. The analysis produced six factors. The analysis accounted for 40% of the total variance, a result that was statistically acceptable. The first factor represented the organization’s attitude towards safety (variables 1, 2, 12, 15, 18 and 19), the second the workers’ own attitudes toward safety (variables 4, 8, 9, 10, 11, 14 and 20) and the third was the daily supervision of safety in the workplace (variables 5, 6 and 7). All three factors were internally consistent. Variable 8 was included in factor 2 even though its loading was under 0.4 because it was consistent with the interpretation of this factor. Because the fourth and fifth and sixth factors consisted of two or less variables, they were rejected.

When the correlation matrix that represented the variables concerning safety precautions of the company received a Kaiser-Meyer-Olkin test index value of 0.94, the results were found to be suitable for analysis. The analysis produced one factor. The solution accounted for 48% of the total variance, which was acceptable. The factor was internally very consistent. Only variable 14 in Table 2 received a loading that was under 0.40. On the basis of these analysis, the determinants of safety climate in the wooden furniture manufacturing industry could be defined as:

The Cronbach-α was 0.83 for factor 1, 0.68 for factor 2, 0.61 for factor 3 and 0.93 for factor 4. Based on the satisfactory reliability level of 0.80, the reliability of factors 2 and 3 were considered too low.

The results suggest that although the determinants of safety climate in the wooden furniture involve four factors, the company responsibility and company safety precautions were found to be the predominant determinants of safety climate. This finding was parallel to the report by Varonen and Mattila (2000), who reported that company responsibility and company safety precautions determined the prevailing safety climate in the Finnish woodworking industry. Further, the results from this study emphasized the fact that the workers’ safety attitudes and safety supervision have little effect on the safety climate in the Malaysian wooden furniture industry. Inevitably, the high accident rate in the wood products industry could be attributed to the poor attitude of the foreign contract-workers towards safety issues, as well as the lack of built-in safety precautions in the factories. Therefore, the safety climate within the wood products industry should be implemented through systematic management and operational approach (Ratnasingam and Bennet, 2009).

Part II: Safety practices: In evaluating the safety practices in the factories involved in the study, it was found that the safety activities of the management received the highest score while anticipation of hazards and safety training received lower scores (Table 3).

The safety climate factor 4, company safety precautions had a statistically correlation with the factor of anticipation of hazards (r = 0.792 at p<0.05), which described the company safety practices. However, safety climate factor 1, 2 and 3 showed no correlation with the company safety practices. In this context, an effective company safety precaution is able to pre-empt any possible safety hazard that may arise in the working environment, while the management’s and safety training appear to have minimum effect on the safety practices. This finding suggest that although the company management may demonstrate a high degree of concern for safety in the working environment, no amount of training and management intervention can replace the effectiveness of the company safety precaution scheme built into the operational system on the factory shop-floor to ensure a safe work environment (Lindell, 1994).

Part III: Safety level: The safety climate factor 1, company responsibility and factor 4, company safety precautions had a statistically significant correlation with the safety level of the work environment (r = 0.882 and 0.820 at p<0.05, respectively), while other factors did not show any such relationship. In this context, the prevailing safety level in the Malaysian wooden furniture industry is affected primarily by company responsibility and company safety precautions, which in turn ensures a safe working environment (Ratnasingam and Bennet, 2009).

Part IV: Occupational accidents: The safety factor 1, company responsibility and factor 4, company safety precautions, had a statistically significant correlation with the accident rate reported by the factories (r = 0.776 and 0.884 at p<0.05, respectively). However, factor 2 and 3 showed no such relationships. The incidence of occupational accidents in the Malaysian wooden furniture industry is also closely linked to company responsibility and company safety precautions, clearly suggesting that a safe operational system must be in place to ensure safety in the work environment (Lindell, 1994; Ratnasingam and Bennet, 2009).

Part V: Health and safety issues: The primary health and safety concerns of the workers in the wooden furniture manufacturing industry can be divided into 5 categories, namely: (1) air-borne dust, (2) noise, (3) chemical exposure, (4) manual handling and (5) occupational accidents. Although other factors, such as poor lighting, poor hygiene and fire hazards were highlighted, these factors were considered insignificant due to their low ratings of importance. The results show that the workers in the Malaysian wooden furniture industry are more concerned about their immediate work environment, as it has a strong bearing on their productive capacity. This is to be expected since as contract workers, their wages are dependent on the number of hours worked (Ratnasingam and Bennet, 2009). Further, the reluctance of workers to use the safety gadgets provided as it is deemed to be uncomfortable and interferes with their productive capacity, suggest that unless the use of safety gadgets by workers are made mandatory, it may be more economical to reduce the safety hazards at the place of origin through technological and operational means.

DISCUSSION

The results of this study, which corresponds to the findings of Varonen and Mattila (2000), Smith et al. (2006), Hahn and Murphy (2008) and Baek et al. (2008), emphasizes that workers safety is highly influenced by the prevailing safety climate factors in the factories. Nevertheless, it must be emphasized that health and safety rules and regulations alone are not the answer to accident prevention. Safety researchers have identified and advocated several approaches in addition to enforcement to help understand this complex problem. Going beyond external regulatory systems, past research to improve occupational safety emphasized the so-called accident prone individual and the ergonomic design of equipment (Sheehy and Chapman, 1987). Subsequent research, however, demonstrated that simply trying to eliminate or control unsafe employees does not solve the root cause of the problem. Geller (1996, 2001) has conclusively shown that the most productive path to reducing accidents is through a greater use of techniques from industrial psychology and organizational science.

The analysis presented in this study enables the identification of primary risk factors for the wooden furniture industry. Risks associated with the immediate work environment (such as air-borne dusts from machining operations, noise emission, chemicals exposure and manual handling of materials as well as the related occupational accidents) are highest concerns of workers (Gazo et al., 2002). Further, the best safety climate within the wooden furniture sector requires a management team that provides its full support for what can be called a culture of safety. Specifically, the senior management of the organization need to do more than just pay lip service to safety, as they need to cultivate an organizational culture that truly believes in and values safety. Leadership for safety must also be shown throughout the managerial ranks in the organization, especially by the plant manager and supervisors (Stewart, 2001). The human resource practices that ensure a workforce that can meet goals for safety, productivity and quality is valuable for increasing safety performance (Geller, 2001). In this context, as shown in this study, the safety precautions taken as part of the standard operating procedures on the factory shop-floor has the strongest influence on creating the highest safety climate, as previously reported by Michael and Leschinsky (2003). Although, training and workers attitude toward safety plays a role in the prevailing safety climate (Veigle and Horst, 1982), their influence is somewhat limited by the quality of the workforce in the Malaysian wooden furniture industry, which is predominated by contract foreign-workers. According to Personick and Biddle (1989), the nature of the workforce is crucial in determining the motivation towards safety and training and as shown in this study, appears to limit the influences of training and supervision on the prevailing safety climate in the industry.

Industrial implications: Health and safety regulation alone will not ensure the highest safety climate within the wooden furniture industry. It’s the management’s commitment and built-in safety precautions that would ensure a safe work environment, which in turn would ensure a productive workforce. Managers of wooden furniture mills would be wise to remember that a successful safety program not only requires their own buy-in, but also requires bottom-up involvement from the workforce. An effective system of manufacturing practices with built-in safety precautions, as advocated by the ISO 18000 scheme will encourage the safety-related involvement of all employees, is even more valuable when dealing with contract foreign-workers, as in the case of the Malaysian wooden furniture industry, to drive for improved safety performances in the wooden furniture industry.

CONCLUSION

Even though the working environment in the wooden furniture industry is deemed unsafe, in reality the prevailing safety level is dependent on the management commitment and safety precautions taken. Improving safety performance should be a primary concern for managers at all levels, as it has far reaching economic and social bearings in the competitive wooden furniture manufacturing industry.