4.1. Questionnaire Validation and Safety-Climate Assessment
The three proposed safety-climate factors were tested by CFA by using SEM with multiple-factor models as previously researched in [
39]. The model assumes that each factor represents the safety climate. The safety-climate factors in this model are connected to each other by using the covariance symbols, which indicate the un-analyzed association, or no effect of the direction is implied [
40]. This model was tested in SEM with the assumption that all exogenous variables are covaried. In SEM, the factors are allowed to be covaried if each of them has at least three indicators (items) [
40]. The multiple-factors model was used because this research only focused on testing the validity of the three proposed safety-climate factors.
According to Huang et al. [
39], the CFA was adopted to confirm the safety-climate scale construct validity. It is stated by Hair et al. [
41] that CFA measures the construct validity and goodness-of-fit of the model. The construct validity is confirmed through convergent validity and discriminant validity. The goodness-of-fit index examines the model quality.
In the CFA test, items with low factor loading (<0.6) need to be removed [
42]. As a result, 13 out of 25 items were retained in this study. These items include three items for safety perception, four items for safety communication and six items for safety-management systems. The constructed multiple-factor safety-climate model is shown in
Figure 1 and is itemized in
Table 2.
Two criteria are used for examining the convergent validity: construct reliability (CR) (>0.7) and average variance extracted (AVE) (>0.5) [
41]. According to [
41], CR confirms the internal consistency of the measures of a construct in representing that same construct. On the other hand, AVE is the convergence summary indicator regarding a set of construct items [
41,
43].
Hair et al. [
41] mentioned that the threshold for the value of the factor loading is 0.5, or 0.7 for an ideal factor loading. When the factor loading is above 0.5, it indicates that the item has a high correspondence with its associated factor. As illustrated in
Table 3, only one item’s value of the factor loading (0.697) was below 0.7, while all of the other 12 items’ values were above 0.7. The resulting Cronbach’s alpha scores indicated a good questionnaire reliability since its scores for all three safety-climate factors were above the threshold value of 0.7 [
41,
44,
45,
46,
47]. By ensuring questionnaire reliability, the measured construct consistency is confirmed. This indicates that the questionnaire with the 13 retained items is an appropriate mechanism to measure safety climate. Furthermore, the resulting value of CR and AVE of the proposed model were also good because they were above the thresholds [
41,
44,
45,
46,
47] (0.7 for CR and 0.5 for AVE).
Another measure to confirm construct validity is discriminant validity [
41], which indicates one construct or one factor is distinctive from the other construct or factor in the model. It is tested by comparing the AVE score to the squared interconstruct correlation (SIC) score. If AVE is higher than SIC, which can be seen in
Table 4, the discriminant validity of the model is confirmed.
Researchers have different preferences regarding the use of the model-fit indices. Vinodkumar and Bhasi [
5] recommended the use of (χ
2/df), root-mean-squared error of approximation (RMSEA), comparative fit index (CFI) and the Tucker–Lewis index (TLI) in their research with a large sample size. Additionally, goodness-of-fit (GFI) and standardized root-mean-residual (SRMR) are considered as two of the most widely used indices [
49]. Hox and Bechger [
50] used the adjusted goodness-of-fit (AGFI) as one of the fit indices for model complexity [
50]. Each fit index has its threshold value. Researchers sometimes have a degree of variation regarding the threshold value used for fit indices. This study used all of the above-mentioned seven fit indices to test the proposed model’s goodness-of-fit, and the results are shown in
Table 5.
Chi-square is used to evaluate the overall model fit [
51] due to the chi-square sensitivity towards the sample size [
59]; the relative chi-square (χ
2/df) was preferred to be used in this study. The χ
2/df obtained in this study is 3.853, which is acceptable [
51,
52,
53]. Higher CFI and TLI mean higher fit on the correspondence data. The CFI and TLI obtained in this study are 0.970 and 0.962, respectively. Since they pass the threshold of 0.9 [
41,
44,
58] and 0.95 [
51], respectively, the goodness-of-fit is confirmed. RMSEA indicates the appreciative error regarding the population’s expected degree of freedom. The RMSEA obtained in this study is 0.058, which is acceptable because it is under 0.08 [
51,
57]. The SRMR is an alternative test statistic based on residuals [
41]. The SRMR obtained in this study is 0.003 and is acceptable because it is under 0.08 [
51,
56]. The GFI, as an early attempt to produce a fit statistic, and AGFI as the GFI adjustment for a more complex model [
50], are both acceptable in this study as they both are above 0.9 [
41,
51,
54,
55].
Table 6 shows the safety-climate level based on the final safety-climate model. Since the mean score for safety perception, safety communication and safety management is 4.22, 3.97 and 4.23, respectively, it shows a good level of safety climate based on similar safety-climate studies [
5,
23,
32,
34,
35]. Furthermore, more attention should be directed to this safety communication because of its lower score compared to the other two factors.
4.2. Identifying Workers’ Background Influence on Safety Climate
All six proposed hypotheses were tested by using one-way ANOVA to investigate the relationship of workers’ background towards safety climate.
(1) Workers’ gender and safety climate
When workers were categorized based on gender, one-way ANOVA results in
Table 7 show that the workers’ perception of safety communication statistically differs between females and males, with the females’ average value of 4.215 vs the males’ average value of 4.051.
(2) Workers’ job position and safety climate
Workers were categorized into four groups based on job position (executives, middle managers, site supervisors, frontline workers). One-way ANOVA results show that for all three safety-climate factors, there are differences among workers’ job position. As can be seen in
Table 8, there is an indication that safety-climate scores decrease when the job position lowers from the executive group to the frontline workers’ group.
Since the one-way ANOVA test was significant, a post-hoc test was conducted to perform a pairwise comparison among the four groups to confirm the findings. While pairwise comparisons showed a mixed result among the four different groups, it did strongly indicate that for all three safety-climate factors, group 1 (executive) has a higher score than the scores of group 4 (frontline worker).
(3) Workers’ job attribute and safety climate
Workers were categorized into seven groups based on job attribute (operation, maintenance, R&D, transport and storage, industrial safety and environmental protection, administrative management, and other). One-way ANOVA results in
Table 9 show that for any of the three safety-climate factors, there are no significant differences among the job attribute groups.
(4) Workers’ organization vs safety climate
Workers were categorized into six groups based on organization (main company, satellite company 1–satellite company 5). One-way ANOVA results in
Table 10 show that for all three safety-climate factors, there are significant differences among the six organization groups.
Since the one-way ANOVA test was significant, a post-hoc test was conducted to perform a pairwise comparison among the six groups. While pairwise comparisons showed a mixed result among the six different groups, they did strongly indicate that for all three safety-climate factors, which include safety perception, safety communication and safety-management systems, group 2 (satellite company 1) has the highest scores compared to group 1 (main company), group 3 (satellite company 2), group 5 (satellite company 4) and group 6 (satellite company 2). On the other hand, group 3 (satellite company 2) has the lowest scores compared to group 1 (main company), group 2 (satellite company 1) and group 5 (satellite company 4).
(5) Workers’ status and safety climate
Workers were categorized into two groups based on workers’ status (main-company workers, satellite-company workers). One-way ANOVA results in
Table 11 show that for safety perception and safety-management systems, main-company workers differ from the satellite-company workers. While satellite-company workers had higher safety-perception scores, their safety-management systems scores were actually lower.
(6) Number of training sessions and safety climate
Workers were categorized into four groups based on the number of in-house training sessions they received from the qualified safety and hygiene professionals (never, once, twice, three times, four times or more). One-way ANOVA results in
Table 12 show that for all three safety-climate factors, there are significant differences in terms of the number of training sessions. For the safety-management system, the safety-climate score increases with the number of training sessions received.
Since the one-way ANOVA test was significant, a post-hoc test was conducted to perform a pairwise comparison among the six groups. While pairwise comparisons showed a mixed result among the five different groups, they did strongly indicate that for all three safety-climate factors, group 1 (no training) has a lower score than group 4 (three-times training) and group 5 (four-times-or-more training).