International Journal of Business Management (IJBM)
Volume 1 Issue 2 2016
The Effect of Safety Training and Workers Involvement on Healthcare Workers Safety Behavior: The Moderating Role of Consideration of Future Safety Consequences Munir Shehu Mashia, Chandrakantan Subramaniama*, Johanim Joharia a
School of Business Management, Universiti Utara Malaysia, Malaysia
Keyword Safety training Workers involvement Consideration of future safety consequences Safety compliance Safety participation Nurses
ABSTRACT This paper proposes that consideration of future safety consequences (CFSC) would moderate the relationships between safety training and workers involvement on healthcare workers (HCWs) safety behaviors (safety compliance and safety participation). Survey data was obtained among 229 nurses from Abuja secondary health facilities, Nigeria. SmartPLS 3.0 was applied to test the hypotheses that comprised both the direct effect of safety training and workers involvement on safety participation and safety compliance and moderating role of CFSC on these relationships and consequently bootstrapping was conducted to investigate the standard error of the estimate and t-values. The findings showed that safety training positively relates to safety compliance and safety participation and workers involvement positively relates to safety compliance and participation. Furthermore, CFSC moderates the relationships between workers involvement and safety compliance. The research provides empirical evidence on the significance of CFSC as moderator. This contributes to the utility of Social Exchange Theory (SET) and Construal Level Theory (CLT). Furthermore, in order to achieve an optimally safe hospital environment, hospital management should provide employees with safety training and involve them in the safety activities and consider individual CFSC when making decisions on how to improve hospital safety.
*Corresponding Author. Email address:
[email protected] 1.
INTRODUCTION Organizational accidents and injuries cause huge amount of employee’s lives
and property damages every year (Zhou & Jiang, 2015). The hidden costs may range from four to five times the direct costs (Heinrich et al., 1980). Work-related 46
injuries and accidents are the major concern for nurses in various hospitals, given the risky nature of hospitals environment (Nixon et al., 2015). Nurses frequently encountered with daily hazards which includes: physical, biological, and chemical hazards (Nixon et al., 2015). Physical hazards range from environmental conditions that may resulted to falls, cuts or electrical shocks. Biological hazards on the other hand, range from exposure to blood-borne pathogens such as HIV/AIDS, bacteria, hepatitis, and tuberculosis among others as a result of injecting patients, drawing, or suturing of blood from the patients (Perry, Parker & Jagger, 2003). Chemical hazards includes nurse’s contact with hazardous agents ranging from carcinogens, corrosives and toxic (Ford & Wiggins, 2012). According to American Nurses Association (2011), in the year 2011 alone, 40% of hospitals nurses reported occupational injuries. In financial term, annual back injuries alone has been projected to cost 16 billion dollars in medical treatment, worker’s compensation benefits, employee turnover costs due to injuries (White, 2010). Nigeria is not immune to these issues (Akinwale & Olusanya, 2015; Aluko et al., 2016; Mashi, 2014). For example, a report from the Federal Capital Territory Administration (FCTA) reported over 100 HCWs recently suffered from needle stick injuries, Hepatitis B and HIV/AIDS due to exposure to healthcare waste (Adejoro, 2014). As Nigeria is aspiring to achieve its Vision 20:2020, the Vision reflects the country to be among the world leading economy in the year 2020 (National Planning Commission, 2010), occupational injuries and diseases that may impair productive citizens deserves special attention. Due to the high cost of injuries highlighted above, occupational safety researchers and practitioners have identified the importance of safety training, — which involves the acquisition of knowledge and skills that improve employee safe behavior in the hospitals (Vredenburgh, 2002). This construct is regarded as an important leading indicator of safety (e.g., Beus, McCord & Zohar, 2016; Christian, Bradley, Wallace & Burke, 2009; McGonagle et al., 2016; Shea et al., 2016; Sinelnikov, Inouye & Kerper, 2015; Zohar, 2010), and increase employee positive safety behaviors (Cooper & Phillips, 2004; Neal & Griffin, 2004). 47
Another important leading safety indicator is workers’ involvement in to safety (Beus et al., 2016; Christian et al., 2009), —which involves the behavior-based technique which includes employees in an upward information flow and safety decision process (Vredenburgh, 2002). In safety management literature, there is a call to incorporate other variables to moderate organizational factors with employee safety behavior (Christian et al., 2009; Foster & Nichols 2015; Mickey et al., 2015; Zohar, 2010) due to the inconsistency in the findings (Ismail, Asumeng & Nyarko, 2015; Vinodkumar & Bhasi, 2010). This inconclusiveness in safety literature concerning these relationships calls for more research to examine possible moderators to explain these relationships (Baron & Kenny, 1986). Therefore, to identify possible constructs that can moderate these relationships is significance to practitioners to use to reduce hospital injuries. This paper addresses this research gap by investigating an important personality variable potentially vital that may elicit the relationship between safety training, workers involvement and healthcare workers safety behavior— consideration of future safety consequences (CFSC)—which Probst, Graso, Estrada and Gree (2013) define as the “degree to which employees consider the future versus immediate consequences of their safety-related behaviors” (p. 125). Specifically, in this paper we investigate the moderating effects of CFSC on relationships between safety training, workers involvement and healthcare workers safety behavior (safety compliance and safety participation) among nurses in Abuja secondary health facilities in Nigeria. We argued in this paper that CFSC will moderate these relationships because of the following reasons: firstly, the extent literature confirmed that consideration of future consequences (CFC) has an influence on the workers behavior of violating the organizational rules and procedures (Takemura & Komatsu, 2013). Secondly, recent empirical study has presented that individual high in CFC reported higher intentions and sustain volunteerism (helping) others in the organization (Maki, Dwyer & Snyder, 2016). Therefore, we argued that by integrating these constructs will provide additional evidence to practitioners on how to improve safety 48
compliance and participation in the organization. Such that, the relationships between safety training, workers involvement and healthcare workers safety behavior is expected to be stronger for the healthcare workers high in CFSC than for those employees who are low in CFSC. In doing so, we advance the general understanding in safety literature and contributes to safety management research, and we offer additional information on the functioning of CFSC as an important variable for hospital managements to use to improve healthcare worker safety. Hence, the objectives of this study therefore are twofold: to examine the influence of safety training and workers involvement on healthcare workers safety behavior and to assess the moderating role of CFSC on the relationships.
2.
LITERATURE REVIEW
2.1 SAFETY PERFORMANCE In extent literature, due to the dearth of measures to assess the effectiveness of organizational safety programs (Glendon & Litherland, 2001), no agreement is reached on the actual safety performance components (Fernández-Muñiz et al., 2007). Historically, to assess safety performance, studies focused on the direct safety performance outcomes such as employees compensation cost, injuries frequency among others (Moore & Viscusi, 1989). Nevertheless, these measures were recognized as a poor measures of safety (Glendon & Mckenna, 1995) because they were inadequately sensitive, retrospective and in some cases risk exposure is ignored. These outcomes are occasional and thus, forms a skewed distribution (Christian et al., 2009). Additionally, the high rates of under-reporting among the industrial players in Nigeria (Tandberg et al., 1991) has resulted in suggesting that safety performance outcomes recorded by the hospitals are too unreliable to understand hospital safety (Cooper, 2000). Due to the inadequacy of injuries and accident data highlighted above, many researchers used safety behavior as the dependent variable in an effort to understand safety performance (Barbaranelli, Petitta & Probst, 2015). Safety behavior “refers to the employee rational reactions to dangerous external stimuli which conform to 49
safety procedures to achieve the desired security objectives” (Zhang, Li & Zuo, 2015, p. 984). In other words, it is defined as “the safety-related actions or behaviors that workers exhibit in almost all types of work to promote their safety and that of others” (Burke & Signal, 2010, p. 3). Beus, McCord and Zohar (2016) defined safety performance behavior “as any workplace behaviors that affect the likelihood of physical harm to persons” (p. 3). Employee safety compliance and participation are the main components of safety performance behavior used in Griffin and Neal (2000) model that described the actual behaviors that workers exhibit in the workplace (Griffin & Neal, 2000). Safety compliance is defined as “generally mandated” behaviors (Neal, Griffin & Hart, 2000, p. 101) which they drawn from the two main components of general job performance from the work of Borman and Motowidlo (1993)—task performance and contextual performance—safety compliance was used as task performance and therefore refers to the core activities that workers carry out to preserve safety at work. These behaviors includes following standard work procedures or wearing personal protective equipment (Neal & Griffin, 2006). Workers safety participation, on the other hand is defined as behaviors “frequently voluntary” (Neal, Griffin & Hart, 2000, p. 101). In other words, are behavior “that may not directly contribute to workplace safety, but that do help to develop an environment that supports safety” (Griffin & Neal, 2000, p. 349) and can be associated to safety improvement. These safety behaviors includes voluntarily participating in safety activities, attending safety meetings, or helping colleagues with safety-related matters (Neal & Griffin, 2006).
2.2 SAFETY TRAINING Various antecedents were empirically tested in an effort to understand safety performance across various work setting. For instance, Hayes, Perander, Smecko, and Trask (1998) and Lee and Dalal (2016) explored how safety climate and culture were important in predicting workers safety performance in the organizations. Additionally, in their model, Griffin & Neal (2000) regarded safety knowledge and 50
safety motivation as proximal factors that have a positive relationship with workers safety behavior. Safety leadership was also found to have a positive relationship with workers safety behavior (Smith, Eldridge, & DeJoy, 2016). Other study used individual characteristics such as personality and age differences (e.g., Siu, Phillips & Leung, 2003), level of education (Gyekye & Salminen, 2009) among others. Training is “refers to instruction and practice for acquiring skills and knowledge of rules, concepts, or attitudes necessary to function effectively in specified task situations” (Cohen, Colligan, Sinclair, Newman & Schuler, 1998, p. 11). Safety training is an important risk prevention and control strategies to guarantee every employee is safe in a good workplace conditions (Cohen, 1998). Safety training is defined as “instruction in hazard recognition and control measures, learning safe work practices and proper use of personal protective equipment, and acquiring knowledge of emergency procedures and preventive actions” (Cohen, 1998, p. 11). Safety training has been recognized as an important organizational characteristic distinguishing organization with successful safety program (Zohar, 1980), and is an effective means for employees to enhance their skills and knowledge of safety in the organizations (Shea et al., 2016). Literature in occupational safety supports the view that safety training is a key factor in maintaining and changing workers attitude toward safety (Ali et al., 2009; Boughaba, Hassane & Roukia, 2014; Donald & Cantre, 1994; Keffane, 2014; Mearns, Whitaker & Flin, 2003; Vinodkumar & Bhasi, 2010; Zohar, 1980). Organizations can improve workers safety behavior via keeping them aware of health and safety practices through seminars, workshops, training on the job among others (Mearns, Hope, Ford & Tetrick, 2010). Study conducted in the US among the representatives of 57 projects summited that higher safety performance is attained with safety training (Hinze, Hallowell & Baud, 2013). Similar studies also found that companies can transfer safety knowledge through workers orientation, toolbox talks, and training sessions among others (Hallowell, 2012; Lu & Yang 2011; Vredenburgh, 2002). In addition, meta-analytic findings show that perceptions of safety training positively related to safety compliance and participation (Christian et 51
al., 2009). Meta-analysis studies also reported strong empirical evidence of the effectiveness of safety training on employees’ safety behaviors (Ricci, Chiesi, Bisio, Panari, & Pelosi, 2016; Robson et al., 2012). Taken together, there are clear evidence in the literature that workers perception of safety training is significantly related to workers safety behaviors. Based on the above submission, empirical evidence suggests that safety training is important in understanding worker’s safety compliance and participation. Therefore, we hypothesized that: Hypothesis 1a: Safety training is positively related to safety compliance. Hypothesis 2a: Safety training is positively related to safety participation.
2.3 WORKERS INVOLVEMENT IN TO SAFETY Various antecedents were empirically tested in an effort to understand safety performance across various work setting. For instance, Hayes, Perander, Smecko, and Trask (1998) and Lee and Dalal (2016) explored how safety climate and culture were important in predicting workers safety performance in the organizations. Additionally, in their model, Griffin & Neal (2000) regarded safety knowledge and safety motivation as proximal factors that have a positive relationship with workers safety behavior. Safety leadership was also found to have a positive relationship with workers safety behavior (Smith, Eldridge, & DeJoy, 2016). Other study used individual characteristics such as personality and age differences (e.g., Siu, Phillips & Leung, 2003), level of education (Fernández-Muñiz et al., 2009; Gyekye & Salminen, 2009) among others. Employee involvement is a vital factor in the organization safety program used to reduce injuries and accidents (Vinodkumar & Bhasi, 2010). Employee involvement is the “extent employees could influence and control OHS management issues at the workplace” (Masso, 2015, p. 64). In other words, employee’s involvement into safety management process involves upward communication flow among individuals or groups and decision-making process within the organization (Vredenburgh, 2000) because employees use to make suggestions about safety improvements, especially when new technologies and 52
materials were introduced (Butler & Park, 2005). This factor is regarded among the important indicator of positive organizational safety culture because is the best ways to achieve safety ownership (Cooper, 1998; Ford & Tetrick, 2011; Liu, Bartram, Casimir & Leggat, 2014). Employee’s involvement is a fundamental element of safety management since it help organization to achieve main objectives and goal of occupational safety and health implementation and improvement in organizational safety conditions for the benefit of both employees and organizations (Podgórski, 2005). High employee’s involvement in the organization’s strategic safety decisions can reduce lost-time frequency rates (LTFR) (Shannon et al., 1996). Employee’s involvement was examined to lower the frequency of unsafe behavior and injuries in the organizations (Camuffo, De Stefano & Paolino, 2015; Rooney, 1992). Within the hospital environment, Garrett and Perry (1996) found that employee’s involvement in to safety decisions reduced injuries effectively within one year. Vinodkumar and Bhasi (2010) reported employee involvement significantly related with safety participation. Keffane and Delhomme (2013) also reported employee involvement predicts safety compliance in a study aimed to understand the performance of road safety practices in France. Based on the above submission, empirical evidence suggests that employee’s involvement is important in understanding employee’s safety compliance and participation. Therefore, we hypothesized that: Hypothesis 1b: Employee’s involvement is positively related to safety compliance. Hypothesis 2b: Employee’s involvement is positively related to safety participation.
2.4 CONSIDERATION OF FUTURE SAFETY CONSEQUENCES Consideration of future consequences (CFC) is an individual differences variable that explain how individuals differ in the extent to which they consider distant versus immediate consequences of their potential behaviors. CFC is defined as “The extent to which people consider the potential distant outcomes of their 53
current behaviors and the extent to which they are influenced by these potential outcomes” (Strathman, Gleicher, Boninger & Edwards, 1994, p. 743). Relative to low CFC individuals, individuals high in CFC reported less use of alcohol and tobacco (Strathman et al., 1994), exercise regularly (Ouellette et al., 2005), less aggression (Joireman et al., 2003) participate in pro-environmental behavior (Joireman et al., 2001), high academic performance (Peters, Joireman & Ridgway, 2005) among others. Probst et al. (2013) extended the concept to safety and define consideration of future safety consequences (CFSC) as the “degree to which employees consider the future versus immediate consequences of their safetyrelated behaviors” (Probst et al., 2013, p. 125) and is related to various safety outcomes (Probst et al., 2013). We argue in this paper that consideration of future safety consequences (CFSC) would provide additional explanation on what boundary condition safety training and employee involvement can influence safety compliance and participation. Therefore, the following hypothesis are advanced: Hypothesis 3a:
The positive relationship between safety training and safety
compliance will be stronger when consideration of future safety consequences is high. Hypothesis 3b: The positive relationship between workers involvement and safety compliance will be stronger when consideration of future safety consequences is high. Hypothesis 3c: The positive relationship between safety training and safety participation will be stronger when consideration of future safety consequences is high. Hypothesis 3d: The positive relationship between workers involvement and safety participation will be stronger when consideration of future safety consequences is high.
54
2.5 CONCEPTUAL FRAMEWORK AND UNDERLINING THEORIES This paper conceptualized that safety training and workers involvement which are the independent variables influence the healthcare workers safety behavior (safety participation and compliance). Also the CFSC is expected to moderate these relationships. These relationships are shown in Figure 1 below. The framework is underpinned by two theories i.e Social Exchange Theory (SET) (Blau, 1964) and Construal Level Theory (CLT) (Liberman & Trope, 1998). The SET “is one of the most influential conceptual paradigms for understanding workplace behavior” (Cropanzano & Mitchell, 2005, P. 874). The primary tenets of this theory is the reciprocity of commitments between employees and employer over time (Blau, 1964). When an organizations exhibits a readiness to make workplace safe and healthy, the employee oblige by engaging in desirable behavior such as high compliance with work procedures and reducing undesirable behavior such as unsafe behavior. In this paper, SET is theoretically applied to explain the direct relationships between safety training, workers involvement and healthcare workers safety behavior (Neal & Griffin, 2006). When hospital cares for their workers safety (i.e., the hospitals give workers training and involve them in to safety decision processes), the workers are likely to develop tacit obligations to perform their duties, using behavior beneficial to the hospitals. When hospital management offers adequate training to the workers, the HCWs would accordingly carried out their responsibilities efficiently and safely, which then results in better safety performance. On the other hand, Construal Level Theory (CLT) (Liberman & Trope, 1998) posits that employees have distinctive psychological links with events and objects grounded on perceived social and temporal distances, taking along a remarkable wrinkle to the discussion of individual safety behavior. According to this theory, people construe distant future events using abstract representations. In contrast, people who choose their behavior thinking only about immediate events using concrete term (Trope & Liberman, 2010). This theory (Liberman & Trope, 1998) is widely used in an effort to understand individual’s decision over time in the area of 55
psychology (e.g., Fujita, & Sasota, 2011). Drawing from CLT (Trope & Liberman, 2010), this paper identify CFSC as plausible moderator that permit further examination of safety training, workers involvement and safety behavior relationships. Drawing from CLT theory, the study proposes that CFSC can play an important role theoretically in explaining the moderating effects of CFSC on safety training, workers involvement and safety behavior in that healthcare workers framed their safety behavior in two different ways (i.e high-level vs. low-level construal) (Liberman & Trope, 1998). Those with low-level construal frame their safety behavior after immediate consideration (low-CFSC workers) while healthcare workers with high-level construal is expected to frame their safety behavior weighing at the future considerations.
Fig 1. Conceptual Framework 3.
METHODOLOGY
3.1 SAMPLE AND DATA COLLECTION The research methodology employed in this study was quantitative research method using questionnaires to test the conceptual model. The study covered 12 secondary health facilities with total population of 1063 nurses and the required samples sizes is 278 based on Krejcie and Morgan (1970) table of sample determination. Four health facilities were randomly selected using cluster sampling technique (the type of probability sampling) using the recommendation of Gay and 56
Diehl (1992) five steps technique of selecting clusters with the total number of 317 nurses. Therefore, all the 317 nurses in these four facilities were responded to the questionnaire. Of the 317 questionnaire distributed, 229 valid questionnaires were returned and used which make the response rate of 72%. The 229 response is enough for this study going by the G*power requirement, the minimum sample size of 153 is required. Since the model had a 3 predictors and 4 interactions, we set the effect size as medium (0.15) and required power of 0.95. The data was collected by the researcher and the assistance of two research assistance. This study was approved by the health and human services of the FCT. A cover letter was attached to the questionnaire informing the nurses of the study goal. Respondents were informed that participation was voluntary and that anonymity was guaranteed.
3.2 DATA ANALYSIS TECHNIQUE The study employed Partial Least Square Structural Equation Modeling (PLS SEM) SmartPLS 3.0 software (Ringle et al., 2015) to compute both the measurement and structural models (Anderson & Gerbing, 1988). The rationales for using PLS are: PLS path models are estimate with a small sample and with nonnormal data (Haenlein & Kaplan, 2004). PLS has the likelihood of providing accurate computations of moderating effect because its accounts for error (Helm, Eggert & Garnefeld, 2010). The two-step technique as recommended by Anderson and Gerbing (1988) and suggestion of Hair et al. (2011), the bootstrapping technique (5000 resample) was also used to ascertain the significance levels for the path coefficient.
3.3 MEASURES Six items adapted from Vinodkumar and Bhasi (2010) were used to measure safety training. Internal consistency reliability of the items was 0.82. Sample items include: “Newly recruits are trained adequately to learn safety rules and procedures” and “safety training given to me is adequate to enable me to assess hazards in the workplace”. Four items adapted from Vinodkumar and Bhasi (2010) 57
were used to measure employee involvement. The internal consistency reliability of the items was 0.69. Sample items include: “Management always welcomes opinions from employees before making final decisions on safety-related matters”, and “my company has safety committees consisting of representatives of management and employees”. Four items adopted from Neal and Griffin (2000) were used to measure safety compliance. The items reported internal consistency reliability of 0.94. Sample items include: “I carry out my work in a safe manner” and “I use all the necessary safety equipment to do my job”. Four items adopted from Neal and Griffin (2000) were used to measure safety participation. The items reported internal consistency reliability of 0.89. Sample items include: “I promote the safety program within the organization” and “I voluntarily carry out tasks or activities that help to improve workplace safety”. Six items adapted from Probst et al. (2013) were used to measure CFSC. The items reported internal consistency reliability of 0.71. Sample items include: “Even though accidents reporting can take a lot of time and effort, it helps other workers in the future” and “I sometimes need to compromise safety in order to meet service delivery”. All the items in this section were measured using five-point Likert scale ranging from 1= strongly disagree to 5= strongly agree.
4.
RESULTS AND ANALYSIS
4.1 RESPONDENTS’ PROFILE Based on the demographics characteristics of the respondents, majority of the respondents are females 157(68.6%) while male consisted of 72 (31.4 %). Majority of the respondents were of Hausa ethnic group 59 (29.8%), followed by Yoruba ethnic group 51 (22.3%). Majority of the respondents are nursing 11 in term of designation. Majority of the respondents are married 169 (73.8%). Also majority of the respondents have nursing certificates 142(62%). The mean age and standard deviation of the respondents were (M=14.67 SD=9.82). The respondents’ mean years of experience and standard deviation as healthcare worker were (M=14.67
58
SD=9.82). The respondents mean organizational tenure and standard deviation were (M=4.67 SD=2.31).
4.2 DESCRIPTIVE STATISTICS Table 1 presents the descriptive statistics, including the constructs means and standard deviations and the reliability of the variables for descriptive purposes. As presented in Table 1 the mean value of all the constructs ranged between 3.198 and 4.138. Composite reliabilities also ranged between 0.835 and 0.921 demonstrating high reliability for all the variables in this study (Hair et al., 2014). Similarly, Cronbach's Alpha value also ranged between 0.705 and 0.880 demonstrating high reliability for all the variables (Hair et al., 2014). Table 1: Mean, Standard deviation and Reliability of the Study Variables Variable Mean Standard Composite Cronbach's deviation Reliability Alpha Safety Compliance Safety Participation Safety Training Workers involvement Consideration of Future Safety Consequences
3.256 3.975 3.258 3.198 4.138
0.784 0.566 0.927 0.907 0.546
0.835 0.854 0.921 0.876 0.917
0.705 0.743 0.896 0.788 0.880
4.3 COMMON METHOD VARIANCE Common method variance (CMV) in a study occur when two or more selfreported measures are acquired from the same respondents at the same point of time, the relationship between the constructs may be influenced by CMV(Podsakoff et al., 2003). This type of variance is attributed to the measurement method rather than the constructs. In this study, CMV was assessed (Podsakoff et al., 2003) using Harman’s (1976) one-factor test principle component factor analysis. The rotation shows that common method bias is not an issue in this study. No single factor accounted for more than 50% of the variance (Podsakoff et al., 2003). The first factor accounted for 31.7 percent of the variance. 59
4.4 MEASUREMENT MODEL EVALUATION To evaluate the measurement model in this paper, two types of validity were assessed. Firstly, we assessed the convergent validity and secondly, discriminant validity was assessed. Convergent validity is determined by examining the composite reliability, loadings and average variance extracted (AVE) (Gholami et al., 2013). As reported from Table 2 below, each construct has achieved the loadings above 0.7, Composite reliability (CR) of all the constructs were all higher than 0.7 and Average variance extracted (AVE) is above 0.5 as recommended by Hair et al. (2014) (see Table 3).
Table 2: Convergent Validity Constructs Items Loadings Consideration of Future Safety Consequences CFSC2 0.894 CFSC3 0.837
Safety Compliance
Safety Participation
Safety Training
Workers Involvement
CFSC5 CFSC6 COM1 COM2 COM4 PAR2
0.867 0.827 0.82 0.775 0.783 0.79
PAR3 PAR4 STR1 STR2 STR3
0.833 0.816 0.891 0.815 0.763
STR4 STR5 STR6
0.887 0.795 0.718
WKI1 WKI3
0.797 0.835
WKI4
0.881
AVE 0.734
CR 0.917
0.629
0.835
0.661
0.854
0.663
0.921
0.703
0.876
Note: AVE = average variance extracted CR= Composite reliability
The discriminant validity (the extent to which items measure distinct concepts) was assessed following the Fornell and Larcker (1981) criterion by comparing the 60
square root of the AVE with the correlations among constructs (see Table 3). As shown from Table 3, the square root of the AVEs (values in bolded) on the diagonals were greater than the corresponding row and column values indicating the measures were discriminant.
Constructs
Table 3: Discriminant Validity Fornell-Larcker criterion 1 2 3
4
1. CFSC
0.857
2. COM
0.058
0.793
3. PAR
0.234
0.305
0.813
4. STR
0.024
0.684
0.405
0.814
5. WKI
-0.035
0.68
0.315
0.657
5
0.838
Note: Diagonals (in bold) signify the average variance extracted whereas the other entries represent the squared correlations CFSC = Consideration of Future Safety Consequences COM= Safety Compliance Par = Safety Participation STR= Safety Training WKI= Workers Involvement
In addition to Fornell and Larcker (1981) criterion, the HTMT ratio was examined as this criterion is regarded to be a more reliable criterion for evaluating discriminant validity than the Fornell–Larcker criterion (Henseler et al. 2014; Henseler, Ringle, & Sarstedt, 2015). The HTMT criterion in this study shows that discriminant validity is achieved. The highest correlation found is between safety training and workers Involvement 0.78, which is within the conventional yardstick of 0.85 (Henseler et al., 2015) as shown in Tables 4. Therefore, both the two types of validity in this study were achieved. Table 4: Discriminant Validity Heterotrait-monotrait ratio (HTMT) 1
2
3
4
5
1. CFSC 2. COM 0.136 3. PAR 0.286 0.419 4. STR 0.077 0.69 0.494 5. WKI 0.069 0.71 0.409 0.78 Note: CFSC = Consideration of Future Safety Consequences COM= Safety Compliance Par = Safety Participation STR= Safety Training WKI= Workers Involvement 61
4.5 STRUCTURAL MODEL EVALUATION Since the measurement model above is achieved in term of reliability and validity, we evaluated the structural model to assess the hypothesized relationships among the variable in this study (Hair et al., 2014). As presented in Table 4 and Figure 2 below, we evaluated the standardize beta values and the t-values (Hair et al., 2014). The t-values were obtain using bootstrapping procedure with 5000 resamples. In addition, we also calculated the predictive relevance (Q 2) of the model and effect sizes of each predictors on the dependent variables (f 2) (Hair et al., 2014). Figure 2 and Table 4 show the estimates for the full structural model, which includes moderator variable (i.e., CFSC) in this study. All relationships in this study are represented by standardized beta values. Additionally, in testing the relationships of the structural model, the significance level was set at p Safety Participation Workers Involvement -> Safety Participation CFSC*Safety training -> Safety Compliance
0.408
0.079
0.402
0.085
0.301
0.077
0.125
0.077
0.027
0.088
CFSC*Worker Involvement -> Safety Compliance CFSC*Safety Training -> Safety Participation
0.159
0.092
0.122
0.174
CFSC*Worker involvement -> Safety Participation
0.080
0.117
1b 2a 2b 3a
3b 3c
3d
tvalu e 5.13 8 4.72 1 3.91 3 1.62 4 0.30 5
Pvalue
Decision
0.000 *** 0.000 *** 0.000 *** 0.052 * 0.380
1.72 5 0.70 0
0.043 ** 0.242
0.68 4
0.247
Supporte d Supporte d Supporte d Supporte d Not Supporte d Supporte d Not Supporte d Not Supporte d
Note: ***p < 0.01 **p
