Influence of Risk Perception on Task and Contextual Performance: A Case of Work-Related Musculoskeletal Disorders in Nurses

Abstract

Assessing the risk perception of workers can be very informative in predicting their behavioral outcomes, including task and contextual performance. Yet, research to assess the effect of risk perception on task performance and contextual performance remains scarce. Thus, this study explored the effect of risk perception of work-related musculoskeletal disorders on task and contextual performance in nurses. This study further examined safety behavior as a mediator of these relationships. Using structural equation modeling, the researchers examined these relationships by employing a cross-sectional survey with a quantitative approach. The data was collected via questionnaires from 382 nurses who work in three major hospitals in Accra Metropolis, Ghana. The results showed that nurses’ risk perception positively influenced their task and contextual performance, and that safety behavior partially mediated the effects of risk perception on task and contextual performance. This study offers a theoretical framework and empirical evidence for the concept of risk perception and its association with safety behavior, task, and contextual performance. To the best of our knowledge, this is the first study to assess the relationships that exist among these variables. Thus, future studies are needed to verify the causality of the relationships.

Keywords

risk perception task performance contextual performance work-related musculoskeletal disorders nurses

Introduction

Work-related musculoskeletal disorders (WMSDs) originate from work tasks such as handling of heavy machines, exertion, and pulling. Usually, when these activities are done repeatedly over a long period, they can cause injuries to different parts of the body (The National Institute for Occupational Safety and Health (NIOSH), 2012). The nursing population constitutes about 33% of the hospital workforce at high risk of WMSDs (Koepsell, 1992; Stubbs et al., 1983; Videman et al., 1984), possibly due to patient handling, repetitive movements, and awkward postures held for a long period (Campo et al., 2008; S. Yang et al., 2018). In Asia, the prevalence rates for Thailand, China, and Saudi Arabia were 61.5% to 91.7%, 78.6%, and 85% respectively (Attar, 2014; Thinkhamrop et al., 2017; Yan et al., 2017). In Europe, the prevalence rates for France, Estonia, and Portugal were 10% to 50%, 70%, and 89% respectively (Freimann et al., 2016; Pelissier et al., 2014; Ribeiro et al., 2017). In the Americas, it ranged from 35.1% to 47% in the USA (Trinkoff et al., 2002) and from 32.8% to 57.1% in Brazil (Fonseca & Fernandes, 2010). In Africa, it was 80.8% in Uganda (Munabi et al., 2014), 68.9% in Zambia (Nkhata et al., 2015), and 78% in Nigeria (Tinubu et al., 2010). In Ghana, a study reported a prevalence of 78.5% WMSDs in nurses (Abledu & Offei, 2015). This situation in Ghana is presumably due to the fact that there is an estimation of the nurse to population ratio at 1:1,000 in the health sector (Appiah-Denkyira, 2015). This shortage in nursing staff, with an increasing number of people seeking medical care from major hospitals, has resulted in prolonged working hours or intense schedules; thus exposing the nurses to WMSDs.

There is a body of research on risk perception of WMSDs in nurses (Abedini et al., 2014; Kim & Lee, 2019; Torres et al., 2010; L. Yang et al., 2016). However, only a small part of this research focuses on the risk perception of WMSDs to nursing performance (Garosi et al., 2020). Risk perception is often considered as a subjective assessment of the likelihood of experiencing an accident or disease caused by exposure to a source of risk (Rundmo, 2000). Factors like the individuals’ knowledge, experience, values, attitudes, and emotions can affect the thinking and judgment about the seriousness and acceptability of risks (Wachinger et al., 2013). Risk perception plays an essential role in individual behavior. In fact, a study revealed that there is a causal association between workers’ perception of occupational risks and their behavior while working (Stewart-Taylor & Cherrie, 1998), and that the workers’ level of risk perception influenced them to adopt safety actions. This supports the view that risk perception is informative in the prediction of employees’ behavioral outcomes (Burke et al., 2011).

Likewise, several studies (Bagnasco et al., 2020; Polgar, 2000; Trevino et al., 2018) support a causal relationship between risk perception and behavior at work in nurses. L. Yang et al. (2016) noted that risk perception of occupational HIV exposure was positively associated with attitudes of self-protection. Jemmott et al. (1992) also proposed that nurses’ risk perception of HIV infection was positively related to their intention to avoid AIDS patients care. However, Polgar (2000) asserted that occupational HIV risk perception was inversely associated with willingness to care for patients with AIDS. Furthermore, a study concluded that nurses’ risk perception of WMSDs was inversely related to their performance at work (Abedini et al., 2014), though this study did not specifically look at nurses’ risk perception of WMSDs and their performance at work.

Greenslade and Jimmieson (2007) have categorized nursing performance into task versus contextual performance. Task performance is defined as behaviors that contribute directly to the organization’s technical core, as well as those activities that are typically and officially recognized as part of a workers’ job description (Coleman & Borman, 2000). It is also associated with the use of technical skills and job-specific knowledge to accomplish mandatory duties. In nursing performance, task performance entails behaviors related to technical care. It includes administering medications and treatment to patients as well as supporting patients with daily living activities. Contextual performance, on the contrary, refers to those behaviors that maintain the broader social environment in which the technical core must function. It includes more discretionary behaviors that assist the organization to function (Borman & Motowidlo, 1993). It does not specifically fall under the job description but supports the organization in achieving its task-specific roles. For instance, in contextual performance, nurses suggest innovative ways through which the overall quality of the hospital can be improved as well as volunteer to be part of committees within the hospital.

Greenslade and Jimmieson (2007) suggest that in assessing nursing performance, it is suitable to assess both task and contextual performance, yet few studies (Aboagye et al., 2020; Chen et al., 2019; Tong, 2018) have examined task performance concurrently with contextual performance in nurses. Therefore, this present study is a novel attempt to address this gap in the literature by using structural equation modeling to evaluate the effect of the nurses’ risk perception of WMSDs on their task and contextual performance. The study further examines whether the nurses’ risk perception can prompt them to adopt a safety behavior and whether their safety behavior relates to their task and contextual performance. If these predictions are supported, then the third objective is to examine the mediation effect of safety behavior on the risk perception-performance relationship.

Theoretical Background, Hypotheses, and Research model

While research examining the effect of risk perception on safety behavior has proliferated in the past years (Arezes & Miguel, 2008; Ji et al., 2011; Lu & Yan, 2013), a dearth of information exists on how risk perception affects performance through safety behavior. The present paper uses the Psychometric paradigm of risk perception, the Performance theory, and a model on safety behavior to conceptualize a framework to explain how risk perception may enhance job performance through safety behavior.

This study adopts the psychometric paradigm of risk perception to assess the factors that contribute to risk perception of WMSDs in nurses. The psychometric paradigm discusses risk perception as a concept categorized under multiple characteristics. Fischhoff (1978) investigated nine risk characteristics in his earlier study. Consequently, other studies have increased the aforementioned characteristics to include characteristics such as benefits vs. risks, information value (Benthin et al., 1993), and vulnerability (Portell et al., 2014). This paradigm directly addresses the psychological risk dimensions that affect judgments of physical, environmental, and material risks in ways that go further than their objective outcomes (Fischhoff, 1978; Slovic et al., 1986).

This study also adopts Borman and Motowidlo’s (1993) Performance Theory to provide a clear theoretical basis for assessing the nurses’ performance at work. These researchers categorized performance at work into a “two-factor theory” that consists of task and contextual performance. They considered behaviors as task performance or contextual performance based on the characteristics of the behaviors themselves.

Furthermore, this study adopts Neal and Griffin’s (2006) model on safety behavior, to show a distinction between two types of safety behavior: safety compliance and safety participation. Safety behavior is often seen as closed and opened actions that are adopted by the individual to prevent feared outcomes and maintain a sense of safety (Lümker, 2012). The safety compliance aspect of this is associated with a person’s core activities that need to be carried out to maintain safety at the workplace. These activities involve adhering to standard work procedures and wearing personal protective equipment (Neal & Griffin, 2006). Safety participation, on the other hand, is associated with behaviors that are not related directly to the individual’s personal safety but help in developing a safe environment. These behaviors involve assisting workmates with safety-related issues and partaking in voluntary safety activities (Neal & Griffin, 2006).

Hypotheses Development

Risk perception, task, and contextual performance

Several studies have assessed the connection between risk perception and safety performance (Jahangiri et al., 2013; Rodrigues et al., 2015); nonetheless, the connection between risk perception and job performance remains unexplored. Research has revealed that safety performance and job performance are not mutually exclusive. This may be as a result of the similarity of problems that arise when both criteria are collectively low, and the similarity of benefits when both criteria are high (Drew, 2014). Given the fact that there is an interplay between risk perception and safety performance, we assert that risk perception also has a relationship with job performance. Though some researchers suggest that risk perception may inversely relate to nursing performance (Abedini et al., 2014), to date, the degree to which task and contextual performance is influenced by risk perception in nurses has not been empirically assessed. For this current study, as depicted in Figure 1, we propose that

Hypothesis 1: Risk perception has negative effects on task performance (H1a) and contextual performance (H1b).

Figure 1.

Proposed research model.

Risk perception, safety behavior, task, and contextual performance

The connection between risk perception and safety behavior at work has been reported in several studies (Arezes & Miguel, 2008; Gaube et al., 2019; Si et al., 2019; Yasin et al., 2019). Prior research explained that when employees perceive risks to be high or unfavorable, they adopt safety behaviors to control the risks (Ji et al., 2018; Kouabenan et al., 2015; Lu & Yan, 2013). One study reported that workers who perceived risk as very high adopted the use of hearing protection devices (Arezes & Miguel, 2008). In like manner, some researchers affirmed that nurses with a high level of risk perception of influenza infection were more likely to get vaccinated (Zhang et al., 2011). In summary, nurses who perceive high risks are likely to adopt safety actions as effective preventive measures to alleviate the risks. Hence, we hypothesize:

Hypothesis 2: Risk perception has a positive effect on safety behavior.

Employees’ behavior, to a large extent, contributes to achieving successful organizational goals. Othman et al. (2019) noted that highly productive work is influenced by the behavior of workers who are dedicated to the organization. Research on safety among nurses has revealed that the nurses’ safety behavior has contributed immensely to improving their performance at work (Asgari et al., 2016). Therefore, we expect that safety behavior can improve task and contextual performance in nurses. On this basis, the following hypothesis is proposed:

Hypothesis 3: Safety behavior has positive effects on task performance (H3a) and contextual performance (H3b).

Based on the proposed hypotheses thus far, we expect mediation effects of safety behavior on the relationship between risk perception and task performance, as well as the relationship between risk perception and contextual performance. While a high level of risk perception reduces employee performance (Yin et al., 2019), it also increases the level of adopting safety behavior (Arezes & Miguel, 2008), which in turn enhances performance (Asamani, 2020). Therefore, it can be inferred that safety behavior mediates the impact of risk perception on performance:

Hypothesis 4: Safety behavior mediates the impacts of risk perception on task performance (H4a) and contextual performance (H4b).

Method

Research design, Setting, and Sampling

A quantitative cross-sectional survey was conducted to assess the influence of risk perception of WMSDs on task and contextual performance in nurses. Participants for the study were registered professional nurses from three purposively selected hospitals in the Accra Metropolis in Ghana. These hospitals were selected based on their suitability in location and the existence of relevant and sophisticated structures suitable to care for a large number of Ghanaians. Furthermore, stratified random sampling was employed to separate the target population of 1,939 into three strata: General Nurses at 1,131, Public Health Nurses at 547, and Midwives at 261. Applying Yamane’s sample size formula (Yamane, 1967), at a confidence level of 95% and an error margin of 0.05, we calculated a sample size of 332. Nonetheless, considering a non-response rate of 20%, we finalized on using a sample size of 398, which meets the threshold of sample size recommended for studies that employ structural equation modeling (Guilford, 1954; Velicer & Fava, 1998). Simple random sample technique was then used to select the sample sizes from each stratum: General Nurses at 398 × 58.32% = 232, Public Health Nurses at 398 × 28.21% = 112 and Midwives 398 × 13.46% = 54.

Data Collection and Ethical issues

This study was approved by the affiliated university. More so, to adhere to research ethics, we formally sent letters conveying the significance of the study to the hospital management of the three selected hospitals. After the necessary approval was given, we delivered a verbal presentation to explain the study to the participants. Participants were assured of maximum protection of their rights and privacy in the study. They were not permitted to disclose their names, working units, or any personal information that could be linked to them on the questionnaire. Besides, participants were expected to willingly and voluntarily give their responses without any coercion or oppression from the researchers or the hospital management. They were assured that there were no expected risks associated with participating in the study. Moreover, participants were informed that completing and returning the questionnaire was considered as informed consent for participation in the study.

Primary data was collected within 6 months by the first and third authors, along with two research assistants. We adopted the “Drop-Off/Pick-Up Method” in administering the questionnaires. Particularly, we personally distributed the questionnaires to the nurses in the selected hospitals and arranged to return at an appropriate and precise time and date to pick up the questionnaires. However, we visited the hospitals frequently to remind the participants of the study, and expedite the questionnaire collection process. If by any means, any selected participant refused to fill, another was randomly selected to replace that participant. This ensured a high response rate for our study. The questionnaire included an introductory paragraph explaining the significance of the study and a statement of review for the protection of the human subject. The contact information of researchers was also given to enable the participants to freely get in touch with researchers in case they have further questions.

Measurement

We employed a questionnaire as the survey instrument to collect the primary data. We adopted this method of survey because it is the most common tool as recommended by Sjöberg (1998) for studies on risk perception. The questionnaire had a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items in the questionnaire were adapted from prior literature to enhance the validity of the study. The wording of the questionnaire was modified to suit the purpose of this study.

Risk perception

The measures of risk perception were developed within the psychometric paradigm from Fischhoff (1978) and Benthin et al. (1993). The constructs were personal knowledge of risk, expert knowledge of risk, familiarity of risk, immediacy of effects of risk, catastrophic likelihood of risk, common-dread of risk, severity of consequences of risk, control over risk, avoidability of risk, and benefits vs. risks. Sample of the measurement items included “To what extent do you know the harm the risk can cause you?” “I am familiar with this risk at my workplace” and “There is a high potential for the risk to affect my performance at work.” In a past study (Lemée et al., 2018), the measures of the psychometric scale demonstrated adequate internal consistency (Cronbach α = 0.72), as well as strong convergent and discriminant validity. In our study, the measure of reliability, Cronbach’s α = 0.941, demonstrated appropriate internal consistency.

Task and contextual performance

The measures of task and contextual performance were adapted from the work of Greenslade and Jimmieson (2007). These measures distinguished task roles from contextual roles in nursing performance. Thus, the constructs for task performance were technical care, emotional support, and coordination of care among key members. Sample items included “I can explain to other nurses about the nature of the patient’s condition and how to handle it” and “I can show care and concern to patients.” The constructs for contextual performance were job-task support, interpersonal support, and organizational support. Samples of the measurement items included “I can help nurses in the unit to resolve work problems” and “I can make innovative suggestions to improve the overall quality of the department.” Greenslade and Jimmieson’s (2007) study indicated an excellent measure of reliability (Cronbach’s α ranged from 0.80 to 0.94). Likewise, in our study, the Cronbach’s α = 0.938 for task performance and Cronbach’s α = 0.913 for contextual performance demonstrated a suitable measure of reliability.

Safety behavior

The measures were drawn from the work of Neal and Griffin (2006). The measurement scale indicated an appropriate measure of reliability. These measures were used to create a holistic evaluation of safety behavior, which covered both safety compliance and safety participation. Items such as “I use the correct safety procedures for carrying out my job” and “I put in extra effort to improve the safety of the workplace” were used to measure safety compliance and safety participation respectively. In the current study, the safety behavior scale showed appropriate internal consistency (Cronbach’s α = 0.903).

Demographic Characteristics

Demographic variables—gender (1 = Male, 2 = Female), age (1 = 20–30 to 3 = Above 50), educational qualification (1 = Secondary/Technical/Vocational Certificate to 5 = Bachelor’s Degree), and work (1 = Less than 1 year to 4 = Above 10 years)—were included in the study to characterize and control our sample. Thus, they were not integrated into the model, as they could cause variations in our dependent and independent variables (Aquino et al., 2004). They were entered as stand-alone variables (Hancock & Mueller, 2013).

Three hundred and ninety-eight nurses were approached to fill out the questionnaires for the study; however, 384 (96.5%) nurses participated in the study. Two participants were excluded due to incomplete responses. Therefore, after the data cleaning process, 382 questionnaires were used for the data analyses. As depicted in Supplemental Appendix 1, the majority of the participants were females (76.2%), with 91 being males at 23.8%. A significantly sizable number of participants were between the ages of 20 and 50 (90.3%). More than half of the participants (51.6%) held a Bachelor’s Degree. The mean score for the number of years the nurses had been working indicated that most of the participants had worked within 1–10 years.

Analysis

For the preliminary analysis, we employed SPSS 23.0 to perform a series of tests including descriptive and correlation analyses of all the variables (see Supplemental Appendix Table 2). In testing the hypothesized relationships in the proposed model, we employed STATA 13 (LP StataCorp, 2015) to perform the Covariance-based structural equation modeling (CB-SEM). CB-SEM was employed for this study because it integrates many standard methods such as correlation, multiple regression (even with multiple dependent variables), and factor analysis (Nachtigall et al., 2003). Again, we employed the CB-SEM because it allows researchers to examine relationships among latent variables with multiple observed measures. The measurement errors in the relationships among these latent variables are thus accounted for, leading to more accurate, and often stronger, relationships between latent variables (Buhi et al., 2007). CB-SEM involves two steps: assessing the measurement model and assessing the structural model. We employed the maximum likelihood estimation (MLE) to estimate the coefficients and test the significance of the hypotheses, for the reason that the MLE is known to give the least-biased parameter estimates (Johnson & Wichern, 2007).

Mediation Effect Analysis

Mediation effect, also known as an indirect effect, is considered as the situation whereby an independent variable has an effect on a dependent variable through a mediating variable (Baron & Kenny, 1986; MacKinnon et al., 2000). In the present study, we employed a bootstrapping approach to test the mediation effects, by using 1,000 bootstrap samples with a 95% bias-corrected confidence interval. The bootstrap method is known to provide an appropriate and precise confidence interval (CI) for mediated effect (MacKinnon et al., 2004). If the 95% bias-corrected confidence interval of a mediation effect does not contain zero, then the mediation effect is considered statistically significant (Preacher & Hayes, 2004).

Results

Assessment of the Measurement model

Prior to the hypotheses testing, confirmatory factor analysis (CFA) was conducted to examine if the measurement model fits the sample data appropriately. As recommended by researchers, loading for all items greater than 0.50 on their proposed latent variables can be maintained (Bagozzi & Yi, 1988; Fornell & Larcker, 1981; Hair et al., 2006). In the present study, standardized loadings for all items, except one, were greater than 0.60. This indicates that nine items were retained (see Table 1) while one item (avoidability) was deleted because of poor loading (0.340). To evaluate the overall fit of the proposed model, we employed several indices in the CFA. For the Tucker-Lewis index (TLI), comparative fit index (CFI), normed fit index (NFI), and incremental fit index (IFI), values greater than 0.95 indicate an acceptable model fit. For root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR), values less than 0.06 and 0.08 respectively, indicate an acceptable model fit (Schreiber et al., 2006). The CFA results in Table 1 showed that the data was fit for both the proposed main effect model (χ²= 3969.44, df = 210, p < 0.001, CFI = 0.972, TLI = 0.954, NFI = 0.950, IFI = 0.979, RMSEA = 0.051, SRMR = 0.061) and the mediated model (χ²= 4772.412, df = 253, p < 0.001, CFI = 0.966, TLI = 0.973, NFI = 0.958, IFI = 0.951, RMSEA = 0.053, SRMR = 0.073). In testing for the convergent validity, we examined the composite reliability and the average variance extracted (AVE). The values of the composite reliability ranged from 0.865 to 0.963, above the threshold of 0.60 (Hair et al., 2006). The values of the AVE were all above the recommended value of 0.50 (Hair, 2011).

Table 1.

Factor Loadings, Composite Reliability, and Average Variance Extracted.

	Factor Loading	Mean (Std.Dev.)	CR	AVE
rp1	0.971
rp2	0.962
rp3	0.905
rp4	0.892
rp5	0.889
rp6	0.637
rp7	0.855
rp8	0.947
rp10	0.673
Risk perception		55.261(11.031)	0.961	0.751

ss1	0.926
ss2	0.953
Safety behavior		16.029 (2.077)	0.963	0.9054

fq1	0.855
fq2	0.941
fq3	0.926
Contextual Performance		12.257(2.166)	0.917	0.827

gz1	0.883
gz2	0.821
gz3	0.901
Task Performance		12.623(2.619)	0.865	0.733

Note. r1 = Personal knowledge, r2 = Expert knowledge, r3 = Familiarity, r4 = Immediacy of effect, r5 = Catastrophic likelihood, r6 = Common-dread, r7 = Severity of consequences, r8 = Control, r10 = Benefits vs. risks, ss1 = Safety compliance, ss2 = Safety participation, fq1 = Job-task support, fq2 = Interpersonal support, fq3 = Organizational support, gz1 = Technical care, gz2 = Emotional support, gz3 = Coordination of care among key members, CR = Composite reliability, AVE = Average variance extracted.

Discriminant validity is demonstrated when the square root of AVE for each construct is greater than the correlations between the construct and all other constructs (Fornell & Larcker, 1981). Supplemental Appendix 2 depicts that the square roots of the AVE values were higher than correlations with other constructs. This finding indicated an acceptable discriminant validity of our measurement instrument.

Tests of Hypotheses

Hypothesis 1 tested whether risk perception would negatively influence task performance (H1a) and contextual performance (H1b). As indicated in Supplemental Appendix 3, risk perception had positive effects on task (β = 0.55, p < 0.001) and contextual (β = 0.51, p < 0.001) performance; thereby H1a and H1b were not supported. Hypothesis 2 explored the relationship between risk perception and safety behavior. As shown in Figure 2, the standardized coefficient for the relationship was (β = 0.12, p < 0.05), indicating a positive effect of risk perception on safety behavior. Thus, hypothesis 2 was supported. Hypothesis 3 tested whether safety behavior would positively influence task performance (H3a) and contextual performance (H3b). As indicated in Figure 2, the standardized coefficients for the associations between safety behavior and task performance as well as safety behavior and contextual performance were (β = 0.02, p < 0.05) and (β = 0.01, p < 0.05) respectively. This indicated positive effects of safety behavior on task and contextual performance; thereby supporting H3a and H3b respectively.

Figure 2.

Mediation effect model. n = 382, ***p < 0.001, **p < 0.01, *p < 0.05 (χ²= 4772.412, df = 253, p < 0.001), CFI = 0.960, TLI = 0.973, NFI = 0.958, IFI = 0.951, RMSEA = 0.053, SRMR = 0.073.

Mediation Effect

For the mediation effect, hypothesis 4 indicated that we expected safety behavior to mediate the impacts of risk perception on task (H4a) and contextual performance (H4b). After controlling for safety behavior as a mediator, the standardized coefficients showed that the relationship between risk perception and task performance was reduced but significant (β = 0.49, p < 0.01) (see Figure 2 or Supplemental Appendix 4). Likewise, the relationship between risk perception and contextual performance was reduced but significant (β = 0.46, p < 0.05). Given the fact that there is an indication of partial mediation when the independent variable’s effect is reduced but still significant when the mediator is introduced (Baron & Kenny, 1986), these results provide evidence for partial mediation.

Furthermore, the results for the significance of the mediation effects in H4a and H4b were (Estimate: 0.060**; Lower Bound: 0.086; Upper Bound: 0.136) and (Estimate: 0.050*; Lower Bound: 0.070; Upper Bound: 0.111) respectively, providing evidence that safety behavior significantly mediated the impacts of risk perception on task performance and contextual performance. Thus H4a and H4b were supported.

Discussion

Contrary to our expectations, it was found that the nurses’ risk perception of WMSDs had a direct positive relationship with their task and contextual performance, signifying that how the nurses perceived the risk associated with WMSDs did not interfere with their performance at work. One possible explanation is due to their high level of knowledge of the risk and high level of perceived control over the risk. Some scholars have pointed out that the level of knowledge is a significant factor in predicting perception of risk (Grasmück & Scholz, 2005; Klerck & Sweeney, 2007), and that there is a correlation between perceived knowledge and perceived control (Frewer et al., 1996). That is, a high level of knowledge can lead to a high level of control of the risk, which in turn can enhance performance. Knowledge management at the workplace plays a key role in achieving higher performance (Grant, 1996; Hecker, 2012). Likewise, a review of 100 studies concluded that a high level of perceived control was associated with high levels of job performance (Spector, 1986).

Another possible explanation for this positive relationship is due to the benefits derived from performing their routines at work. Fischhoff (1978) acknowledged that people who are exposed to risks report less desire for the regulation of the activity by authorities and greater benefits relative to the risks. The importance of the benefits in the form of motivation has long been considered as a contributing factor for resilience¹ in risky situations (Newman, 2005; Tedeschi & Kilmer, 2005). Moreover, recent studies report a connection between resilience and enhanced employee performance in stressful or turbulent environments (Avey et al., 2010; Luthans et al., 2011; Paul et al., 2016). A study of resilience toward stressful situations in nurses described how nurses in high-stress areas scored high on measures of burnout but still felt personal accomplishment related to their work due to their level of resilience (Rushton et al., 2015). Yet, the finding that resilience toward risk may enhance the nurses’ performance at work requires specific attention. As levels of resilience rises, the individual’s perseverance also grows. This helps him/ her to face challenging situations at work without much panic (Paul et al., 2016).

Further, results on the task and contextual performance open up an interesting issue about how nurses perform their duties. The findings show that risk perception’s effect on task performance is more than its effect on contextual performance. Apparently, task performance is made up of mandatory roles such as the heavy lifting of hospital equipment and patient-handling tasks, which are performed on a daily basis. These roles consistently expose the nurses to the risks associated with WMSDs. This consistent exposure can then lead to a high level of perception about the risk. In fact, researchers claim that a high level of risk perception is associated with high physical workload (Lee et al., 2013).

In the second hypothesis, our results provide empirical evidence in support of the positive relationship between risk perception and safety behavior. This indicates that nurses with high levels of risk perception are more likely to adopt safety behaviors. Theories like the Protection Motivation Theory (Rogers, 1975), have described risk perception as a factor in adopting health safety behaviors. According to Kuttschreuter (2006), individuals with a high perception of food risks adopted safety behavior that involved the prevention of consuming these foods.

Similarly, in the third hypothesis, there are positive relationships between safety behavior and task performance as well as safety behavior and contextual performance. This line of reasoning is consistent with previous literature (Al Yousef, 2014; Asgari et al., 2016; Turner et al., 1990).

Our study supports the hypothesis that safety behavior serves as a mediator between risk perception as the predictor and task and contextual performance as the outcomes. The mediation is partial, which means that risk perception has direct effects on task and contextual performance, as well as indirect effects through safety behavior. The fundamental explanation of the relationship between risk perception and task and contextual performance is that risk perception influences the nurses to adopt safety behavior. This combination of risk perception and safety behavior results in outcomes in terms of task performance and contextual performance. This indicates that risk perception is linked to safety behavior (Zhang et al., 2011) and nurses who adopt safety behavior perform better at work (Asgari et al., 2016). However, temporal precedence is a required condition for establishing a cause-effect relationship (Mill, 1948). Thus, the absence of empirical evidence on temporal precedence of risk perception before safety behavior and safety behavior before performance limits the ability to make strong inferences about the causal direction. For example, our findings indicated a significant effect of risk perception on safety behavior. At the same time, however, it may be equally possible that the adoption of safety behavior in nurses may have an effect on their perception of risk.

Conclusion

Employing both the psychometric paradigm of risk perception and job performance theory, we have developed a model to show the effect of risk perception on task and contextual performance. This study explored the relationship between the nurses’ risk perception and work outcomes including safety behavior, task, and contextual performance. The findings of our study confirmed that risk perception is a significant predictor of safety behavior, task, and contextual performance. Given this, it was revealed that risk perception had positive effects on task and contextual performance. It was also revealed that risk perception had a positive effect on safety behavior, while safety behavior positively predicted task and contextual performance. The study further showed that safety behavior partially mediated the impacts of risk perception on task and contextual performance.

Implications

This study has some important implications. First, nursing management could organize sessions to obtain an insight into the causes and perceptions of WMSDs in nurses and address them by improving the working environment. This could be done by reducing nursing staff shortages, re-designing roles, reassessing shift timings, and giving breaks in-between the working hours to reduce workload. This could be a beneficial step, especially for nurses who are highly exposed to WMSDs. Second, education on the appropriate handling of patients and heavy equipment could be provided to nurses to prevent and reduce the risks of WMSDs. Third, training on effective safety measures could be provided to enable nurses to adopt appropriate safety behaviors that can enhance their performance at work.

Limitations and Future Research

This study has some limitations. The main limitation of this study is that it employed a cross-sectional design, and thus cannot establish temporal precedence, which is considered as a prerequisite if one intends to firmly establish mediation effects (Kendall et al., 2017). One important aspect of cross-sectional studies is that their correlational evidence serves as a key first step aimed at establishing a mediation effect in a longitudinal study (Maxwell et al., 2011). Therefore, this present study is a starting point for future studies that aim to use a longitudinal approach to verify the causality of the relationships.

Secondly, concerning causality, it was discussed that safety behavior mediates the effects of risk perception on task and contextual performance; however, plausibly risk perception can mediate this relationship. To enumerate, safety behavior can predict risk perception, as well as task and contextual performance. For instance, adopting safety behavior may reduce the fear and vulnerability toward risks or may increase the knowledge of safety measures toward risks. This may result in better performance among employees exposed to situations of risk. Therefore, future research can assess the mediating role of risk perception between safety behavior and performance.

Furthermore, the use of nurses as the study population limits the generalization of the results in the hospital setting. To generalize these findings, future research should examine the impact of risk perception on job performance among other hospital staff. Finally, socio-demographic factors are considered as one of the most important predictors of risk perception, and that gender for instance has a significant association with risk perception (Ngo et al., 2020). For example, a study revealed that females are more likely to perceive high risks of hazards than males (O’Neill et al., 2016). Thus, future research can evaluate the role of socio-demographic factors in the impact of nurses’ risk perception on task and contextual performance in the context of WMSDs.

Supplemental Material

Supplemental Material, sj-eps-1-ehp-10.1177_0163278720975071 - Influence of Risk Perception on Task and Contextual Performance: A Case of Work-Related Musculoskeletal Disorders in Nurses

Supplemental Material, sj-eps-1-ehp-10.1177_0163278720975071 for Influence of Risk Perception on Task and Contextual Performance: A Case of Work-Related Musculoskeletal Disorders in Nurses by Abigail Konadu Aboagye, Baozhen Dai and Ernest Kay Bakpa in Evaluation & the Health Professions

Supplemental Material

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Footnotes

Acknowledgment

The authors would like to sincerely thank the participants who were used for this study. The authors would also like to thank Natalie Glover for her contribution to this paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Nature Science Foundation of China under Grant number 71774069.

ORCID iD

Abigail Konadu Aboagye

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Note

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