Civility norms, safety climate, and safety outcomes: a preliminary investigation.

Journal of Occupational Health Psychology 2014, Vol. 19, No. 4, 437– 452

© 2014 American Psychological Association 1076-8998/14/$12.00 http://dx.doi.org/10.1037/a0037110

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Civility Norms, Safety Climate, and Safety Outcomes: A Preliminary Investigation Alyssa K. McGonagle

Benjamin M. Walsh

Wayne State University

University of Illinois at Springfield

Lisa M. Kath

Stephanie L. Morrow

San Diego State University

U.S. Nuclear Regulatory Commission, Rockville, Maryland

Working environments that are both civil and safe are good for business and employee well-being. Civility has been empirically linked to such important outcomes as organizational performance and individuals’ positive work-related attitudes, yet research relating civility to safety is lacking. In this study, we link perceptions of civility norms to perceptions of safety climate and safety outcomes. Drawing on social exchange theory, we proposed and tested a model in 2 samples wherein civility norms indirectly relate to safety outcomes through associations with various safety climate facets. Our results supported direct relationships between civility and management safety climate and coworker safety climate. Additionally, indirect effects of civility norms on unsafe behaviors and injuries were observed. Indirect effects of civility norms on unsafe behaviors were observed through coworker safety climate and work-safety tension. Indirect effects of civility norms on injuries were observed through management safety climate and work-safety tension for full-time employees, although these effects did not hold for part-time employees. This study provides initial evidence that researchers and practitioners may want to look beyond safety climate to civility norms to more comprehensively understand the origins of unsafe behaviors and injuries and to develop appropriate preventive interventions. Keywords: civility norms, injuries, safety climate, unsafe behaviors

has evolved from a focus on individual factors (e.g., employee knowledge, skills, and motivation) to include contextual factors, such as safety climate (Zohar, 1980). Safety climate, which refers to employees’ perception of the value of safety as reflected in a company’s policies, practices, and procedures (Neal & Griffin, 2006), acts as a frame of reference that informs the ways employees behave in terms of safety (Zohar, 1980). Importantly, safety climate has a substantial influence on employees’ safety performance and injuries on the job (Christian, Bradley, Wallace, & Burke, 2009). Although safety climate research has greatly advanced our understanding of safety performance and injuries, much less attention has been given to other contextual variables that may contribute to the creation of a positive safety climate and worker safety outcomes. In the present study, we examine individual perceptions of civility norms, or the degree to which norms for respectful treatment exist (Walsh et al., 2012), as they relate to individual safety climate perceptions and safety outcomes. More specifically, we propose that civility norms will indirectly relate to safety outcomes (i.e., unsafe behaviors and on-the-job injuries) through associations with specific psychosocial safety climate dimensions (i.e., management safety climate, coworker safety climate) and worksafety tension (felt conflict between job tasks and safety), using social exchange theory as a theoretical framework. An overview of the conceptual model is depicted in Figure 1. By studying civility norms, considering multiple safety climate dimensions, and focusing on two different safety outcome vari-

Worker safety is a critical concern to organizations. In 2010 alone, 4,690 U.S. workers suffered fatal occupational injuries (Bureau of Labor Statistics, 2010a) and nearly 3.1 million U.S. workers suffered nonfatal injuries (Bureau of Labor Statistics, 2010b). The study of workplace safety, which started in the 1930s,

This article was published Online First June 16, 2014. Alyssa K. McGonagle, Department of Psychology, Wayne State University; Benjamin M. Walsh, Department of Management, University of Illinois at Springfield; Lisa M. Kath, Department of Psychology, San Diego State University; Stephanie L. Morrow, U.S. Nuclear Regulatory Commission, Rockville, Maryland. A previous version of this paper was presented at the 2009 APA Work, Stress, and Health Conference in San Juan, Puerto Rico. This project was partially funded through Volpe National Transportation Systems Center Contract DTRT57-07-P-80165. The Volpe Center received funding for the contract through an Interagency Agreement with the Federal Railroad Administration, US Department of Transportation. The recommendations of this study are those of the authors and do not represent the views of the Volpe National Transportation Systems Center, the Federal Railroad Administration, the US Department of Transportation, the US Nuclear Regulatory Commission, or the US Government. We thank Mengqiao Liu, Tiana Kent, and Daniel Wiegert (Wayne State University) for assistance with coding worker injuries. Correspondence concerning this article should be addressed to Alyssa K. McGonagle, Department of Psychology, Wayne State University, 5057 Woodward Avenue, 7th Floor, Detroit, MI 48202. E-mail: alyssa [email protected] 437

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Figure 1.

Conceptual model and hypotheses.

ables, this study makes important contributions to the organizational literature. First, research on the linkage between civility and safety is lacking (for an exception see Haines, Stringer, & Duku, 2007). Civility is an important variable to consider; research on the negative effects of incivility abounds, and interventions have been shown to improve civility (Leiter, Laschinger, Day, & Gilin-Oore, 2011; Osatuke, Moore, Ward, Dyrenforth, & Belton, 2009). Second, by demonstrating that the indirect effects of civility norms on safety outcomes are not uniform, we underscore the importance of both measuring multiple dimensions of safety climate and considering the context within which these linkages are examined. We begin discussing our proposed model by first describing civility norms and how they may engender a social exchange process that includes safety. We then elaborate on specific safety climate dimensions and the mechanisms through which civility norms may relate to safety outcomes.

Civility Norms Civility norms indicate general norms for respect in the workplace (Walsh et al., 2012). Positive norms for civility help to sustain civil behaviors among workers (i.e., behaviors characterized by a show of concern and regard for others; Andersson & Pearson, 1999; Pearson, Andersson, & Porath, 2000). Managers and employees alike have a responsibility for fostering and maintaining civility in their work groups. Managers may foster civility in their working environments through such actions as modeling civil behavior, hiring employees who will contribute to civility norms, teaching civility, and, importantly, creating group norms by clearly stating expectations, rewarding good behavior, and penalizing bad behavior with regard to civility (Porath & Pearson, 2010, 2013). Employees may promote civility through their daily interactions with other employees and managers. Organizations and their employees reap multiple benefits from working in civil contexts. In such contexts, workplace incivility (a form of low-intensity mistreatment; Andersson & Pearson, 1999; Cortina & Magley, 2009; Cortina, Magley, Williams, & Langhout, 2001) occurs less frequently and more positive work attitudes are observed among employees (Leiter et al., 2011; Walsh et al., 2012). Because norms for civility encourage respectful behaviors among employees, they should also promote helping, facilitate

communication, and have a positive effect on the overall working environment (Gill & Sypher, 2009). Research also suggests that civility facilitates organizational performance (King et al., 2011). We propose that the positive effects of civility norms may engender a positive climate for safety through a process of social exchange. Social exchange theory (Blau, 1964; Homans, 1961; Thibaut & Kelley, 1959) states that individuals weigh potential benefits and costs of social relationships, ultimately seeking to maximize benefits and minimize costs. Costs refer not only to economic exchanges, but also social exchanges (Blau, 1964). Social exchanges work to create “enduring social patterns” (Cropanzano & Mitchell, 2005; p. 882). Relational mechanisms through which social exchanges are transmitted include perceived organizational support, team support, supervisory support, leadermember exchange, and trust (Cropanzano & Mitchell, 2005). Zohar (2010) provides additional support for this idea through his assertion that safety climate perceptions are often the result of social exchange relationships between leaders and workers. We argue that civility may be considered another mechanism through which reciprocity is generated; being treated in a respectful manner should generate a reciprocity norm (in this case, for safety). When workers feel they are treated respectfully, they are inclined to reciprocate and follow safety directives. When supervisors feel they have been treated respectfully, they are also inclined to reciprocate by providing support for workers’ safety. As a relatively new construct to the organizational literature, civility norms have not been studied in relation to employee safety. The limited research that has been conducted has focused instead on the relation between uncivil behavior and safety climate. Haines and colleagues (2007) surveyed 87 operating room nurses and found that incivility was associated with worse safety climate and also with lesser use of recommended operating room practices. Yet, Haines et al. (2007) do not provide theory linking incivility with safety climate and the measured safety behaviors. Additionally, all of the study constructs were measured via self-report. The present study builds upon these initial findings by addressing some of its methodological and theoretical limitations. Other non–safety-specific supportive constructs have also been related to workplace safety. Nahrgang, Morgeson, and Hoffman (2011) found that emphasis on teamwork, advice and assistance


CIVILITY NORMS AND SAFETY

from others, and leadership each related to safety outcomes as part of a larger meta-analysis. Closely related to the present study, Wallace and colleagues (2006) also found that safety climate mediated relationships between management-employee relations and perceived organizational support and worker accidents. Our study expands this past research, particularly Wallace et al.’s (2006) findings, in three important ways. First, we study an additional safety outcome (unsafe behaviors), which is a leading indicator for injuries (Christian et al., 2009; Clarke, 2006). Second, we test a separate and distinct construct, civility norms, as indirectly relating to worker safety outcomes. Finally, we study specific safety climate dimensions as mechanisms for the indirect effects of civility norms on safety outcomes, rather than treating safety climate as a single construct (which ignores this dimensionality).

Safety Climate Climate reflects how individuals make sense of their environments; particularly how people perceive formal and informal organizational policies, practices, and procedures (Reichers & Schneider, 1990). Climate may be conceptualized as an individuallevel construct, reflecting individuals’ personal perceptions of work policies, practices, and procedures (James et al., 2008; James & James, 1989; James & Jones, 1974; Schneider, 1973), or a group-level construct reflecting shared perceptions (James, James, & Ashe, 1990). We examine individuals’ perceptions as they pertain to safety (“psychological climate”). This approach is consistent with a significant body of research relating psychological climate perceptions to important organizational outcomes (e.g., job performance; Byrne, Stoner, Thompson, & Hochwarter, 2005; job attitudes and behaviors; King, Hebl, George, & Matusik, 2010; work attitudes, motivation, and performance; Parker et al., 2003), and research relating psychological perceptions of safety climate to safety outcomes (e.g., Clarke, 2009; Griffin & Neal, 2000; Morrow et al., 2010; Probst & Estrada, 2010; Seo, 2005). Employees use multiple cues when evaluating whether they perceive safety to be valued in their organization, such as how their managers respond to safety concerns, how coworkers behave when it comes to doing a job safely, and how one’s work is designed to emphasize or de-emphasize the priority of safety. These cues tend to manifest in the research literature as different dimensions of safety climate. Researchers have examined a number of different dimensions, and little consensus exists as to which fully constitute the safety climate construct. Lack of consistent construct definitions, in fact, is one major criticism of safety climate research (Christian et al., 2009). One review of the safety literature found more than 100 different dimensions (Flin, Mearns, O’Connor, & Bryden, 2000). However, some commonalities exist. In line with Morrow et al. (2010), we chose to examine three dimensions based on the notion that they represent the three primary elements of the working environment: the hierarchical social environment (management safety), the lateral social environment (coworker safety), and the job itself (work-safety tension). The most commonly studied safety climate dimension is management safety (e.g., Seo, Torabi, Blair, & Ellis, 2004; Zohar, 1980), which refers to perceptions of the extent to which management values safety, prioritizes safety, provides resources to act safely, and punishes unsafe behaviors. From a social exchange perspective, we posit that the extent to which managers feel that

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they are treated respectfully by their subordinates (i.e., the extant levels of civility norms) should influence the degree to which managers reciprocate with tendencies to maintain workers’ safety by, for instance, enforcing safety rules, providing resources needed to work safely, and generally working to make the environment safer. These reciprocated tendencies would then be reflected in workers’ perceptions of management safety climate. We therefore expect that civility norms will positively relate to perceptions of management safety climate. A second commonly studied safety climate dimension is coworker safety, which refers to the extent to which coworkers appear to value safety based on their actions and the extent to which coworkers follow safety rules (e.g., Hayes, Perander, Smecko, & Trask, 1998; Zohar, 1980). Social exchange theory may also be applied to coworker safety climate. The extent to which workers feel that they are treated with respect by their managers and coworkers (norms for civility) may influence their propensity to reciprocate by behaving in a way that promotes safety in the workplace (e.g., generally demonstrating that they care about workplace safety; reminding others of the need to work safely). This effect would be especially relevant in contexts where one’s safety behaviors affect others’ levels of safety. This reciprocated propensity to care about safety would be reflected in individuals’ coworker safety climate perceptions. Hence, we expect that civility norms will also relate positively to perceptions of coworker safety climate. Hypothesis 1: Civility norms will be positively related to management safety climate. Hypothesis 2: Civility norms will be positively related to coworker safety climate. Work-safety tension refers to the extent to which safety and other work pressures (e.g., productivity) are perceived as competing priorities (McGonagle & Kath, 2010; Morrow et al., 2010). This construct is studied under different labels including work pressure (Flin et al., 2000), perceived effects of required work pace on safety (Zohar, 1980), and worker involvement in safety (Dedobbeleer & Béland, 1991). Whereas the linkages between civility norms and management and coworker safety climate through social exchange are fairly straightforward, the relationship of civility norms with work-safety tension is less so. Zohar (2010) argues that safety climate includes the relative perceptions of the priorities of safety versus productivity or efficiency. We agree, and argue further that this evaluation, which is akin to perceptions of work-safety tension, is influenced by management and coworker safety climate. Specifically, the degree to which one feels their manager(s) and coworkers value safety will influence his or her perceptions of the relative priority of safety in light of competing demands (Zohar, 2010). A manager who highly values safety is likely to make the relative priority of safety clear, resulting in lower levels of work-safety tension. Similarly, workers may take cues from coworkers regarding the relative priority of safety versus competing work demands; additionally, they may also look to their peers to help them make sense of managers’ messages regarding safety. When managers and coworkers appear to highly value safety, lower levels of tension between productivity and safety may exist as workers perceive a clear priority of safety in light of competing demands.


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Hypothesis 3: Management safety climate will be negatively related to work-safety tension. Hypothesis 4: Coworker safety climate will be negatively related to work-safety tension.

Hypothesis 8b: Civility norms will have a negative indirect relationship with worker injuries, through associations with management safety climate and work-safety tension. Hypothesis 9a: Civility norms will have a negative indirect relationship with unsafe behaviors through associations with coworker safety climate and work-safety tension.


Safety Outcomes We study two safety outcomes: individuals’ unsafe behaviors and workplace injuries. We propose that civility norms will indirectly relate to both unsafe behaviors and injuries. For ease of illustrating the hypotheses (see Figure 1) we denote hypotheses regarding unsafe behaviors with “a” and those denoting injuries with “b.” The first outcome of interest, unsafe behaviors, includes behaviors that go against requirements necessary to maintain one’s immediate safety at work. Unsafe behaviors—those that may be thought of as the opposite of behaviors termed “safety compliance” (Griffin & Neal, 2000)—are problematic because they are leading indicators of worker accidents and injuries (Hofmann & Stetzer, 1996; Neal & Griffin, 2006). It is likely that individuals who experience greater levels of work-safety tension will also tend to exhibit more unsafe behaviors—as they are confronted with unclear priorities and a tension regarding safety and productivity. Our second outcome of interest—injuries—is an important behavioral criterion due to harmful implications for individual and organizational welfare. We propose that work-safety tension will positively relate to injuries, because high levels of work-safety tension indicate that enacting safe behaviors is in conflict with getting the job done (Zohar, 2010). Hypothesis 5a: Work-safety tension will be positively related to unsafe behaviors. Hypothesis 5b: Work-safety tension will be positively related to worker injuries. In addition, based on prior research findings (e.g., Christian et al., 2009), we propose that management safety climate will directly relate to injuries (Sample 2) and coworker safety climate will directly relate to unsafe behaviors (Sample 1) and injuries (Sample 2). Note that because management safety is excluded from Sample 1, we only test relationships involving management safety with injuries; therefore there is no Hypothesis 6a or Hypothesis 8a. Hypothesis 6b: Management safety climate will be negatively related to worker injuries. Hypothesis 7a: Coworker safety climate will be negatively related to unsafe behaviors. Hypothesis 7b: Coworker safety climate will be negatively related to worker injuries. Finally, we propose indirect relationships of civility norms to unsafe behaviors through the aforementioned paths. Specifically, in line with theory of social exchange, civility norms will relate to management and coworker safety climate, which will in turn relate to work-safety tension, which will in turn relate to unsafe behaviors and injuries.

Hypothesis 9b: Civility norms will have a negative indirect relationship with worker injuries, through associations with coworker safety climate and work-safety tension. Two separate samples of employees in different industries were used to test our hypotheses. Individuals’ unsafe behaviors were examined in a sample of full-time railroad employees (Sample 1) and workplace injuries were examined in both part-time and fulltime grocery store employees separately (Sample 2, with full-time and part-time subsamples). Notably, Sample 2 afforded an analysis of potential model differences attributable to workers’ employment status as full-time or part-time. Social exchange theory was used as a framework for developing hypotheses concerning indirect relationships between civility norms and safety outcomes, but research indicates that mechanisms of social exchange may differ for full-time and part-time workers. For example, evidence suggests that part-time employees are less motivated by prosocial reasons than full-time workers (Stamper & Van Dyne, 2001). Consequently, we expect that the hypotheses will hold better for the Sample 2 full-time worker subsample than they will for the part-time worker subsample.

Method Sample 1 was used to test Hypotheses 2, 4, 5a, 7a, and 9a, and Sample 2 was used to test Hypotheses 1, 2, 3, 4, 5b, 6b, 7b, 8b, and 9b. Specific differences between the Sample 1 and Sample 2 datasets and rationale for excluding management safety climate from Sample 1 hypothesis testing are explained below.

Sample 1 Participants and procedure. Participants were nonmanagement full-time mechanical workers employed by a large North American railroad. They worked at one of three locations and specialized in the maintenance and repair of rail cars or diesel locomotives. A total of 635 employees were selected for participation, and 421 returned completed surveys (66% response rate). Individual participants with greater than 20% missing survey responses were removed from the dataset (final n ⫽ 385). This was done in an attempt to ensure data quality (as those with large amounts of missing data may not have provided reliable responses). Because only 1% of the workforce was female, we did not ask respondents to report gender. Respondents were predominately older workers: 80.3% were age 41 or older. Approximately 65% of the sample had at least 21 years of experience both in the railroad industry and in their current organizations. All participants worked at least 30 hours per week. Site administrators, who were also members of the employees’ union, asked for volunteers to complete an anonymous paper and pencil survey about safety during working hours. No individually identifying information was collected. Participants were asked to seal their surveys in



unmarked envelopes and were instructed to either return the surveys to the site administrator or mail them directly to the researchers (who were not affiliated with the participants’ employer). We note that the unsafe behaviors and safety climate data from Sample 1 have been previously published (see Morrow et al., 2010). The current paper focuses on the previously unexplored indirect effects of civility norms on unsafe behaviors using that data. Measures. Items were selected based on consideration of constructs included in research of similar industries and consultation with subject matter experts in the organization. All items were subjected to a confirmatory factor analysis and evidence for discriminant validity was found (see Measurement Models section under Results, along with Table 4). Unless otherwise noted, all items had a Likert-type response scale ranging from 1 (strongly disagree) to 5 (strongly agree). Coefficient alphas for measures are reported in Table 1. Civility norms. The four-item Civility Norms Questionnaire – Brief (CNQ-B; Walsh et al., 2012) was used to measure perceptions of civility. A sample item is, Respectful treatment is the norm in your unit/workgroup. Coworker safety. Three items were used to assess perceived coworker safety, adapted from Zohar’s (1980) six-item effect of safe behavior on social status scale as reported by Mueller, DaSilva, Townsend, and Tetrick (1999). The original items referenced the “best” workers; in consultation with subject matter experts we decided to drop the “best” for this study. A sample item is, Workers in my unit expect other workers to behave safely. Work-safety tension. Work-safety tension was measured with four items, two of which were modified from Zohar’s (1980) effect of work pace on safety scale by Mueller et al. (1999), and another one was from Dedobbeleer and Béland’s (1991) measure of worker involvement in safety. The fourth item was adapted from Mueller et al. (1999) by Hofmann and Mark (2006); see also Hughes, Chang, and Mark (2009). A sample item is, My job duties often interfere with my ability to comply with safety regulations. Unsafe behaviors. Six items from Hofmann and Stetzer’s (1996) unsafe behaviors scale were used. The National Health and Safety Representatives from the participants’ union and a Safety Specialist on a policy committee from the participating organization were asked to choose the six items from the original 29-item scale that were most relevant to the current sample of workers. Participants were asked to indicate the frequency with which they had personally engaged in the behavior described by each item (e.g., Not wearing fall protection for a job that had a risk for a

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fall). Participants responded using a five-point scale ranging from 1 (never) to 5 (more than once a week).

Sample 2 Participants and procedure. Participants were employees of a medium-sized grocery store chain in the northeastern United States. Participants were from all departments within the stores, including bakery, cleaning, deli, frozen foods and dairy, floral, food service, front end, grocery, health and beauty care, meat, produce, and seafood departments. Nonmanagement employees from 54 stores (n ⫽ 1,995) were invited to participate in a confidential online survey regarding workplace safety. Of these, 1,069 participated (response rate of 54%). Participants with excessive missing data were removed from the sample (resulting in 964 employees). The injuries and safety climate data from Sample 2 have been previously published (see McGonagle & Kath, 2010); the current paper focuses on the previously unexplored indirect effects of civility norms on worker injuries using that data. Because, as noted above, research suggests that social exchange processes may operate differently for part-time workers as compared with full-time workers (e.g., Stamper & Van Dyne, 2001), we chose to examine two subgroups of workers in Sample 2 separately: those who reported working at least 30 hours per week on average (n ⫽ 402 participants) and those who reported working less than 30 hours per week (n ⫽ 536 participants). The first subsample (those working at least 30 hours per week) matched Sample 1 in terms of number of hours worked per week. Our expectation was that full-time worker status may be necessary to realize the full effects of civility norms on safety outcomes, so we focused on the first subsample of full-time workers to establish our model and test hypotheses. To check whether our expectation was accurate, we then tested the model on the part-time worker subsample (see the Supplemental Results section). For the sake of brevity, and except as otherwise noted, we refer to the full-time worker subsample of Sample 2 as simply “Sample 2” or “Sample 2 (full-time workers).” In the Supplemental Results section, where analyses are extended to part-time workers, we refer to the “parttime worker subsample of Sample 2.” In Sample 2 (full-time workers), 51% of respondents were female, and the average age of respondents was 35.50 (SD ⫽ 14.50). Respondents worked, on average, 36.6 hours per week (SD ⫽ 5.65). Respondents had an average of 5.9 years’ tenure in their current jobs (SD ⫽ 6.32). In the part-time worker subsample

Table 1 Sample 1 Zero-Order Correlations, Descriptive Statistics, and Coefficient Alphas Variable 1. 2. 3. 4. 5. 6.

Civility norms Coworker safety Work-safety tension Unsafe behaviors Industry tenure (years) Hours worked

M

SD

1

2

3

4

5

6

3.09 3.80 2.25 1.53 6.38 —

0.92 0.72 0.87 0.60 1.84 —

(.87) .40ⴱⴱ ⫺.12ⴱ ⫺.10 ⫺.14ⴱⴱ ⫺.08

(.81) ⫺.29ⴱⴱ ⫺.19ⴱⴱ ⫺.24ⴱⴱ ⫺.14ⴱⴱ

(.84) .38ⴱⴱ .08 ⫺.07

(.67) .11ⴱ .09

(—) .15ⴱⴱ

(—)

Note. Listwise n ⫽ 364. Coefficient alphas are in parentheses along the diagonal, where applicable. Hours worked was reported in categories ranging from 2 (30 – 40 hours) to 6 (more than 70 hours). ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.


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of Sample 2, 70% of respondents were female, and the average age of respondents was 38.01 (SD ⫽ 18.05). Respondents worked, on average 20.5 hours per week (SD ⫽ 5.74), and respondents had an average of 4.9 years’ tenure in their current jobs (SD ⫽ 5.39). Measures. Unless otherwise noted, all items had a Likert-type response scale ranging from 1 (strongly disagree) to 5 (strongly agree). Coefficient alphas for all measures are reported in Table 2. We conducted confirmatory factor analysis on all items and found evidence for discriminant validity (see “Measurement Model” section under Results, along with Table 4). The same civility norms scale reported in the Sample 1 measures section was used in Sample 2, yet the referent was changed from the individual’s workgroup to his or her store. The same measure of coworker safety used in Sample 1 was also used with Sample 2, yet with the original item wording (i.e., including the adjective “best”). Additionally, three of the four work-safety tension items overlapped between the two samples; for Sample 2 one item was from Mueller et al. (1999), one was from Dedobbeleer and Béland (1991), and two were modified from Mueller et al. (1999) as reported in Hughes et al. (2009). The other study scales were as follows. Management safety. Six items were used—three were from Neal and Griffin’s (2006) safety climate scale and three were from Hofmann and Morgeson’s (1999) upward safety communication scale. A sample item is, My store manager gives safety a high priority. Injuries. Organizational records of every documented workrelated employee injury were obtained for a two-year time period following the survey administration. Upon inspection of the injury data, it was apparent that some injuries may not have occurred on the job; these injuries were reported on the job but may have happened elsewhere or may be the result of a chronic condition (e.g., “Pain in lower back while bending down at home,” “Felt stiffness in left upper arm, then gave out”). In an effort to only include job-related injuries, three student coders were trained to code the injuries prior to data analysis. Coders were instructed to categorize injuries as “job-related” if there was enough information provided to discern that the injury happened on the job (e.g., if equipment was involved, if the nature of the injury made it likely that it happened in a grocery store, or if a work-related location was mentioned). Coders independently evaluated each injury based on the following question: Did it (the injury) happen on the job? Potential codes were 0 (no), 1 (yes), and “999” (not enough information to decide), with a yes denoting a job-related injury. Fleiss’ Kappa was calculated to determine interrater reliability. Kappa was .61, which indicates moderate to substantial agreement, according to Landis and Loch (1977). Discrepancies were discussed between the three coders and the first author, and consensus was reached regarding the job-relatedness of each injury. Only those coded as “job-related” were included in the analyses (73 job-related injuries total in the subsample of 402 full-time workers and 58 job-related injuries total in the subsample of 536 part-time workers). In Sample 2 (full-time workers), 57 people had injuries in total (some with multiple injuries); 43 individuals had one job-related injury, 12 individuals had two job-related injuries, and two individuals had three job-related injuries postsurvey). In the part-time worker subsample of Sample 2, 52 individuals had one job-related injury and three individuals had two job-related injuries. The total injury count for full-time and part-time workers

combined before coding was 144; 29 injuries were coded as either not job-related or not enough information to ascertain.

Data Analysis Strategy Sample 1 was used to test hypotheses about unsafe behaviors, and Sample 2 (full-time workers) was used to test hypotheses about injuries. Because of the referent for civility norms in Sample 1 (unit/workgroup), we did not conduct a test of linkages with management safety using Sample 1,1 but we do test linkages with management safety climate in Sample 2 (where the civility referent was the entire store). Structural equation modeling in Mplus version 6.11 (Muthén & Muthén, 1998 –2010) was used to test our hypotheses. Robust maximum likelihood (MLR) estimation, which provides a chi-square and standard errors that are robust to nonnormality (Kaplan, 2009), was used in all analyses to accommodate positively skewed distributions in unsafe behaviors and injuries. Given that injuries is a count variable, a Poisson distribution was specified to appropriately estimate effects where injuries was the outcome (Coxe, West, & Aiken, 2009). Furthermore, Sample 2 data were clustered such that employees were nested within departments in stores. Our focus was on psychological climate perceptions at the individual level for all variables, and injuries did not vary significantly across departments or stores. Nonetheless, potential nonindependence in the data resulting from this nested data structure was accounted for in Mplus by calculating standard errors while taking nonindependence into account (Muthén & Muthén, 1998 –2010). We tested a series of measurement models before examining structural models and testing our specific hypotheses (Anderson & Gerbing, 1988). In the measurement models, items served as indicators of all latent variables. Because of the complexity of models with Poisson distributed outcomes (injuries in our case), observed (i.e., nonlatent) scores were used to represent the civility and safety climate dimensions, and injuries was modeled as an observed count variable for Sample 2. This approach helped to minimize model complexity by limiting the number of estimated parameters. Saturated models with all possible paths were tested first, and nonsignificant paths were removed before estimating final trimmed models. Finally, the Sample 2 mediator residuals for management and coworker safety were allowed to covary as recommended by Preacher and Hayes (2008) for multiple mediator models. Fit for models predicting unsafe behaviors was assessed with several indices including the comparative fit index (CFI), root mean squared error of approximation (RMSEA), and standardized root mean squared residual (SRMR). Hu and Bentler (1999) state that values of .95 or higher for CFI, .06 or lower for SRMR and .08 1 We thank an anonymous reviewer for pointing out that the referent for the civility measure for Sample 1 (“unit/workgroup”) creates potential problems for Sample 1 model testing when including management safety. Because it is unclear whether respondents would include managers in their assessment of civility according to this referent, and the inclusion of managers in this referent is important for the social exchange conceptual linkage in our model from civility norms to management safety climate, we excluded management safety from model testing using Sample 1. The referent for civility norms in Sample 2 (e.g., “everyone in your store”) is more inclusive; therefore management safety climate is included in Sample 2 model testing.


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Table 2 Sample 2 (Full-Time Workers) Zero-Order Correlations, Descriptive Statistics, and Reliability Estimates Variable


1. 2. 3. 4. 5. 6. 7.

Civility norms Coworker safety Management safety Work-safety tension Injuries Job tenure (years) Hours worked

M

SD

1

2

3

4

5

6

7

3.80 3.74 3.94 2.31 0.18 5.09 36.59

0.77 0.68 0.78 0.82 0.49 6.32 5.65

(.81) .51ⴱⴱ .58ⴱⴱ ⫺.25ⴱⴱ .02 .01 ⫺.09

(.73) .61ⴱⴱ ⫺.22ⴱⴱ .06 .01 ⫺.01

(.95) ⫺.28ⴱⴱ .00 .01 ⫺.06

(.84) .10ⴱ ⫺.05 .15ⴱⴱ

(—) ⫺.07 .03

(—) .09

(—)

Note. Listwise n ⫽ 374. Coefficient alphas are in parentheses along the diagonal, where applicable. Job tenure excluded from model testing because of nonsignificant correlations with substantive variables. Hours worked correlated with work-safety tension in model testing as a control variable. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

or lower for RMSEA indicate good model-data fit. Because MLR estimation was used, chi-square difference testing of nested models was not directly possible, so instead we compared the fit of nested models with log-likelihood difference tests.2 The resulting test statistic is distributed as chi-square with degrees of freedom equal to the difference in parameters estimated in the models. When the models were non-nested we used the Bayesian information criterion (BIC) to compare the fit of structural models. The better-fitting model is that which has lower values for BIC (Kaplan, 2009). Unstandardized coefficients are reported for all effects. Finally, standard errors for the indirect effects were calculated using the delta method (cf. MacKinnon, Lockwood, & Williams, 2004). This is because alternative methods such as bootstrapping were unavailable in Sample 1 as a result of MLR estimation and in Sample 2 as a result of the MLR estimation and the clustered nature of the data. We consulted the literature for guidance regarding the inclusion of control variables. One demographic variable that consistently emerged as relating to safety is tenure (e.g., Beus, Bergman & Payne, 2010; Goldenhar, Williams, & Swanson, 2003; Liao, Arvey, Butler, & Nutting, 2001). Positive relationships between injuries and tenure were found in Goldenhar et al. (2003) and Liao et al. (2001). Similarly, Beus et al. (2010) found a positive relationship between tenure and safety climate strength. Given the apparent importance of tenure for safety, we examined relationships between tenure and our study variables to evaluate it for inclusion as a control variable, in line with recommendations of Carlson and Wu (2012). Additionally, because number of hours worked should relate to number of injuries (as hours worked represents exposure to potential hazards) and because more hours worked provides more opportunity for behaving unsafely, we also examined hours worked as a control variable in both samples.

Results Descriptive Statistics Tables 1 and 2 report means, standard deviations, zero-order correlations, and reliability estimates for all variables in Sample 1 and Sample 2 (full-time workers). Civility norms had no significant bivariate correlation with unsafe behaviors (r ⫽ ⫺.10, p ⬎ .05) or injuries (r ⫽ .02, p ⬎ .05). However, civility norms had significant relationships with each safety climate dimension in both samples. In addition, industry tenure (Sample 1) and job

tenure (Sample 2) and hours worked (both samples) were examined as potential control variables in hypothesis tests. Industry tenure was significantly correlated with multiple study variables in Sample 1 (see Table 1), but job tenure was not in Sample 2 (see Table 2). Consequently, we controlled for industry tenure in model testing using Sample 1 by allowing correlations where significant ones were found but did not include job tenure in Sample 2 hypothesis testing (see Carlson & Wu, 2012). Hours worked was correlated with coworker safety in Sample 1 and work-safety tension in Sample 2 full-time workers; therefore, we used it as a control variable in analysis of both samples.

Measurement Models Table 3 presents fit indices for measurement models using Sample 1 and Sample 2 (full-time workers). The fit indices provide discriminant validity evidence and support for assessing civility norms, safety climate dimensions, and outcomes (with the exception of injuries which was not included in measurement model testing) as unique constructs. In each sample, the hypothesized factor structure provided significantly better fit to the data than comparison models based on log-likelihood difference testing. Given this support, we proceeded to test the structural models.

Sample 1 Hypothesis Testing: Unsafe Behaviors Unstandardized coefficients for the final model after trimming nonsignificant paths are presented in Figure 2, and model fit statistics are shown in Table 4. Hours worked was included as a control variable correlating with coworker safety. The model provided good fit to the data, and fit did not decrease significantly relative to the saturated model, ␹2(3) ⫽ 2.14, p ⬎ .05. Hypotheses 2 and 4 were supported, as civility norms were related to coworker safety (b ⫽ .37, p ⬍ .01), and coworker safety was significantly related to work-safety tension (b ⫽ ⫺.28, p ⬍ .01). Work-safety tension was positively related to unsafe behaviors (b ⫽ .28, p ⬍ .01), supporting Hypothesis 5a. However, Hypothesis 7a was not supported because coworker safety was not significantly related to unsafe behaviors (and this effect was removed from the model prior to final estimates being generated). The influence of civility 2 For more detail on the steps to conduct difference testing using model log-likelihoods, please see the Mplus Web site: http://www.statmodel.com/ chidiff.shtml.


444

Table 3 Measurement Model Fit Indices Log-likelihood

Scaling correction factor

Parameters

CFI

RMSEA

SRMR

model model model model

⫺7842.89 ⫺8041.54 ⫺8135.14 ⫺8473.68

1.69 1.70 1.63 1.60

57 54 52 51

.94 .76 .68 .39

.05 .09 .11 .15

.04 .10 .12 .16

model model model model

⫺6912.22 ⫺6983.54 ⫺7297.89 ⫺7429.29

1.87 1.88 1.80 1.74

57 54 52 51

.98 .94 .78 .72

.03 .06 .11 .13

.04 .05 .11 .12


Model Sample 1 4-factor 3-factor 2-factor 1-factor Sample 2 4-factor 3-factor 2-factor 1-factor

Note. Sample 1 n ⫽ 385. Sample 2 (full-time workers) n ⫽ 402. Robust maximum likelihood estimation used in all models. In Sample 1, the 2-factor model includes civility (factor 1) and all safety-related items (factor 2); the 3-factor model includes civility (factor 1), all safety climate items (factor 2), and unsafe behaviors (factor 3); and the 4-factor model includes civility (factor 1), coworker safety (factor 2), work-safety tension (factor 3), and unsafe behaviors (factor 4). In Sample 2, the 2-factor model includes civility (factor 1) and all safety climate items (factor 2); the 3-factor model includes civility (factor 1), coworker safety and management safety items (factor 2), and work-safety tension (factor 3); and the 4-factor model includes civility (factor 1), coworker safety (factor 2), management safety (factor 3), and work-safety tension (factor 4). The Model log-likelihoods, scaling correction factors, and the number of parameters were used to compare fit of nested models (http://www .statmodel.com/chidiff.shtml). The 4-factor models provide significantly better fit to the data than nested models in both samples. CFI ⫽ comparative fit index; RMSEA ⫽ root mean squared error of approximation; SRMR ⫽ standardized root mean squared residual.

norms on unsafe behaviors was entirely indirect. The indirect effect through coworker safety and work-safety tension was significant (indirect effect ⫽ ⫺.03, p ⬍ .01), thereby supporting Hypothesis 9a.

Sample 2 (Full-Time Workers) Hypothesis Testing: Injuries Unstandardized coefficients for the final model are presented in Figure 3 (note that hours worked was included as a control variable with a correlation to work-safety tension). A lower BIC value of the trimmed model (5381.48) relative to the saturated model (5401.44) shows that the final model provided better fit to the data. Support was observed for Hypotheses 1 and 2, as civility norms were positively related to both management safety (b ⫽ .56, p ⬍ .01) and coworker safety (b ⫽ .44, p ⬍ .01). Hypothesis 3 was supported, management safety was negatively related to worksafety tension (b ⫽ ⫺.31, p ⬍ .01).Yet, coworker safety was not related to work-safety tension in this model (Hypothesis 4), and

Figure 2.

this relation was removed from the model prior to generating final estimates. Work-safety tension was the only variable to have a significant direct effect on injuries (b ⫽ .32, p ⬍ .05), supporting Hypothesis 5b. Hypotheses 6b and 7b were not supported as management safety and coworker safety had nonsignificant direct effects on injuries, and both paths were removed prior to estimating the final model. Similar to Sample 1, the influence of civility norms on injuries was entirely indirect in nature in Sample 2 (full-time workers). The specific indirect effect through management safety and work-safety tension was ⫺.06, p ⫽ .05, which provided some support for Hypothesis 8b (but not Hypothesis 9b).

Supplemental Results To supplement our hypothesis testing, we examined the parttime worker subsample of Sample 2 separately to see whether the model established using the full-time worker sample would also apply to the part-time workers (n ⫽ 562). In addition, we conducted tests of alternative models and compared the fit of our final

Sample 1 final unstandardized coefficients for effects on unsafe behaviors.

ⴱⴱ

p ⬍ .01.


445

Table 4 Sample 1 Structural Model Fit Indices: Unsafe Behaviors Model

Log-likelihood

Scaling correction factor

Parameters

CFI

RMSEA

SRMR

Saturated model Final model

⫺8958.28 ⫺8959.61

1.65 1.67

64 61

.93 .93

.05 .05

.05 .05


Note. n ⫽ 388. Robust maximum likelihood estimation used in all models. Model log-likelihoods, scaling correction factors, and the number of parameters were used to compare fit of nested models (http://www .statmodel.com/chidiff.shtml). Models did not statistically differ in terms of fit: ␹2(3) ⫽ 2.14, p ⬎ .05. CFI ⫽ comparative fit index; RMSEA ⫽ root mean squared error of approximation; SRMR ⫽ standardized root mean squared residual.

models with the fit of these alternatives. Finally, because we unexpectedly did not find significant relationships between management safety and coworker safety and unsafe behaviors and injuries, we tested the significance of indirect effects of each of these variables on the outcomes through work-safety tension. Model testing using the Sample 2 part-time workers. Research on social exchange asserts that part-time employees are “more likely to develop economic rather than social exchange relationships with their employers” (Stamper & Van Dyne, 2001, p. 520) and therefore reciprocate by working for transactional reasons (i.e., pay) rather than prosocial reasons as compared with full-time workers. Because of aforementioned potential differences in the social exchange mechanisms for part-time workers, we chose to focus our hypothesis testing on the full-time workers from Sample 2. To test the expectation that part-time workers may have limited opportunities for social exchange to drive the same relationships, we tested our model on the part-time worker subsample of Sample 2 (n ⫽ 536). Table 5 reports means, standard deviations, zero-order correlations, and reliability estimates for all variables in the part-time worker subsample of Sample 2. As with the full-time worker subsample of Sample 2, civility norms was not significantly correlated with injuries (r ⫽ ⫺.06, p ⬎ .05). We ran models in Mplus to compare model fit and parameter estimates between the Sample 2 (full-time workers – those working greater than or equal to 30 hours per week; n ⫽ 402) and the part-time worker subsample of Sample 2 (those working less than 30 hours per week; n ⫽ 536; note that 26 participants were missing data on this variable). We included a correlation of (a) hours worked with coworker safety climate and (b) a path from job tenure to injuries,3 in addition to the correlation of hours worked with work-safety tension used in the model for Sample 2 (full-time workers) hypothesis testing to control for the effects of these two variables in both subsamples, due to observed significant bivariate correlations (as displayed in Table 5). We first examined BIC values for the model using each dataset separately because, as the models were not nested, log-likelihood or chi square difference testing was not possible, and traditional multiple groups analysis was not possible because of the Poisson-distributed outcome variable (injuries). The fit of the model with full-time workers was BIC ⫽ 5003.34, compared with the BIC of the model with parttime workers, which was 6394.37. The lower value of the BIC for the model with the full-time workers indicated better fit to the data. Using latent class analysis, we then conducted statistical comparisons of model parameters between the two groups using log likelihood difference testing, as described earlier in this manuscript. We tested a series of models in which we estimated differ-

ences in model fit when the paths were constrained to be equal between the two groups. When all four substantive paths were constrained to be equal between the two groups, the log likelihood difference test was nonsignificant: ␹2(4) ⫽ 4.19, p ⬎ .05. Therefore, we tested each path separately, so that there was one degree of freedom difference between the nested models in each case. Results, presented in Table 6, show that the path from work-safety tension to injuries differed significantly between the two groups but the remaining three paths did not. Specifically, in the unconstrained model, the path between work-safety tension and injuries in Sample 2 (full-time workers) was b ⫽ .32, p ⬍ .05 and the same path in the part-time subsample of Sample 2 was b ⫽ ⫺.12, p ⬎ .05. Figure 4 displays unstandardized coefficients from the unconstrained models for each subsample in the latent class analysis. Alternative models. We tested two alternative models using the Sample 2 (full-time worker) data. We chose to test two specific plausible alternative models of the many we could possibly test based on concerns about the positioning of management safety climate in the model as an outcome of civility rather than as a predictor or alongside civility in driving perceptions of coworker safety climate, work-safety tension, and injuries.4 Because managers play a role in the creation and maintenance of both safety climate and civility norms, it is possible that management safety climate could be driving workers’ perceptions of both coworker safety and civility. Note that because the alternative models involve the management safety climate variable, Sample 1 is not appropriate for these model tests. We tested the following: (a) a model in which both civility norms and management safety related to coworker safety, which in turn related to work-safety tension, which in turn related to injuries; and (b) a model in which management safety related to both coworker safety and civility norms, which in turn related to work-safety tension, which in turn related to injuries. See Figure 5 for illustrations of alternative models. We used BIC values to compare model fit; the better-fitting model is that which has a lower BIC (Kaplan, 2009). The BIC value for our final model for Sample 2 was 5381.48; the BIC value for alternative model (a) was 6335.76 and the BIC value for alternative model (b) was 5381.93. Our final model as theorized had the lowest BIC value, and therefore demonstrated the best fit. 3 Note that we were unable to add a correlation of job tenure with injuries because of injuries being a count variable; we therefore added it as a model path (see Figure 4). 4 We thank an anonymous reviewer for this suggestion.



446

Figure 3. Final model (hypothesis testing) for Sample 2 (full-time workers) – unstandardized coefficients. BIC ⫽ 5381.48. ⴱ p ⬍ .05, ⴱⴱ p ⬍ .01.

part-time worker subsample of Sample 2. Central to our study, we also found indirect effects of civility norms on safety outcomes via safety climate dimensions in both Sample 1 and Sample 2 (fulltime workers), although the specific nature of the indirect effects varied by sample. Our results provide initial evidence that civility is an important variable to consider alongside safety climate when assessing safety outcomes. Below we consider our findings within the broader literatures on civility and safety, elaborate on practical implications, and consider the strengths and limitations of this research. Individual perceptions of civility norms related to the safety climate dimensions of management safety and coworker safety as expected. In line with social exchange theory, civility norms may lead managers and coworkers to reciprocate by advocating for workers’ safety and upholding safe work practices. It is important to note that, although we used social exchange as a framework for our study, we did not explicitly test the mechanisms of social exchange that may have acted as conduits from civility to safety climate. Examples of these specific mechanisms may include, for instance, leader–member exchange and coworker helping behaviors. Future research should explicitly operationalize and test these or other mechanisms to provide a better test of social exchange theory as applied to safety. As expected, we also found support for a linkage between management safety climate and work-safety tension. Management support for safety establishes conditions under which individuals may feel less tension between their work duties and behaving

Indirect effects of management and coworker safety. Unexpectedly, management safety climate did not directly lead to injuries and coworker safety climate did not directly relate to unsafe behaviors or injuries. Because it is possible that indirect effects exist between these variables and injuries and unsafe behaviors through work-safety tension, we tested these effects. First, we tested the indirect effect of coworker safety on unsafe behaviors through work-safety tension (Sample 1) and found that it was statistically significant (indirect effect ⫽ ⫺.08, p ⬍ .01). We also tested an indirect effect of management safety climate to injuries through work-safety tension (Sample 2, full-time workers) and found that it was statistically significant (indirect effect ⫽ ⫺.10, p ⫽ .05). We did not test an indirect effect of coworker safety on injuries because its relationship with work-safety tension was nonsignificant in Sample 2 (full-time workers).

Discussion Using social exchange theory, we proposed and found partial support for a model in which civility norms indirectly related to worker unsafe behaviors and injuries through relationships with three safety climate dimensions (management safety, coworker safety, and work-safety tension). Although not all of our hypotheses were supported, we found consistent support for the links between civility norms and management and coworker safety. In Sample 1 and Sample 2 (full-time workers), we also found support for relationships between work-safety tension and safety outcomes—yet this finding did not hold up when examining the

Table 5 Sample 2 (Part-Time Workers) Zero-Order Correlations, Descriptive Statistics, and Reliability Estimates Variable 1. 2. 3. 4. 5. 6. 7.

Civility norms Coworker safety Management safety Work-safety tension Injuries Job tenure (years) Hours worked

M

SD

1

2

3

4

5

6

7

3.97 3.72 4.00 2.21 0.11 4.88 20.49

0.63 0.66 0.72 0.81 0.33 5.39 5.74

(.72) .48ⴱⴱ .52ⴱⴱ ⫺.24ⴱⴱ ⫺.06 ⫺.02 ⫺.04

(.63) .60ⴱⴱ ⫺.15ⴱⴱ .04 .02 .10ⴱ

(.95) ⫺.27ⴱⴱ .03 .03 .07

(.85) ⫺.03 ⫺.04 .01

(—) .14ⴱⴱ .09

(—) ⫺.05

(—)

Note. Listwise n ⫽ 487. Coefficient alphas are in parentheses along the diagonal, where applicable. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.


447

Table 6 Sample 2 Latent Class Analysis Model Comparison Results (Full-Time Workers Versus PartTime Workers) Model

Scaling Estimate and Parameters Log likelihood correction factor significance


Unconstrained model Constrained path 1. Work-safety tension – Injuries 2. Management safety – Work-safety tension 3. Civility – Management safety 4. Civility – Coworker safety

38

⫺10597.91

1.43

n/a

37 37 37 37

⫺10600.15 ⫺10597.95 ⫺10598.38 ⫺10598.10

1.44 1.43 1.43 1.43

4.23ⴱ 0.06 0.66 0.26

Note. Estimates indicate the log-likelihood difference value for parameters in a latent class that includes individuals working 30⫹ hours per week versus a latent class that includes individuals working less than 30 hours per week. The log likelihood difference statistic is distributed as chi-square with degrees of freedom equal to the difference in parameters estimated in the models. For more information see the Mplus Web site: http://www.statmodel.com/chidiff.shtml. ⴱ p ⬍ .05.

safely. This aligns with Zohar’s (2010) assertion that leaders directly affect workers’ perceptions of the perceived priority of safety in relation to operational demands. This also corroborates a large research base that shows management commitment to safety and safety-specific leadership to be critical to maintaining workplace safety (e.g., Kelloway, Mullen, & Francis, 2006; Zohar, 2002). Yet, unexpectedly, although coworker safety climate related to work-safety tension in Sample 1, it did not in Sample 2 (full-time workers), and, therefore, the indirect effect of civility on injuries in Sample 2 was also nonsignificant. The inconsistent relationships between coworker safety and work-safety tension may possibly be attributed to differences between the two samples in terms of safety interdependence. Sample 1 workers, who performed maintenance on rail cars and diesel locomotives, were more interdependent in terms of keeping each other safe. For instance, leaving a worksite where significant dangers remain without alerting other workers could result in another worker injuring himself. Rushing through a task involving the lifting of a heavy rail car could result in another worker being crushed. It follows that this interdependence may strengthen the correspondence between coworker safety climate and work safety tension. In Sample 2, grocery store workers faced hazards in terms of interacting with equipment (knives, shopping carts, etc.), but the risk to injury caused by unsafe behaviors was mainly to the self; therefore it follows that coworker safety climate perceptions would not be as strongly related to work-safety tension and injuries. Our findings suggest that a potentially fruitful area for future research may involve examining whether coworker safety climate is relatively more important to keeping workers safe when task interdependence is high versus when it is low. Additionally, the relative importance of management and coworker safety in keeping workers safe may depend on the level of task interdependence. Work-safety tension related to both unsafe behaviors and injuries as expected in Sample 1 and Sample 2 (full-time workers). This underscores recent commentary by Zohar (2010), which espouses the importance of operationalizing safety climate in terms of the relative priority of safety versus other demands (productivity, task completion, etc.) As work-safety tension reflects an individual’s assessment of the relative priority of safety

and job demands, it is likely more proximal to injuries than management safety and coworker safety (which reflect others’ demonstration of values regarding safety, and therefore may be more distal; Morrow et al., 2010). These results also corroborate research on the importance of job duties interfering with safe working (e.g., Brown & Holmes, 1986; Dedobbeleer & Béland, 1991; McGonagle & Kath, 2010; McLain & Jarrell, 2007). Yet, as noted, the relationship between work-safety tension and injuries was nonsignificant in the part-time worker subsample of Sample 2. We suspected that there may be differences in the modeled relationships between full-time and part-time workers in Sample 2 because of potential differences in how social exchange functions for full-time versus part-time workers. Research on social exchange suggests that social exchange mechanisms may not apply to part-time workers to the same extent they do to full-time workers (Stamper & Van Dyne, 2001). We found that the established model from full-time workers in Samples 1 and 2 was not supported when tested on the part-time worker subsample of Sample 2. Yet, unexpectedly, the results of a latent class analysis indicated that the lack of support for the model for individuals working less than 30 hours per week was driven by the aforementioned nonsignificant relationship between work-safety tension and injuries, rather than differences in the paths from civility to management and coworker safety. Safety researchers note that hours worked may significantly predict the number of injuries on the job (e.g., Lamberg, 2004; Trimpop, Karkcaldy, Athanasou, & Cooper, 2000)—which makes sense considering hours worked as an exposure to conditions that can lead to injuries; however, there is a lack of research examining whether relationships between safety climate constructs and safety outcomes (injuries, unsafe behaviors) are moderated by hours worked. Our findings suggest that this may be an important area for future research. Management safety climate did not directly lead to injuries, and coworker safety climate did not directly relate to unsafe behaviors or injuries. Yet, as we noted in the Supplemental Results section, an indirect effect of management safety climate on injuries and an indirect effect of coworker safety climate on unsafe behaviors was found. Given these results, combined with Zohar’s (2010) focus on safety climate conceptualized as the tension between operational duties and safety, future tests of causal links between management


448


Figure 4. Latent class analysis unstandardized coefficients for each Sample 2 subsample (A ⫽ full-time workers and B ⫽ part-time workers). Results are from fully unconstrained model testing. ⴱ p ⬍ .05, ⴱⴱ p ⬍ .01. Control variables are hours worked and job tenure.

and coworker safety (and other safety climate facets) to worksafety tension and safety outcomes (using longitudinal studies) may be important as safety climate researchers continue working to more precisely understand safety climate constructs. Finally, civility norms displayed significant indirect effects to unsafe behaviors through coworker safety climate and work-safety tension (Sample 1) and to injuries through management safety climate and work-safety tension (Sample 2 full-time workers)— yet no indirect effects were found to injuries through coworker safety in Sample 2. Based on industry-related differences between the samples (as elaborated above), paired with the differences in the mechanisms we found through which civility norms indirectly related to safety outcomes, we conclude that it is important for researchers to study (a) multiple safety climate dimensions and (b) industry differences, especially those regarding safety hazards and safety interdependence. It is important for us to note that, whereas we framed civility norms as an antecedent to both management and coworker safety climate and found support for this conceptualization, it is also

possible that management safety is an antecedent to both civility norms and coworker safety. The difference in model fit between our final model and the alternative model that positions management safety as a predictor of both civility and coworker safety is notably small. Additionally, and as discussed in more detail in our limitations section, the cross-sectional nature of the survey data in both samples does not lend itself to causal testing that could help us empirically determine the process by which civility influences unsafe behaviors and injuries. We chose the variable ordering based on theory of social exchange, but we cannot eliminate the possibility of alternative variable ordering. Regardless of the way the variables are ordered, it is clear that civility is related to safety. We hope to see future research further delineate the relationship between civility and safety. Taken as a whole, this study contributes to the safety and civility literatures by providing initial theoretical and empirical support linking civility norms with safety climate and safety outcomes. Research has associated civility with positive work attitudes (Leiter et al., 2011; Walsh et al., 2012) and organizational perfor-



449

Figure 5. Results of alternative model testing using Sample 2 (full-time workers) with unstandardized estimates. For comparative purposes, the BIC value for the final model based on hypothesized model testing in Figure 3 was 5381.48. ⴱ p ⬍ .05, ⴱⴱ p ⬍ .01.

mance (King et al., 2011). Further, a related construct, incivility, has been linked to declines in employee well-being (Bowling & Beehr, 2006; Cortina et al., 2001; Lim & Cortina, 2005) and lower performance (Caza & Cortina, 2007; Porath & Erez, 2007, 2009; Sakurai & Jex, 2012; Sliter, Sliter, & Jex, 2012). We expand on preliminary work linking incivility and safety (Haines et al., 2007) by providing a theoretically grounded and more rigorous test of the relationships between civility norms, safety climate, and safety outcomes in two additional industries.

Implications for Practice Although preliminary, our results point to the potential importance of intervening when civility is lacking, as it may have implications for safety. Civility interventions have become a recent focus of attention for researchers and practitioners. One initiative—Civility, Respect, and Engagement in the Workforce (CREW)— has proven effective at improving levels of civility, decreasing incivility, and driving increases in job satisfaction and decreases in employee absences (Leiter et al., 2011; Osatuke et al., 2009). Future research is warranted to both replicate our study findings linking civility with safety outcomes and to test the effectiveness of civility interventions on safety outcomes. Overall, our findings suggest that the civility-safety link may prove to be a promising area of continuing inquiry for researchers and practitioners.

Limitations and Additional Future Directions Our study is not without limitations, and many of these limitations provide the basis for future research. First, measurement and conceptualization issues are prevalent in safety climate research (Christian et al., 2009). We chose to examine three of the most commonly studied safety climate dimensions—those which represent three major psychosocial aspects of the working environment: management, coworkers, and the job itself. Yet, other dimensions exist (e.g., safety system, competence; Flin et al., 2000). Future research may attempt to more comprehensively examine the safety climate domain in relation to civility. Another limitation pertains to levels of analysis. Our sole focus was on psychological (individual) perceptions as opposed to the shared perceptions. A recent meta-analysis (Christian et al., 2009) found that group-level safety climate was more strongly related to safety outcomes than (individual) psychological climate. Therefore, the relationships reported may be attenuated. Future research may attempt to examine civility norms and safety climate at the group level. Because much of our data were self-report and gathered at one time point (with the exception of injuries which occurred after the survey administration and were obtained from organizational records), concerns may also exist about common method variance biasing the observed relationships (e.g., Brannick, Chan, Conway, Lance, & Spector, 2010). Yet, we implemented some procedural recommendations from Podsakoff et al. (2003) to minimize these


450


concerns. First, scales were separated in the surveys such that items about civility norms and safety climate were not adjacent to each other. Second, the Sample 1 survey was anonymous, which reduces motivation for respondents to intentionally distort responses based on social desirability (another distinct source of common method bias). The Sample 2 survey was not anonymous, but confidentiality was assured. Finally, in Sample 1, a different response scale was used for the unsafe behaviors measure than for other scales. Notably, in their meta-analysis, Christian et al. (2009) did not find differences based on the use of self-report measures versus secondary source measures in relationships between safety climate, safety performance, and safety outcomes. The fact that our model established using Sample 2 (full-time workers) was not supported in the part-time worker subsample of Sample 2 because of the nonsignificant relationship of work-safety tension with injuries in that subsample is another important consideration. As mentioned, safety issues relating to part-time and full-time works deserves more attention in future research. Relatedly, we recognize that our cutoff of 30 hours per week for employment status may be seen as arbitrary, and note this as a study limitation.5 As noted, the link between civility norms and safety climate, although grounded in theory of social exchange, cannot be assumed to be causal based on the data presented. Future research should examine the link between civility and safety climate longitudinally. Relatedly, as leadership has been posited to have an influence on both civility (e.g., Porath & Pearson, 2010, 2013) and safety climate (e.g., Zohar, 2010), researchers may explicitly measure leadership variables that may affect both civility norms and safety climate and their indirect links to safety outcomes. One possibility is ethical leadership, which includes demonstrating appropriate ethical behavior to create behavioral norms and treating people fairly (Mayer, Aquino, Greenbaum, & Kuenzi, 2012). Ethical leadership is just one possible antecedent of safety behavior that also relates to respectful treatment. We hope to see work in this area continue.

Conclusion The current study makes an important and understudied link between workplace civility and safety. It contributes to the safety literature by introducing civility as an indirect predictor of worker unsafe behaviors and injuries. It also contributes to the burgeoning civility literature by linking the construct to workplace safety. We hope that the initial evidence provided by this study stimulates continued research into the relationship between civility and safety.

5 Availability of benefits such as health insurance many times differentiates part-time from full-time worker status, and benefits also may have implications for differences in social exchange relationships for part-time versus full-time workers (Stamper & Van Dyne, 2001). U.S. law currently mandates that employers make health insurance available to employees who work, on average, 30 or more hours per week (Patient Protection & Affordable Care Act, 2010), making 30 hours per week one reasonable cutoff for full-time status. Another is 35 hours per week based on the definition of full-time work by the U.S. Bureau of Labor Statistics (2008).

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Received September 10, 2012 Revision received March 17, 2014 Accepted March 19, 2014 䡲

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